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  Subjects -> MATHEMATICS (Total: 864 journals)
    - APPLIED MATHEMATICS (68 journals)
    - GEOMETRY AND TOPOLOGY (19 journals)
    - MATHEMATICS (643 journals)
    - MATHEMATICS (GENERAL) (40 journals)
    - NUMERICAL ANALYSIS (19 journals)
    - PROBABILITIES AND MATH STATISTICS (75 journals)

MATHEMATICS (643 journals)                  1 2 3 4 | Last

Showing 1 - 200 of 538 Journals sorted alphabetically
Abakós     Open Access   (Followers: 3)
Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg     Hybrid Journal   (Followers: 2)
Academic Voices : A Multidisciplinary Journal     Open Access   (Followers: 2)
Accounting Perspectives     Full-text available via subscription   (Followers: 6)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 16)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 4)
ACM Transactions on Mathematical Software (TOMS)     Hybrid Journal   (Followers: 6)
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 20)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 1)
Acta Mathematica     Hybrid Journal   (Followers: 10)
Acta Mathematica Hungarica     Hybrid Journal   (Followers: 2)
Acta Mathematica Scientia     Full-text available via subscription   (Followers: 5)
Acta Mathematica Sinica, English Series     Hybrid Journal   (Followers: 5)
Acta Mathematica Vietnamica     Hybrid Journal  
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal  
Advanced Science Letters     Full-text available via subscription   (Followers: 4)
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 3)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Complex Systems     Hybrid Journal   (Followers: 7)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 15)
Advances in Decision Sciences     Open Access   (Followers: 4)
Advances in Difference Equations     Open Access   (Followers: 1)
Advances in Fixed Point Theory     Open Access   (Followers: 5)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 9)
Advances in Linear Algebra & Matrix Theory     Open Access   (Followers: 1)
Advances in Materials Sciences     Open Access   (Followers: 15)
Advances in Mathematical Physics     Open Access   (Followers: 6)
Advances in Mathematics     Full-text available via subscription   (Followers: 10)
Advances in Numerical Analysis     Open Access   (Followers: 3)
Advances in Operations Research     Open Access   (Followers: 11)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Pure and Applied Mathematics     Hybrid Journal   (Followers: 5)
Advances in Pure Mathematics     Open Access   (Followers: 4)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Aequationes Mathematicae     Hybrid Journal   (Followers: 2)
African Journal of Educational Studies in Mathematics and Sciences     Full-text available via subscription   (Followers: 5)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Afrika Matematika     Hybrid Journal   (Followers: 1)
Air, Soil & Water Research     Open Access   (Followers: 7)
AKSIOMA Journal of Mathematics Education     Open Access   (Followers: 1)
Algebra and Logic     Hybrid Journal   (Followers: 2)
Algebra Colloquium     Hybrid Journal   (Followers: 4)
Algebra Universalis     Hybrid Journal   (Followers: 2)
Algorithmic Operations Research     Full-text available via subscription   (Followers: 5)
Algorithms     Open Access   (Followers: 9)
Algorithms Research     Open Access  
American Journal of Biostatistics     Open Access   (Followers: 9)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 3)
American Journal of Mathematical Analysis     Open Access  
American Journal of Mathematics     Full-text available via subscription   (Followers: 7)
American Journal of Operations Research     Open Access   (Followers: 5)
American Mathematical Monthly     Full-text available via subscription   (Followers: 6)
An International Journal of Optimization and Control: Theories & Applications     Open Access   (Followers: 7)
Analele Universitatii Ovidius Constanta - Seria Matematica     Open Access   (Followers: 1)
Analysis     Hybrid Journal   (Followers: 2)
Analysis and Applications     Hybrid Journal   (Followers: 1)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 4)
Analysis Mathematica     Full-text available via subscription  
Annales Mathematicae Silesianae     Open Access  
Annales mathématiques du Québec     Hybrid Journal   (Followers: 4)
Annales UMCS, Mathematica     Open Access   (Followers: 1)
Annales Universitatis Paedagogicae Cracoviensis. Studia Mathematica     Open Access  
Annali di Matematica Pura ed Applicata     Hybrid Journal   (Followers: 1)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Data Science     Hybrid Journal   (Followers: 8)
Annals of Discrete Mathematics     Full-text available via subscription   (Followers: 6)
Annals of Mathematics     Full-text available via subscription  
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 6)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of the Alexandru Ioan Cuza University - Mathematics     Open Access  
Annals of the Institute of Statistical Mathematics     Hybrid Journal   (Followers: 1)
Annals of West University of Timisoara - Mathematics     Open Access  
Annuaire du Collège de France     Open Access   (Followers: 5)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applications of Mathematics     Hybrid Journal   (Followers: 1)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Mathematics     Open Access   (Followers: 3)
Applied Mathematics     Open Access   (Followers: 4)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 4)
Applied Mathematics - A Journal of Chinese Universities     Hybrid Journal  
Applied Mathematics Letters     Full-text available via subscription   (Followers: 1)
Applied Mathematics Research eXpress     Hybrid Journal   (Followers: 1)
Applied Numerical Analysis & Computational Mathematics     Hybrid Journal   (Followers: 5)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
Arab Journal of Mathematical Sciences     Open Access   (Followers: 2)
Arabian Journal of Mathematics     Open Access   (Followers: 2)
Archive for Mathematical Logic     Hybrid Journal   (Followers: 1)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 4)
Archive of Numerical Software     Open Access  
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
Arnold Mathematical Journal     Hybrid Journal   (Followers: 1)
Artificial Satellites : The Journal of Space Research Centre of Polish Academy of Sciences     Open Access   (Followers: 17)
Asia-Pacific Journal of Operational Research     Hybrid Journal   (Followers: 3)
Asian Journal of Algebra     Open Access   (Followers: 1)
Asian Journal of Current Engineering & Maths     Open Access  
Asian-European Journal of Mathematics     Hybrid Journal   (Followers: 2)
Australian Mathematics Teacher, The     Full-text available via subscription   (Followers: 6)
Australian Primary Mathematics Classroom     Full-text available via subscription   (Followers: 1)
Australian Senior Mathematics Journal     Full-text available via subscription   (Followers: 1)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Axioms     Open Access  
Baltic International Yearbook of Cognition, Logic and Communication     Open Access  
Basin Research     Hybrid Journal   (Followers: 3)
BIBECHANA     Open Access  
BIT Numerical Mathematics     Hybrid Journal  
BoEM - Boletim online de Educação Matemática     Open Access  
Boletim Cearense de Educação e História da Matemática     Open Access  
Boletim de Educação Matemática     Open Access  
Boletín de la Sociedad Matemática Mexicana     Hybrid Journal  
Bollettino dell'Unione Matematica Italiana     Full-text available via subscription   (Followers: 1)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 19)
Bruno Pini Mathematical Analysis Seminar     Open Access  
Buletinul Academiei de Stiinte a Republicii Moldova. Matematica     Open Access   (Followers: 5)
Bulletin des Sciences Mathamatiques     Full-text available via subscription   (Followers: 4)
Bulletin of Dnipropetrovsk University. Series : Communications in Mathematical Modeling and Differential Equations Theory     Open Access   (Followers: 1)
Bulletin of Mathematical Sciences     Open Access   (Followers: 2)
Bulletin of the Brazilian Mathematical Society, New Series     Hybrid Journal  
Bulletin of the London Mathematical Society     Hybrid Journal   (Followers: 3)
Bulletin of the Malaysian Mathematical Sciences Society     Hybrid Journal  
Calculus of Variations and Partial Differential Equations     Hybrid Journal  
Canadian Journal of Science, Mathematics and Technology Education     Hybrid Journal   (Followers: 18)
Carpathian Mathematical Publications     Open Access   (Followers: 1)
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CAUCHY     Open Access   (Followers: 1)
CEAS Space Journal     Hybrid Journal  
CHANCE     Hybrid Journal   (Followers: 5)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
ChemSusChem     Hybrid Journal   (Followers: 7)
Chinese Annals of Mathematics, Series B     Hybrid Journal  
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
Chinese Journal of Mathematics     Open Access  
Clean Air Journal     Full-text available via subscription   (Followers: 2)
Cogent Mathematics     Open Access   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 4)
Collectanea Mathematica     Hybrid Journal  
College Mathematics Journal     Full-text available via subscription   (Followers: 1)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 13)
Commentarii Mathematici Helvetici     Hybrid Journal   (Followers: 1)
Communications in Contemporary Mathematics     Hybrid Journal  
Communications in Mathematical Physics     Hybrid Journal   (Followers: 1)
Communications On Pure & Applied Mathematics     Hybrid Journal   (Followers: 3)
Complex Analysis and its Synergies     Open Access   (Followers: 2)
Complex Variables and Elliptic Equations: An International Journal     Hybrid Journal  
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 9)
Comptes Rendus Mathematique     Full-text available via subscription   (Followers: 1)
Computational and Applied Mathematics     Hybrid Journal   (Followers: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 4)
Computational Methods and Function Theory     Hybrid Journal  
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 5)
Concrete Operators     Open Access   (Followers: 4)
Confluentes Mathematici     Hybrid Journal  
COSMOS     Hybrid Journal  
Cryptography and Communications     Hybrid Journal   (Followers: 12)
Cuadernos de Investigación y Formación en Educación Matemática     Open Access  
Cubo. A Mathematical Journal     Open Access  
Czechoslovak Mathematical Journal     Hybrid Journal   (Followers: 1)
Demographic Research     Open Access   (Followers: 11)
Demonstratio Mathematica     Open Access  
Dependence Modeling     Open Access  
Design Journal : An International Journal for All Aspects of Design     Hybrid Journal   (Followers: 28)
Developments in Clay Science     Full-text available via subscription   (Followers: 1)
Developments in Mineral Processing     Full-text available via subscription   (Followers: 3)
Dhaka University Journal of Science     Open Access  
Differential Equations and Dynamical Systems     Hybrid Journal   (Followers: 2)
Discrete Mathematics     Hybrid Journal   (Followers: 7)
Discrete Mathematics & Theoretical Computer Science     Open Access  
Discrete Mathematics, Algorithms and Applications     Hybrid Journal   (Followers: 2)
Discussiones Mathematicae Graph Theory     Open Access   (Followers: 1)
Doklady Mathematics     Hybrid Journal  
Duke Mathematical Journal     Full-text available via subscription   (Followers: 1)
Edited Series on Advances in Nonlinear Science and Complexity     Full-text available via subscription  
Electronic Journal of Graph Theory and Applications     Open Access   (Followers: 2)
Electronic Notes in Discrete Mathematics     Full-text available via subscription   (Followers: 2)
Elemente der Mathematik     Full-text available via subscription   (Followers: 3)
Energy for Sustainable Development     Hybrid Journal   (Followers: 9)
Enseñanza de las Ciencias : Revista de Investigación y Experiencias Didácticas     Open Access  
Ensino da Matemática em Debate     Open Access  
Entropy     Open Access   (Followers: 4)
ESAIM: Control Optimisation and Calculus of Variations     Full-text available via subscription   (Followers: 1)
European Journal of Combinatorics     Full-text available via subscription   (Followers: 4)
European Journal of Mathematics     Hybrid Journal   (Followers: 1)
European Scientific Journal     Open Access   (Followers: 2)
Experimental Mathematics     Hybrid Journal   (Followers: 3)
Expositiones Mathematicae     Hybrid Journal   (Followers: 2)
Facta Universitatis, Series : Mathematics and Informatics     Open Access  
Fasciculi Mathematici     Open Access  
Finite Fields and Their Applications     Full-text available via subscription   (Followers: 4)
Fixed Point Theory and Applications     Open Access   (Followers: 1)
Formalized Mathematics     Open Access   (Followers: 2)

        1 2 3 4 | Last

Journal Cover Algorithms
  [SJR: 0.357]   [H-I: 17]   [9 followers]  Follow
    
  This is an Open Access Journal Open Access journal
   ISSN (Print) 1999-4893
   Published by MDPI Homepage  [148 journals]
  • Algorithms, Vol. 10, Pages 5: Efficient Algorithms for the Maximum Sum
           Problems

    • Authors: Sung Bae, Tong-Wook Shinn, Tadao Takaoka
      First page: 5
      Abstract: We present efficient sequential and parallel algorithms for the maximum sum (MS) problem, which is to maximize the sum of some shape in the data array. We deal with two MS problems; the maximum subarray (MSA) problem and the maximum convex sum (MCS) problem. In the MSA problem, we find a rectangular part within the given data array that maximizes the sum in it. The MCS problem is to find a convex shape rather than a rectangular shape that maximizes the sum. Thus, MCS is a generalization of MSA. For the MSA problem, O ( n ) time parallel algorithms are already known on an ( n , n ) 2D array of processors. We improve the communication steps from 2 n − 1 to n, which is optimal. For the MCS problem, we achieve the asymptotic time bound of O ( n ) on an ( n , n ) 2D array of processors. We provide rigorous proofs for the correctness of our parallel algorithm based on Hoare logic and also provide some experimental results of our algorithm that are gathered from the Blue Gene/P super computer. Furthermore, we briefly describe how to compute the actual shape of the maximum convex sum.
      PubDate: 2017-01-04
      DOI: 10.3390/a10010005
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 6: Using Force-Field Grids for Sampling
           Translation/Rotation of Partially Rigid Macromolecules

    • Authors: Mihaly Mezei
      First page: 6
      Abstract: An algorithm is presented for the simulation of two partially flexible macromolecules where the interaction between the flexible parts and rigid parts is represented by energy grids associated with the rigid part of each macromolecule. The proposed algorithm avoids the transformation of the grid upon molecular movement at the expense of the significantly lesser effect of transforming the flexible part.
      PubDate: 2017-01-04
      DOI: 10.3390/a10010006
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 7: Backtracking-Based Iterative Regularization
           Method for Image Compressive Sensing Recovery

    • Authors: Lingjun Liu, Zhonghua Xie, Jiuchao Feng
      First page: 7
      Abstract: This paper presents a variant of the iterative shrinkage-thresholding (IST) algorithm, called backtracking-based adaptive IST (BAIST), for image compressive sensing (CS) reconstruction. For increasing iterations, IST usually yields a smoothing of the solution and runs into prematurity. To add back more details, the BAIST method backtracks to the previous noisy image using L2 norm minimization, i.e., minimizing the Euclidean distance between the current solution and the previous ones. Through this modification, the BAIST method achieves superior performance while maintaining the low complexity of IST-type methods. Also, BAIST takes a nonlocal regularization with an adaptive regularizor to automatically detect the sparsity level of an image. Experimental results show that our algorithm outperforms the original IST method and several excellent CS techniques.
      PubDate: 2017-01-06
      DOI: 10.3390/a10010007
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 8: Modeling Delayed Dynamics in Biological
           Regulatory Networks from Time Series Data

    • Authors: Emna Ben Abdallah, Tony Ribeiro, Morgan Magnin, Olivier Roux, Katsumi Inoue
      First page: 8
      Abstract: Background: The modeling of Biological Regulatory Networks (BRNs) relies on background knowledge, deriving either from literature and/or the analysis of biological observations. However, with the development of high-throughput data, there is a growing need for methods that automatically generate admissible models. Methods: Our research aim is to provide a logical approach to infer BRNs based on given time series data and known influences among genes. Results: We propose a new methodology for models expressed through a timed extension of the automata networks (well suited for biological systems). The main purpose is to have a resulting network as consistent as possible with the observed datasets. Conclusion: The originality of our work is three-fold: (i) identifying the sign of the interaction; (ii) the direct integration of quantitative time delays in the learning approach; and (iii) the identification of the qualitative discrete levels that lead to the systems’ dynamics. We show the benefits of such an automatic approach on dynamical biological models, the DREAM4(in silico) and DREAM8 (breast cancer) datasets, popular reverse-engineering challenges, in order to discuss the precision and the computational performances of our modeling method.
      PubDate: 2017-01-09
      DOI: 10.3390/a10010008
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 9: Elite Opposition-Based Social Spider
           Optimization Algorithm for Global Function Optimization

    • Authors: Ruxin Zhao, Qifang Luo, Yongquan Zhou
      First page: 9
      Abstract: The Social Spider Optimization algorithm (SSO) is a novel metaheuristic optimization algorithm. To enhance the convergence speed and computational accuracy of the algorithm, in this paper, an elite opposition-based Social Spider Optimization algorithm (EOSSO) is proposed; we use an elite opposition-based learning strategy to enhance the convergence speed and computational accuracy of the SSO algorithm. The 23 benchmark functions are tested, and the results show that the proposed elite opposition-based Social Spider Optimization algorithm is able to obtain an accurate solution, and it also has a fast convergence speed and a high degree of stability.
      PubDate: 2017-01-08
      DOI: 10.3390/a10010009
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 10: Estimating the Local Radius of Convergence
           for Picard Iteration

    • Authors: Ştefan Măruşter
      First page: 10
      Abstract: In this paper, we propose an algorithm to estimate the radius of convergence for the Picard iteration in the setting of a real Hilbert space. Numerical experiments show that the proposed algorithm provides convergence balls close to or even identical to the best ones. As the algorithm does not require to evaluate the norm of derivatives, the computing effort is relatively low.
      PubDate: 2017-01-09
      DOI: 10.3390/a10010010
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 11: Acknowledgement to Reviewers of Algorithms
           in 2016

    • Authors: Algorithms Editorial Office
      First page: 11
      Abstract: The editors of Algorithms would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2016.[...]
      PubDate: 2017-01-10
      DOI: 10.3390/a10010011
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 12: Coupled Least Squares Identification
           Algorithms for Multivariate Output-Error Systems

    • Authors: Wu Huang, Feng Ding
      First page: 12
      Abstract: This paper focuses on the recursive identification problems for a multivariate output-error system. By decomposing the system into several subsystems and by forming a coupled relationship between the parameter estimation vectors of the subsystems, two coupled auxiliary model based recursive least squares (RLS) algorithms are presented. Moreover, in contrast to the auxiliary model based recursive least squares algorithm, the proposed algorithms provide a reference to improve the identification accuracy of the multivariate output-error system. The simulation results confirm the effectiveness of the proposed algorithms.
      PubDate: 2017-01-12
      DOI: 10.3390/a10010012
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 13: A Fault Detection and Data Reconciliation
           Algorithm in Technical Processes with the Help of Haar Wavelets Packets

    • Authors: Paolo Mercorelli
      First page: 13
      Abstract: This article is focused on the detection of errors using an approach that is signal based. The proposed algorithm considers several criteria: soft, hard and very hard recognition error. After the recognition of the error, the error is replaced. In this sense, different strategies for data reconciliation are associated with the proposed criteria error detection. Algorithms in several industrial software platforms are used for detecting errors of sensors. Computer simulations confirm the validation of the presented applications. Results with actual sensor measurements in industrial processes are presented.
      PubDate: 2017-01-14
      DOI: 10.3390/a10010013
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 14: Kernel Clustering with a Differential
           Harmony Search Algorithm for Scheme Classification

    • Authors: Yu Feng, Jianzhong Zhou, Muhammad Tayyab
      First page: 14
      Abstract: This paper presents a kernel fuzzy clustering with a novel differential harmony search algorithm to coordinate with the diversion scheduling scheme classification. First, we employed a self-adaptive solution generation strategy and differential evolution-based population update strategy to improve the classical harmony search. Second, we applied the differential harmony search algorithm to the kernel fuzzy clustering to help the clustering method obtain better solutions. Finally, the combination of the kernel fuzzy clustering and the differential harmony search is applied for water diversion scheduling in East Lake. A comparison of the proposed method with other methods has been carried out. The results show that the kernel clustering with the differential harmony search algorithm has good performance to cooperate with the water diversion scheduling problems.
      PubDate: 2017-01-14
      DOI: 10.3390/a10010014
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 15: Toward Personalized Vibrotactile Support
           When Learning Motor Skills

    • Authors: Olga Santos
      First page: 15
      Abstract: Personal tracking technologies allow sensing of the physical activity carried out by people. Data flows collected with these sensors are calling for big data techniques to support data collection, integration and analysis, aimed to provide personalized support when learning motor skills through varied multisensorial feedback. In particular, this paper focuses on vibrotactile feedback as it can take advantage of the haptic sense when supporting the physical interaction to be learnt. Despite each user having different needs, when providing this vibrotactile support, personalization issues are hardly taken into account, but the same response is delivered to each and every user of the system. The challenge here is how to design vibrotactile user interfaces for adaptive learning of motor skills. TORMES methodology is proposed to facilitate the elicitation of this personalized support. The resulting systems are expected to dynamically adapt to each individual user’s needs by monitoring, comparing and, when appropriate, correcting in a personalized way how the user should move when practicing a predefined movement, for instance, when performing a sport technique or playing a musical instrument.
      PubDate: 2017-01-16
      DOI: 10.3390/a10010015
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 16: Length-Bounded Hybrid CPU/GPU Pattern
           Matching Algorithm for Deep Packet Inspection

    • Authors: Yi-Shan Lin, Chun-Liang Lee, Yaw-Chung Chen
      First page: 16
      Abstract: Since frequent communication between applications takes place in high speed networks, deep packet inspection (DPI) plays an important role in the network application awareness. The signature-based network intrusion detection system (NIDS) contains a DPI technique that examines the incoming packet payloads by employing a pattern matching algorithm that dominates the overall inspection performance. Existing studies focused on implementing efficient pattern matching algorithms by parallel programming on software platforms because of the advantages of lower cost and higher scalability. Either the central processing unit (CPU) or the graphic processing unit (GPU) were involved. Our studies focused on designing a pattern matching algorithm based on the cooperation between both CPU and GPU. In this paper, we present an enhanced design for our previous work, a length-bounded hybrid CPU/GPU pattern matching algorithm (LHPMA). In the preliminary experiment, the performance and comparison with the previous work are displayed, and the experimental results show that the LHPMA can achieve not only effective CPU/GPU cooperation but also higher throughput than the previous method.
      PubDate: 2017-01-18
      DOI: 10.3390/a10010016
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 17: A Preconditioned Iterative Method for
           Solving Systems of Nonlinear Equations Having Unknown Multiplicity

    • Authors: Fayyaz Ahmad, Toseef Bhutta, Umar Sohaib, Malik Zaka Ullah, Ali Alshomrani, Shamshad Ahmad, Shahid Ahmad
      First page: 17
      Abstract: A modification to an existing iterative method for computing zeros with unknown multiplicities of nonlinear equations or a system of nonlinear equations is presented. We introduce preconditioners to nonlinear equations or a system of nonlinear equations and their corresponding Jacobians. The inclusion of preconditioners provides numerical stability and accuracy. The different selection of preconditioner offers a family of iterative methods. We modified an existing method in a way that we do not alter its inherited quadratic convergence. Numerical simulations confirm the quadratic convergence of the preconditioned iterative method. The influence of preconditioners is clearly reflected in the numerically achieved accuracy of computed solutions.
      PubDate: 2017-01-18
      DOI: 10.3390/a10010017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 18: Imperialist Competitive Algorithm with
           Dynamic Parameter Adaptation Using Fuzzy Logic Applied to the Optimization
           of Mathematical Functions

    • Authors: Emer Bernal, Oscar Castillo, José Soria, Fevrier Valdez
      First page: 18
      Abstract: In this paper we are presenting a method using fuzzy logic for dynamic parameter adaptation in the imperialist competitive algorithm, which is usually known by its acronym ICA. The ICA algorithm was initially studied in its original form to find out how it works and what parameters have more effect upon its results. Based on this study, several designs of fuzzy systems for dynamic adjustment of the ICA parameters are proposed. The experiments were performed on the basis of solving complex optimization problems, particularly applied to benchmark mathematical functions. A comparison of the original imperialist competitive algorithm and our proposed fuzzy imperialist competitive algorithm was performed. In addition, the fuzzy ICA was compared with another metaheuristic using a statistical test to measure the advantage of the proposed fuzzy approach for dynamic parameter adaptation.
      PubDate: 2017-01-23
      DOI: 10.3390/a10010018
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 19: Pressure Control for a Hydraulic Cylinder
           Based on a Self-Tuning PID Controller Optimized by a Hybrid Optimization
           Algorithm

    • Authors: Ru Wang, Chao Tan, Jing Xu, Zhongbin Wang, Jingfei Jin, Yiqiao Man
      First page: 19
      Abstract: In order to improve the performance of the hydraulic support electro-hydraulic control system test platform, a self-tuning proportion integration differentiation (PID) controller is proposed to imitate the actual pressure of the hydraulic support. To avoid the premature convergence and to improve the convergence velocity for tuning PID parameters, the PID controller is optimized with a hybrid optimization algorithm integrated with the particle swarm algorithm (PSO) and genetic algorithm (GA). A selection probability and an adaptive cross probability are introduced into the PSO to enhance the diversity of particles. The proportional overflow valve is installed to control the pressure of the pillar cylinder. The data of the control voltage of the proportional relief valve amplifier and pillar pressure are collected to acquire the system transfer function. Several simulations with different methods are performed on the hydraulic cylinder pressure system. The results demonstrate that the hybrid algorithm for a PID controller has comparatively better global search ability and faster convergence velocity on the pressure control of the hydraulic cylinder. Finally, an experiment is conducted to verify the validity of the proposed method.
      PubDate: 2017-01-23
      DOI: 10.3390/a10010019
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 20: Computing a Clique Tree with the Algorithm
           Maximal Label Search

    • Authors: Anne Berry, Geneviève Simonet
      First page: 20
      Abstract: The algorithm MLS (Maximal Label Search) is a graph search algorithm that generalizes the algorithms Maximum Cardinality Search (MCS), Lexicographic Breadth-First Search (LexBFS), Lexicographic Depth-First Search (LexDFS) and Maximal Neighborhood Search (MNS). On a chordal graph, MLS computes a PEO (perfect elimination ordering) of the graph. We show how the algorithm MLS can be modified to compute a PMO (perfect moplex ordering), as well as a clique tree and the minimal separators of a chordal graph. We give a necessary and sufficient condition on the labeling structure of MLS for the beginning of a new clique in the clique tree to be detected by a condition on labels. MLS is also used to compute a clique tree of the complement graph, and new cliques in the complement graph can be detected by a condition on labels for any labeling structure. We provide a linear time algorithm computing a PMO and the corresponding generators of the maximal cliques and minimal separators of the complement graph. On a non-chordal graph, the algorithm MLSM, a graph search algorithm computing an MEO and a minimal triangulation of the graph, is used to compute an atom tree of the clique minimal separator decomposition of any graph.
      PubDate: 2017-01-25
      DOI: 10.3390/a10010020
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 21: Concurrent vs. Exclusive Reading in
           Parallel Decoding of LZ-Compressed Files

    • Authors: Sergio Agostino, Bruno Carpentieri, Raffaele Pizzolante
      First page: 21
      Abstract: Broadcasting a message from one to many processors in a network corresponds to concurrent reading on a random access shared memory parallel machine. Computing the trees of a forest, the level of each node in its tree and the path between two nodes are problems that can easily be solved with concurrent reading in a time logarithmic in the maximum height of a tree. Solving such problems with exclusive reading requires a time logarithmic in the number of nodes, implying message passing between disjoint pairs of processors on a distributed system. Allowing concurrent reading in parallel algorithm design for distributed computing might be advantageous in practice if these problems are faced on shallow trees with some specific constraints. We show an application to LZC (Lempel-Ziv-Compress)-compressed file decoding, whose parallelization employs these computations on such trees for realistic data. On the other hand, zipped files do not have this advantage, since they are compressed by the Lempel–Ziv sliding window technique.
      PubDate: 2017-01-28
      DOI: 10.3390/a10010021
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 22: Evaluation of Diversification Techniques
           for Legal Information Retrieval

    • Authors: Marios Koniaris, Ioannis Anagnostopoulos, Yannis Vassiliou
      First page: 22
      Abstract: “Public legal information from all countries and international institutions is part of the common heritage of humanity. Maximizing access to this information promotes justice and the rule of law”. In accordance with the aforementioned declaration on free access to law by legal information institutes of the world, a plethora of legal information is available through the Internet, while the provision of legal information has never before been easier. Given that law is accessed by a much wider group of people, the majority of whom are not legally trained or qualified, diversification techniques should be employed in the context of legal information retrieval, as to increase user satisfaction. We address the diversification of results in legal search by adopting several state of the art methods from the web search, network analysis and text summarization domains. We provide an exhaustive evaluation of the methods, using a standard dataset from the common law domain that we objectively annotated with relevance judgments for this purpose. Our results: (i) reveal that users receive broader insights across the results they get from a legal information retrieval system; (ii) demonstrate that web search diversification techniques outperform other approaches (e.g., summarization-based, graph-based methods) in the context of legal diversification; and (iii) offer balance boundaries between reinforcing relevant documents or sampling the information space around the legal query.
      PubDate: 2017-01-29
      DOI: 10.3390/a10010022
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 23: An Architectural Based Framework for the
           Distributed Collection, Analysis and Query from Inhomogeneous Time Series
           Data Sets and Wearables for Biofeedback Applications

    • Authors: James Lee, David Rowlands, Nicholas Jackson, Raymond Leadbetter, Tomohito Wada, Daniel James
      First page: 23
      Abstract: The increasing professionalism of sports persons and desire of consumers to imitate this has led to an increased metrification of sport. This has been driven in no small part by the widespread availability of comparatively cheap assessment technologies and, more recently, wearable technologies. Historically, whilst these have produced large data sets, often only the most rudimentary analysis has taken place (Wisbey et al in: “Quantifying movement demands of AFL football using GPS tracking”). This paucity of analysis is due in no small part to the challenges of analysing large sets of data that are often from disparate data sources to glean useful key performance indicators, which has been a largely a labour intensive process. This paper presents a framework that can be cloud based for the gathering, storing and algorithmic interpretation of large and inhomogeneous time series data sets. The framework is architecture based and technology agnostic in the data sources it can gather, and presents a model for multi set analysis for inter- and intra- devices and individual subject matter. A sample implementation demonstrates the utility of the framework for sports performance data collected from distributed inertial sensors in the sport of swimming.
      PubDate: 2017-02-01
      DOI: 10.3390/a10010023
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 24: Problems on Finite Automata and the
           Exponential Time Hypothesis

    • Authors: Henning Fernau, Andreas Krebs
      First page: 24
      Abstract: We study several classical decision problems on finite automata under the (Strong) Exponential Time Hypothesis. We focus on three types of problems: universality, equivalence, and emptiness of intersection. All these problems are known to be CoNP-hard for nondeterministic finite automata, even when restricted to unary input alphabets. A different type of problems on finite automata relates to aperiodicity and to synchronizing words. We also consider finite automata that work on commutative alphabets and those working on two-dimensional words.
      PubDate: 2017-02-05
      DOI: 10.3390/a10010024
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 25: An On-Line Tracker for a Stochastic Chaotic
           System Using Observer/Kalman Filter Identification Combined with Digital
           Redesign Method

    • Authors: Tseng-Hsu Chien, Yeong-Chin Chen
      First page: 25
      Abstract: This is the first paper to present such a digital redesign method for the (conventional) OKID system and apply this novel technique for nonlinear system identification. First, the Observer/Kalman filter Identification (OKID) method is used to obtain the lower-order state-space model for a stochastic chaos system. Then, a digital redesign approach with the high-gain property is applied to improve and replace the observer identified by OKID. Therefore, the proposed OKID combined with an observer-based digital redesign novel tracker not only suppresses the uncertainties and the nonlinear perturbations, but also improves more accurate observation parameters of OKID for complex Multi-Input Multi-Output systems. In this research, Chen’s stochastic chaotic system is used as an illustrative example to demonstrate the effectiveness and excellence of the proposed methodology.
      PubDate: 2017-02-15
      DOI: 10.3390/a10010025
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 26: Analysis and Improvement of Fireworks
           Algorithm

    • Authors: Xi-Guang Li, Shou-Fei Han, Chang-Qing Gong
      First page: 26
      Abstract: The Fireworks Algorithm is a recently developed swarm intelligence algorithm to simulate the explosion process of fireworks. Based on the analysis of each operator of Fireworks Algorithm (FWA), this paper improves the FWA and proves that the improved algorithm converges to the global optimal solution with probability 1. The proposed algorithm improves the goal of further boosting performance and achieving global optimization where mainly include the following strategies. Firstly using the opposition-based learning initialization population. Secondly a new explosion amplitude mechanism for the optimal firework is proposed. In addition, the adaptive t-distribution mutation for non-optimal individuals and elite opposition-based learning for the optimal individual are used. Finally, a new selection strategy, namely Disruptive Selection, is proposed to reduce the running time of the algorithm compared with FWA. In our simulation, we apply the CEC2013 standard functions and compare the proposed algorithm (IFWA) with SPSO2011, FWA, EFWA and dynFWA. The results show that the proposed algorithm has better overall performance on the test functions.
      PubDate: 2017-02-17
      DOI: 10.3390/a10010026
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 27: Fragile Watermarking for Image
           Authentication Using the Characteristic of SVD

    • Authors: Heng Zhang, Chengyou Wang, Xiao Zhou
      First page: 27
      Abstract: Digital image authentication has become a hot topic in the last few years. In this paper, a pixel-based fragile watermarking method is presented for image tamper identification and localization. By analyzing the left and right singular matrices of SVD, it is found that the matrix product between the first column of the left singular matrix and the transposition of the first column in the right singular matrix is closely related to the image texture features. Based on this characteristic, a binary watermark consisting of image texture information is generated and inserted into the least significant bit (LSB) of the original host image. To improve the security of the presented algorithm, the Arnold transform is applied twice in the watermark embedding process. Experimental results indicate that the proposed watermarking algorithm has high security and perceptual invisibility. Moreover, it can detect and locate the tampered region effectively for various malicious attacks.
      PubDate: 2017-02-17
      DOI: 10.3390/a10010027
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 28: Mining Domain-Specific Design Patterns: A
           Case Study †

    • Authors: Vassiliki Gkantouna, Giannis Tzimas
      First page: 28
      Abstract: Domain-specific design patterns provide developers with proven solutions to common design problems that arise, particularly in a target application domain, facilitating them to produce quality designs in the domain contexts. However, research in this area is not mature and there are no techniques to support their detection. Towards this end, we propose a methodology which, when applied on a collection of websites in a specific domain, facilitates the automated identification of domain-specific design patterns. The methodology automatically extracts the conceptual models of the websites, which are subsequently analyzed in terms of all of the reusable design fragments used in them for supporting common domain functionalities. At the conceptual level, we consider these fragments as recurrent patterns consisting of a configuration of front-end interface components that interrelate each other and interact with end-users to support certain functionality. By performing a pattern-based analysis of the models, we locate the occurrences of all the recurrent patterns in the various website designs which are then evaluated towards their consistent use. The detected patterns can be used as building blocks in future designs, assisting developers to produce consistent and quality designs in the target domain. To support our case, we present a case study for the educational domain.
      PubDate: 2017-02-21
      DOI: 10.3390/a10010028
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 29: Stable Analysis of Compressive Principal
           Component Pursuit

    • Authors: Qingshan You, Qun Wan
      First page: 29
      Abstract: Compressive principal component pursuit (CPCP) recovers a target matrix that is a superposition of low-complexity structures from a small set of linear measurements. Pervious works mainly focus on the analysis of the existence and uniqueness. In this paper, we address its stability. We prove that the solution to the related convex programming of CPCP gives an estimate that is stable to small entry-wise noise. We also provide numerical simulation results to support our result. Numerical results show that the solution to the related convex program is stable to small entry-wise noise under board condition.
      PubDate: 2017-02-21
      DOI: 10.3390/a10010029
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 30: Towards Efficient Positional Inverted Index
           †

    • Authors: Petr Procházka, Jan Holub
      First page: 30
      Abstract: We address the problem of positional indexing in the natural language domain. The positional inverted index contains the information of the word positions. Thus, it is able to recover the original text file, which implies that it is not necessary to store the original file. Our Positional Inverted Self-Index (PISI) stores the word position gaps encoded by variable byte code. Inverted lists of single terms are combined into one inverted list that represents the backbone of the text file since it stores the sequence of the indexed words of the original file. The inverted list is synchronized with a presentation layer that stores separators, stop words, as well as variants of the indexed words. The Huffman coding is used to encode the presentation layer. The space complexity of the PISI inverted list is O ( ( N − n ) ⌈ log 2 b N ⌉ + ( ⌊ N − n α ⌋ + n ) × ( ⌈ log 2 b n ⌉ + 1 ) ) where N is a number of stems, n is a number of unique stems, α is a step/period of the back pointers in the inverted list and b is the size of the word of computer memory given in bits. The space complexity of the presentation layer is O ( − ∑ i = 1 N ⌈ log 2 p i n ( i ) ⌉ − ∑ j = 1 N ′ ⌈ log 2 p j ′ ⌉ + N ) with respect to p i n ( i ) as a probability of a stem variant at position i, p j ′ as the probability of separator or stop word at position j and N ′ as the number of separators and stop words.
      PubDate: 2017-02-22
      DOI: 10.3390/a10010030
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 31: Optimization-Based Approaches to Control of
           Probabilistic Boolean Networks

    • Authors: Koichi Kobayashi, Kunihiko Hiraishi
      First page: 31
      Abstract: Control of gene regulatory networks is one of the fundamental topics in systems biology. In the last decade, control theory of Boolean networks (BNs), which is well known as a model of gene regulatory networks, has been widely studied. In this review paper, our previously proposed methods on optimal control of probabilistic Boolean networks (PBNs) are introduced. First, the outline of PBNs is explained. Next, an optimal control method using polynomial optimization is explained. The finite-time optimal control problem is reduced to a polynomial optimization problem. Furthermore, another finite-time optimal control problem, which can be reduced to an integer programming problem, is also explained.
      PubDate: 2017-02-22
      DOI: 10.3390/a10010031
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 32: A New Quintic Spline Method for Integro
           Interpolation and Its Error Analysis

    • Authors: Feng-Gong Lang
      First page: 32
      Abstract: In this paper, to overcome the innate drawbacks of some old methods, we present a new quintic spline method for integro interpolation. The method is free of any exact end conditions, and it can reconstruct a function and its first order to fifth order derivatives with high accuracy by only using the given integral values of the original function. The approximation properties of the obtained integro quintic spline are well studied and examined. The theoretical analysis and the numerical tests show that the new method is very effective for integro interpolation.
      PubDate: 2017-03-03
      DOI: 10.3390/a10010032
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 33: Large Scale Implementations for Twitter
           Sentiment Classification

    • Authors: Andreas Kanavos, Nikolaos Nodarakis, Spyros Sioutas, Athanasios Tsakalidis, Dimitrios Tsolis, Giannis Tzimas
      First page: 33
      Abstract: Sentiment Analysis on Twitter Data is indeed a challenging problem due to the nature, diversity and volume of the data. People tend to express their feelings freely, which makes Twitter an ideal source for accumulating a vast amount of opinions towards a wide spectrum of topics. This amount of information offers huge potential and can be harnessed to receive the sentiment tendency towards these topics. However, since no one can invest an infinite amount of time to read through these tweets, an automated decision making approach is necessary. Nevertheless, most existing solutions are limited in centralized environments only. Thus, they can only process at most a few thousand tweets. Such a sample is not representative in order to define the sentiment polarity towards a topic due to the massive number of tweets published daily. In this work, we develop two systems: the first in the MapReduce and the second in the Apache Spark framework for programming with Big Data. The algorithm exploits all hashtags and emoticons inside a tweet, as sentiment labels, and proceeds to a classification method of diverse sentiment types in a parallel and distributed manner. Moreover, the sentiment analysis tool is based on Machine Learning methodologies alongside Natural Language Processing techniques and utilizes Apache Spark’s Machine learning library, MLlib. In order to address the nature of Big Data, we introduce some pre-processing steps for achieving better results in Sentiment Analysis as well as Bloom filters to compact the storage size of intermediate data and boost the performance of our algorithm. Finally, the proposed system was trained and validated with real data crawled by Twitter, and, through an extensive experimental evaluation, we prove that our solution is efficient, robust and scalable while confirming the quality of our sentiment identification.
      PubDate: 2017-03-04
      DOI: 10.3390/a10010033
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 34: A Novel, Gradient Boosting Framework for
           Sentiment Analysis in Languages where NLP Resources Are Not Plentiful: A
           Case Study for Modern Greek

    • Authors: Vasileios Athanasiou, Manolis Maragoudakis
      First page: 34
      Abstract: Sentiment analysis has played a primary role in text classification. It is an undoubted fact that some years ago, textual information was spreading in manageable rates; however, nowadays, such information has overcome even the most ambiguous expectations and constantly grows within seconds. It is therefore quite complex to cope with the vast amount of textual data particularly if we also take the incremental production speed into account. Social media, e-commerce, news articles, comments and opinions are broadcasted on a daily basis. A rational solution, in order to handle the abundance of data, would be to build automated information processing systems, for analyzing and extracting meaningful patterns from text. The present paper focuses on sentiment analysis applied in Greek texts. Thus far, there is no wide availability of natural language processing tools for Modern Greek. Hence, a thorough analysis of Greek, from the lexical to the syntactical level, is difficult to perform. This paper attempts a different approach, based on the proven capabilities of gradient boosting, a well-known technique for dealing with high-dimensional data. The main rationale is that since English has dominated the area of preprocessing tools and there are also quite reliable translation services, we could exploit them to transform Greek tokens into English, thus assuring the precision of the translation, since the translation of large texts is not always reliable and meaningful. The new feature set of English tokens is augmented with the original set of Greek, consequently producing a high dimensional dataset that poses certain difficulties for any traditional classifier. Accordingly, we apply gradient boosting machines, an ensemble algorithm that can learn with different loss functions providing the ability to work efficiently with high dimensional data. Moreover, for the task at hand, we deal with a class imbalance issues since the distribution of sentiments in real-world applications often displays issues of inequality. For example, in political forums or electronic discussions about immigration or religion, negative comments overwhelm the positive ones. The class imbalance problem was confronted using a hybrid technique that performs a variation of under-sampling the majority class and over-sampling the minority class, respectively. Experimental results, considering different settings, such as translation of tokens against translation of sentences, consideration of limited Greek text preprocessing and omission of the translation phase, demonstrated that the proposed gradient boosting framework can effectively cope with both high-dimensional and imbalanced datasets and performs significantly better than a plethora of traditional machine learning classification approaches in terms of precision and recall measures.
      PubDate: 2017-03-06
      DOI: 10.3390/a10010034
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 35: A Geo-Clustering Approach for the Detection
           of Areas-of-Interest and Their Underlying Semantics

    • Authors: Evaggelos Spyrou, Michalis Korakakis, Vasileios Charalampidis, Apostolos Psallas, Phivos Mylonas
      First page: 35
      Abstract: Living in the “era of social networking”, we are experiencing a data revolution, generating an astonishing amount of digital information every single day. Due to this proliferation of data volume, there has been an explosion of new application domains for information mined from social networks. In this paper, we leverage this “socially-generated knowledge” (i.e., user-generated content derived from social networks) towards the detection of areas-of-interest within an urban region. These large and homogeneous areas contain multiple points-of-interest which are of special interest to particular groups of people (e.g., tourists and/or consumers). In order to identify them, we exploit two types of metadata, namely location-based information included within geo-tagged photos that we collect from Flickr, along with plain simple textual information from user-generated tags. We propose an algorithm that divides a predefined geographical area (i.e., the center of Athens, Greece) into “tile”-shaped sub-regions and based on an iterative merging procedure, it aims to detect larger, cohesive areas. We examine the performance of the algorithm both in a qualitative and quantitative manner. Our experiments demonstrate that the proposed geo-clustering algorithm is able to correctly detect regions that contain popular tourist attractions within them with very promising results.
      PubDate: 2017-03-18
      DOI: 10.3390/a10010035
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 9, Pages 62: Noise Reduction of Steel Cord Conveyor Belt
           

    • Authors: Hong-Wei Ma, Hong-Wei Fan, Qing-Hua Mao, Xu-Hui Zhang, Wang Xing
      First page: 62
      Abstract: In order to reduce the noise of a defect electromagnetic signal of the steel cord conveyor belt used in coal mines, a new signal noise reduction method by combined use of the improved threshold wavelet and Empirical Mode Decomposition (EMD) is proposed. Firstly, the denoising method based on the improved threshold wavelet is applied to reduce the noise of a defect electromagnetic signal obtained by an electromagnetic testing system. Then, the EMD is used to decompose the denoised signal and then the effective Intrinsic Mode Function (IMF) is extracted by the dominant eigenvalue strategy. Finally, the signal reconstruction is carried out by utilizing the obtained IMF. In order to verify the proposed noise reduction method, the experiments are carried out in two cases including the defective joint and steel wire rope break. The experimental results show that the proposed method in this paper obtains the higher Signal to Noise Ratio (SNR) for the defect electromagnetic signal noise reduction of steel cord conveyor belts.
      PubDate: 2016-09-26
      DOI: 10.3390/a9040062
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 63: A Practical and Robust Execution Time-Frame
           Procedure for the Multi-Mode Resource-Constrained Project Scheduling
           Problem with Minimal and Maximal Time Lags

    • Authors: Angela Chen, Yun-Chia Liang, Jose Padilla
      First page: 63
      Abstract: Modeling and optimizing organizational processes, such as the one represented by the Resource-Constrained Project Scheduling Problem (RCPSP), improve outcomes. Based on assumptions and simplification, this model tackles the allocation of resources so that organizations can continue to generate profits and reinvest in future growth. Nonetheless, despite all of the research dedicated to solving the RCPSP and its multi-mode variations, there is no standardized procedure that can guide project management practitioners in their scheduling tasks. This is mainly because many of the proposed approaches are either based on unrealistic/oversimplified scenarios or they propose solution procedures not easily applicable or even feasible in real-life situations. In this study, we solve a more true-to-life and complex model, Multimode RCPSP with minimal and maximal time lags (MRCPSP/max). The complexity of the model solved is presented, and the practicality of the proposed approach is justified depending on only information that is available for every project regardless of its industrial context. The results confirm that it is possible to determine a robust makespan and to calculate an execution time-frame with gaps lower than 11% between their lower and upper bounds. In addition, in many instances, the solved lower bound obtained was equal to the best-known optimum.
      PubDate: 2016-09-24
      DOI: 10.3390/a9040063
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 64: Theorietage der Gesellschaft für Informatik
           in Speyer 2015—Special Issue

    • Authors: Henning Fernau
      First page: 64
      Abstract: We briefly report on the national workshops on Formal Languages and Automata Theory as well as on Algorithms and Complexity Theory held in early Autumn, 2015.
      PubDate: 2016-09-26
      DOI: 10.3390/a9040064
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 65: Local Convergence Analysis of an Eighth
           Order Scheme Using Hypothesis Only on the First Derivative

    • Authors: Ioannis Argyros, Ramandeep Behl, Sandile Motsa
      First page: 65
      Abstract: In this paper, we propose a local convergence analysis of an eighth order three-step method to approximate a locally unique solution of a nonlinear equation in a Banach space setting. Further, we also study the dynamic behaviour of that scheme. In an earlier study, Sharma and Arora (2015) did not discuss these properties. Furthermore, the order of convergence was shown using Taylor series expansions and hypotheses up to the fourth order derivative or even higher of the function involved which restrict the applicability of the proposed scheme. However, only the first order derivatives appear in the proposed scheme. To overcome this problem, we present the hypotheses for the proposed scheme maximum up to first order derivative. In this way, we not only expand the applicability of the methods but also suggest convergence domain. Finally, a variety of concrete numerical examples are proposed where earlier studies can not be applied to obtain the solutions of nonlinear equations on the other hand our study does not exhibit this type of problem/restriction.
      PubDate: 2016-09-29
      DOI: 10.3390/a9040065
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 66: Fault Sensing Using Fractal Dimension and
           Wavelet

    • Authors: Mei Wang, Liang Zhu, Yanan Guo
      First page: 66
      Abstract: A new fusion sensing (FS) method was proposed by using the improved fractal box dimension (IFBD) and a developed maximum wavelet coefficient (DMWC) for fault sensing of an online power cable. There are four strategies that were used. Firstly, the traditional fractal box dimension was improved to enlarge the feature distances between the different fault classes. Secondly, the IFBD recognition algorithm was proposed by using the improved fractal dimension feature extracted from the three-phase currents for the first stage of fault recognition. Thirdly, the DMWC recognition algorithm was developed based on the K-transform and wavelet analysis to establish the relationship between the maximum wavelet coefficient and the fault class. Fourthly, the FS method was formed by combining the IFBD algorithm and the DMWC algorithm in order to recognize the 10 types of short circuit faults of online power. The designed test system proved that the FS method increased the fault recognition accuracy obviously. In addition, the parameters of the initial angle, transient resistance, and fault distance had no influence on the FS method.
      PubDate: 2016-10-11
      DOI: 10.3390/a9040066
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 67: Comparison of Different Algorithms to
           Orthorectify WorldView-2 Satellite Imagery

    • Authors: Oscar Belfiore, Claudio Parente
      First page: 67
      Abstract: Due to their level of spatial detail (pixel dimensions equal to or less than 1 m), very high-resolution satellite images (VHRSIs) need particular georeferencing and geometric corrections which require careful orthorectification. Although there are several dedicated algorithms, mainly commercial and free software for geographic information system (GIS) and remote sensing applications, the quality of the results may be inadequate in terms of the representation scale for which these images are intended. This paper compares the most common orthorectification algorithms in order to define the best approach for VHRSIs. Both empirical models (such as 2D polynomial functions, PFs; or 3D rational polynomial functions, RPFs) and rigorous physical and deterministic models (such as Toutin) are considered. Ground control points (GCPs) and check points (CPs)—whose positions in the image as, well as in the real world, are known—support algorithm applications. Tests were executed on a WorldView-2 (WV-2) panchromatic image of an area near the Gulf of Naples in Campania (Italy) to establish the best-performing algorithm. Combining 3D RPFs with 2D PFs produced the best results.
      PubDate: 2016-10-11
      DOI: 10.3390/a9040067
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 68: An Overview on the Applications of Matrix
           Theory in Wireless Communications and Signal Processing

    • Authors: Xu Wang, Erchin Serpedin
      First page: 68
      Abstract: This paper overviews the key applications enabled by matrix theory in two major fields of interest in electrical engineering, namely wireless communications and signal processing. The paper focuses on the fundamental role played by matrices in modeling and optimization of wireless communication systems, and in detection, extraction and processing of the information embedded in signals. Among the major applications in wireless communications, the role of matrix representations and decompositions in characterizing multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) communication systems is described. In addition, this paper points out the important contribution made by matrices in solving signal estimation and detection problems. Special attention is given to the implementation of matrices in sensor array signal processing and the design of adaptive filters. Furthermore, the crucial role played by matrices in representing and processing digital images is depicted by several illustrative applications. This paper concludes with some applications of matrix theory in the area of compressive sensing of signals and by outlining a few open research problems for future study.
      PubDate: 2016-10-14
      DOI: 10.3390/a9040068
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 69: A New Fuzzy Harmony Search Algorithm Using
           Fuzzy Logic for Dynamic Parameter Adaptation

    • Authors: Cinthia Peraza, Fevrier Valdez, Mario Garcia, Patricia Melin, Oscar Castillo
      First page: 69
      Abstract: In this paper, a new fuzzy harmony search algorithm (FHS) for solving optimization problems is presented. FHS is based on a recent method using fuzzy logic for dynamic adaptation of the harmony memory accepting (HMR) and pitch adjustment (PArate) parameters that improve the convergence rate of traditional harmony search algorithm (HS). The objective of the method is to dynamically adjust the parameters in the range from 0.7 to 1. The impact of using fixed parameters in the harmony search algorithm is discussed and a strategy for efficiently tuning these parameters using fuzzy logic is presented. The FHS algorithm was successfully applied to different benchmarking optimization problems. The results of simulation and comparison studies demonstrate the effectiveness and efficiency of the proposed approach.
      PubDate: 2016-10-14
      DOI: 10.3390/a9040069
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 70: Plant Electrical Signal Classification Based
           on Waveform Similarity

    • Authors: Yang Chen, Dong-Jie Zhao, Zi-Yang Wang, Zhong-Yi Wang, Guiliang Tang, Lan Huang
      First page: 70
      Abstract: (1) Background: Plant electrical signals are important physiological traits which reflect plant physiological state. As a kind of phenotypic data, plant action potential (AP) evoked by external stimuli—e.g., electrical stimulation, environmental stress—may be associated with inhibition of gene expression related to stress tolerance. However, plant AP is a response to environment changes and full of variability. It is an aperiodic signal with refractory period, discontinuity, noise, and artifacts. In consequence, there are still challenges to automatically recognize and classify plant AP; (2) Methods: Therefore, we proposed an AP recognition algorithm based on dynamic difference threshold to extract all waveforms similar to AP. Next, an incremental template matching algorithm was used to classify the AP and non-AP waveforms; (3) Results: Experiment results indicated that the template matching algorithm achieved a classification rate of 96.0%, and it was superior to backpropagation artificial neural networks (BP-ANNs), supported vector machine (SVM) and deep learning method; (4) Conclusion: These findings imply that the proposed methods are likely to expand possibilities for rapidly recognizing and classifying plant action potentials in the database in the future.
      PubDate: 2016-10-15
      DOI: 10.3390/a9040070
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 71: A Variable Block Insertion Heuristic for the
           Blocking Flowshop Scheduling Problem with Total Flowtime Criterion

    • Authors: Mehmet Tasgetiren, Quan-Ke Pan, Damla Kizilay, Kaizhou Gao
      First page: 71
      Abstract: In this paper, we present a variable block insertion heuristic (VBIH) algorithm to solve the blocking flowshop scheduling problem with the total flowtime criterion. In the VBIH algorithm, we define a minimum and a maximum block size. After constructing the initial sequence, the VBIH algorithm starts with a minimum block size being equal to one. It removes the block from the current sequence and inserts it into the partial sequence sequentially with a predetermined move size. The sequence, which is obtained after several block moves, goes under a variable local search (VLS), which is based on traditional insertion and swap neighborhood structures. If the new sequence obtained after the VLS local search is better than the current sequence, it replaces the current sequence. As long as it improves, it keeps the same block size. However, if it does not improve, the block size is incremented by one and a simulated annealing-type of acceptance criterion is used to accept the current sequence. This process is repeated until the block size reaches at the maximum block size. Furthermore, we present a novel constructive heuristic, which is based on the profile fitting heuristic from the literature. The proposed constructive heuristic is able to further improve the best known solutions for some larger instances in a few seconds. Parameters of the constructive heuristic and the VBIH algorithm are determined through a design of experiment approach. Extensive computational results on the Taillard’s well-known benchmark suite show that the proposed VBIH algorithm outperforms the discrete artificial bee colony algorithm, which is one of the most efficient algorithms recently in the literature. Ultimately, 52 out of the 150 best known solutions are further improved with substantial margins.
      PubDate: 2016-10-20
      DOI: 10.3390/a9040071
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 72: Engineering a Combinatorial Laplacian
           Solver: Lessons Learned

    • Authors: Daniel Hoske, Dimitar Lukarski, Henning Meyerhenke, Michael Wegner
      First page: 72
      Abstract: Linear system solving is a main workhorse in applied mathematics. Recently, theoretical computer scientists contributed sophisticated algorithms for solving linear systems with symmetric diagonally-dominant (SDD) matrices in provably nearly-linear time. These algorithms are very interesting from a theoretical perspective, but their practical performance was unclear. Here, we address this gap. We provide the first implementation of the combinatorial solver by Kelner et al. (STOC 2013), which is appealing for implementation due to its conceptual simplicity. The algorithm exploits that a Laplacian matrix (which is SDD) corresponds to a graph; solving symmetric Laplacian linear systems amounts to finding an electrical flow in this graph with the help of cycles induced by a spanning tree with the low-stretch property. The results of our experiments are ambivalent. While they confirm the predicted nearly-linear running time, the constant factors make the solver much slower for reasonable inputs than basic methods with higher asymptotic complexity. We were also not able to use the solver effectively as a smoother or preconditioner. Moreover, while spanning trees with lower stretch indeed reduce the solver’s running time, we experience again a discrepancy in practice: in our experiments, simple spanning tree algorithms perform better than those with a guaranteed low stretch. We expect that our results provide insights for future improvements of combinatorial linear solvers.
      PubDate: 2016-10-31
      DOI: 10.3390/a9040072
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 73: Community Structure Detection for Directed
           Networks through Modularity Optimisation

    • Authors: Lingjian Yang, Jonathan Silva, Lazaros Papageorgiou, Sophia Tsoka
      First page: 73
      Abstract: Networks constitute powerful means of representing various types of complex systems, where nodes denote the system entities and edges express the interactions between the entities. An important topological property in complex networks is community structure, where the density of edges within subgraphs is much higher than across different subgraphs. Each of these subgraphs forms a community (or module). In literature, a metric called modularity is defined that measures the quality of a partition of nodes into different mutually exclusive communities. One means of deriving community structure is modularity maximisation. In this paper, a novel mathematical programming-based model, DiMod, is proposed that tackles the problem of maximising modularity for directed networks.
      PubDate: 2016-11-01
      DOI: 10.3390/a9040073
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 74: An Optimization Algorithm for the Design of
           an Irregularly-Shaped Bridge Based on the Orthogonal Test and Analytic
           Hierarchy Process

    • Authors: Hanbing Liu, Xin He, Xianqiang Wang, Yubo Jiao, Gang Song
      First page: 74
      Abstract: Irregularly-shaped bridges are usually adopted to connect the main bridge and ramps in urban overpasses, which are under significant flexion-torsion coupling effects and in complicated stress states. In irregular-shaped bridge design, the parameters such as ramp radius, bifurcation diaphragm stiffness, box girder height, and supporting condition could affect structural performance in different manners. In this paper, the influence of various parameters on three indices, including maximum stress, the stress variation coefficient, and the fundamental frequency of torsional vibration, is investigated and analyzed based on orthogonal test method. Through orthogonal analysis, the major influence parameters and corresponding optimal values for these indices are achieved. Combining with the analytic hierarchy process (AHP), the hierarchical structure model of the multi-indices orthogonal test is established and a comprehensive weight analysis method is proposed to reflect the parameter influence on overall mechanical properties of an irregularly-shaped bridge. Influence order and optimal values of parameters for overall mechanical properties are determined based on the weight of factors and levels calculated by the comprehensive weight analysis method. The results indicate that the comprehensive weight analysis method is superior to the overall balance method, which verifies the effectiveness and accuracy of the comprehensive weight analysis in the parameter optimization of the multi-indices orthogonal test for an irregularly-shaped bridge. Optimal parameters obtained in this paper can provide reference and guidance for parameter control in irregularly-shaped bridge design.
      PubDate: 2016-11-05
      DOI: 10.3390/a9040074
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 75: A Modified Iterative Algorithm for Split
           Feasibility Problems of Right Bregman Strongly Quasi-Nonexpansive Mappings
           in Banach Spaces with Applications

    • Authors: Anantachai Padcharoen, Poom Kumam, Yeol Cho, Phatiphat Thounthong
      First page: 75
      Abstract: In this paper, we present a new iterative scheme for finding a common element of the solution set F of the split feasibility problem and the fixed point set F ( T ) of a right Bregman strongly quasi-nonexpansive mapping T in p-uniformly convex Banach spaces which are also uniformly smooth. We prove strong convergence theorem of the sequences generated by our scheme under some appropriate conditions in real p-uniformly convex and uniformly smooth Banach spaces. Furthermore, we give some examples and applications to illustrate our main results in this paper. Our results extend and improve the recent ones of some others in the literature.
      PubDate: 2016-11-10
      DOI: 10.3390/a9040075
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 76: A Procedure for Identification of
           Appropriate State Space and ARIMA Models Based on Time-Series
           Cross-Validation

    • Authors: Patrícia Ramos, José Oliveira
      First page: 76
      Abstract: In this work, a cross-validation procedure is used to identify an appropriate Autoregressive Integrated Moving Average model and an appropriate state space model for a time series. A minimum size for the training set is specified. The procedure is based on one-step forecasts and uses different training sets, each containing one more observation than the previous one. All possible state space models and all ARIMA models where the orders are allowed to range reasonably are fitted considering raw data and log-transformed data with regular differencing (up to second order differences) and, if the time series is seasonal, seasonal differencing (up to first order differences). The value of root mean squared error for each model is calculated averaging the one-step forecasts obtained. The model which has the lowest root mean squared error value and passes the Ljung–Box test using all of the available data with a reasonable significance level is selected among all the ARIMA and state space models considered. The procedure is exemplified in this paper with a case study of retail sales of different categories of women’s footwear from a Portuguese retailer, and its accuracy is compared with three reliable forecasting approaches. The results show that our procedure consistently forecasts more accurately than the other approaches and the improvements in the accuracy are significant.
      PubDate: 2016-11-09
      DOI: 10.3390/a9040076
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 77: Algorithms for Drug Sensitivity Prediction

    • Authors: Carlos De Niz, Raziur Rahman, Xiangyuan Zhao, Ranadip Pal
      First page: 77
      Abstract: Precision medicine entails the design of therapies that are matched for each individual patient. Thus, predictive modeling of drug responses for specific patients constitutes a significant challenge for personalized therapy. In this article, we consider a review of approaches that have been proposed to tackle the drug sensitivity prediction problem especially with respect to personalized cancer therapy. We first discuss modeling approaches that are based on genomic characterizations alone and further the discussion by including modeling techniques that integrate both genomic and functional information. A comparative analysis of the prediction performance of four representative algorithms, elastic net, random forest, kernelized Bayesian multi-task learning and deep learning, reflecting the broad classes of regularized linear, ensemble, kernelized and neural network-based models, respectively, has been included in the paper. The review also considers the challenges that need to be addressed for successful implementation of the algorithms in clinical practice.
      PubDate: 2016-11-17
      DOI: 10.3390/a9040077
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 78: A Modified Cloud Particles Differential
           Evolution Algorithm for Real-Parameter Optimization

    • Authors: Wei Li
      First page: 78
      Abstract: The issue of exploration-exploitation remains one of the most challenging tasks within the framework of evolutionary algorithms. To effectively balance the exploration and exploitation in the search space, this paper proposes a modified cloud particles differential evolution algorithm (MCPDE) for real-parameter optimization. In contrast to the original Cloud Particles Differential Evolution (CPDE) algorithm, firstly, control parameters adaptation strategies are designed according to the quality of the control parameters. Secondly, the inertia factor is introduced to effectively keep a better balance between exploration and exploitation. Accordingly, this is helpful for maintaining the diversity of the population and discouraging premature convergence. In addition, the opposition mechanism and the orthogonal crossover are used to increase the search ability during the evolutionary process. Finally, CEC2013 contest benchmark functions are selected to verify the feasibility and effectiveness of the proposed algorithm. The experimental results show that the proposed MCPDE is an effective method for global optimization problems.
      PubDate: 2016-11-18
      DOI: 10.3390/a9040078
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 79: A Two-Stage Method to Test the Robustness of
           the Generalized Approximate Message Passing Algorithm

    • Authors: Qingshan You, Yongjie Luo, Qun Wan
      First page: 79
      Abstract: We propose a two-stage method to test the robustness of the generalized approximate message passing algorithm (GAMP). A pursuit process based on the marginal posterior probability is inserted in the standard GAMP algorithm to find the support of a sparse vector, and a revised GAMP process is used to estimate the amplitudes of the support. The numerical experiments with simulation and real world data confirm the robustness and performance of our proposed algorithm.
      PubDate: 2016-11-22
      DOI: 10.3390/a9040079
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 80: Short-Term Load Forecasting Based on the
           Analysis of User Electricity Behavior

    • Authors: Yuancheng Li, Panpan Guo, Xiang Li
      First page: 80
      Abstract: The smart meter is an important part of the smart grid, and in order to take full advantage of smart meter data, this paper mines the electricity behaviors of smart meter users to improve the accuracy of load forecasting. First, the typical day loads of users are calculated separately according to different date types (ordinary workdays, day before holidays, holidays). Second, the similarity between user electricity behaviors is mined and the user electricity loads are clustered to classify the users with similar behaviors into the same cluster. Finally, the load forecasting model based on the Online Sequential Extreme Learning Machine (OS-ELM) is applied to different clusters to conduct load forecasting and the load forecast is summed to obtain the system load. In order to prove the validity of the proposed method, we performed simulation experiments on the MATLAB platform using smart meter data from the Ireland electric power cooperation. The experimental results show that the proposed method is able to mine the user electricity behaviors deeply, improve the accuracy of load forecasting by the reasonable clustering of users, and reveal the relationship between forecasting accuracy and cluster numbers.
      PubDate: 2016-11-23
      DOI: 10.3390/a9040080
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 81: Cross-Coupled Contouring Control of
           Multi-DOF Robotic Manipulator

    • Authors: Puren Ouyang, Yuqi Hu, Wenhui Yue, Deshun Liu
      First page: 81
      Abstract: Reduction of contour error is a very important issue for high precise contour tracking applications, and many control systems were proposed to deal with contour tracking problems for two/three axial translational motion systems. However, there is no research on cross-coupled contour tracking control for serial multi-DOF robot manipulators. In this paper, the contouring control of multi-DOF serial manipulators is developed for the first time and a new cross-coupled PD (CC-PD) control law is proposed, based on contour errors of the end-effector and tracking errors of the joints. It is a combination of PD control for trajectory tracking at joint level and PD control for contour tracking at the end-effector level. The contour error of the end-effector is transformed to the equivalent tracking errors of the joints using the Jacobian regulation, and the CC-PD control law is implemented in the joint level. Stability analysis of the proposed CC-PD control system is conducted using the Lyapunov method, followed by some simulation studies for linear and nonlinear contour tracking to verify the effectiveness of the proposed CC-PD control system.
      PubDate: 2016-11-24
      DOI: 10.3390/a9040081
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 82: Linear Algorithms for Radioelectric Spectrum
           Forecast

    • Authors: Luis Pedraza, Cesar Hernandez, Ingrid Paez, Jorge Ortiz, E. Rodriguez-Colina
      First page: 82
      Abstract: This paper presents the development and evaluation of two linear algorithms for forecasting reception power for different channels at an assigned spectrum band of global systems for mobile communications (GSM), in order to analyze the spatial opportunity for reuse of frequencies by secondary users (SUs) in a cognitive radio (CR) network. The algorithms employed correspond to seasonal autoregressive integrated moving average (SARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH), which allow for a forecast of channel occupancy status. Results are evaluated using the following criteria: availability and occupancy time for channels, different types of mean absolute error, and observation time. The contributions of this work include a more integral forecast as the algorithm not only forecasts reception power but also the occupancy and availability time of a channel to determine its precision percentage during the use by primary users (PUs) and SUs within a CR system. Algorithm analyses demonstrate a better performance for SARIMA over GARCH algorithm in most of the evaluated variables.
      PubDate: 2016-12-02
      DOI: 10.3390/a9040082
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 83: Nonsmooth Levenberg-Marquardt Type Method
           for Solving a Class of Stochastic Linear Complementarity Problems with
           Finitely Many Elements

    • Authors: Zhimin Liu, Shouqiang Du, Ruiying Wang
      First page: 83
      Abstract: Our purpose of this paper is to solve a class of stochastic linear complementarity problems (SLCP) with finitely many elements. Based on a new stochastic linear complementarity problem function, a new semi-smooth least squares reformulation of the stochastic linear complementarity problem is introduced. For solving the semi-smooth least squares reformulation, we propose a feasible nonsmooth Levenberg–Marquardt-type method. The global convergence properties of the nonsmooth Levenberg–Marquardt-type method are also presented. Finally, the related numerical results illustrate that the proposed method is efficient for the related refinery production problem and the large-scale stochastic linear complementarity problems.
      PubDate: 2016-12-06
      DOI: 10.3390/a9040083
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 84: Evaluation of Cloud Services: A Fuzzy
           Multi-Criteria Group Decision Making Method

    • Authors: Santoso Wibowo, Hepu Deng, Wei Xu
      First page: 84
      Abstract: This paper presents a fuzzy multi-criteria group decision making method for evaluating the performance of Cloud services in an uncertain environment. Intuitionistic fuzzy numbers are used to better model the subjectivity and imprecision in the performance evaluation process. An effective algorithm is developed based on the technique for order preference by similarity to the ideal solution and the Choquet integral operator for adequately solving the performance evaluation problem. An example is presented for demonstrating the applicability of the proposed method for solving the multi-criteria group decision making problem in real situations.
      PubDate: 2016-12-16
      DOI: 10.3390/a9040084
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 85: A Differentiated Anonymity Algorithm for
           Social Network Privacy Preservation

    • Authors: Yuqin Xie, Mingchun Zheng
      First page: 85
      Abstract: Devising methods to publish social network data in a form that affords utility without compromising privacy remains a longstanding challenge, while many existing methods based on k-anonymity algorithms on social networks may result in nontrivial utility loss without analyzing the social network topological structure and without considering the attributes of sparse distribution. Toward this objective, we explore the impact of the attributes of sparse distribution on data utility. Firstly, we propose a new utility metric that emphasizes network structure distortion and attribute value loss. Furthermore, we design and implement a differentiated k-anonymity l-diversity social network anonymity algorithm, which seeks to protect users’ privacy in social networks and increase the usability of the published anonymized data. Its key idea is that it divides a node into two child nodes and only anonymizes sensitive values to satisfy anonymity requirements. The evaluation results show that our method can effectively improve the data utility as compared to generalized anonymizing algorithms.
      PubDate: 2016-12-14
      DOI: 10.3390/a9040085
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 86: Moving Mesh Strategies of Adaptive Methods
           for Solving Nonlinear Partial Differential Equations

    • Authors: Qinjiao Gao, Shenggang Zhang
      First page: 86
      Abstract: This paper proposes moving mesh strategies for the moving mesh methods when solving the nonlinear time dependent partial differential equations (PDEs). Firstly we analyse Huang’s moving mesh PDEs (MMPDEs) and observe that, after Euler discretion they could be taken as one step of the root searching iteration methods. We improve Huang’s MMPDE by adding one Lagrange speed term. The proposed moving mesh PDE could draw the mesh to equidistribution quickly and stably. The numerical algorithm for the coupled system of the original PDE and the moving mesh equation is proposed and the computational experiments are given to illustrate the validity of the new method.
      PubDate: 2016-12-15
      DOI: 10.3390/a9040086
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 87: A No Reference Image Quality Assessment
           Metric Based on Visual Perception

    • Authors: Yan Fu, Shengchun Wang
      First page: 87
      Abstract: Nowadays, how to evaluate image quality reasonably is a basic and challenging problem. In view of the present no reference evaluation methods, they cannot reflect the human visual perception of image quality accurately. In this paper, we propose an efficient general-purpose no reference image quality assessment (NRIQA) method based on visual perception, and effectively integrates human visual characteristics into the NRIQA fields. First, a novel algorithm for salient region extraction is presented. Two characteristics graphs of texture and edging of the original image are added to the Itti model. Due to the normalized luminance coefficients of natural images obey the generalized Gauss probability distribution, we utilize this characteristic to extract statistical features in the regions of interest (ROI) and regions of non-interest respectively. Then, the extracted features are fused to be an input to establish the support vector regression (SVR) model. Finally, the IQA model obtained by training is used to predict the quality of the image. Experimental results show that this method has good predictive ability, and the evaluation effect is better than existing classical algorithms. Moreover, the predicted results are more consistent with human subjective perception, which can accurately reflect the human visual perception to image quality.
      PubDate: 2016-12-16
      DOI: 10.3390/a9040087
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 88: Which, When, and How: Hierarchical
           Clustering with Human–Machine Cooperation

    • Authors: Huanyang Zheng, Jie Wu
      First page: 88
      Abstract: Human–Machine Cooperations (HMCs) can balance the advantages and disadvantages of human computation (accurate but costly) and machine computation (cheap but inaccurate). This paper studies HMCs in agglomerative hierarchical clusterings, where the machine can ask the human some questions. The human will return the answers to the machine, and the machine will use these answers to correct errors in its current clustering results. We are interested in the machine’s strategy on handling the question operations, in terms of three problems: (1) Which question should the machine ask? (2) When should the machine ask the question (early or late)? (3) How does the machine adjust the clustering result, if the machine’s mistake is found by the human? Based on the insights of these problems, an efficient algorithm is proposed with five implementation variations. Experiments on image clusterings show that the proposed algorithm can improve the clustering accuracy with few question operations.
      PubDate: 2016-12-21
      DOI: 10.3390/a9040088
      Issue No: Vol. 9, No. 4 (2016)
       
  • Algorithms, Vol. 9, Pages 43: Opposition-Based Adaptive Fireworks
           Algorithm

    • Authors: Chibing Gong
      First page: 43
      Abstract: A fireworks algorithm (FWA) is a recent swarm intelligence algorithm that is inspired by observing fireworks explosions. An adaptive fireworks algorithm (AFWA) proposes additional adaptive amplitudes to improve the performance of the enhanced fireworks algorithm (EFWA). The purpose of this paper is to add opposition-based learning (OBL) to AFWA with the goal of further boosting performance and achieving global optimization. Twelve benchmark functions are tested in use of an opposition-based adaptive fireworks algorithm (OAFWA). The final results conclude that OAFWA significantly outperformed EFWA and AFWA in terms of solution accuracy. Additionally, OAFWA was compared with a bat algorithm (BA), differential evolution (DE), self-adapting control parameters in differential evolution (jDE), a firefly algorithm (FA), and a standard particle swarm optimization 2011 (SPSO2011) algorithm. The research results indicate that OAFWA ranks the highest of the six algorithms for both solution accuracy and runtime cost.
      PubDate: 2016-07-08
      DOI: 10.3390/a9030043
      Issue No: Vol. 9, No. 3 (2016)
       
  • Algorithms, Vol. 9, Pages 44: A Gentle Introduction to Applications of
           Algorithmic Metatheorems for Space and Circuit Classes

    • Authors: Till Tantau
      First page: 44
      Abstract: Algorithmic metatheorems state that if a problem can be described in a certain logic and the inputs are structured in a certain way, then the problem can be solved with a certain amount of resources. As an example, by Courcelle’s Theorem, all monadic second-order (“in a certain logic”) properties of graphs of bounded tree width (“structured in a certain way”) can be solved in linear time (“with a certain amount of resources”). Such theorems have become valuable tools in algorithmics: if a problem happens to have the right structure and can be described in the right logic, they immediately yield a (typically tight) upper bound on the time complexity of the problem. Perhaps even more importantly, several complex algorithms rely on algorithmic metatheorems internally to solve subproblems, which considerably broadens the range of applications of these theorems. This paper is intended as a gentle introduction to the ideas behind algorithmic metatheorems, especially behind some recent results concerning space and circuit classes, and tries to give a flavor of the range of their applications.
      PubDate: 2016-07-09
      DOI: 10.3390/a9030044
      Issue No: Vol. 9, No. 3 (2016)
       
  • Algorithms, Vol. 9, Pages 45: Designing a Framework to Improve Time Series
           Data of Construction Projects: Application of a Simulation Model and
           Singular Spectrum Analysis

    • Authors: Zahra Hojjati Tavassoli, Seyed Iranmanesh, Ahmad Tavassoli Hojjati
      First page: 45
      Abstract: During a construction project life cycle, project costs and time estimations contribute greatly to baseline scheduling. Besides, schedule risk analysis and project control are also influenced by the above factors. Although many papers have offered estimation techniques, little attempt has been made to generate project time series data as daily progressive estimations in different project environments that could help researchers in generating general and customized formulae in further studies. This paper, however, is an attempt to introduce a new simulation approach to reflect the data regarding time series progress of the project, considering the specifications and the complexity of the project and the environment where the project is performed. Moreover, this simulator can equip project managers with estimated information, which reassures them of the execution stages of the project although they lack historical data. A case study is presented to show the usefulness of the model and its applicability in practice. In this study, singular spectrum analysis has been employed to analyze the simulated outputs, and the results are separated based on their signal and noise trends. The signal trend is used as a point-of-reference to compare the outputs of a simulation employing S-curve technique results and the formulae corresponding to earned value management, as well as the life of a given project.
      PubDate: 2016-07-18
      DOI: 10.3390/a9030045
      Issue No: Vol. 9, No. 3 (2016)
       
  • Algorithms, Vol. 9, Pages 46: Affinity Propagation Clustering Using Path
           Based Similarity

    • Authors: Yuan Jiang, Yuliang Liao, Guoxian Yu
      First page: 46
      Abstract: Clustering is a fundamental task in data mining. Affinity propagation clustering (APC) is an effective and efficient clustering technique that has been applied in various domains. APC iteratively propagates information between affinity samples, updates the responsibility matrix and availability matrix, and employs these matrices to choose cluster centers (or exemplars) of respective clusters. However, since it mainly uses negative Euclidean distance between exemplars and samples as the similarity between them, it is difficult to identify clusters with complex structure. Therefore, the performance of APC deteriorates on samples distributed with complex structure. To mitigate this problem, we propose an improved APC based on a path-based similarity (APC-PS). APC-PS firstly utilizes negative Euclidean distance to find exemplars of clusters. Then, it employs the path-based similarity to measure the similarity between exemplars and samples, and to explore the underlying structure of clusters. Next, it assigns non-exemplar samples to their respective clusters via that similarity. Our empirical study on synthetic and UCI datasets shows that the proposed APC-PS significantly outperforms original APC and other related approaches.
      PubDate: 2016-07-21
      DOI: 10.3390/a9030046
      Issue No: Vol. 9, No. 3 (2016)
       
  • Algorithms, Vol. 9, Pages 47: A Hybrid Course Recommendation System by
           Integrating Collaborative Filtering and Artificial Immune Systems

    • Authors: Pei-Chann Chang, Cheng-Hui Lin, Meng-Hui Chen
      First page: 47
      Abstract: This research proposes a two-stage user-based collaborative filtering process using an artificial immune system for the prediction of student grades, along with a filter for professor ratings in the course recommendation for college students. We test for cosine similarity and Karl Pearson (KP) correlation in affinity calculations for clustering and prediction. This research uses student information and professor information datasets of Yuan Ze University from the years 2005–2009 for the purpose of testing and training. The mean average error and confusion matrix analysis form the testing parameters. A minimum professor rating was tested to check the results, and observed that the recommendation systems herein provide highly accurate results for students with higher mean grades.
      PubDate: 2016-07-22
      DOI: 10.3390/a9030047
      Issue No: Vol. 9, No. 3 (2016)
       
  • Algorithms, Vol. 9, Pages 48: Semi-Supervised Classification Based on Low
           Rank Representation

    • Authors: Xuan Hou, Guangjun Yao, Jun Wang
      First page: 48
      Abstract: Graph-based semi-supervised classification uses a graph to capture the relationship between samples and exploits label propagation techniques on the graph to predict the labels of unlabeled samples. However, it is difficult to construct a graph that faithfully describes the relationship between high-dimensional samples. Recently, low-rank representation has been introduced to construct a graph, which can preserve the global structure of high-dimensional samples and help to train accurate transductive classifiers. In this paper, we take advantage of low-rank representation for graph construction and propose an inductive semi-supervised classifier called Semi-Supervised Classification based on Low-Rank Representation (SSC-LRR). SSC-LRR first utilizes a linearized alternating direction method with adaptive penalty to compute the coefficient matrix of low-rank representation of samples. Then, the coefficient matrix is adopted to define a graph. Finally, SSC-LRR incorporates this graph into a graph-based semi-supervised linear classifier to classify unlabeled samples. Experiments are conducted on four widely used facial datasets to validate the effectiveness of the proposed SSC-LRR and the results demonstrate that SSC-LRR achieves higher accuracy than other related methods.
      PubDate: 2016-07-22
      DOI: 10.3390/a9030048
      Issue No: Vol. 9, No. 3 (2016)
       
  • Algorithms, Vol. 9, Pages 49: Data Filtering Based Recursive and Iterative
           Least Squares Algorithms for Parameter Estimation of Multi-Input Output
           Systems

    • Authors: Jiling Ding
      First page: 49
      Abstract: This paper discusses the parameter estimation problems of multi-input output-error autoregressive (OEAR) systems. By combining the auxiliary model identification idea and the data filtering technique, a data filtering based recursive generalized least squares (F-RGLS) identification algorithm and a data filtering based iterative least squares (F-LSI) identification algorithm are derived. Compared with the F-RGLS algorithm, the proposed F-LSI algorithm is more effective and can generate more accurate parameter estimates. The simulation results confirm this conclusion.
      PubDate: 2016-07-26
      DOI: 10.3390/a9030049
      Issue No: Vol. 9, No. 3 (2016)
       
  • Algorithms, Vol. 9, Pages 50: Utilizing Network Structure to Accelerate
           Markov Chain Monte Carlo Algorithms

    • Authors: Ahmad Askarian, Rupei Xu, András Faragó
      First page: 50
      Abstract: We consider the problem of estimating the measure of subsets in very large networks. A prime tool for this purpose is the Markov Chain Monte Carlo (MCMC) algorithm. This algorithm, while extremely useful in many cases, still often suffers from the drawback of very slow convergence. We show that in a special, but important case, it is possible to obtain significantly better bounds on the convergence rate. This special case is when the huge state space can be aggregated into a smaller number of clusters, in which the states behave approximately the same way (but their behavior still may not be identical). A Markov chain with this structure is called quasi-lumpable. This property allows the aggregation of states (nodes) into clusters. Our main contribution is a rigorously proved bound on the rate at which the aggregated state distribution approaches its limit in quasi-lumpable Markov chains. We also demonstrate numerically that in certain cases this can indeed lead to a significantly accelerated way of estimating the measure of subsets. The result can be a useful tool in the analysis of complex networks, whenever they have a clustering that aggregates nodes with similar (but not necessarily identical) behavior.
      PubDate: 2016-07-29
      DOI: 10.3390/a9030050
      Issue No: Vol. 9, No. 3 (2016)
       
  • Algorithms, Vol. 9, Pages 51: A Multi-Objective Harmony Search Algorithm
           for Sustainable Design of Floating Settlements

    • Authors: Cemre Cubukcuoglu, Ioannis Chatzikonstantinou, Mehmet Tasgetiren, I. Sariyildiz, Quan-Ke Pan
      First page: 51
      Abstract: This paper is concerned with the application of computational intelligence techniques to the conceptual design and development of a large-scale floating settlement. The settlement in question is a design for the area of Urla, which is a rural touristic region located on the west coast of Turkey, near the metropolis of Izmir. The problem at hand includes both engineering and architectural aspects that need to be addressed in a comprehensive manner. We thus adapt the view as a multi-objective constrained real-parameter optimization problem. Specifically, we consider three objectives, which are conflicting. The first one aims at maximizing accessibility of urban functions such as housing and public spaces, as well as special functions, such as a marina for yachts and a yacht club. The second one aims at ensuring the wind protection of the general areas of the settlement, by adequately placing them in between neighboring land masses. The third one aims at maximizing visibility of the settlement from external observation points, so as to maximize the exposure of the settlement. To address this complex multi-objective optimization problem and identify lucrative alternative design solutions, a multi-objective harmony search algorithm (MOHS) is developed and applied in this paper. When compared to the Differential Evolution algorithm developed for the problem in the literature, we demonstrate that MOHS achieves competitive or slightly better performance in terms of hyper volume calculation, and gives promising results when the Pareto front approximation is examined.
      PubDate: 2016-07-30
      DOI: 10.3390/a9030051
      Issue No: Vol. 9, No. 3 (2016)
       
  • Algorithms, Vol. 9, Pages 52: Control for Ship Course-Keeping Using
           Optimized Support Vector Machines

    • Authors: Weilin Luo, Hongchao Cong
      First page: 52
      Abstract: Support vector machines (SVM) are proposed in order to obtain a robust controller for ship course-keeping. A cascaded system is constructed by combining the dynamics of the rudder actuator with the dynamics of ship motion. Modeling errors and disturbances are taken into account in the plant. A controller with a simple structure is produced by applying an SVM and L2-gain design. The SVM is used to identify the complicated nonlinear functions and the modeling errors in the plant. The Lagrangian factors in the SVM are obtained using on-line tuning algorithms. L2-gain design is applied to suppress the disturbances. To obtain the optimal parameters in the SVM, then particle swarm optimization (PSO) method is incorporated. The stability and robustness of the close-loop system are confirmed by Lyapunov stability analysis. Numerical simulation is performed to demonstrate the validity of the proposed hybrid controller and its superior performance over a conventional PD controller.
      PubDate: 2016-08-10
      DOI: 10.3390/a9030052
      Issue No: Vol. 9, No. 3 (2016)
       
  • Algorithms, Vol. 9, Pages 53: Faster Force-Directed Graph Drawing with the
           Well-Separated Pair Decomposition

    • Authors: Fabian Lipp, Alexander Wolff, Johannes Zink
      First page: 53
      Abstract: The force-directed paradigm is one of the few generic approaches to drawing graphs. Since force-directed algorithms can be extended easily, they are used frequently. Most of these algorithms are, however, quite slow on large graphs, as they compute a quadratic number of forces in each iteration. We give a new algorithm that takes only O ( m + n log n ) time per iteration when laying out a graph with n vertices and m edges. Our algorithm approximates the true forces using the so-called well-separated pair decomposition. We perform experiments on a large number of graphs and show that we can strongly reduce the runtime, even on graphs with less than a hundred vertices, without a significant influence on the quality of the drawings (in terms of the number of crossings and deviation in edge lengths).
      PubDate: 2016-08-04
      DOI: 10.3390/a9030053
      Issue No: Vol. 9, No. 3 (2016)
       
  • Algorithms, Vol. 9, Pages 54: Sign Function Based Sparse Adaptive
           Filtering Algorithms for Robust Channel Estimation under Non-Gaussian
           Noise Environments

    • Authors: Tingping Zhang, Guan Gui
      First page: 54
      Abstract: Robust channel estimation is required for coherent demodulation in multipath fading wireless communication systems which are often deteriorated by non-Gaussian noises. Our research is motivated by the fact that classical sparse least mean square error (LMS) algorithms are very sensitive to impulsive noise while standard SLMS algorithm does not take into account the inherent sparsity information of wireless channels. This paper proposes a sign function based sparse adaptive filtering algorithm for developing robust channel estimation techniques. Specifically, sign function based least mean square error (SLMS) algorithms to remove the non-Gaussian noise that is described by a symmetric α-stable noise model. By exploiting channel sparsity, sparse SLMS algorithms are proposed by introducing several effective sparse-promoting functions into the standard SLMS algorithm. The convergence analysis of the proposed sparse SLMS algorithms indicates that they outperform the standard SLMS algorithm for robust sparse channel estimation, which can be also verified by simulation results.
      PubDate: 2016-08-12
      DOI: 10.3390/a9030054
      Issue No: Vol. 9, No. 3 (2016)
       
  • Algorithms, Vol. 9, Pages 55: A Novel AHRS Inertial Sensor-Based Algorithm
           for Wheelchair Propulsion Performance Analysis

    • Authors: Jonathan Shepherd, Tomohito Wada, David Rowlands, Daniel James
      First page: 55
      Abstract: With the increasing rise of professionalism in sport, athletes, teams, and coaches are looking to technology to monitor performance in both games and training in order to find a competitive advantage. The use of inertial sensors has been proposed as a cost effective and adaptable measurement device for monitoring wheelchair kinematics; however, the outcomes are dependent on the reliability of the processing algorithms. Though there are a variety of algorithms that have been proposed to monitor wheelchair propulsion in court sports, they all have limitations. Through experimental testing, we have shown the Attitude and Heading Reference System (AHRS)-based algorithm to be a suitable and reliable candidate algorithm for estimating velocity, distance, and approximating trajectory. The proposed algorithm is computationally inexpensive, agnostic of wheel camber, not sensitive to sensor placement, and can be embedded for real-time implementations. The research is conducted under Griffith University Ethics (GU Ref No: 2016/294).
      PubDate: 2016-08-17
      DOI: 10.3390/a9030055
      Issue No: Vol. 9, No. 3 (2016)
       
  • Algorithms, Vol. 9, Pages 56: Multiple Artificial Neural Networks with
           Interaction Noise for Estimation of Spatial Categorical Variables

    • Authors: Xiang Huang, Zhizhong Wang
      First page: 56
      Abstract: This paper presents a multiple artificial neural networks (MANN) method with interaction noise for estimating the occurrence probabilities of different classes at any site in space. The MANN consists of several independent artificial neural networks, the number of which is determined by the neighbors around the target location. In the proposed algorithm, the conditional or pre-posterior (multi-point) probabilities are viewed as output nodes, which can be estimated by weighted combinations of input nodes: two-point transition probabilities. The occurrence probability of a certain class at a certain location can be easily computed by the product of output probabilities using Bayes’ theorem. Spatial interaction or redundancy information can be measured in the form of interaction noises. Prediction results show that the method of MANN with interaction noise has a higher classification accuracy than the traditional Markov chain random fields (MCRF) model and can successfully preserve small-scale features.
      PubDate: 2016-08-20
      DOI: 10.3390/a9030056
      Issue No: Vol. 9, No. 3 (2016)
       
  • Algorithms, Vol. 9, Pages 57: Uniform Page Migration Problem in Euclidean
           Space

    • Authors: Amanj Khorramian, Akira Matsubayashi
      First page: 57
      Abstract: The page migration problem in Euclidean space is revisited. In this problem, online requests occur at any location to access a single page located at a server. Every request must be served, and the server has the choice to migrate from its current location to a new location in space. Each service costs the Euclidean distance between the server and request. A migration costs the distance between the former and the new server location, multiplied by the page size. We study the problem in the uniform model, in which the page has size D = 1 . All request locations are not known in advance; however, they are sequentially presented in an online fashion. We design a 2.75 -competitive online algorithm that improves the current best upper bound for the problem with the unit page size. We also provide a lower bound of 2.732 for our algorithm. It was already known that 2.5 is a lower bound for this problem.
      PubDate: 2016-08-23
      DOI: 10.3390/a9030057
      Issue No: Vol. 9, No. 3 (2016)
       
  • Algorithms, Vol. 9, Pages 58: LR Parsing for LCFRS

    • Authors: Laura Kallmeyer, Wolfgang Maier
      First page: 58
      Abstract: LR parsing is a popular parsing strategy for variants of Context-Free Grammar (CFG). It has also been used for mildly context-sensitive formalisms, such as Tree-Adjoining Grammar. In this paper, we present the first LR-style parsing algorithm for Linear Context-Free Rewriting Systems (LCFRS), a mildly context-sensitive extension of CFG which has received considerable attention in the last years in the context of natural language processing.
      PubDate: 2016-08-27
      DOI: 10.3390/a9030058
      Issue No: Vol. 9, No. 3 (2016)
       
  • Algorithms, Vol. 9, Pages 59: Binary Cockroach Swarm Optimization for
           Combinatorial Optimization Problem

    • Authors: Ibidun Obagbuwa, Ademola Abidoye
      First page: 59
      Abstract: The Cockroach Swarm Optimization (CSO) algorithm is inspired by cockroach social behavior. It is a simple and efficient meta-heuristic algorithm and has been applied to solve global optimization problems successfully. The original CSO algorithm and its variants operate mainly in continuous search space and cannot solve binary-coded optimization problems directly. Many optimization problems have their decision variables in binary. Binary Cockroach Swarm Optimization (BCSO) is proposed in this paper to tackle such problems and was evaluated on the popular Traveling Salesman Problem (TSP), which is considered to be an NP-hard Combinatorial Optimization Problem (COP). A transfer function was employed to map a continuous search space CSO to binary search space. The performance of the proposed algorithm was tested firstly on benchmark functions through simulation studies and compared with the performance of existing binary particle swarm optimization and continuous space versions of CSO. The proposed BCSO was adapted to TSP and applied to a set of benchmark instances of symmetric TSP from the TSP library. The results of the proposed Binary Cockroach Swarm Optimization (BCSO) algorithm on TSP were compared to other meta-heuristic algorithms.
      PubDate: 2016-09-02
      DOI: 10.3390/a9030059
      Issue No: Vol. 9, No. 3 (2016)
       
  • Algorithms, Vol. 9, Pages 60: HMM Adaptation for Improving a Human
           Activity Recognition System

    • Authors: Rubén San-Segundo, Juan Montero, José Moreno-Pimentel, José Pardo
      First page: 60
      Abstract: When developing a fully automatic system for evaluating motor activities performed by a person, it is necessary to segment and recognize the different activities in order to focus the analysis. This process must be carried out by a Human Activity Recognition (HAR) system. This paper proposes a user adaptation technique for improving a HAR system based on Hidden Markov Models (HMMs). This system segments and recognizes six different physical activities (walking, walking upstairs, walking downstairs, sitting, standing and lying down) using inertial signals from a smartphone. The system is composed of a feature extractor for obtaining the most relevant characteristics from the inertial signals, a module for training the six HMMs (one per activity), and the last module for segmenting new activity sequences using these models. The user adaptation technique consists of a Maximum A Posteriori (MAP) approach that adapts the activity HMMs to the user, using some activity examples from this specific user. The main results on a public dataset have reported a significant relative error rate reduction of more than 30%. In conclusion, adapting a HAR system to the user who is performing the physical activities provides significant improvement in the system’s performance.
      PubDate: 2016-09-02
      DOI: 10.3390/a9030060
      Issue No: Vol. 9, No. 3 (2016)
       
  • Algorithms, Vol. 9, Pages 61: Noncircular Sources-Based Sparse
           Representation Algorithm for Direction of Arrival Estimation in MIMO Radar
           with Mutual Coupling

    • Authors: Weidong Zhou, Jing Liu, Pengxiang Zhu, Wenhe Gong, Jiaxin Hou
      First page: 61
      Abstract: In this paper, a reweighted sparse representation algorithm based on noncircular sources is proposed, and the problem of the direction of arrival (DOA) estimation for multiple-input multiple-output (MIMO) radar with mutual coupling is addressed. Making full use of the special structure of banded symmetric Toeplitz mutual coupling matrices (MCM), the proposed algorithm firstly eliminates the effect of mutual coupling by linear transformation. Then, a reduced dimensional transformation is exploited to reduce the computational complexity of the proposed algorithm. Furthermore, by utilizing the noncircular feature of signals, the new extended received data matrix is formulated to enlarge the array aperture. Finally, based on the new received data, a reweighted matrix is constructed, and the proposed method further designs the joint reweighted sparse representation scheme to achieve the DOA estimation by solving the l 1 -norm constraint minimization problem. The proposed method enlarges the array aperture due to the application of signal noncircularity, and in the presence of mutual coupling, the proposed algorithm provides higher resolution and better angle estimation performance than ESPRIT-like, l 1 -SVD and l 1 -SRDML (sparse representation deterministic maximum likelihood) algorithms. Numerical experiment results verify the effectiveness and advantages of the proposed method.
      PubDate: 2016-09-08
      DOI: 10.3390/a9030061
      Issue No: Vol. 9, No. 3 (2016)
       
  • Algorithms, Vol. 9, Pages 24: Structural Damage Localization by the
           Principal Eigenvector of Modal Flexibility Change

    • Authors: Cui-Hong Li, Qiu-Wei Yang, Bing-Xiang Sun
      First page: 24
      Abstract: Using the principal eigenvector (PE) of modal flexibility change, a new vibration-based algorithm for structural defect localization was presented in this paper. From theoretical investigations, it was proven that the PE of modal flexibility variation has a turning point with a sharp peak in its curvature at the damage location. A three-span continuous beam was used as an example to illustrate the feasibility and superiority of the proposed PE algorithm for damage localization. Furthermore, defect localization was also performed using the well-known uniform load surface approach for comparison. Numerical results demonstrated that the PE algorithm can locate structural defects with good accuracy, whereas the ULS approach occasionally missed one or two defect locations. It was found that the PE algorithm may be promising for structural defect assessment.
      PubDate: 2016-04-13
      DOI: 10.3390/a9020024
      Issue No: Vol. 9, No. 2 (2016)
       
  • Algorithms, Vol. 9, Pages 25: Primary User Localization Algorithm Based on
           Compressive Sensing in Cognitive Radio Networks

    • Authors: Fang Ye, Xun Zhang, Yibing Li, Hui Huang
      First page: 25
      Abstract: In order to locate source signal more accurately in authorized frequency bands, a novel primary user localization algorithm based on compressive sensing (PU-CSL) in cognitive radio networks (CRNs) is proposed in this paper. In comparison to existing centroid locating algorithms, PU-CSL shows higher locating accuracy for integrally exploring correlation between source signal and secondary users (SUs). Energy detection is first adopted for collecting the energy fingerprint of source signal at each SU, then degree of correlation between source signal and SUs is reconstructed based on compressive sensing (CS), which determines weights of centroid coordinates. A weighted centroid scheme is finally utilized to estimate source position. Simulation results show that PU-CSL has smaller maximum error of positioning and root-mean-square error. Moreover, the proposed PU-CSL algorithm possess excellent location accuracy and strong anti-noise performance.
      PubDate: 2016-04-14
      DOI: 10.3390/a9020025
      Issue No: Vol. 9, No. 2 (2016)
       
  • Algorithms, Vol. 9, Pages 26: siEDM: An Efficient String Index and Search
           Algorithm for Edit Distance with Moves

    • Authors: Yoshimasa Takabatake, Kenta Nakashima, Tetsuji Kuboyama, Yasuo Tabei, Hiroshi Sakamoto
      First page: 26
      Abstract: Although several self-indexes for highly repetitive text collections exist, developing an index and search algorithm with editing operations remains a challenge. Edit distance with moves (EDM) is a string-to-string distance measure that includes substring moves in addition to ordinal editing operations to turn one string into another. Although the problem of computing EDM is intractable, it has a wide range of potential applications, especially in approximate string retrieval. Despite the importance of computing EDM, there has been no efficient method for indexing and searching large text collections based on the EDM measure. We propose the first algorithm, named string index for edit distance with moves (siEDM), for indexing and searching strings with EDM. The siEDM algorithm builds an index structure by leveraging the idea behind the edit sensitive parsing (ESP), an efficient algorithm enabling approximately computing EDM with guarantees of upper and lower bounds for the exact EDM. siEDM efficiently prunes the space for searching query strings by the proposed method, which enables fast query searches with the same guarantee as ESP. We experimentally tested the ability of siEDM to index and search strings on benchmark datasets, and we showed siEDM’s efficiency.
      PubDate: 2016-04-15
      DOI: 10.3390/a9020026
      Issue No: Vol. 9, No. 2 (2016)
       
  • Algorithms, Vol. 9, Pages 27: The Effect of Preprocessing on Arabic
           Document Categorization

    • Authors: Abdullah Ayedh, Guanzheng TAN, Khaled Alwesabi, Hamdi Rajeh
      First page: 27
      Abstract: Preprocessing is one of the main components in a conventional document categorization (DC) framework. This paper aims to highlight the effect of preprocessing tasks on the efficiency of the Arabic DC system. In this study, three classification techniques are used, namely, naive Bayes (NB), k-nearest neighbor (KNN), and support vector machine (SVM). Experimental analysis on Arabic datasets reveals that preprocessing techniques have a significant impact on the classification accuracy, especially with complicated morphological structure of the Arabic language. Choosing appropriate combinations of preprocessing tasks provides significant improvement on the accuracy of document categorization depending on the feature size and classification techniques. Findings of this study show that the SVM technique has outperformed the KNN and NB techniques. The SVM technique achieved 96.74% micro-F1 value by using the combination of normalization and stemming as preprocessing tasks.
      PubDate: 2016-04-18
      DOI: 10.3390/a9020027
      Issue No: Vol. 9, No. 2 (2016)
       
  • Algorithms, Vol. 9, Pages 28: Alternating Direction Method of Multipliers
           for Generalized Low-Rank Tensor Recovery

    • Authors: Jiarong Shi, Qingyan Yin, Xiuyun Zheng, Wei Yang
      First page: 28
      Abstract: Low-Rank Tensor Recovery (LRTR), the higher order generalization of Low-Rank Matrix Recovery (LRMR), is especially suitable for analyzing multi-linear data with gross corruptions, outliers and missing values, and it attracts broad attention in the fields of computer vision, machine learning and data mining. This paper considers a generalized model of LRTR and attempts to recover simultaneously the low-rank, the sparse, and the small disturbance components from partial entries of a given data tensor. Specifically, we first describe generalized LRTR as a tensor nuclear norm optimization problem that minimizes a weighted combination of the tensor nuclear norm, the l1-norm and the Frobenius norm under linear constraints. Then, the technique of Alternating Direction Method of Multipliers (ADMM) is employed to solve the proposed minimization problem. Next, we discuss the weak convergence of the proposed iterative algorithm. Finally, experimental results on synthetic and real-world datasets validate the efficiency and effectiveness of the proposed method.
      PubDate: 2016-04-19
      DOI: 10.3390/a9020028
      Issue No: Vol. 9, No. 2 (2016)
       
  • Algorithms, Vol. 9, Pages 29: An Improved Dynamic Joint Resource
           Allocation Algorithm Based on SFR

    • Authors: Yibing Li, Xueying Diao, Ge Dong, Fang Ye
      First page: 29
      Abstract: Inter-cell interference (ICI) is the main factor affecting system capacity and spectral efficiency. Effective spectrum resource management is an important and challenging issue for the design of wireless communication systems. The soft frequency reuse (SFR) is regarded as an interesting approach to significantly eliminate ICI. However, the allocation of resource is fixed prior to system deployment in static SFR. To overcome this drawback, this paper adopts a distributed method and proposes an improved dynamic joint resource allocation algorithm (DJRA). The improved scheme adaptively adjusts resource allocation based on the real-time user distribution. DJRA first detects the edge-user distribution vector to determine the optimal scheme, which guarantees that all the users have available resources and the number of iterations is reduced. Then, the DJRA maximizes the throughput for each cell via optimizing resource and power allocation. Due to further eliminate interference, the sector partition method is used in the center region and in view of fairness among users, the novel approach adds the proportional fair algorithm at the end of DJRA. Simulation results show that the proposed algorithm outperforms previous approaches for improving the system capacity and cell edge user performance.
      PubDate: 2016-04-22
      DOI: 10.3390/a9020029
      Issue No: Vol. 9, No. 2 (2016)
       
  • Algorithms, Vol. 9, Pages 30: Comment on: On the Kung-Traub Conjecture for
           Iterative Methods for Solving Quadratic Equations. Algorithms 2016, 9, 1

    • Authors: Fayyaz Ahmad
      First page: 30
      Abstract: Kung-Traub conjecture states that an iterative method without memory for finding the simple zero of a scalar equation could achieve convergence order 2 d − 1 , and d is the total number of function evaluations. In an article “Babajee, D.K.R. On the Kung-Traub Conjecture for Iterative Methods for Solving Quadratic Equations, Algorithms 2016, 9, 1, doi:10.3390/a9010001”, the author has shown that Kung-Traub conjecture is not valid for the quadratic equation and proposed an iterative method for the scalar and vector quadratic equations. In this comment, we have shown that we first reported the aforementioned iterative method.
      PubDate: 2016-04-26
      DOI: 10.3390/a9020030
      Issue No: Vol. 9, No. 2 (2016)
       
  • Algorithms, Vol. 9, Pages 31: Improved Direct Linear Transformation for
           Parameter Decoupling in Camera Calibration

    • Authors: Zhenqing Zhao, Dong Ye, Xin Zhang, Gang Chen, Bin Zhang
      First page: 31
      Abstract: For camera calibration based on direct linear transformation (DLT), the camera’s intrinsic and extrinsic parameters are simultaneously calibrated, which may cause coupling errors in the parameters and affect the calibration parameter accuracy. In this paper, we propose an improved direct linear transformation (IDLT) algorithm for calibration parameter decoupling. This algorithm uses a linear relationship of calibration parameter errors and obtains calibration parameters by moving a three-dimensional template. Simulation experiments were conducted to compare the calibration accuracy of DLT and IDLT algorithms with image noise and distortion. The results show that the IDLT algorithm calibration parameters achieve higher accuracy because the algorithm removes the coupling errors.
      PubDate: 2016-04-29
      DOI: 10.3390/a9020031
      Issue No: Vol. 9, No. 2 (2016)
       
  • Algorithms, Vol. 9, Pages 32: Uniform vs. Nonuniform Membership for Mildly
           Context-Sensitive Languages: A Brief Survey

    • Authors: Henrik Björklund, Martin Berglund, Petter Ericson
      First page: 32
      Abstract: Parsing for mildly context-sensitive language formalisms is an important area within natural language processing. While the complexity of the parsing problem for some such formalisms is known to be polynomial, this is not the case for all of them. This article presents a series of results regarding the complexity of parsing for linear context-free rewriting systems and deterministic tree-walking transducers. We discuss the difference between uniform and nonuniform complexity measures and how parameterized complexity theory can be used to investigate how different aspects of the formalisms influence how hard the parsing problem is. The main results we survey are all hardness results and indicate that parsing is hard even for relatively small values of parameters such as rank and fan-out in a rewriting system.
      PubDate: 2016-05-11
      DOI: 10.3390/a9020032
      Issue No: Vol. 9, No. 2 (2016)
       
  • Algorithms, Vol. 9, Pages 33: Mining Branching Rules from Past Survey Data
           with an Illustration Using a Geriatric Assessment Survey for Older Adults
           with Cancer

    • Authors: Daniel Jeske, Jeffrey Longmate, Vani Katheria, Arti Hurria
      First page: 33
      Abstract: We construct a fast data mining algorithm that can be used to identify high-frequency response patterns in historical surveys. Identification of these patterns leads to the derivation of question branching rules that shorten the time required to complete a survey. The data mining algorithm allows the user to control the error rate that is incurred through the use of implied answers that go along with each branching rule. The context considered is binary response questions, which can be obtained from multi-level response questions through dichotomization. The algorithm is illustrated by the analysis of four sections of a geriatric assessment survey used by oncologists. Reductions in the number of questions that need to be asked in these four sections range from 33% to 54%.
      PubDate: 2016-05-13
      DOI: 10.3390/a9020033
      Issue No: Vol. 9, No. 2 (2016)
       
  • Algorithms, Vol. 9, Pages 34: A State Recognition Approach for Complex
           Equipment Based on a Fuzzy Probabilistic Neural Network

    • Authors: Jing Xu, Zhongbin Wang, Chao Tan, Xinhua Liu
      First page: 34
      Abstract: Due to the traditional state recognition approaches for complex electromechanical equipment having had the disadvantages of excessive reliance on complete expert knowledge and insufficient training sets, real-time state identification system was always difficult to be established. The running efficiency cannot be guaranteed and the fault rate cannot be reduced fundamentally especially in some extreme working conditions. To solve these problems, an online state recognition method for complex equipment based on a fuzzy probabilistic neural network (FPNN) was proposed in this paper. The fuzzy rule base for complex equipment was established and a multi-level state space model was constructed. Moreover, a probabilistic neural network (PNN) was applied in state recognition, and the fuzzy functions and quantification matrix were presented. The flowchart of proposed approach was designed. Finally, a simulation example of shearer state recognition and the industrial application with an accuracy of 90.91% were provided and the proposed approach was feasible and efficient.
      PubDate: 2016-05-20
      DOI: 10.3390/a9020034
      Issue No: Vol. 9, No. 2 (2016)
       
  • Algorithms, Vol. 9, Pages 35: Application of the Energy-Conserving
           Integration Method to Hybrid Simulation of a Full-Scale Steel Frame

    • Authors: Tianlin Pan, Bin Wu, Yongsheng Chen, Guoshan Xu
      First page: 35
      Abstract: The nonlinear unconditionally stable energy-conserving integration method (ECM) is a new method for solving a continuous equation of motion. To our knowledge, there is still no report about its application on a hybrid test. Aiming to explore its effect on hybrid tests, the nonlinear beam-column element program is developed for computation. The program contains both the ECM and the average acceleration method (AAM). The comparison of the hybrid test results with thesetwo methods validates the effectiveness of the ECM in the hybrid simulation. We found that the energy error of hybrid test by using ECM is less than that of AAM. In addition, a new iteration strategy with reduction factor is presented to avoid the overshooting phenomena during iteration process with the finite element program.
      PubDate: 2016-05-21
      DOI: 10.3390/a9020035
      Issue No: Vol. 9, No. 2 (2016)
       
  • Algorithms, Vol. 9, Pages 36: Robust Hessian Locally Linear Embedding
           Techniques for High-Dimensional Data

    • Authors: Xianglei Xing, Sidan Du, Kejun Wang
      First page: 36
      Abstract: Recently manifold learning has received extensive interest in the community of pattern recognition. Despite their appealing properties, most manifold learning algorithms are not robust in practical applications. In this paper, we address this problem in the context of the Hessian locally linear embedding (HLLE) algorithm and propose a more robust method, called RHLLE, which aims to be robust against both outliers and noise in the data. Specifically, we first propose a fast outlier detection method for high-dimensional datasets. Then, we employ a local smoothing method to reduce noise. Furthermore, we reformulate the original HLLE algorithm by using the truncation function from differentiable manifolds. In the reformulated framework, we explicitly introduce a weighted global functional to further reduce the undesirable effect of outliers and noise on the embedding result. Experiments on synthetic as well as real datasets demonstrate the effectiveness of our proposed algorithm.
      PubDate: 2016-05-26
      DOI: 10.3390/a9020036
      Issue No: Vol. 9, No. 2 (2016)
       
  • Algorithms, Vol. 9, Pages 37: A New Multi-Step Iterative Algorithm for
           Approximating Common Fixed Points of a Finite Family of Multi-Valued
           Bregman Relatively Nonexpansive Mappings

    • Authors: Wiyada Kumam, Pongsakorn Sunthrayuth, Phond Phunchongharn, Khajonpong Akkarajitsakul, Parinya Ngiamsunthorn, Poom Kumam
      First page: 37
      Abstract: In this article, we introduce a new multi-step iteration for approximating a common fixed point of a finite class of multi-valued Bregman relatively nonexpansive mappings in the setting of reflexive Banach spaces. We prove a strong convergence theorem for the proposed iterative algorithm under certain hypotheses. Additionally, we also use our results for the solution of variational inequality problems and to find the zero points of maximal monotone operators. The theorems furnished in this work are new and well-established and generalize many well-known recent research works in this field.
      PubDate: 2016-05-30
      DOI: 10.3390/a9020037
      Issue No: Vol. 9, No. 2 (2016)
       
  • Algorithms, Vol. 9, Pages 38: A 3/2-Approximation Algorithm for the Graph
           Balancing Problem with Two Weights

    • Authors: Daniel Page, Roberto Solis-Oba
      First page: 38
      Abstract: In the pursuit of finding subclasses of the makespan minimization problem on unrelated parallel machines that have approximation algorithms with approximation ratio better than 2, the graph balancing problem has been of current interest. In the graph balancing problem each job can be non-preemptively scheduled on one of at most two machines with the same processing time on either machine. Recently, Ebenlendr, Krčál, and Sgall (Algorithmica 2014, 68, 62–80.) presented a 7 / 4 -approximation algorithm for the graph balancing problem. Let r , s ∈ Z + . In this paper we consider the graph balancing problem with two weights, where a job either takes r time units or s time units. We present a 3 / 2 -approximation algorithm for this problem. This is an improvement over the previously best-known approximation algorithm for the problem with approximation ratio 1.652 and it matches the best known inapproximability bound for it.
      PubDate: 2016-06-08
      DOI: 10.3390/a9020038
      Issue No: Vol. 9, No. 2 (2016)
       
  • Algorithms, Vol. 9, Pages 39: Review of Recent Type-2 Fuzzy Controller
           Applications

    • Authors: Kevin Tai, Abdul-Rahman El-Sayed, Mohammad Biglarbegian, Claudia Gonzalez, Oscar Castillo, Shohel Mahmud
      First page: 39
      Abstract: Type-2 fuzzy logic controllers (T2 FLC) can be viewed as an emerging class of intelligent controllers because of their abilities in handling uncertainties; in many cases, they have been shown to outperform their Type-1 counterparts. This paper presents a literature review on recent applications of T2 FLCs. To follow the developments in this field, we first review general T2 FLCs and the most well-known interval T2 FLS algorithms that have been used for control design. Certain applications of these controllers include robotic control, bandwidth control, industrial systems control, electrical control and aircraft control. The most promising applications are found in the robotics and automotive areas, where T2 FLCs have been demonstrated and proven to perform better than traditional controllers. With the development of enhanced algorithms, along with the advancement in both hardware and software, we shall witness increasing applications of these frontier controllers.
      PubDate: 2016-06-09
      DOI: 10.3390/a9020039
      Issue No: Vol. 9, No. 2 (2016)
       
  • Algorithms, Vol. 9, Pages 40: A Direct Search Algorithm for Global
           Optimization

    • Authors: Enrique Baeyens, Alberto Herreros, José Perán
      First page: 40
      Abstract: A direct search algorithm is proposed for minimizing an arbitrary real valued function. The algorithm uses a new function transformation and three simplex-based operations. The function transformation provides global exploration features, while the simplex-based operations guarantees the termination of the algorithm and provides global convergence to a stationary point if the cost function is differentiable and its gradient is Lipschitz continuous. The algorithm’s performance has been extensively tested using benchmark functions and compared to some well-known global optimization algorithms. The results of the computational study show that the algorithm combines both simplicity and efficiency and is competitive with the heuristics-based strategies presently used for global optimization.
      PubDate: 2016-06-13
      DOI: 10.3390/a9020040
      Issue No: Vol. 9, No. 2 (2016)
       
  • Algorithms, Vol. 9, Pages 41: Visual and Textual Sentiment Analysis of a
           Microblog Using Deep Convolutional Neural Networks

    • Authors: Yuhai Yu, Hongfei Lin, Jiana Meng, Zhehuan Zhao
      First page: 41
      Abstract: Sentiment analysis of online social media has attracted significant interest recently. Many studies have been performed, but most existing methods focus on either only textual content or only visual content. In this paper, we utilize deep learning models in a convolutional neural network (CNN) to analyze the sentiment in Chinese microblogs from both textual and visual content. We first train a CNN on top of pre-trained word vectors for textual sentiment analysis and employ a deep convolutional neural network (DNN) with generalized dropout for visual sentiment analysis. We then evaluate our sentiment prediction framework on a dataset collected from a famous Chinese social media network (Sina Weibo) that includes text and related images and demonstrate state-of-the-art results on this Chinese sentiment analysis benchmark.
      PubDate: 2016-06-21
      DOI: 10.3390/a9020041
      Issue No: Vol. 9, No. 2 (2016)
       
  • Algorithms, Vol. 9, Pages 42: Joint Antenna Selection and Beamforming
           Algorithms for Physical Layer Multicasting with Massive Antennas

    • Authors: Xinhua Wang, Jinlu Sheng
      First page: 42
      Abstract: We investigate the problem of minimizing the total power consumption under the constraint of the signal-to-noise ratio (SNR) requirement for the physical layer multicasting system with large-scale antenna arrays. In contrast with existing work, we explicitly consider both the transmit power and the circuit power scaling with the number of antennas. The joint antenna selection and beamforming technique is proposed to minimize the total power consumption. The problem is a challenging one, which aims to minimize the linear combination of ℓ 0 -norm and ℓ 2 -norm. To our best knowledge, this minimization problem has not yet been well solved. A random decremental antenna selection algorithm is designed, which is further modified by an approximation of the minimal transmit power based on the asymptotic orthogonality of the channels. Then, a more efficient decremental antenna selection algorithm is proposed based on minimizing the ℓ 0 norm. Performance results show that the ℓ 0 norm minimization algorithm greatly outperforms the random selection algorithm in terms of the total power consumption and the average run time.
      PubDate: 2016-06-22
      DOI: 10.3390/a9020042
      Issue No: Vol. 9, No. 2 (2016)
       
  • Algorithms, Vol. 10, Pages 1: MultiAspect Graphs: Algebraic Representation
           and Algorithms

    • Authors: Klaus Wehmuth, Éric Fleury, Artur Ziviani
      First page: 1
      Abstract: We present the algebraic representation and basic algorithms for MultiAspect Graphs (MAGs). A MAG is a structure capable of representing multilayer and time-varying networks, as well as higher-order networks, while also having the property of being isomorphic to a directed graph. In particular, we show that, as a consequence of the properties associated with the MAG structure, a MAG can be represented in matrix form. Moreover, we also show that any possible MAG function (algorithm) can be obtained from this matrix-based representation. This is an important theoretical result since it paves the way for adapting well-known graph algorithms for application in MAGs. We present a set of basic MAG algorithms, constructed from well-known graph algorithms, such as degree computing, Breadth First Search (BFS), and Depth First Search (DFS). These algorithms adapted to the MAG context can be used as primitives for building other more sophisticated MAG algorithms. Therefore, such examples can be seen as guidelines on how to properly derive MAG algorithms from basic algorithms on directed graphs. We also make available Python implementations of all the algorithms presented in this paper.
      PubDate: 2016-12-25
      DOI: 10.3390/a10010001
      Issue No: Vol. 10, No. 1 (2016)
       
  • Algorithms, Vol. 10, Pages 2: A Tensor Decomposition Based Multiway
           Structured Sparse SAR Imaging Algorithm with Kronecker Constraint

    • Authors: Yu-Fei Gao, Xun-Chao Cong, Yue Yang, Qun Wan, Guan Gui
      First page: 2
      Abstract: This paper investigates a structured sparse SAR imaging algorithm for point scattering model based on tensor decomposition. Several SAR imaging schemes have been developed by researchers for improving the imaging quality. For a typical SAR target scenario, the scatterers distribution usually has the feature of structured sparsity. Without considering this feature thoroughly, the existing schemes have still certain drawbacks. The classic matching pursuit algorithms can obtain clearer imaging results, but the cost is resulting in an extreme complexity and a huge computation resource consumption. Therefore, this paper put forward a tensor-based SAR imaging algorithm by means of multiway structured sparsity which makes full use of the above geometrical feature of the scatterers distribution. The spotlight SAR observation signal is formulated as a Tucker model considering the Kronecker constraint, and then a sparse reconstruction algorithm is introduced by utilizing the structured sparsity of the scene. The proposed tensor-based SAR imaging model is able to take advantage of the Kronecker information in each mode, which ensures the robustness for the signal reconstruction. Both the algorithm complexity analysis and numerical simulations show that the proposed method requires less computation than the existing sparsity-driven SAR imaging algorithms. The imaging realizations based on the practical measured data also indicate that the proposed algorithm is superior to the reference methods even in the severe noisy environment, under the condition of multiway structured sparsity.
      PubDate: 2016-12-25
      DOI: 10.3390/a10010002
      Issue No: Vol. 10, No. 1 (2016)
       
  • Algorithms, Vol. 10, Pages 3: A Pilot-Pattern Based Algorithm for
           MIMO-OFDM Channel Estimation

    • Authors: Guomin Li, Guisheng Liao
      First page: 3
      Abstract: An improved pilot pattern algorithm for facilitating the channel estimation in multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems is proposed in this paper. The presented algorithm reconfigures the parameter in the least square (LS) algorithm, which belongs to the space-time block-coded (STBC) category for channel estimation in pilot-based MIMO-OFDM system. Simulation results show that the algorithm has better performance in contrast to the classical single symbol scheme. In contrast to the double symbols scheme, the proposed algorithm can achieve nearly the same performance with only half of the complexity of the double symbols scheme.
      PubDate: 2016-12-28
      DOI: 10.3390/a10010003
      Issue No: Vol. 10, No. 1 (2016)
       
  • Algorithms, Vol. 10, Pages 4: Dependent Shrink of Transitions for
           Calculating Firing Frequencies in Signaling Pathway Petri Net Model

    • Authors: Atsushi Mizuta, Qi-Wei Ge, Hiroshi Matsuno
      First page: 4
      Abstract: Despite the recent rapid progress in high throughput measurements of biological data, it is still difficult to gather all of the reaction speed data in biological pathways. This paper presents a Petri net-based algorithm that can derive estimated values for non-valid reaction speeds in a signaling pathway from biologically-valid data. In fact, these reaction speeds are reflected based on the delay times in the timed Petri net model of the signaling pathway. We introduce the concept of a “dependency relation” over a transition set of a Petri net and derive the properties of the dependency relation through a structural analysis. Based on the theoretical results, the proposed algorithm can efficiently shrink the transitions with two elementary structures into a single transition repeatedly to reduce the Petri net size in order to eventually discover all transition sets with a dependency relation. Finally, to show the usefulness of our algorithm, we apply our algorithm to the IL-3 Petri net model.
      PubDate: 2016-12-31
      DOI: 10.3390/a10010004
      Issue No: Vol. 10, No. 1 (2016)
       
 
 
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