Abraham T. Mathew

Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network

Magnetic resonance imaging (MRI) is a non-invasive diagnostic tool very frequently used for brain imaging. The classification of MRI images of normal and pathological brain conditions pose a challenge from technological and clinical point of view, since MR imaging focuses on soft tissue anatomy and generates a large information set and these can act as a mirror reflecting the conditions of the brain. A new approach by integrating wavelet entropy based spider web plots and probabilistic neural network is proposed for the classification of MRI brain images. The two step method for classification uses (1) wavelet entropy based spider web plots for the feature extraction and (2) probabilistic neural network for the classification. The spider web plot is a geometric construction drawn using the entropy of the wavelet approximation components and the areas calculated are used as feature set for classification. Probabilistic neural network provides a general solution to the pattern classification problems and the classification accuracy is found to be 100%.

Fuzzy Clustered Probabilistic and Multi Layered Feed Forward Neural Networks for Electrocardiogram Arrhythmia Classification

The role of electrocardiogram (ECG) as a noninvasive technique for detecting and diagnosing cardiac problems cannot be overemphasized. This paper introduces a fuzzy C-mean (FCM) clustered probabilistic neural network (PNN) for the discrimination of eight types of ECG beats. The performance has been compared with FCM clustered multi layered feed forward network (MLFFN) trained with back propagation algorithm. Important parameters are extracted from each ECG beat and feature reduction has been carried out using FCM clustering. The cluster centers form the input of neural network classifiers. The extensive analysis using the MIT-BIH arrhythmia database has shown an average classification accuracy of 97.54% with FCM clustered MLFFN and 99.58% with FCM clustered PNN. Fuzzy clustering improves the classification speed as well. The result reveals the capability of the FCM clustered PNN in the computer-aided diagnosis of ECG abnormalities.

Improving BTC image compression using a fuzzy complement edge operator

A simple and easy to implement technique for improving block truncation coding (BTC) is proposed. The method is based on replacement of bit block obtained using the conventional BTC method with the fuzzy logical bit block (LBB) such that the sample mean and standard deviation in each image block are preserved. This fuzzy LBB is obtained from the fuzzy edge image by using the Yager involutive fuzzy complement edge operator (YIFCEO). The input image is encoded with the block mean and standard deviation and the fuzzy LBB. Experimental results show an improvement of visual quality of reconstructed images and peak signal-to-noise ratio (PSNR) when compared to the conventional BTC. The raggedness and jagged appearance and the ringing artifacts at sharp edges are greatly reduced in the reconstructed images. With the use of YIFCEO, the proposed method is shown to be more flexible to determine the visual quality of the reconstructed images.

Online Static Security Assessment Module Using Artificial Neural Networks

Fast and accurate contingency selection and ranking method has become a key issue to ensure the secure operation of power systems. In this paper multi-layer feed forward artificial neural network (MLFFN) and radial basis function network (RBFN) are proposed to implement the online module for power system static security assessment. The security classification, contingency selection and ranking are done based on the composite security index which is capable of accurately differentiating the secure and non-secure cases. For each contingency case as well as for base case condition, the composite security index is computed using the full Newton Raphson load flow analysis. The proposed artificial neural network (ANN) models take loading condition and the probable contingencies as the input and assess the system security by screening the credible contingencies and ranking them in the order of severity based on composite security index. The numerical results of applying the proposed approach to IEEE 118-bus test system demonstrate its effectiveness for online power system static security assessment. The comparison of the ANN models with the model based on Newton Raphson load flow analysis in terms of accuracy and computational speed indicate that the proposed model is effective and reliable in the fast evaluation of the security level of power systems. The proposed online static security assessment (OSSA) module realized using the ANN models are found to be suited for online application.

Nonlocal-means image denoising technique using robust M-estimator

Edge preserved smoothing techniques have gained importance for the purpose of image processing applications. A good edge preserving filter is given by nonlocal-means filter rather than any other linear model based approaches. This paper explores a different approach of nonlocal-means filter by using robust M-estimator function rather than the exponential function for its weight calculation. Here the filter output at each pixel is the weighted average of pixels with surrounding neighborhoods using the chosen robust M-estimator function. The main direction of this paper is to identify the best robust M-estimator function for nonlocal-means denoising algorithm. In order to speed up the computation, a new patch classification method is followed to eliminate the uncorrelated patches from the weighted averaging process. This patch classification approach compares favorably to existing techniques in respect of quality versus computational time. Validations using standard test images and brain atlas images have been analyzed and the results were compared with the other known methods. It is seen that there is reason to believe that the proposed refined technique has some notable points.

Classification of Arrhythmia Using Hybrid Networks

Reliable detection of arrhythmias based on digital processing of Electrocardiogram (ECG) signals is vital in providing suitable and timely treatment to a cardiac patient. Due to corruption of ECG signals with multiple frequency noise and presence of multiple arrhythmic events in a cardiac rhythm, computerized interpretation of abnormal ECG rhythms is a challenging task. This paper focuses a Fuzzy C- Mean (FCM) clustered Probabilistic Neural Network (PNN) and Multi Layered Feed Forward Network (MLFFN) for the discrimination of eight types of ECG beats. Parameters such as fourth order Auto Regressive (AR) coefficients along with Spectral Entropy (SE) are extracted from each ECG beat and feature reduction has been carried out using FCM clustering. The cluster centers form the input of neural network classifiers. The extensive analysis of Massachusetts Institute of Technology- Beth Israel Hospital (MIT-BIH) arrhythmia database shows that FCM clustered PNNs is superior in cardiac arrhythmia classification than FCM clustered MLFFN with an overall accuracy of 99.05%, 97.14%, respectively.

Condition Monitoring of Induction Motors Using Wavelet Based Analysis of Vibration Signals

Condition monitoring of machines has its roots in the human ECG analysis for detecting cardiac arrhythmias. Condition monitoring in industry is desirable for increasing machinery availability, reducing consequential damage, and improving the operational efficiency. This is very significant in industries that use heavy duty machines for various processes. A monstrous three-phase AC induction motor to drive a city water supply pump or very big, high power motors used in mills, huge generators for generating power in hydel plants etc. depict a few of them. Also, for safety and economic considerations, there is a need to monitor the behavior of motors working in critical production processes as well. This paper demonstrates how the condition of an induction motor can be monitored by the analysis of the acoustic signal that represents the non-stationary vibration data. The analysis has been done using various signal processing algorithms and a robust fault detection scheme has been developed using the PSD (power spectral density) concept in wavelet decomposition.

Rise time based characterization of sub-millimeter SMA helical actuator for sensorless displacement estimation

Bio mimicking micro/miniature robots require design aesthetics, simplicity, low power, lower computational requirement, resilient operation and repeatability. The main choice of actuators in such systems are Shape Memory Alloys (SMA). The larger strain and reduced size of the SMA sub millimeter diameter helical springs make them a potential choice in such systems. Sensorless position/ displacement control of these type of SMA spring actuators have been studied extensively due to the difficulty in physically attaching feedback sensors, which are larger than SMA actuators. For position estimation, electrical parameters like Inductance and Resistance were considered in the past. This paper presents a new approach based on the pulse response i.e. Rise time variation along the actuation of SMA spring. Considering SMA spring as an RL element, a characterization technique is proposed for sensorless position estimation based on rise time variation. The proposed method is independent of ambient temperature variations. Investigations have shown that rise time based position control through PWM based current drives can be implemented for manipulating displacement without a sensor.

A Composite Security Index for On-line Steady-state Security Evaluation

Abstract On-line critical contingency selection and ranking based on a composite security index is presented in this article. A new method for obtaining a composite scalar index, in terms of both line flow and bus voltage limit violations, has been developed based on the concept of hyper-ellipse inscribed within a hyper-box. The proposed index provides an efficient method for filtering and ranking the credible contingencies and is defined in such a way that a proper differentiation between the secure and insecure states are possible. The composite security index can completely eliminate the masking problem in contingency selection and ranking. In addition, it is seen that if the system is insecure, the exact location of limit violations in buses and branches can be determined by monitoring the violation vector so that proper preventive and corrective control actions can be taken. Extensive simulations have been done using IEEE 30-bus and IEEE 118-bus systems, and the results show that the proposed approach is effective in security assessment.

Robust Estimation Approach for NL-Means Filter

Edge preserved smoothing techniques have gained importance for the purpose of image denoising. A good edge preserving filter is given by NL-means filter than any other linear model based approaches. Since the weight function in NL-means filter is closely related to the error norm and influence function in robust estimation framework, this paper explores a refined approach of NL-means filter by using robust estimation function rather than the usual exponential function for its weight calculation. Here the filter output at each pixel is the weighted average of pixels in the surrounding neighborhoods using the chosen robust M-estimator function. Validations using various test images have been analyzed and the results were compared with the other known recent methods. There is a reason to believe that this refined algorithm has some interesting and notable points.