Faisal Mohd-Yasin

Expert System for Power Quality Disturbance Classifier

Identification and classification of voltage and current disturbances in power systems are important tasks in the monitoring and protection of power system. Most power quality disturbances are non-stationary and transitory and the detection and classification have proved to be very demanding. The concept of discrete wavelet transform for feature extraction of power disturbance signal combined with artificial neural network and fuzzy logic incorporated as a powerful tool for detecting and classifying power quality problems. This paper employes a different type of univariate randomly optimized neural network combined with discrete wavelet transform and fuzzy logic to have a better power quality disturbance classification accuracy. The disturbances of interest include sag, swell, transient, fluctuation, and interruption. The system is modeled using VHSIC hardware description language (VHDL), a hardware description language, followed by extensive testing and simulation to verify the functionality of the system that allows efficient hardware implementation of the same. This proposed method classifies, and achieves 98.19% classification accuracy for the application of this system on software-generated signals and utility sampled disturbance events.

Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction

Electromyography gives an electrical representation of neuromuscular activation associated with a contracting muscle. The electromyography signal acquires noise while travelling though different media. The wavelet transform is employed for removing noise from surface electromyography (SEMG) and higher order statistics are applied for analysing the signal. With the appropriate choice of wavelet, it is possible to remove interference noise (denoise) effectively in order to analyse the SEMG. Daubechies wavelets (db2, db4, db5, db6, db8), symmlet (sym4, sym5) and the orthogonal Meyer (dmey) wavelet can efficiently remove noise from the recorded SEMG signals. However, the most effective wavelet for SEMG denoising is chosen by calculating the root mean square difference and signal-to-noise ratio values. Results for both root mean square difference and signal-to-noise ratio show that wavelet db2 performs denoising best out of the wavelets. Furthermore, the higher order statistics method is applied for SEMG signal analysis because of its unique properties when applied to random time series, such as parameter estimation, testing of Gaussianity and linearity, deterministic and non-deterministic signal detection etc. Gaussianity and linearity tests as part of higher order statistics are conducted to understand changes in muscle contraction and to quantify the effectiveness of the noise removal process. According to the results, the SEMG signal becomes less Gaussian and more linear with increased force.

Techniques of EMG signal analysis: detection, processing, classification and applications (Correction)

This paper was originally published in Biological Procedures Online (BPO) on March 23, 2006. It was brought to the attention of the journal and authors that reference 74 was incorrect. The original citation for reference 74, “Stanford V. Biosignals offer potential for direct interfaces and health monitoring. Pervasive Computing, IEEE 2004; 3(1):99–103.” should read “Costanza E, Inverso SA, Allen R. ‘Toward Subtle Intimate Interfaces for Mobile Devices Using an EMG Controller’ in Proc CHI2005, April 2005, Portland, OR, USA.”

Noise in MEMS

This review provides a comprehensive survey of noise research in MEMS. Some background on noise and on MEMS is provided. We review noise production mechanisms, and highlight work on the theory and modeling of noise in MEMS. Then noise measurements in the specific types of MEMS are reviewed. Inertial MEMS (accelerometers and angular rate sensors), pressure and acoustic sensors, optical MEMS, RF MEMS, surface acoustic wave devices, flow sensors, and chemical and biological MEMS, as well as data storage devices and magnetic MEMS, are reviewed. We indicate opportunities for additional experimental and computational research on noise in MEMS.

The FPGA prototyping of iris recognition for biometric identification employing neural network

In this paper, we present the realization of iris recognition for biometric identification employing neural network on Altera FLEX10 K FPGA device that allows for efficient hardware implementation. This method consists of two main parts, which are image processing and recognition. Image processing is implemented by using MATLAB and backpropagation method was used for recognition. The iris recognition neural network architecture is comprised of three layers: input layer with three neurons, hidden layer with two neurons and output layer with one neuron. Sigmoid transfer function is used for both hidden layer and output layer neurons. The timing analysis for the validation, functionality and performance of the model is performed using Aldec active HDL and the logic synthesis was performed using Synplify. Iris vector from captured human iris has been used to validate the effectiveness of the model. Test on the sample of 100 data showed an accuracy of 88.6% in recognizing the sample of irises.

Prototyping of Wavelet Transform, Artificial Neural Network and Fuzzy Logic for Power Quality Disturbance Classifier

Abstract Identification and classification of voltage and current disturbances in power systems are important tasks in their monitoring and protection. Introduction of knowledge-based approaches, in conjunction with signal processing and decision fusion techniques, enable us to identify delicate power quality related events. This article focuses on the application of wavelet transform technique to extract features from power quality disturbance waveforms and their classification using a combination of artificial neural network and fuzzy logic. The disturbances of interest include sag, swell, transient, fluctuation and interruption waveform. The system is modelled using VHDL and synthesized to Mercury EP1M120F484C5 FPGA, tested and validated. Comparisons, verification and analysis on disturbance signals validate the utility of this approach and achieved a classification accuracy of 98.19%. This novel and efficient method, and also implementation of the method in hardware based on FPGA technology, showed improved performance over existing approaches for power quality disturbance detection and classification.

A modified-set partitioning in hierarchical trees algorithm for real-time image compression

Among all algorithms based on wavelet transform and zerotree quantization, Said and Pearlman’s set partitioning in hierarchical trees (SPIHT) algorithm is well known for its simplicity and efficiency. SPIHT’s high memory requirement is a major drawback to hardware implementation. In this study, we present a modification of SPIHT named modified SPIHT (MSPIHT), which requires less execution time at a low bit rate and less working memory than SPIHT. The MSPIHT coding algorithm is modified with the use of one list to store the coordinates of wavelet coefficients instead of three lists of SPIHT; defines two terms, number of error bits and absolute zerotree; and merges the sorting pass and the refinement pass together as one scan pass. Comparison of MSPIHT with SPIHT on different test image shows that MSPIHT reduces execution time at most 7 times for coding a 512 × 512 grayscale image; reduces execution time at most 11 times at a low bit rate; saves at least 0.5625 MB of memory; and reduces minor peak signal-to noise ratio (PSNR) values, thereby making it highly promising for real-time and memory limited mobile communications.

Measurement of noise characteristics of MEMS accelerometers

Microelectromechanical systems (MEMS) are devices that have static or movable components with dimensions on the scale of a micrometer. One particular device that is widely used commercially is the MEMS accelerometer. Such accelerometers typically contain some movable micro beams that measure acceleration in one or two orthogonal directions. Major markets for MEMS accelerometers are automobile airbag triggers, earthquake detection circuits and health care. MEMS accelerometers have advantages over conventional accelerometers because they are smaller, lighter and cheaper. Since MEMS accelerometers are used in many systems, the noise characteristics of these devices are very important. The noise characteristics will influence the performance of the accelerometers especially when operating at lower g conditions. In this work, we report on the noise characteristics and special measurement techniques for Analog Devices ADXL202, ADXL 105 and ADXL 190 accelerometers.

Partial encryption of compressed images employing FPGA

We present the realization of partial encryption of compressed images on an Altera FLEX10K FPGA device that allows for efficient hardware implementation. The compression algorithm decomposes images into several different parts. A secure encryption algorithm is then used to encrypt only the crucial parts, which are considerably smaller than the original image. This results in significant reduction in processing time and computational requirement for encryption and decryption. The breadth-first traversal linear lossless quadtree decomposition method is used for the partial compression and RSA is used for the encryption. Functional simulations are carried out to verify the functionality of the individual modules and the system on four different images. The comparisons, verification and analysis made validate the advantage of this approach. The design has utilized 2928 units of LC and a system frequency of 13.42 MHz.

Recent advances in the integrated circuit design of RFID transponder

This paper describes recent advances in the design of RFID transponder. Nine architectures of RFID transponder IC are reviewed and compared. We also performed analysis of the circuit design techniques used for all building blocks on selected architectures, focusing on the advanced optimization techniques employed. Apart from that, future improvements for the transponder chip and other future trends in transponder design are also reviewed.