Conferences related to Neurons

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2018 17th International Conference on Information Technology Based Higher Education and Training (ITHET)

The convergence of current technologies provides the infrastructure for transmitting and storing information faster and cheaper. For information to be used in gaining knowledge, however, environments for collecting, storing, disseminating, sharing and constructing knowledge are needed. Such environments, knowledge media, brings together telecommunication, computer and networking technologies, learning theories and cognitive sciences to form meaningful environments that provides for a variety of learner needs. ITHET 2018 will continue with the traditional themes of previous events. However, our special theme for this year is a fundamental one. We have previously had MOOCs as our special theme, but now they are just infrastructure. Even “Blended Learning” is what we all do anyway. In a time of the unprecedented access to knowledge through IT, it is time for us to revisit the fundamental purpose of our educational system. It is certainly not about knowledge anymore.


2018 26th Signal Processing and Communications Applications Conference (SIU)

The general scope of the conference ranges from signal and image processing to telecommunication, and applications of signal processing methods in biomedical and communication problems.

  • 2017 25th Signal Processing and Communications Applications Conference (SIU)

    Signal Processing and Communication Applications (SIU) conference is the most prominent scientific meeting on signal processing in Turkey bringing together researchers working in signal processing and communication fields. Topics include but are not limited to the areas of research listed in the keywords.

  • 2016 24th Signal Processing and Communication Application Conference (SIU)

    Signal Processing Theory, Statistical Signal Processing, Nonlinear Signal Processing, Adaptive Signal Processing, Array and Multichannel Signal Processing, Signal Processing for Sensor Networks, Time-Frequency Analysis, Speech / Voice Processing and Recognition, Computer Vision, Pattern Recognition, Machine Learning for Signal Processing, Human-Machine Interaction, Brain-Computer Interaction, Signal-Image Acquisition and Generation, image Processing, video Processing, Image Printing and Presentation, Image / Video / Audio browsing and retrieval, Image / Video / Audio Watermarking, Multimedia Signal Processing, Biomedical Signal Processing and Image Processing, Bioinformatics, Biometric Signal-Image Processing and Recognition, Signal Processing for Security and Defense, Signal and Image Processing for Remote Sensing, Signal Processing Hardware, Signal Processing Education, Radar Signal Processing, Communication Theory, Communication Networks, Wireless Communications

  • 2015 23th Signal Processing and Communications Applications Conference (SIU)

    Signal Processing Theory Statistical Signal Processing Nonlinear Signal Processing Adaptive Signal Processing Array and Multichannel Signal Processing Signal Processing for Sensor Networks Time-Frequency Analysis Speech / Voice Processing and Recognition Computer Vision Pattern Recognition Machine Learning for Signal Processing Human-Machine Interaction Brain-Computer Interaction Signal-Image Acquisition and Generation image Processing video Processing Image Printing and Presentation Image / Video / Audio browsing and retrieval Image / Video / Audio Watermarking Multimedia Signal Processing Biomedical Signal Processing and Image Processing Bioinformatics Biometric Signal-Image Processing and Recognition Signal Processing for Security and Defense Signal and Image Processing for Remote Sensing Signal Processing Hardware Signal Processing Education Radar Signal Processing Communication Theory Communication Networks Wireless Communications

  • 2014 22nd Signal Processing and Communications Applications Conference (SIU)

    SIU will be held in Trabzon, Turkey at the Karadeniz Technical University Convention and Exhibition Centre on April 23, 2014. SIU is the largest and most comprehensive technical conference focused on signal processing and its applications in Turkey. Last year there were 500 hundred participants. The conference will feature renowned speakers, tutorials, and thematic workshops. Topics include but are not limited to: Signal Procesing, Image Processing, Communication, Computer Vision, Machine Learning, Biomedical Signal Processing,

  • 2013 21st Signal Processing and Communications Applications Conference (SIU)

    Conference will discuss state of the art solutions and research results on existing and future DSP and telecommunication systems, applications, and related standardization activities. Conference will also include invited lectures, tutorials and special sessions.

  • 2012 20th Signal Processing and Communications Applications Conference (SIU)

    Conference will discuss state of the art solutions and research results on existing and future DSP and telecommunication systems, applications, and related standardization activities. Conference will also include invited lectures, tutorials and special sessions.

  • 2011 19th Signal Processing and Communications Applications Conference (SIU)

    Conference will bring together academia and industry professionals as well as students and researchers to present and discuss state of the art solutions and research results on existing and future DSP and telecommunication systems, applications, and related standardization activities. The Conference will also include invited lectures, tutorials and special sessions.

  • 2010 IEEE 18th Signal Processing and Communications Applications Conference (SIU)

    S1.Theory of Signal-Processing S2.Statistical Signal-Processing S3.Multimedia Signal-Processing S4.Biomedical Signal-Processing S5.Sensor Networks S6.Multirate Signal-Processing S7.Pattern Recognition S8.Computer Vision S9.Adaptive Filters S10.Image/Video/Speech Browsing, Retrieval S11.Speech/Audio Coding S12.Speech Processing S13.Human-Machine Interfaces S14.Surveillance Signal Processing S15.Bioinformatics S16.Self-Learning S17.Signal-Processing Education S18.Signal-Processing Systems S1

  • 2009 IEEE 17th Signal Processing and Communications Applications Conference (SIU)

    The scope of the conference is to cover recent topics in theory and applications of Signal Processing and Communications.

  • 2008 IEEE 16th Signal Processing and Communications Applications Conference (SIU)

    Signal Processing, Image Processing, Speech Processing, Pattern Recognition, Human Computer Interaction, Communication, Video and Speech indexing, Computer Vision, Biomedical Signal Processing

  • 2007 IEEE 15th Signal Processing and Communications Applications (SIU)

  • 2006 IEEE 14th Signal Processing and Communications Applications (SIU)

  • 2005 IEEE 13th Signal Processing and Communications Applications (SIU)

  • 2004 IEEE 12th Signal Processing and Communications Applications (SIU)


2018 European Conference on Antennas and Propagation (EuCAP)

Antennas & related topics e.g. theoretical methods, systems, wideband, multiband, UWBPropagation & related topics e.g. modelling/simulation, HF, body-area, urbanAntenna & RCS measurement techniques


2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA)

The colloquium will provide an excellent platform for knowledge exchange between researchers, scientists, academicians and engineers working in the areas of automation, process, scientific research and analysis. This event calls for local and international participation.Field of Interest:


2018 IEEE 16th International Conference on Industrial Informatics (INDIN)

The aim of INDIN´18 is to bring together researchers and practitioners from industry and academia and provide them with a platform to report and discuss recent developments, deployments, technology trends and research results, as well as initiatives related to industrial informatics and their application.


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Periodicals related to Neurons

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Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.


Biomedical Circuits and Systems, IEEE Transactions on

The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Circuits and Systems I: Regular Papers, IEEE Transactions on

Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.


Circuits and Systems II: Express Briefs, IEEE Transactions on

Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.


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Most published Xplore authors for Neurons

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Xplore Articles related to Neurons

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Neural mesh on cellular neural network

[{u'author_order': 1, u'affiliation': u'Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ., Taipei, Taiwan', u'full_name': u'Tai-Hei Wu'}, {u'author_order': 2, u'affiliation': u'Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ., Taipei, Taiwan', u'full_name': u'Yng-Kae Tzeng'}, {u'author_order': 3, u'affiliation': u'Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ., Taipei, Taiwan', u'full_name': u'Cheng-Yuan Liou'}] 2005 9th International Workshop on Cellular Neural Networks and Their Applications, None

This paper presents an implementation of the neural mesh on the cellular neural networks (CNN). An energy function is devised for the mesh to cope with the CNN. This energy can guide various behaviors of the mesh.


Evolutionary optimization-based training of convolutional neural networks for OCR applications

[{u'author_order': 1, u'affiliation': u'Dept. of Autom. & Appl. Inf., “Politeh.” Univ. of Timisoara, Timisoara, Romania', u'full_name': u'Lucian-Ovidiu Fedorovici'}, {u'author_order': 2, u'affiliation': u'Dept. of Autom. & Appl. Inf., “Politeh.” Univ. of Timisoara, Timisoara, Romania', u'full_name': u'Radu-Emil Precup'}, {u'author_order': 3, u'affiliation': u'Dept. of Autom. & Appl. Inf., “Politeh.” Univ. of Timisoara, Timisoara, Romania', u'full_name': u'Florin Dragan'}, {u'author_order': 4, u'affiliation': u'Dept. of Autom. & Appl. Inf., “Politeh.” Univ. of Timisoara, Timisoara, Romania', u'full_name': u'Constantin Purcaru'}] 2013 17th International Conference on System Theory, Control and Computing (ICSTCC), None

This paper presents aspects concerning the implementation of two training algorithms for convolutional neural networks (CNNs) used in optical character recognition (OCR) applications. The two training algorithms involve evolutionary optimization algorithms represented by a Gravitational Search Algorithm (GSA) and a Particle Swarm Optimization (PSO) Algorithm. New CNN training algorithms are offered on the basis of using GSA and PSO algorithms ...


Neural classifiers using one-time updating

[{u'author_order': 1, u'affiliation': u'Dept. of Appl. Inf., Univ. of Macedonia, Thessaloniki, Greece', u'full_name': u'K. I. Diamantaras'}, {u'author_order': 2, u'full_name': u'M. G. Strintzis'}] IEEE Transactions on Neural Networks, 1998

The linear threshold element, or perceptron, is a linear classifier with limited capabilities due to the problems arising when the input pattern set is linearly nonseparable. Assuming that the patterns are presented in a sequential fashion, we derive a theory for the detection of linear nonseparability as soon as it appears in the pattern set. This theory is based on ...


Polynomial prediction of neurons in neural network classifier for breast cancer diagnosis

[{u'author_order': 1, u'affiliation': u'School of Engineering and Technology, Central Queensland University, 160 Ann Street, Brisbane, Australia 4000', u'full_name': u'Peter Mc Leod'}, {u'author_order': 2, u'affiliation': u'School of Engineering and Engineering Technology, Central Queensland University, 160 Ann Street, Brisbane, Australia 4000', u'full_name': u'Brijesh Verma'}] 2015 11th International Conference on Natural Computation (ICNC), None

Post hoc evaluation mechanisms are utilized for determining the configuration of classifiers. Heuristic approaches mean that sub-optimal configurations could be used; resulting in lost training time, sub-optimal performance and can result in inappropriate results especially for large complex datasets. This paper proposes a new technique to determine the number of neurons in feed forward neural network on two large-scale breast ...


A fuzzy neural network tree with heuristic backpropagation learning

[{u'author_order': 1, u'affiliation': u'Dept. of Comput. Sci., Georgia State Univ., Atlanta, GA, USA', u'full_name': u'Yan-Qing Zhang'}, {u'author_order': 2, u'full_name': u"F'u-lai Chung"}] Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on, None

To solve the curse of dimensionality of a conventional fuzzy neural network, a fuzzy neural network tree based on the normal fuzzy reasoning is proposed. The heuristic backpropagation learning algorithm using a divide-and-conquer method is developed to enhance learning quality in term of discovered knowledge, training error and prediction error. Simulations have shown that the fuzzy neural network tree is ...


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Educational Resources on Neurons

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eLearning

No eLearning Articles are currently tagged "Neurons"

IEEE-USA E-Books

  • Multilayer Neural Networks and Backpropagation

    A computationally effective method for training the multilayer perceptrons is the backpropagation algorithm, which is regarded as a landmark in the development of neural network. This chapter presents two different learning methods, batch learning and online learning, on the basis of how the supervised learning of the multilayer perceptron is actually performed. The essence of backpropagation learning is to encode an input-output mapping into the synaptic weights and thresholds of a multilayer perceptron. It is hoped that the network becomes well trained so that it learns enough about the past to generalize to the future. The chapter concludes with cross-validation and generalization. Cross-validation is appealing particularly when people have to design a large neural network with good generalization as the goal in different ways. Generalization is assumed that the test data are drawn from the same population used to generate the training data.

  • Meta-Cognitive Complex-Valued Relaxation Network and Its Sequential Learning Algorithm

    This chapter contains sections titled: * Meta-Cognition in Machine Learning * Meta-Cognition in Complex-Valued Neural Networks * Meta-Cognitive Fully Complex-Valued Relaxation Network * Performance Evaluation of McFCRN: Synthetic Complex-Valued Function Approximation Problem * Performance Evaluation of McFCRN: Real-Valued Classification Problems * Conclusion

  • Wavelet Denoising and Conditioning of Neural Recordings

    This chapter presents wavelet-based denoising algorithms as a preprocessing stage before spike detection and sorting. The first part of the chapter overviews wavelet-based denoising algorithms. The dyadic wavelet transform is compared with a timeinvariant approach, showing that the latter is best suited to the denoising of neural signals. The second part of the chapter shows a sample application with eletroneurographic (ENG) signals recorded from the sciatic nerve of rabbits while the experimenter stimulated the paw of the animal. The wavelet-based denoising is compared with a traditional band-pass filter in two cases: when followed by spike sorting and when followed by traditional rectified bin integration (RBI). The results illustrate the benefits of wavelet denoising over standard band-pass filtering and demonstrate that there is an even more marked improvement when the subsequent step requires signals with high signal-to-noise ratio (SNR), such as in the case of spike sorting.

  • An Online Scheme for Threat Detection Within Mobile Ad Hoc Networks

    This chapter contains sections titled: Introduction Graph Neuron Hierarchical Graph Neuron Case Study I: DHGN for Pattern Recognition Case Study II: GN for Threat Detection in WSN DHGN Approach for Threat Detection in Manet Summary Acknowledgments References

  • Self-Organization

    This chapter presents the principles of Self-Organization, and focuses on Adaptive Resonance Theory (ART) and Self-Organizing Map (SOM) neural networks. It investigates the theoretic basis of formulations of these neural networks, and illustrates a few examples. The structures of these networks and their learning algorithms are also thoroughly explored in the chapter. The ART architecture is a specifically designed neural network to overcome the stability-plasticity dilemma. It is described using nonlinear differential equations. In addition to ART and SOM, there are two other fixed approaches that could be considered fundamental, namely, Neural Gas and the Hierarchical Feature Map. While both are strongly related to the SOM in terms of the learning mechanism, they each have spawned a range of newer architectures are introduced in the chapter. The chapter explains other popular architectures to have emerged based on similar principles of Self-Organization, drawing a distinction between static and dynamic architectures.

  • Fast Handover Authentication Based on Mobility Prediction

    This chapter discusses the characteristics of VANETs and how vehicles are moving on the road. It presents a fast handover authentication scheme based on a vehicle's mobility prediction. First, the chapter introduces a two-layer network architecture based on WiMax and 802.11p, in which broadband wireless Internet access supports both vehicles' safety-related applications and general mobile users' entertainment-related applications. Next, it briefly talks about the multilayer perceptron (MLP) classifier. The proposed seamless authentication scheme is composed of the following four phases: initial vehicle authentication phase, movement prediction phase, preauthentication phase, and handover phase. The chapter analyzes the security properties of the proposed scheme. In particular, it shows how the scheme can effectively resist replay attack and provide forward secrecy. The chapter concludes with a discussion on the performance of the proposed preauthentication scheme.

  • Wireless, Implantable Neuroprostheses: Applying Advanced Technology to Untether the Mind

  • Multilayer Feedforward Neural Network with Multi-Valued Neurons for Brain-Computer Interfacing

    This chapter contains sections titled: * Brain-Computer Interface (BCI) * BCI Based on Steady-State Visual Evoked Potentials * EEG Signal Preprocessing * Decoding Based on MLMVN for Phase-Coded SSVEP BCI * System Validation * Discussion

  • Instantaneous Cross-Correlation Analysis of Neural Ensembles with High Temporal Resolution

    One of the fundamental difficulties in neural assembly studies is the lack of an effective, high-resolution measure of the spatiotemporal structure in spike trains obtained from a single realization. This chapter proposes a systematic approach to estimate the cross-correlation (CC) of spike trains, over time and in only one realization. The solution lies in an alternate defi nition of cross-correlation which suggests that, rather than time averaging as is current practice, ensemble averaging should be used. This observation suggests a natural instantaneous CC (ICC) estimator as required for high temporal resolution and real-time ensemble analysis and decoding. Results are shown in simulated data sets and neural activity of rat motor cortical neurons during a behavioral task.

  • Quaternionic Neural Networks for Associative Memories

    This chapter contains sections titled: * Introduction * Quaternionic Algebra * Stability of Quaternionic Neural Networks * Learning Schemes for Embedding Patterns * Conclusion



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