Mixture of experts (ME) is modular neural network architecture for supervised learning. A double-loop Expectation-Maximization (EM) algorithm has been introduced to the ME network structure for detection of epileptic seizure. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. EEG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT). Then these sub-band frequencies were used as an input to a ME network with two discrete outputs: normal and epileptic. In order to improve accuracy, the outputs of expert networks were combined according to a set of local weights called the ''gating function''. The invariant transformations of the ME probability density functions include the permutations of the expert labels and the translations of the parameters in the gating functions. The performance of the proposed model was evaluated in terms of classification accuracies and the results confirmed that the proposed ME network structure has some potential in detecting epileptic seizures. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network model.

EEG signal classification using wavelet feature extraction and a mixture of expert model

In this work, we proposed a versatile signal processing and analysis framework for Electroencephalogram (EEG). Within this framework the signals were decomposed into the frequency sub-bands using DWT and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients. Principal components analysis (PCA), independent components analysis (ICA) and linear discriminant analysis (LDA) is used to reduce the dimension of data. Then these features were used as an input to a support vector machine (SVM) with two discrete outputs: epileptic seizure or not. The performance of classification process due to different methods is presented and compared to show the excellent of classification process. These findings are presented as an example of a method for training, and testing a seizure prediction method on data from individual petit mal epileptic patients. Given the heterogeneity of epilepsy, it is likely that methods of this type will be required to configure intelligent devices for treating epilepsy to each individual's neurophysiology prior to clinical operation.

EEG signal classification using PCA, ICA, LDA and support vector machines

Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders

Techniques are described for encoding and decoding digital video data using macroblocks that are larger than the macroblocks prescribed by conventional video encoding and decoding standards. For example, the techniques include encoding and decoding a video stream using macroblocks comprising greater than 16×16 pixels. In one example, an apparatus includes a video encoder configured to encode a coded unit comprising a plurality of video blocks, wherein at least one of the plurality of video blocks comprises a size of more than 16×16 pixels and to generate syntax information for the coded unit that includes a maximum size value, wherein the maximum size value indicates a size of a largest one of the plurality of video blocks in the coded unit. The syntax information may also include a minimum size value. In this manner, the encoder may indicate to a decoder the proper syntax decoder to apply to the coded unit.

Video coding with large macroblocks

Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction

Neural classification of lung sounds using wavelet coefficients.

Eyelid and eyeball movement cause changes to the electric fields around the eyes, and consequently over the scalp. As a result electroencephalogram (EEG) recordings are often significantly distorted, causing problems for analysis and interpretation by clinicians, and also the contamination of the EEG with potentials that originate from movements of the eyelid or eyeball are a nuisance for researchers who investigate the electrophysiology of the brain. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. The purpose of this article is to present a survey of the published literature in dealing with ocular artifacts in the EEG, focusing on the relative merits and demerits of a variety of EOG correction procedures.

Removal of ocular artifacts from EEG: A survey

In Multichannel Wireless Mesh Network architecture, topology discovery, traffic profiling, channel assignment and routing are essential. From the existing work done so far, we can observe that no work has been carried out on the combined solution of multichannel assignment with routing protocol and congestion control. In this paper, we propose to design a Distributed Multichannel Assignment with Congestion control (DMAC) routing protocol. In this protocol, a traffic-aware metric provides the solution for multichannel assignment and congestion control. Hence, the proposed protocol can improve the throughput and channel utilization to a very high extent. The proposed algorithm avoids self-interference by not assigning a channel to any link whose incident links have already been assigned channels. By our simulation results, we show that our proposed protocol attains high throughput and delivery ratio along with reduced delay. Copyright © 2011 John Wiley & Sons, Ltd.

Distributed multichannel assignment with congestion control in wireless mesh networks

OFGM-SMED: An Efficient and Robust Foreground Object Detection in Compressed Video Sequences

In this project we present detection the motion in video frames using background frame Matching. These document video surveillance systems have become widely available to ensure safety and security in both the public and private sectors due to incidents of terrorist activity and other social problems. This paper proposes a novel motion detection method with a background model module and an object mask generation module with moving camera. We propose a self- adaptive background matching method to select the background pixel at each frame with regard to background model generation. The quality of the proposed method is analyzed. The experimental results show that our proposed method has high accuracy and performance compared to previous methods using static camera.

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N. Malmurugan

Papers

Neural classification of lung sounds using wavelet coefficients.

Removal of ocular artifacts from EEG: A survey

Distributed multichannel assignment with congestion control in wireless mesh networks

OFGM-SMED: An Efficient and Robust Foreground Object Detection in Compressed Video Sequences

Object Motion Detection in Video Frames Using Background Frame Matching