Marizan Mubin

Model Predictive Torque Ripple Reduction with Weighting Factor Optimization Fed by an Indirect Matrix Converter

Abstract—Model predictive control has emerged as a powerful control tool in the field of power converter and drives system. In this article, a weighting factor optimization for reducing the torque ripple of induction machine fed by an indirect matrix converter is introduced and presented. Therefore, an optimization method is adopted here to calculate the optimum weighting factor corresponding to minimum torque ripple. However, model predictive torque and flux control of the induction machine with conventionally selected weighting factor is being investigated in this article and is compared with the proposed optimum weighting factor based model predictive control algorithm to reduce the torque ripples. The proposed model predictive control scheme utilizes the discrete phenomena of power converter and predicts the future nature of the system variables. For the next sampling period, model predictive method selects the optimized switching state that minimizes a cost function based on optimized weighting factor to actuate the power converter. The introduced weighting factor optimization method in model predictive control algorithm is validated through simulations and shows potential control, tracking of variables with their respective references and consequently reduces the torque ripples corresponding to conventional weighting factor based predictive control method.

Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization

Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.

HMM based automated wheelchair navigation using EOG traces in EEG

This paper presents a wheelchair navigation system based on a hidden Markov model (HMM), which we developed to assist those with restricted mobility. The semi-autonomous system is equipped with obstacle/collision avoidance sensors and it takes the electrooculography (EOG) signal traces from the user as commands to maneuver the wheelchair. The EOG traces originate from eyeball and eyelid movements and they are embedded in EEG signals collected from the scalp of the user at three different locations. Features extracted from the EOG traces are used to determine whether the eyes are open or closed, and whether the eyes are gazing to the right, center, or left. These features are utilized as inputs to a few support vector machine (SVM) classifiers, whose outputs are regarded as observations to an HMM. The HMM determines the state of the system and generates commands for navigating the wheelchair accordingly. The use of simple features and the implementation of a sliding window that captures important signatures in the EOG traces result in a fast execution time and high classification rates. The wheelchair is equipped with a proximity sensor and it can move forward and backward in three directions. The asynchronous system achieved an average classification rate of 98% when tested with online data while its average execution time was less than 1 s. It was also tested in a navigation experiment where all of the participants managed to complete the tasks successfully without collisions.

Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals

Abstract In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification.

Automatic Cryptosporidium and Giardia viability detection in treated water

In the automatic detection of Cryptosporidium and Giardia (oo)cysts in water samples, low contrast and noise in the microscopic images can adversely affect the accuracy of the segmentation results. An improved partial differential equation (PDE) filtering that achieves a better trade-off between noise removal and edge preservation is introduced where the compass operator is utilized to attenuate noise while retaining edge information at the cytoplasm wall and around the nuclei of the (oo)cysts. Then the anatomically important information is separated from the unwanted background noise using the Otsu method to improve the detection accuracy. Once the (oo)cysts are located, a simple technique to classify the two types of protozoans using area, roundness metric and eccentricity is implemented. Finally, the number of nuclei in the cytoplasm of each (oo)cyst is counted to check the viability of individual parasite. The proposed system is tested on 40 microscopic images obtained from treated water samples, and it gives excellent detection and viability rates of 97% and 98%, respectively.

Evaluation Of Different Peak Models Of Eye Blink Eeg For Signal Peak Detection Using Artificial Neural Network

There is a growing interest of research being conducted on detecting eye blink to assist physically impaired people for verbal communication and controlling devices using electroencephalogram (EEG) signal. One particular eye blink can be determined from use of peak points. Therefore, the purpose of peak detection algorithm is to distinguish an actual peak location from a list of peak candidates. The need of a good peak model is important in ensuring a satisfy classification performance. In general, there are various peak models available in literature, which have been tested in several peak detection algorithms. In this study, performance evaluation of the existing peak models is conducted based on Artificial Neural Network (ANN) with particle swarm optimization (PSO) as learning algorithm. This study evaluates the performance of eye blink EEG signal peak detection algorithm for four different peak models which are Dumpalas, Acirs, Lius, and Dingles peak models. To generalize the performance evaluation, two case studies of eye blink EEG signal are considered, which are single and double eye blink signals. It has been observed that the best test performance, in average, is 91.94% and 87.47% for single and double eye blink signals, respectively. These results indicate that the Acirs peak model offers high accuracy of peak detection for the two eye blink EEG signals as compared to other peak models. The result of statistical analysis also indicates that the Acirs peak model is better than Dingles and Dumpalas peak models.

New topology three phase multilevel inverter for grid-connected photovoltaic system

This paper presents a new schematic for three phase seven-level inverters. The proposed topology minimizes switching, reduces short circuiting, is easy to control, and can mask through upper-level switches the work of lower levels. System improvements include low ripple-current, high power conversion and minimized switching, which are verified in simulations using the Matlab/Simulink software package. Experimental results are obtained through the use of field programmable gate arrays (FPGA), and the pulse generation is carried out using the SPARTAN 3A DSP board. The proposed topology has been experimentally implemented for both five-level and seven-level pulse width modulated laboratory inverters.

Feature Selection and Classifier Parameter Estimation for EEG Signal Peak Detection Using Gravitational Search Algorithm

Peak detection is a significant step in analyzing the electroencephalography (EEG) signal because peaks may represent meaningful brain activities. Several approaches can be used for peak point detection such as time domain, frequency domain, time-frequency domain, and nonlinear approaches. The main intention of this study is to find the significant peak features in time domain approach and this can be done using feature selection methods such as gravitational search algorithm (GSA) and particle swarm optimization (PSO). This study focuses on using GSA method, a new computational intelligence algorithm. Moreover, a rule-based classifier is employed to distinguish a peak point based on the selected features. Using GSA, the parameter estimation of the classifier and the peak feature selection can be done simultaneously. Based on the experimental results, the significant peak features of the peak detection algorithm were obtained where the average test accuracy is 77.74%.

Segmentation of overlapping Cryptosporidium and Giardia (oo)cysts using bidirectional contour tracing

a b s t r a c t In the inspection of treated water samples under microscope, knowing the average number of parasite (oo)cysts like Giardia and Cryptosporidium that exist in the samples is crucial as it tells whether the water is safe for consumption. Here, we introduce a new approach using a bidirectional contour tracing technique to segment and enumerate overlapping Cryptosporidium and Giardia (oo)cysts in microscopic images of treated water samples. First the image is denoised and edge detection is performed to detect the boundary of the (oo)cysts using Kirsch operator. The greyscale image is then binarized to identify the position of the (oo)cysts before it is Otsu thresholded to separate weak edge from strong edge. Then bidirectional contour tracing is implemented to isolate overlapping objects where the boundary of the (oo)cysts is traced in two different directions simultaneously. After boundary tracing, a modified ellipse fitting is executed where partial or broken ellipses can be combined to form completed ellipses that represent (oo)cysts. The proposed technique is tested on 40 FITC microscopic images containing overlapping Cryptosporidium and Giardia (oo)cysts in treated water samples. The performance of the technique is comparable to better than those of four well-known ellipse detection methods. The technique is also tested on images containing overlapping blood cells, Cryptosporidium oocysts in dirty background and rice grains, and the results are excellent.

Performance evaluation of vector evaluated gravitational search algorithm II

This paper presents a performance evaluation of a novel Vector Evaluated Gravitational Search Algorithm II (VEGSAII) for multi-objective optimization problems. The VEGSAII algorithm uses a number of populations of particles. In particular, a population of particles corresponds to one objective function to be minimized or maximized. Simultaneous minimization or maximization of every objective function is realized by exchanging a variable between populations. The results shows that the VEGSA is outperformed by other multi-objective optimization algorithms and further enhancements are needed before it can be employed in any application.