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International Joint Conference on Neural Networks (IJCNN)

作者： Gregory Ditzler Robi Polikar Signal Processing and Pattern Recognition Laboratory in the Electrical & Computer Engineering Department Rowan University Glassboro NJ USA

Learning in nonstationary environments, also called learning concept drift, has been receiving increasing attention due to increasingly large number of applications that generate data with drifting distributions. These applications are usually associated with streaming data, either online or in batches, and concept drift algorithms are trained to detect and track the drifting concepts. While concept drift itself is a significantly more complex problem than the traditional machine learning paradigm of data coming from a fixed distribution, the problem is further complicated when obtaining labeled data is expensive, and training must rely, in part, on unlabelled data. Independently from concept drift research, semi-supervised approaches have been developed for learning from (limited) labeled and (abundant) unlabeled data; however, such approaches have been largely absent in concept drift literature. In this contribution, we describe an ensemble of classifiers based approach that takes advantage of both labeled and unlabeled data in addressing concept drift: available labeled data are used to generate classifiers, whose voting weights are determined based on the distances between Gaussian mixture model components trained on both labeled and unlabeled data in a drifting environment.

关键词： Classification algorithms Data models Clustering algorithms Training data Training Testing Algorithm design and analysis

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A block-based 2D Discrete Wavelet Transform structure with new scan method for overlapped sections

A block-based 2D Discrete Wavelet Transform structure with n...

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Middle East Conference on Biomedical engineering (MECBME)

作者： Sayed Ahmad Salehi Rasoul Amirfattahi Department of Electrical and Computer Engineering Digital Signal Processing Research Laboratory Isfahan University of Technology Isfahan Iran

Regarding internal memory size, we proposed a new scan method for block-based structure to provide an efficient parametric structure for two Dimensional Discrete Wavelet Transform (2-D DWT). The main challenge in 2-D DWT structure is the amount of internal memory required to produce wavelet coefficients. The previous structures need fixed memory size depend on input frame size, but in proposed architecture, memory size is parametric and in trade off with the number of output memory access. Lifting scheme is incorporated to implement DWT.

关键词： Discrete wavelet transforms Memory management Hardware Pixel

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A Novel Unsupervised Spike Sorting Algorithm for Intracranial EEG

A Novel Unsupervised Spike Sorting Algorithm for Intracrania...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： R. Yadav A. K. Shah J. A. Loeb M. N. S. Swamy R. Agarwal Center for Signal Processing and Communications (CENSIPCOM) Department of Electrical and Computer Engineering Concordia University Department of Neurology and. the Wayne State University School of Medicine

ISBN: (纸本)9781424441211

This paper presents a novel, unsupervised spike classification algorithm for intracranial EEG. The method combines template matching and principal component analysis (PCA) for building a dynamic patient-specific codebook without a priori knowledge of the spike waveforms. The problem of misclassification due to overlapping classes is resolved by identifying similar classes in the codebook using hierarchical clustering. Cluster quality is visually assessed by projecting inter- and intra-clusters onto a 3D plot. Intracranial EEG from 5 patients was utilized to optimize the algorithm. The resulting codebook retains 82.1% of the detected spikes in non-overlapping and disjoint clusters. Initial results suggest a definite role of this method for both rapid review and quantitation of interictal spikes that could enhance both clinical treatment and research studies on epileptic patients.

关键词： EEG Interictal spike Clustering PCA

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Evaluation of some physiological statements about seizure, using processing of epileptic EEG signals

Evaluation of some physiological statements about seizure, u...

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Iranian Conference on electrical engineering (ICEE)

作者： F. Shayegh R. Amirfattahi S. Sadri K. Ansari-Asl Digital signal Processing Laboratory Department of Electrical and Computer Engineering Isfahan University of Technology Iran Electrical Department Engineering Faculty Shahid Chamran University of Ahvaz Iran

Seizure genesis is usually attributed to imbalance of the excitation and inhibition activities of neurons. It is claimed that slow inhibition mechanism is responsible for controlling the variations of the excitation mechanism, and any deficiency in this function will lead to disturbance of their natural balance. Fast inhibition mechanism is also known to collaborate with slow inhibitory processes. Any proof about the excitation and inhibition mechanisms refers to the ex-vivo or in-vitro experiments, i.e. from the microscopic point of view. Up to now, there is not a straight road into evaluating these hypotheses from the macroscopic view. In this paper we try to interpret the excitation-inhibition sequences extracted from some depth-EEG signals through an identification-based procedure. The aim is to check out whether the statements gained from microscopic observations are re-established in population of neurons in the case of MTLE or not. Using Neyman-Pearson hypothesis test technique we try to answer these questions. We observe that even in population of neurons slow inhibition is responsible for compensating excitatory processes to balance inhibitory /excitatory ratio, such that when it is impaired causes seizures to arise; also the average fast inhibitory process of population of neurons follows its slow inhibition dynamics. Also, we check the differentiation of the excitation-inhibition ratio between normal and ictal states, as well as between normal and pre-ictal states. Ictal activity is properly a state of abnormal high excitation-inhibition ratio but before beginning of seizures there are not unique patterns.

关键词： Brain modeling Electroencephalography Noise Mathematical model Neurons Cost function Equations

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New image compression algorithm using proposed quantization approach

New image compression algorithm using proposed quantization ...

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International Conference on Pattern Analysis and Intelligent Robotics (ICPAIR)

作者： Seyed Mehdi Moghadas Hossein Pourghasem Rasoul Amirfattahi Department of Electrical Engineering Islamic Azad University Najafabad Iran Digital Signal Processing Research Laboratory Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

A new quantization method is proposed in this paper. This method is useful for enhancement of compression quality when each kind of neural network is used to compress the image. By quantizing the image with the proposed method, the numbers of samples which must be reconstructed by neural network is reduced. This causes a remarkable increase in quality of the reconstructed image. For testing the proposed method we use autoassociative transform coding and by merging it with the proposed quantization method a new compression algorithm is obtained. Then results of compression by the merged method are compared with some previous works. Obtained results show that the proposed compression algorithm increases the compression quality of the images remarkably. Compression time and complexity in the merged method is also better than JPEG and make it suitable for the systems with low processor and hardware implementation.

关键词： Image coding Biological neural networks Image reconstruction Transform coding Neurons PSNR Training

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Bayesian Spatiotemporal Multitask Learning for Radar HRRP Target Recognition

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IEEE Transactions on signal processing 2011年第7期59卷 3182-3196页

作者： Lan Du Penghui Wang Hongwei Liu Mian Pan Feng Chen Zheng Bao Department of Electrical and Computer Engineering Duke University Durham NC USA National Laboratory of Radar Signal Processing Xidian University Xi'an China School of Electronic Information and Electrical Engineering Shanghai Jiaotong University Shanghai China

A Bayesian dynamic model based on multitask learning (MTL) is developed for radar automatic target recognition (RATR) using high-resolution range profile (HRRP). The aspect-dependent HRRP sequence is modeled using a truncated stick-breaking hidden Markov model (TSB-HMM) with time-evolving transition probabilities, in which the spatial structure across range cells is described by the hidden Markov structure and the temporal dependence between HRRP samples is described by the time evolution of the transition probabilities. This framework imposes the belief that temporally proximate HRRPs are more likely to be drawn from similar HMMs, while also allowing for possible distant repetition or “innovation”. In addition, as formulated the stick-breaking prior and MTL mechanism are employed to infer the number of hidden states in an HMM and learn the target-dependent states collectively for all targets. The form of the proposed hierarchical model allows efficient variational Bayesian (VB) inference, of interest for large-scale problems. To validate the formulation, example results are presented for an illustrative synthesized dataset and our main application-RATR, for which we consider the measured HRRP data. For the latter, we also make comparisons to the model with the independent state-transition statistics and some other existing statistical models for radar HRRP data.

关键词： Hidden Markov models Data models Radar Bayesian methods Target recognition Analytical models

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A New On-Line/Off-Line Adaptive Antenna Array Beamformer for Tracking the Mobile Targets

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International Journal of communications, Network and System Sciences 2011年第5期4卷 304-312页

作者： Shahriar Shirvani Moghaddam Mahyar Shirvani Moghaddam 1Digital communications signal Processing Research Lab. Faculty of Electrical and Computer Engineering Shahid Rajaee Teacher Training University (SRTTU) Tehran Iran 2Department of Electrical Engineering and Advanced Communication Research Institute Sharif University of Technology Tehran Iran

An adaptive antenna array system adjusts the main lobe of radiation pattern in the direction of desired signal and points the nulls in the direction of undesired signals or interferers. The essential goal of beamforming is to reduce the complexity of weighting process and to decrease the time needed for adjusting the antenna radiation pattern. In this article a new adaptive weighting algorithm is proposed for both least mean squares (LMS) and constant modulus (CM) algorithms. It is appropriate and applicable for antenna array systems with moving targets and also mobile applications as well as sensor networks. By predicting the relative velocity of source, the next location of the source will be estimated and the array weights will be determined using LMS or CM algorithm before arriving to the new point. For the next time associated to the new sampling point, evaluated weights will be used. Furthermore, by updating these weights between two consecutive times the effects of error propagation will be eliminated. Therefore, in addition to reduction in computational complexity at the time of weight allocation, relatively accurate weight allocation can be obtained. Simulation results of this investigation show that the angular error related to both LMS-based and CM-based algorithms is less than the conventional LMS and CM algorithms at different signal to noise ratios (SNRs). On the other hand, due to considering off-line process, online computational complexity of new algorithms is slightly low with respect to previous ones.

关键词： Beamforming Adaptive Antenna Array LMS CM Training-Based Blind

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Single and Multiple Estimation in MIMO Rician Fading Channels

Single and Multiple Estimation in MIMO Rician Fading Channel...

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International Conference on computer Communication and Management（ICCCM 2011）

作者： Hamid Nooralizadeh Shahriar Shirvani Moghaddam Faculty Member of Electrical Engineering Department Islamshahr Branch Islamic Azad University Digital Communications Signal Processing (DCSP) Research Lab. Faculty of Electrical and Computer Engineering Shahid Rajaee Teacher Training University (SRTTU)

In this paper, the performance of the Single-Estimation （SE） and Multiple-Estimation （ME） is investigated in Multiple-Input Multiple-Output （MIMO） Rician fading channels using the Maximum Likelihood （ML） technique and the new Shifted Scaled Least Squares （SSLS） estimator. Analytical and numerical results show that both estimators have lower error in the case of ME than SE. Moreover, it is seen that increasing the channel Rice factor improves the performance of the SSLS estimator. However, the ML estimator cannot exploit the knowledge of the Rician fading channel. As a result, in fast fading MIMO channels with a short coherence time, the SSLS estimator can be used in SE mode. On the other hand, the ML estimator has only good results in the case of ME. This estimator is appropriate for slow fading MIMO channels with a long coherent time.

关键词： Rician fading channel Single Estimation （SE） Multiple Estimation （ME） MIMO ML SSLS.

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A convex relaxation framework for initialization of activation-based inverse electrocardiography

A convex relaxation framework for initialization of activati...

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Joint Meeting of the International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging (NFSI-ICFBI)

作者： Burak Erem Peter M. van Dam Dana H. Brooks Communications and Digital Signal Processing Center Department of Electrical and Computer Engineering Northeastern University Boston MA USA Donders Institute of Brain Cognition and Behaviour Radboud University Nijmegen Netherlands

In inverse electrocardiography (ECG), the problem of finding activation times on the heart noninvasively from body surface potentials is typically formulated as a nonlinear least squares optimization problem. Current solutions rely on iterative algorithms which are sensitive to the presence of local minima. As a result, improved initialization approaches for this problem have been of considerable interest. In this work, we establish a mathematical optimization framework for the inverse problem, formally show that it is non-convex, and construct a convex relaxation whose solution we use to initialize an iterative algorithm for the original problem. We use simulated experiments based on real canine heart data to test and evaluate this method.

关键词： Optimization Electrocardiography Surface waves Convex functions signal to noise ratio Heart beat

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signal recovery in PDM optical communication systems employing independent component analysis

Signal recovery in PDM optical communication systems employi...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： E. S. Rosa L. T. Duarte J. M. T. Romano R. Suyama DSPCom-Laboratory of Signal Processing for Communications School of Electrical and Computer Engineering (FEEC) State University of Campinas-UNICAMP Sao Paulo Brazil Engineering Modeling and Applied Social Sciences-CECS Federal University of ABC UFABC Sao Paulo Brazil

There is a growing interest in employing digital signal processing methods to compensate the distortions typical of the next generation optical systems. For instance, several works considered the multi-user constant modulus algorithm (MU-CMA) to recover the transmitted signals in Polarization Division Multiplexing (PDM) systems. In this work, the same problem is tackled through independent component analysis methods. The obtained results point out that, differently from the MU-CMA, our proposal is robust against sources loss even when polarization dependent loss is present.

关键词： Optical fibers Optical distortion Optical polarization Optical fiber dispersion Integrated optics Chromatic dispersion

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