K. A. Ahmad

Gel electrophoresis image segmentation with Otsu method based on Particle Swarm Optimization

Gel electrophoresis (GE) is an important tool in genomic analysis. It is a process of DNA, RNA and protein molecules separation using electric field applied to a gel matrix. This paper describes the image processing techniques applied on GE image to segment the bands from their background. Numerous pre-processing steps are applied on the image prior to the segmentation technique for the purpose of removing noise in the image. Then multilevel thresholding using Otsu method based on Particle Swarm Optimization is applied. The experimental results show that the PSO-Otsu successfully segmented all the bands.

Multilevel thresholding of gel electrophoresis images using firefly algorithm

Gel electrophoresis (GE) is a process of DNA, RNA and protein molecules separation using electric field applied to a gel matrix. This paper describes the image processing techniques applied on GE image to segment the bands from their background. A few pre-processing steps are applied on the image prior to the segmentation technique for the purpose of removing noise in the image. Multilevel thresholding using Otsu method based on Firefly Algorithm is developed. The experimental results show that the Otsu-FA produced good separation of DNA bands and its background.

Moving vehicle segmentation in a dynamic background using self-adaptive kalman background method

This paper introduce the adaptive kalman filter to modeling dynamic background for background subtraction. Background subtraction is a method to identify object and famous used in moving object segmentation. In this paper we also investigate a comparison study on Gaussian subtraction method, frame differencing method and approximate median method. The detection of object will be shown in the result.

Self adaptive neuro-fuzzy control of FES-assisted paraplegics indoor rowing exercise

This paper describes the development of a self adaptive neuro-fuzzy control mechanism for FES-assisted indoor rowing exercise (FES-rowing). The FES-rowing is introduced as a total body exercise for rehabilitation of function of lower body through the application of functional electrical stimulation (FES). The neuro-fuzzy control technique is a control technique that combines fuzzy logic controller and a neural network, which makes the controller self tuning and adaptive. An adaptive control strategy is purposed to control the FES-rowing with the adaptation of the muscle fitness of the physiological based muscle model in the FLC. The adaptive control is able to modify the control law used by the FLC to cope with the muscle fatigue in adjusting the rowing ergometer inclination and generating the stimulation pulse width required by the system. This study indicates that the self adaptive neuro-fuzzy control developed provides an effective mechanism for automatically adjusting the ergometer inclination and regulating the stimulation pulse width for FES-rowing to overcome muscle fatigue.

GA-tuned fuzzy logic control of knee-FES-ergometer for knee swinging exercise

Knee-FES-ergometer for knee swinging exercise is introduced as a hybrid exercise for restoration of function of the knee for stroke patients through the application of functional electrical stimulation (FES). The aim of the new knee-FES-ergometer is to provide high intensity knee swinging exercise. It is able to reduce required electrical stimulation and will able to elongate the exercise duration while avoiding early muscle fatigue. Fuzzy logic control (FLC) is used to control the knee trajectory for the purpose of smooth knee swinging exercise. However, conventional FLC rely on human experiences and trial and error for parameter identifications. In this work, a genetic algorithm (GA) is used to tune the FLC to maintain a smooth swinging exercise. The performance of the proposed GA tuned FLC is compared with a manually tuned FLC. Results shows that the GA tuned FLC offers encouragingly better performance.

Evaluation of spreading factor inertial weight PSO for FLC of FES-assisted paraplegic indoor rowing exercise

This paper describes the evaluation of the spreading factor inertia weight Particle Swarm Optimization (PSO) for the fuzzy logic control (FLC) of FES-assisted paraplegic indoor rowing exercise (FES-rowing). The FES-rowing is introduced as a total body exercise for rehabilitation of lower extremities through the application of functional electrical stimulation (FES). FLC is used to control the knee trajectories for smooth rowing manoeuvre and minimize the total electrical stimulation required by the muscles. PSO is implemented to optimize the parameter of the FLC. The objective function specified is to minimize the mean squared error of knee angle trajectory. The inertia weight of the PSO is updated using spreading factor technique and it performance is compared to the performance of PSO with time variant inertia weight. In view of good results obtained, it is concluded that Spreading Factor Inertia weight PSO is able to obtain the optimal parameter for FLC of FES-rowing.

Improvement moving vehicle detection using RGB removal shadow segmentation

The detection of moving object in dynamic case especially outdoor case is very challenging for computer vision. One of the problems in dynamic case is detection of shadow in moving object. This paper presents the improvement of moving vehicle detection using removal shadow technique in RGB color space and frame difference technique. In RGB color space, the luminance and chrominance value are being tuned to remove shadow. The result shows about 75% of shadow can be removed from image moving object detection. The result also shows the improvement of moving object detection.

Time-current characteristic measurement of overcurrent relay in power system using multilayer perceptron network

Protection in power system is very important to ensure the systems are in a good condition without any failure. It is necessary that the protection system can operate at the shortest time to clear the fault as soon as possible. Overcurrent relay protection is depending on their time-current characteristic curve. In this particular characteristic curve, the required relay operating time can be determined in order to remove high fault current in the system. If fault current are not remove in correct possible time, it will cause damage of expensive equipment and loss of life as well. Therefore, until now, researchers are always come out with several methods to solve this problem. In this paper, artificial neural network (ANN) using multilayer perceptron network (MLP) is applied to determine an accurate operating time. The MLP network is trained by Levenberg-Marquardt algorithm as for its fastest convergence rate and with a minimum error. Experimental results proved that the proposed method produced acceptable results in determining an accurate operating time. Sum squared error (SSE) produced during training and testing is 1.04% and 0.73%, respectively. In addition, the result reveals that the relay operating time obtained by neural network is 1ms which is considerably fast time for the system to isolate fault.

A miniaturization using Surface Mount Technology of potentiometric indicator system for measuring human stress

This paper concerns miniaturization using Surface Mount Technology (SMT) of potentiometric indicator system for measuring human stress. The piezoresistive MEMS sensor is integrated with potentiometric indicator system that converts a biochemical signal into a measurable signal. The overall project involved transduction stage, filtering, voltage follower, and linearization stage. The design of this new approach is implemented using Cadence OrCAD Capture CIS 15.7 software and the miniaturization part uses SMT and troubleshooting. The advantages of SMT over the older through-hole technique are surface mount allows for much smaller components to be used which, in turn, allows for smaller, more portable and lightweight electronic device application. Details descriptions of the hardware for the circuit miniaturization will be presented. From the previous research, it has been found that for a sensor input range of 1.1 to 1.3kilo ohms, an output range of -100 to +100 millivolts is obtained. The result from the SMT testing indicates discrepancy within 10.75% was found for the last output stage on the average, which could be attributed to tolerance of electronic components and wiring. This development allowed stress measurement with relatively high precision and accuracy.