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  Subjects -> ENGINEERING (Total: 2235 journals)
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ENGINEERING (1194 journals)                  1 2 3 4 5 6 7 8 | Last

3 Biotech     Open Access   (Followers: 7)
3D Research     Hybrid Journal   (Followers: 24)
AAPG Bulletin     Full-text available via subscription   (Followers: 7)
AASRI Procedia     Open Access   (Followers: 14)
Abstract and Applied Analysis     Open Access   (Followers: 3)
Aceh International Journal of Science and Technology     Open Access   (Followers: 2)
ACS Nano     Full-text available via subscription   (Followers: 179)
Acta Geotechnica     Hybrid Journal   (Followers: 8)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 7)
Acta Polytechnica : Journal of Advanced Engineering     Open Access  
Acta Scientiarum. Technology     Open Access  
Acta Universitatis Cibiniensis. Technical Series     Open Access   (Followers: 1)
Active and Passive Electronic Components     Open Access   (Followers: 4)
Adaptive Behavior     Hybrid Journal   (Followers: 9)
Adsorption     Hybrid Journal   (Followers: 7)
Advanced Engineering Forum     Full-text available via subscription   (Followers: 1)
Advanced Science     Open Access   (Followers: 2)
Advanced Science Focus     Free   (Followers: 3)
Advanced Science Letters     Full-text available via subscription   (Followers: 6)
Advanced Science, Engineering and Medicine     Partially Free   (Followers: 8)
Advanced Synthesis & Catalysis     Hybrid Journal   (Followers: 6)
Advances in Artificial Neural Systems     Open Access   (Followers: 6)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 3)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Complex Systems     Hybrid Journal   (Followers: 9)
Advances in Engineering Software     Hybrid Journal   (Followers: 23)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 13)
Advances in Fuzzy Systems     Open Access   (Followers: 9)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 13)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 14)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 18)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 6)
Advances in Natural Sciences: Nanoscience and Nanotechnology     Open Access   (Followers: 17)
Advances in Operations Research     Open Access   (Followers: 10)
Advances in OptoElectronics     Open Access   (Followers: 4)
Advances in Physics Theories and Applications     Open Access   (Followers: 8)
Advances in Polymer Science     Hybrid Journal   (Followers: 38)
Advances in Porous Media     Full-text available via subscription   (Followers: 3)
Advances in Remote Sensing     Open Access   (Followers: 11)
Advances in Science and Research (ASR)     Open Access   (Followers: 7)
Aerobiologia     Hybrid Journal   (Followers: 3)
African Journal of Science, Technology, Innovation and Development     Hybrid Journal   (Followers: 5)
Afrique Science : Revue Internationale des Sciences et Technologie     Open Access   (Followers: 1)
AIChE Journal     Hybrid Journal   (Followers: 24)
Ain Shams Engineering Journal     Open Access   (Followers: 5)
Alexandria Engineering Journal     Open Access   (Followers: 1)
AMB Express     Open Access  
American Journal of Applied Sciences     Open Access   (Followers: 33)
American Journal of Engineering and Applied Sciences     Open Access   (Followers: 11)
American Journal of Engineering Education     Open Access   (Followers: 5)
American Journal of Environmental Engineering     Open Access   (Followers: 10)
American Journal of Industrial and Business Management     Open Access   (Followers: 20)
Analele Universitatii Ovidius Constanta - Seria Chimie     Open Access  
Annals of Combinatorics     Hybrid Journal   (Followers: 6)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Regional Science     Hybrid Journal   (Followers: 7)
Annals of Science     Hybrid Journal   (Followers: 6)
Antarctic Science     Hybrid Journal   (Followers: 2)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applicable Analysis: An International Journal     Hybrid Journal   (Followers: 1)
Applied Catalysis A: General     Hybrid Journal   (Followers: 6)
Applied Catalysis B: Environmental     Hybrid Journal   (Followers: 5)
Applied Clay Science     Hybrid Journal   (Followers: 3)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 9)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 4)
Applied Nanoscience     Open Access   (Followers: 8)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 7)
Applied Physics Research     Open Access   (Followers: 4)
Applied Sciences     Open Access   (Followers: 2)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 3)
Arabian Journal for Science and Engineering     Hybrid Journal   (Followers: 4)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
Archives of Foundry Engineering     Open Access  
Archives of Thermodynamics     Open Access   (Followers: 6)
Arkiv för Matematik     Hybrid Journal  
ASEE Prism     Full-text available via subscription   (Followers: 1)
Asian Engineering Review     Open Access  
Asian Journal of Applied Science and Engineering     Open Access  
Asian Journal of Applied Sciences     Open Access   (Followers: 4)
Asian Journal of Biotechnology     Open Access   (Followers: 4)
Asian Journal of Control     Hybrid Journal  
Asian Journal of Current Engineering & Maths     Open Access  
Asian Journal of Technology Innovation     Hybrid Journal   (Followers: 9)
Assembly Automation     Hybrid Journal   (Followers: 1)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 2)
ATZagenda     Hybrid Journal  
ATZextra worldwide     Hybrid Journal   (Followers: 1)
Australasian Physical & Engineering Sciences in Medicine     Hybrid Journal   (Followers: 2)
Australian Journal of Multi-Disciplinary Engineering     Full-text available via subscription   (Followers: 2)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 5)
Avances en Ciencias e Ingeniería     Open Access  
Bangladesh Journal of Scientific and Industrial Research     Open Access   (Followers: 1)
Basin Research     Hybrid Journal   (Followers: 3)
Batteries     Open Access   (Followers: 1)
Bautechnik     Hybrid Journal   (Followers: 1)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 12)
Beni-Suef University Journal of Basic and Applied Sciences     Open Access   (Followers: 3)
BER : Manufacturing Survey : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Motor Trade Survey     Full-text available via subscription   (Followers: 1)
BER : Retail Sector Survey     Full-text available via subscription   (Followers: 2)

        1 2 3 4 5 6 7 8 | Last

Journal Cover Autonomous Mental Development, IEEE Transactions on
  [5 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1943-0604
   Published by Institute of Electrical and Electronics Engineers (IEEE) Homepage  [177 journals]
  • IEEE Computational Intelligence Society Information
    • Abstract: Provides a listing of board members, committee members and society officers.
      PubDate: Dec. 2015
      Issue No: Vol. 7, No. 4 (2015)
       
  • IEEE Transactions on Autonomous Mental Development information for authors
    • Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: Dec. 2015
      Issue No: Vol. 7, No. 4 (2015)
       
  • Table of Contents
    • Abstract: Presents the table of contents for this issue of the publication.
      PubDate: Dec. 2015
      Issue No: Vol. 7, No. 4 (2015)
       
  • IEEE Transactions on Autonomous Mental Development publication information
    • Abstract: Provides a listing of the editors, board members, and current staff for this issue of the publication.
      PubDate: Dec. 2015
      Issue No: Vol. 7, No. 4 (2015)
       
  • Guest Editorial Multimodal Modeling and Analysis Informed by Brain
           Imaging—Part II
    • Authors: Han; J;Liu, T;Guo, C;Weng, J;
      Pages: 269 - 272
      Abstract: Human brains are the ultimate recipients and assessors of multimedia contents and semantics. Recent developments of neuroimaging techniques have enabled us to probe human brain activities during free viewing of multimedia contents. This special issue mainly focuses on the synergistic combinations of cognitive neuroscience, brain imaging, and multimedia analysis. It aims to capture the latest advances in the research community working on brain imaging informed multimedia analysis, as well as computational model of the brain processes driven by multimedia contents. This issue contains the second set of papers from the double Special Issue.
      PubDate: Dec. 2015
      Issue No: Vol. 7, No. 4 (2015)
       
  • Types, Locations, and Scales from Cluttered Natural Video and Actions
    • Authors: Xiaoying Song;Wenqiang Zhang;Juyang Weng;
      Pages: 273 - 286
      Abstract: We model the autonomous development of brain-inspired circuits through two modalities-video stream and action stream that are synchronized in time. We assume that such multimodal streams are available to a baby through inborn reflexes, self-supervision, and caretaker's supervision, when the baby interacts with the real world. By autonomous development, we mean that not only that the internal (inside the “skull”) self-organization is fully autonomous, but the developmental program (DP) that regulates the computation of the network is also task nonspecific. In this work, the task-nonspecificity is reflected by the fact that the actions associated with an attended object in a cluttered, natural, and dynamic scene is taught after the DP is finished and the “life” has begun. The actions correspond to neuronal firing patterns representing object type, object location and object scale, but learning is directly from unsegmented cluttered scenes. Along the line of where-what networks (WWN), this is the first one that explicitly models multiple “brain” areas-each for a different range of object scales. Among experiments, large natural video experiments were conducted. To show the power of automatic attention in unknown cluttered backgrounds, the last experimental group demonstrated disjoint tests in the presence of large within-class variations (object 3-D-rotations in very different unknown backgrounds), but small between-class variations (small object patches in large similar and different unknown backgrounds), in contrast with global classification tests such as ImageNet and Atari Games.
      PubDate: Dec. 2015
      Issue No: Vol. 7, No. 4 (2015)
       
  • Randomized Structural Sparsity-Based Support Identification with
           Applications to Locating Activated or Discriminative Brain Areas: A
           Multicenter Reproducibility Study
    • Authors: Yilun Wang;Sheng Zhang;Junjie Zheng;Heng Chen;Huafu Chen;
      Pages: 287 - 300
      Abstract: In this paper, we focus on how to locate the relevant or discriminative brain regions related with external stimulus or certain mental decease, which is also called support identification, based on the neuroimaging data. The main difficulty lies in the extremely high dimensional voxel space and relatively few training samples, easily resulting in an unstable brain region discovery (or called feature selection in context of pattern recognition). When the training samples are from different centers and have between-center variations, it will be even harder to obtain a reliable and consistent result. Corresponding, we revisit our recently proposed algorithm based on stability selection and structural sparsity. It is applied to the multicenter MRI data analysis for the first time. A consistent and stable result is achieved across different centers despite the between-center data variation while many other state-of-the-art methods such as two sample t-test fail. Moreover, we have empirically showed that the performance of this algorithm is robust and insensitive to several of its key parameters. In addition, the support identification results on both functional MRI and structural MRI are interpretable and can be the potential biomarkers.
      PubDate: Dec. 2015
      Issue No: Vol. 7, No. 4 (2015)
       
  • Beyond Subjective Self-Rating: EEG Signal Classification of Cognitive
           Workload
    • Authors: Zarjam; P.;Epps, J.;Lovell, N.H.;
      Pages: 301 - 310
      Abstract: Cognitive workload is an important indicator of mental activity that has implications for human-computer interaction, biomedical and task analysis applications. Previously, subjective rating (self-assessment) has often been a preferred measure, due to its ease of use and relative sensitivity to the cognitive load variations. However, it can only be feasibly measured in a post-hoc manner with the user's cooperation, and is not available as an online, continuous measurement during the progress of the cognitive task. In this paper, we used a cognitive task inducing seven different levels of workload to investigate workload discrimination using electroencephalography (EEG) signals. The entropy, energy, and standard deviation of the wavelet coefficients extracted from the segmented EEGs were found to change very consistently in accordance with the induced load, yielding strong significance in statistical tests of ranking accuracy. High accuracy for subject-independent multichannel classification among seven load levels was achieved, across the twelve subjects studied. We compare these results with alternative measures such as performance, subjective ratings, and reaction time (response time) of the subjects and compare their reliability with the EEG-based method introduced. We also investigate test/re-test reliability of the recorded EEG signals to evaluate their stability over time. These findings bring the use of passive brain-computer interfaces (BCI) for continuous memory load measurement closer to reality, and suggest EEG as the preferred measure of working memory load.
      PubDate: Dec. 2015
      Issue No: Vol. 7, No. 4 (2015)
       
  • Local Multimodal Serial Analysis for Fusing EEG-fMRI: A New Method to
           Study Familial Cortical Myoclonic Tremor and Epilepsy
    • Authors: Li Dong;Pu Wang;Yi Bin;Jiayan Deng;Yongjie Li;Leiting Chen;Cheng Luo;Dezhong Yao;
      Pages: 311 - 319
      Abstract: Integrating information of neuroimaging multimodalities, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), has become popularly for investigating various types of epilepsy. However, there are also some problems for the analysis of simultaneous EEG-fMRI data in epilepsy: one is the variation of HRFs, and another is low signal-to-noise ratio (SNR) in the data. Here, we propose a new multimodal unsupervised method, termed local multimodal serial analysis (LMSA), which may compensate for these deficiencies in multimodal integration. A simulation study with comparison to the traditional EEG-informed fMRI analysis which directly implemented the general linear model (GLM) was conducted to confirm the superior performance of LMSA. Then, applied to the simultaneous EEG-fMRI data of familial cortical myoclonic tremor and epilepsy (FCMTE), some meaningful information of BOLD changes related to the EEG discharges, such as the cerebellum and frontal lobe (especially in the inferior frontal gyrus), were found using LMSA. These results demonstrate that LMSA is a promising technique for exploring various data to provide integrated information that will further our understanding of brain dysfunction.
      PubDate: Dec. 2015
      Issue No: Vol. 7, No. 4 (2015)
       
  • Discriminating Bipolar Disorder From Major Depression Based on SVM-FoBa:
           Efficient Feature Selection With Multimodal Brain Imaging Data
    • Authors: Nan-Feng Jie;Mao-Hu Zhu;Xiao-Ying Ma;Osuch; E.A.;Wammes, M.;Theberge, J.;Huan-Dong Li;Yu Zhang;Tian-Zi Jiang;Jing Sui;Calhoun, V.D.;
      Pages: 320 - 331
      Abstract: Discriminating between bipolar disorder (BD) and major depressive disorder (MDD) is a major clinical challenge due to the absence of known biomarkers; hence a better understanding of their pathophysiology and brain alterations is urgently needed. Given the complexity, feature selection is especially important in neuroimaging applications, however, feature dimension and model understanding present serious challenges. In this study, a novel feature selection approach based on linear support vector machine with a forward-backward search strategy (SVM-FoBa) was developed and applied to structural and resting-state functional magnetic resonance imaging data collected from 21 BD, 25 MDD and 23 healthy controls. Discriminative features were drawn from both data modalities, with which the classification of BD and MDD achieved an accuracy of 92.1% (1000 bootstrap resamples). Weight analysis of the selected features further revealed that the inferior frontal gyrus may characterize a central role in BD-MDD differentiation, in addition to the default mode network and the cerebellum. A modality-wise comparison also suggested that functional information outweighs anatomical by a large margin when classifying the two clinical disorders. This work validated the advantages of multimodal joint analysis and the effectiveness of SVM-FoBa, which has potential for use in identifying possible biomarkers for several mental disorders.
      PubDate: Dec. 2015
      Issue No: Vol. 7, No. 4 (2015)
       
  • Design of a Multimodal EEG-based Hybrid BCI System with Visual Servo
           Module
    • Authors: Feng Duan;Dongxue Lin;Wenyu Li;Zhao Zhang;
      Pages: 332 - 341
      Abstract: Current EEG-based brain-computer interface technologies mainly focus on how to independently use SSVEP, motor imagery, P300, or other signals to recognize human intention and generate several control commands. SSVEP and P300 require external stimulus, while motor imagery does not require it. However, the generated control commands of these methods are limited and cannot control a robot to provide satisfactory service to the user. Taking advantage of both SSVEP and motor imagery, this paper aims to design a hybrid BCI system that can provide multimodal BCI control commands to the robot. In this hybrid BCI system, three SSVEP signals are used to control the robot to move forward, turn left, and turn right; one motor imagery signal is used to control the robot to execute the grasp motion. In order to enhance the performance of the hybrid BCI system, a visual servo module is also developed to control the robot to execute the grasp task. The effect of the entire system is verified in a simulation platform and a real humanoid robot, respectively. The experimental results show that all of the subjects were able to successfully use this hybrid BCI system with relative ease.
      PubDate: Dec. 2015
      Issue No: Vol. 7, No. 4 (2015)
       
  • EEG-Based Perceived Tactile Location Prediction
    • Authors: Deng Wang;Yadong Liu;Dewen Hu;Blohm; G.;
      Pages: 342 - 348
      Abstract: Previous studies have attempted to investigate the peripheral neural mechanisms implicated in tactile perception, but the neurophysiological data in humans involved in tactile spatial location perception to help the brain orient the body and interact with its surroundings are not well understood. In this paper, we use single-trial electroencephalogram (EEG) measurements to explore the perception of tactile stimuli located on participants' right forearm, which were approximately equally spaced centered on the body midline, 2 leftward and 2 rightward of midline. An EEG-based signal analysis approach to predict the location of the tactile stimuli is proposed. Offline classification suggests that tactile location can be detected from EEG signals in single trial (four-class classifier for location discriminate can achieve up to 96.76%) with a short response time (600 milliseconds after stimulus presentation). From a human-machine-interaction (HMI) point of view, this could be used to design a real-time reactive control machine for patients, e.g., suffering from hypoesthesia.
      PubDate: Dec. 2015
      Issue No: Vol. 7, No. 4 (2015)
       
  • An Adaptive Motion-Onset VEP-Based Brain-Computer Interface
    • Authors: Rui Zhang;Peng Xu;Rui Chen;Teng Ma;Xulin Lv;Fali Li;Peiyang Li;Tiejun Liu;Dezhong Yao;
      Pages: 349 - 356
      Abstract: Motion-onset visual evoked potential (mVEP) has been recently proposed for EEG-based brain-computer interface (BCI) system. It is a scalp potential of visual motion response, and typically composed of three components: P1, N2, and P2. Usually several repetitions are needed to increase the signal-to-noise ratio (SNR) of mVEP, but more repetitions will cost more time thus lower the efficiency. Considering the fluctuation of subject's state across time, the adaptive repetitions based on the subject's real-time signal quality is important for increasing the communication efficiency of mVEP-based BCI. In this paper, the amplitudes of the three components of mVEP are proposed to build a dynamic stopping criteria according to the practical information transfer rate (PITR) from the training data. During online test, the repeated stimulus stopped once the predefined threshold was exceeded by the real-time signals and then another circle of stimulus newly began. Evaluation tests showed that the proposed dynamic stopping strategy could significantly improve the communication efficiency of mVEP-based BCI that the average PITR increases from 14.5 bit/min of the traditional fixed repetition method to 20.8 bit/min. The improvement has great value in real-life BCI applications because the communication efficiency is very important.
      PubDate: Dec. 2015
      Issue No: Vol. 7, No. 4 (2015)
       
  • 2015 Index IEEE Transactions on Autonomous Mental Development Vol. 7
    • Pages: 357 - 362
      Abstract: Presents the 2015 author/subject index for this publication.
      PubDate: Dec. 2015
      Issue No: Vol. 7, No. 4 (2015)
       
 
 
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