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  Subjects -> ELECTRONICS (Total: 181 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Acta Electronica Malaysia     Open Access  
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 6)
Advances in Electronics     Open Access   (Followers: 79)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Power Electronics     Open Access   (Followers: 33)
Advancing Microelectronics     Hybrid Journal  
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 318)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 24)
Annals of Telecommunications     Hybrid Journal   (Followers: 9)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 9)
Archives of Electrical Engineering     Open Access   (Followers: 13)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 28)
Bioelectronics in Medicine     Hybrid Journal  
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 19)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 36)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 12)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 1)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access   (Followers: 1)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 47)
China Communications     Full-text available via subscription   (Followers: 8)
Chinese Journal of Electronics     Hybrid Journal  
Circuits and Systems     Open Access   (Followers: 15)
Consumer Electronics Times     Open Access   (Followers: 5)
Control Systems     Hybrid Journal   (Followers: 270)
Edu Elektrika Journal     Open Access   (Followers: 1)
Electrica     Open Access  
Electronic Design     Partially Free   (Followers: 106)
Electronic Markets     Hybrid Journal   (Followers: 7)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
Electronics     Open Access   (Followers: 86)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 10)
Electronics For You     Partially Free   (Followers: 93)
Electronics Letters     Hybrid Journal   (Followers: 26)
Elkha : Jurnal Teknik Elektro     Open Access  
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 51)
Energy Harvesting and Systems     Hybrid Journal   (Followers: 4)
Energy Storage Materials     Full-text available via subscription   (Followers: 3)
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 11)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 10)
Frequenz     Hybrid Journal   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 197)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 4)
IACR Transactions on Symmetric Cryptology     Open Access  
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 97)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 77)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 46)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 9)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 1)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 67)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 70)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 56)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 20)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 40)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 19)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 26)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 70)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 5)
IET Cyber-Physical Systems : Theory & Applications     Open Access   (Followers: 1)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 35)
IET Nanodielectrics     Open Access  
IET Power Electronics     Hybrid Journal   (Followers: 46)
IET Smart Grid     Open Access  
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 18)
IETE Journal of Education     Open Access   (Followers: 4)
IETE Journal of Research     Open Access   (Followers: 11)
IETE Technical Review     Open Access   (Followers: 13)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 58)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 25)
Informatik-Spektrum     Hybrid Journal   (Followers: 2)
Instabilities in Silicon Devices     Full-text available via subscription   (Followers: 1)
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 13)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 18)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 10)
International Journal of Antennas and Propagation     Open Access   (Followers: 11)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 4)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 6)
International Journal of Control     Hybrid Journal   (Followers: 11)
International Journal of Electronics     Hybrid Journal   (Followers: 7)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 13)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 2)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Hybrid Intelligence     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 14)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 8)
International Journal of Nanoscience     Hybrid Journal   (Followers: 1)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 4)
International Journal of Power Electronics     Hybrid Journal   (Followers: 24)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 10)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 4)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 6)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 3)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 10)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 4)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 1)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 25)
Journal of Electrical Bioimpedance     Open Access  
Journal of Electrical Bioimpedance     Open Access   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 7)
Journal of Electrical, Electronics and Informatics     Open Access  
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 7)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 8)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 6)
Journal of Electronics (China)     Hybrid Journal   (Followers: 4)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
Journal of Engineered Fibers and Fabrics     Open Access   (Followers: 2)
Journal of Field Robotics     Hybrid Journal   (Followers: 3)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 170)
Journal of Information and Telecommunication     Open Access   (Followers: 1)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 3)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 7)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 9)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal  
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal  
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 10)
Journal of Nuclear Cardiology     Hybrid Journal  
Journal of Optoelectronics Engineering     Open Access   (Followers: 4)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 29)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 11)
Journal of Semiconductors     Full-text available via subscription   (Followers: 5)
Journal of Sensors     Open Access   (Followers: 26)
Journal of Signal and Information Processing     Open Access   (Followers: 9)
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer     Open Access  
Jurnal Rekayasa Elektrika     Open Access  
Jurnal Teknik Elektro     Open Access  
Jurnal Teknologi Elektro     Open Access  
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 12)
Magnetics Letters, IEEE     Hybrid Journal   (Followers: 7)
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 2)
Metrology and Measurement Systems     Open Access   (Followers: 5)
Microelectronics and Solid State Electronics     Open Access   (Followers: 19)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 33)
Nanotechnology, Science and Applications     Open Access   (Followers: 6)
Nature Electronics     Hybrid Journal  
Networks: an International Journal     Hybrid Journal   (Followers: 5)
Open Electrical & Electronic Engineering Journal     Open Access  
Open Journal of Antennas and Propagation     Open Access   (Followers: 8)
Optical Communications and Networking, IEEE/OSA Journal of     Full-text available via subscription   (Followers: 15)
Paladyn. Journal of Behavioral Robotics     Open Access   (Followers: 1)
Power Electronics and Drives     Open Access   (Followers: 1)
Problemy Peredachi Informatsii     Full-text available via subscription  
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 7)
Pulse     Full-text available via subscription   (Followers: 5)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Recent Advances in Communications and Networking Technology     Hybrid Journal   (Followers: 3)
Recent Advances in Electrical & Electronic Engineering     Hybrid Journal   (Followers: 9)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 5)
Revue Méditerranéenne des Télécommunications     Open Access  
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 54)
Semiconductors and Semimetals     Full-text available via subscription   (Followers: 1)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Services Computing, IEEE Transactions on     Hybrid Journal   (Followers: 4)
Software Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 75)
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 13)
Solid-State Electronics     Hybrid Journal   (Followers: 9)
Superconductor Science and Technology     Hybrid Journal   (Followers: 2)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 3)
Technical Report Electronics and Computer Engineering     Open Access  
TELE     Open Access  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 9)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 6)
Visión Electrónica : algo más que un estado sólido     Open Access   (Followers: 1)
Wireless and Mobile Technologies     Open Access   (Followers: 6)
Wireless Power Transfer     Full-text available via subscription   (Followers: 4)
Women in Engineering Magazine, IEEE     Full-text available via subscription   (Followers: 11)
Електротехніка і Електромеханіка     Open Access  

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Similar Journals
Journal Cover
IEEE Transactions on Signal and Information Processing over Networks
Number of Followers: 12  
 
  Full-text available via subscription Subscription journal
ISSN (Print) 2373-776X
Published by IEEE Homepage  [191 journals]
  • IEEE Transactions on Signal and Information Processing over Networks
           publication information
    • Abstract: Provides a listing of current staff, committee members and society officers.
      PubDate: June 2019
      Issue No: Vol. 5, No. 2 (2019)
       
  • IEEE Transactions on Signal and Information Processing over Networks
    • Abstract: Provides a listing of current committee members and society officers.
      PubDate: June 2019
      Issue No: Vol. 5, No. 2 (2019)
       
  • Distributed Estimation From Relative Measurements of Heterogeneous and
           Uncertain Quality
    • Authors: Chiara Ravazzi;Nelson P. K. Chan;Paolo Frasca;
      Pages: 203 - 217
      Abstract: This paper studies the problem of estimation from relative measurements in a graph, in which a vector indexed over the nodes has to be reconstructed from pairwise measurements of differences between its components associated with nodes connected by an edge. In order to model heterogeneity and uncertainty of the measurements, we assume them to be affected by additive noise distributed according to a Gaussian mixture. In this original setup, we formulate the problem of computing the maximum-likelihood estimates and we design two novel algorithms, based on least squares (LS) regression and expectation maximization (EM). The first algorithm (LS-EM) is centralized and performs the estimation from relative measurements, the soft classification of the measurements, and the estimation of the noise parameters. The second algorithm (Distributed LS-EM) is distributed and performs estimation and soft classification of the measurements, but requires the knowledge of the noise parameters. We provide rigorous proofs of convergence for both algorithms and we present numerical experiments to evaluate their performance and compare it with solutions from the literature. The experiments show the robustness of the proposed methods against different kinds of noise and, for the Distributed LS-EM, against errors in the knowledge of noise parameters.
      PubDate: June 2019
      Issue No: Vol. 5, No. 2 (2019)
       
  • Distributed Bernoulli Filtering Using Likelihood Consensus
    • Authors: Giuseppe Papa;Rene Repp;Florian Meyer;Paolo Braca;Franz Hlawatsch;
      Pages: 218 - 233
      Abstract: We consider the detection and tracking of a target in a decentralized sensor network. The presence of the target is uncertain, and the sensor measurements are affected by clutter and missed detections. The state-evolution model and the measurement model may be nonlinear and non-Gaussian. For this practically relevant scenario, we propose a particle-based distributed Bernoulli filter (BF) that provides to each sensor approximations of the Bayes-optimal estimates of the target presence probability and the target state. The proposed method uses all the measurements in the network while requiring only local intersensor communication. This is enabled by an extension of the likelihood consensus method that reaches consensus on the likelihood function under both the target presence and target absence hypotheses. We also propose an adaptive pruning of the likelihood expansion coefficients that yields a significant reduction of intersensor communication. Finally, we present a new variant of the likelihood consensus method that is suited to networks containing star-connected sensor groups. Simulation results show that in challenging scenarios, including a heterogeneous sensor network with significant noise and clutter, the performance of the proposed distributed BF approaches that of the optimal centralized multisensor BF. We also demonstrate that the proposed distributed BF outperforms a state-of-the-art distributed BF at the expense of a higher amount of intersensor communication.
      PubDate: June 2019
      Issue No: Vol. 5, No. 2 (2019)
       
  • Distributed Estimation Over an Adaptive Diffusion Network Based on the
           Family of Affine Projection Algorithms
    • Authors: Mohammad Shams Esfand Abadi;Mohammad Saeed Shafiee;
      Pages: 234 - 247
      Abstract: This paper utilizes the family of affine projection algorithms (APAs) for distributed estimation in the adaptive diffusion networks. The diffusion APA (DAPA), the diffusion selective partial update (SPU) APA (DSPU-APA), the diffusion selective regressor (SR) APA (DSR-APA), and the diffusion dynamic selection (DS) APA (DDS-APA) are introduced in a unified way. In DSPU-APA, the weight coefficients are partially updated at each node during the adaptation. Therefore, the DSPU-APA has lower computational complexity in comparison to the DAPA. In addition, the convergence speed of the DSPU-APA is close to the DAPA. In DSR-APA, a subset of input regressors is optimally selected at each node during the adaptation. The dynamic selection of input regressors is performed in the DDS-APA. These strategies improve the performance of the conventional DAPA in terms of the steady-state error and computational complexity features. Also, by combining these algorithms, the DSPU-SR-APA and the DSPU-DS-APA are established, which are computationally efficient. The mean-square performance of the proposed algorithms is analyzed in the nonstationary environment and the generic relations for the theoretical learning curve and the steady-state error are derived. The analysis is based on the spatial-temporal energy conservation relation. The validity of the theoretical results and the good performance of the introduced algorithms are demonstrated by several computer simulations in diffusion networks.
      PubDate: June 2019
      Issue No: Vol. 5, No. 2 (2019)
       
  • Widely Linear Complex-Valued Diffusion Subband Adaptive Filter Algorithm
    • Authors: Pengwei Wen;Jiashu Zhang;
      Pages: 248 - 257
      Abstract: The adaptive algorithms applied to distributed networks are usually real-valued diffusion subband adaptive filter algorithms. However, it cannot be used for processing the complex-valued signals. In this paper, a novel augmented complex-valued diffusion normalized subband adaptive filter (D-ACNSAF) algorithm is proposed for distributed estimation over networks. In order to deal with the noncircular complex-valued signals, the D-ACNSAF algorithm uses the widely linear model for a diffusion network. Due to the second-order statistical properties of signal, the D-ACNSAF algorithm can process the circular and non-circular complex-valued signals simultaneously. Moreover, the stability and mean-square steady-state analysis of the proposed algorithm are derived based on the spatial-temporal energy conservation principle. Computer simulation experiments on complex-valued system identification and prediction show that the proposed algorithm has better performance (lower mean-square deviation and faster convergence rate) than diffusion complex least-mean-square and diffusion augmented complex least-mean-square algorithms. And the simulation results are consistent with the analysis results.
      PubDate: June 2019
      Issue No: Vol. 5, No. 2 (2019)
       
  • Online Sparse Multi-Output Gaussian Process Regression and Learning
    • Authors: Le Yang;Ke Wang;Lyudmila Mihaylova;
      Pages: 258 - 272
      Abstract: This paper proposes an approach for online training of a sparse multi-output Gaussian process (GP) model using sequentially obtained data. The considered model combines linearly multiple latent sparse GPs to produce correlated output variables. Each latent GP has its own set of inducing points to achieve sparsity. We show that given the model hyperparameters, the posterior over the inducing points is Gaussian under Gaussian noise since they are linearly related to the model outputs. However, the inducing points from different latent GPs would become correlated, leading to a full covariance matrix cumbersome to handle. Variational inference is thus applied and an approximate regression technique is obtained, with which the posteriors over different inducing point sets can always factorize. As the model outputs are non-linearly dependent on the hyperparameters, a novel marginalized particle filer (MPF)-based algorithm is proposed for the online inference of the inducing point values and hyperparameters. The approximate regression technique is incorporated in the MPF and its distributed realization is presented. Algorithm validation using synthetic and real data is conducted, and promising results are obtained.
      PubDate: June 2019
      Issue No: Vol. 5, No. 2 (2019)
       
  • Online Contextual Influence Maximization With Costly Observations
    • Authors: Anıl Ömer Sarıtaç;Altuğ Karakurt;Cem Tekin;
      Pages: 273 - 289
      Abstract: In the online contextual influence maximization problem with costly observations, the learner faces a series of epochs in each of which a different influence spread process takes place over a network. At the beginning of each epoch, the learner exogenously influences (activates) a set of seed nodes in the network. Then, the influence spread process takes place over the network, through which other nodes get influenced. The learner has the option to observe the spread of influence by paying an observation cost. The goal of the learner is to maximize its cumulative reward, which is defined as the expected total number of influenced nodes over all epochs minus the observation costs. We depart from the prior work in three aspects: 1) the learner does not know how the influence spreads over the network, i.e., it is unaware of the influence probabilities; 2) influence probabilities depend on the context; and 3) observing influence is costly. We consider two different influence observation settings: costly edge-level feedback, in which the learner freely observes the set of influenced nodes, but pays to observe the influence outcomes on the edges of the network; and costly node-level feedback, in which the learner pays to observe whether a node is influenced or not. Since the offline influence maximization problem itself is NP-hard, for these settings, we develop online learning algorithms that use an approximation algorithm as a subroutine to obtain the set of seed nodes in each epoch. When the influence probabilities are Hölder continuous functions of the context, we prove that these algorithms achieve sublinear regret (for any sequence of contexts) with respect to an approximation oracle that knows the influence probabilities for all contexts. Our numerical results on several networks illustrate that the proposed algorithms perform on par with the state-of-the-art methods even when the observations are cost free.
      PubDate: June 2019
      Issue No: Vol. 5, No. 2 (2019)
       
  • A Bayesian Algorithm for Distributed Network Localization Using Distance
           and Direction Data
    • Authors: Hassan Naseri;Visa Koivunen;
      Pages: 290 - 304
      Abstract: A reliable, accurate, and affordable positioning service is highly required in wireless networks. High-resolution estimates of distance and direction data are available in most current and emerging wireless systems. Combining these two sensing modalities can improve the estimation performance and identifiability of the cooperative localization problem, and reduce its sensitivity to the geometry of anchor nodes, i.e., the reference nodes with known locations. However, this is still an open and challenging research problem. In this paper, the novel Message Passing Hybrid Localization (MPHL) algorithm is proposed to solve the problem of cooperative distributed localization using joint distance and direction estimates without any prior information. A statistical model is formulated for the problem, and approximate minimum mean square error (MMSE) estimates of the node locations are computed. The proposed MPHL algorithm is a distributed technique based on belief propagation and Markov chain Monte Carlo sampling. Numerical results are presented showing that the average localization error is significantly reduced in almost every simulation scenario, about 50% in most cases, compared to the state of the art. This improvement in localization performance is due to close approximation of a statistically optimal MMSE estimator.
      PubDate: June 2019
      Issue No: Vol. 5, No. 2 (2019)
       
  • Improved Bounds for Max Consensus in Wireless Networks
    • Authors: Aida Nowzari;Michael G. Rabbat;
      Pages: 305 - 319
      Abstract: In consensus problems, the goal is for the nodes of a network to converge to a certain quantity or a function of their values using local communications. In the maximum value consensus problem, the objective of these communications is for all the nodes to converge to the maximum of their initial values. There are two existing algorithms for the maximum value consensus problem in asynchronous networks: RANDOM-PAIRWISE-MAX and RANDOM-BROADCAST-MAX for which the bounds on the mean convergence time have been derived in the literature. In this paper, we derive tighter bounds on the expected convergence time of these two algorithms when run on grid networks and random geometric graphs, respectively-two models commonly used to capture salient properties of wireless networks. We show that RANDOM-PAIRWISE-MAX run on a 2-D grid graph with n nodes converges in expectation after O(n3/2) iterations, and RANDOM-BROADCAST-MAX run on a random geometric graph with n nodes converges in expectation after O((n/ log n)3/2) iterations. These bounds improve over the previous best-known upper bounds by factors of √n log n and log n + log2 n, respectively. Experiments illustrate that the proposed bounds can be up to 95% tighter than the previous state-of-the-art bounds. Furthermore, we enhance the proposed bounds by introducing probabilistic network link failures, e.g., to model packet drops in wireless networks.
      PubDate: June 2019
      Issue No: Vol. 5, No. 2 (2019)
       
  • Cross-Layer MAC Protocol for Unbiased Average Consensus Under Random
           Interference
    • Authors: César Asensio-Marco;Daniel Alonso-Román;Baltasar Beferull-Lozano;
      Pages: 320 - 333
      Abstract: Wireless Sensor Networks have been revealed as a powerful technology to solve many different problems through sensor nodes cooperation. One important cooperative process is the so-called average gossip algorithm, which constitutes a building block to perform many inference tasks in an efficient and distributed manner. From the theoretical designs proposed in most previous work, this algorithm requires instantaneous symmetric links in order to reach average consensus. However, in a realistic scenario wireless communications are subject to interferences and other environmental factors, which results in random instantaneous topologies that are, in general, asymmetric. Consequently, the estimation of the average obtained by the gossip algorithm is a random variable, which its realizations may significantly differ from the average value. In the present paper, we first derive a sufficient conditions for any MAC protocol to guarantee that the expected value of the obtained consensus random variable is the average of the initial values (unbiased estimator), while the variance of the estimator is minimum. Then, we propose a cross-layer and distributed link scheduling protocol based on carrier sense, which besides avoiding collisions, ensures both an unbiased estimation and close to minimum variance values. Extensive numerical results are presented to show the validity and efficiency of the proposed approach.
      PubDate: June 2019
      Issue No: Vol. 5, No. 2 (2019)
       
  • Derivation and Analysis of the Primal-Dual Method of Multipliers Based on
           Monotone Operator Theory
    • Authors: Thomas William Sherson;Richard Heusdens;W. Bastiaan Kleijn;
      Pages: 334 - 347
      Abstract: In this paper, we present a novel derivation of an existing algorithm for distributed optimization termed the primal-dual method of multipliers (PDMM). In contrast to its initial derivation, monotone operator theory is used to connect PDMM with other first-order methods such as Douglas-Rachford splitting and the alternating direction method of multipliers, thus, providing insight into its operation. In particular, we show how PDMM combines a lifted dual form in conjunction with Peaceman-Rachford splitting to facilitate distributed optimization in undirected networks. We additionally demonstrate sufficient conditions for primal convergence for strongly convex differentiable functions and strengthen this result for strongly convex functions with Lipschitz continuous gradients by introducing a primal geometric convergence bound.
      PubDate: June 2019
      Issue No: Vol. 5, No. 2 (2019)
       
  • Sampling of Graph Signals via Randomized Local Aggregations
    • Authors: Diego Valsesia;Giulia Fracastoro;Enrico Magli;
      Pages: 348 - 359
      Abstract: Sampling of signals defined over the nodes of a graph is one of the crucial problems in graph signal processing, whereas in classical signal processing, sampling is a well-defined operation; when we consider a graph signal, many new challenges arise and defining an efficient sampling strategy is not straightforward. Recently, several works have addressed this problem. The most common techniques select a subset of nodes to reconstruct the entire signal. However, such methods often require the knowledge of the signal support and the computation of the sparsity basis before sampling. Instead, in this paper, we propose a new approach to this issue. We introduce a novel technique that combines localized sampling with compressed sensing. We first choose a subset of nodes and then, for each node of the subset, we compute random linear combinations of signal coefficients localized at the node itself and its neighborhood. The proposed method provides theoretical guarantees in terms of reconstruction and stability to noise for any graph and any orthonormal basis, even when the support is not known.
      PubDate: June 2019
      Issue No: Vol. 5, No. 2 (2019)
       
  • Graph Learning From Filtered Signals: Graph System and Diffusion Kernel
           Identification
    • Authors: Hilmi E. Egilmez;Eduardo Pavez;Antonio Ortega;
      Pages: 360 - 374
      Abstract: This paper introduces a novel graph signal processing framework for building graph-based models from classes of filtered signals. In our framework, graph-based modeling is formulated as a graph system identification problem, where the goal is to learn a weighted graph (a graph Laplacian matrix) and a graph-based filter (a function of graph Laplacian matrices). In order to solve the proposed problem, an algorithm is developed to jointly identify a graph and a graph-based filter (GBF) from multiple signal/data observations. Our algorithm is valid under the assumption that GBFs are one-to-one functions. The proposed approach can be applied to learn diffusion (heat) kernels, which are popular in various fields for modeling diffusion processes. In addition, for specific choices of graph-based filters, the proposed problem reduces to a graph Laplacian estimation problem. Our experimental results demonstrate that the proposed algorithm outperforms the current state-of-the-art methods. We also implement our framework on a real climate dataset for modeling of temperature signals.
      PubDate: June 2019
      Issue No: Vol. 5, No. 2 (2019)
       
  • Real-Time Cooperative Analytics for Ambient Noise Tomography in Sensor
           Networks
    • Authors: Maria Valero;Fangyu Li;Sili Wang;Fan-Chi Lin;WenZhan Song;
      Pages: 375 - 389
      Abstract: The transformative integration of sensor networks and geophysical imaging techniques enables the creation of a system to monitor and analyze seismic data in real time as well as image various subsurface structures, properties, and dynamics. Ambient noise seismic imaging is a technique widely used in geophysical exploration for investigating subsurface structures using recorded background raw ambient noise data. The current state-of-the-art of ambient noise monitoring relies on gathering these high volumes of raw data back to a centralized server or base station to pre-process, cross-correlate, analyze frequency-time components, and generate subsurface tomography. However, modern computational sensors (for example, those with ~1.2 GHz of processor and ~1 GB of memory) can be not only used for recording raw vibration data but also performing in situ processing and cooperative computing to generate subsurface imaging in real time. In this paper, we present a distributed solution to apply ambient noise tomography over large dense networks and perform in-network computing on huge seismic samples while avoiding centralized computation and expensive data collection. Results show that our approach can detect subsurface velocity variations in real time while meeting network bandwidth constraints and reducing communication cost (~-75%).
      PubDate: June 2019
      Issue No: Vol. 5, No. 2 (2019)
       
  • Distributionally Robust Radio Frequency Localization
    • Authors: Nachikethas A. Jagadeesan;Bhaskar Krishnamachari;
      Pages: 390 - 403
      Abstract: We consider the problem of estimating the location of an RF-device using observations such as received signal strengths, generated according to an uncertain distribution from a set of transmitters with known locations. We present a distributionally robust formulation of the localization problem that explicitly takes into account the uncertainty in the distribution that generates the observations. We identify the structure of the robust solution and demonstrate how to construct the optimization problem so that it is easily computed, and always yields the optimal solution. We show that the robust estimate outperforms traditional methods in the presence of modeling errors, while remaining close to the traditional estimate when the modeling is exact. This suggests that the formulation presented here is an attractive option in applications where we use a model that may not be an exact fit to our environment or if changes in our environment have induced errors in an empirically derived model.
      PubDate: June 2019
      Issue No: Vol. 5, No. 2 (2019)
       
 
 
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