Ahmad Zuri Sha'ameri

Analysis and classification of heart sounds and murmurs based on the instantaneous energy and frequency estimations

This paper proposes the use of the instantaneous energy and the frequency estimation in the classification of the heart sounds and murmurs for common heart diseases. It has been known that the present of the heart murmurs in ones heart sound indicates that there is a potential heart problem. Thus, the goal of this work is to develop a technique for detecting and classifying murmurs. Such a technique can be used as part of a heart diagnostic system. The analysis is performed based on a set of 102 data for various heart sounds. To discriminate the various heart sounds, the instantaneous energy and frequency estimation is used to estimate the features of heart sound. The techniques used to estimate the instantaneous frequency are the central finite difference frequency estimation (CFDFE) and zero crossing frequency estimation (ZCFE). From the instantaneous energy and frequency estimate, the energy and frequencies of the heart sounds are defined as the features of the heart sounds that can uniquely discriminate the various heart sounds.

Adaptive optimal kernel smooth-windowed wigner-ville distribution for digital communication signal

Time-frequency distributions (TFDs) are powerful tools to represent the energy content of time-varying signal in both time and frequency domains simultaneously but they suffer from interference due to cross-terms. Various methods have been described to remove these cross-terms and they are typically signal-dependent. Thus, there is no single TFD with a fixed window or kernel that can produce accurate time-frequency representation (TFR) for all types of signals. In this paper, a globally adaptive optimal kernel smooth-windowed Wigner-Ville distribution (AOK-SWWVD) is designed for digital modulation signals such as ASK, FSK, and M-ary FSK, where its separable kernel is determined automatically from the input signal, without prior knowledge of the signal. This optimum kernel is capable of removing the cross-terms and maintaining accurate time-frequency representation at SNR as low as 0 dB. It is shown that this system is comparable to the system with prior knowledge of the signal.

Power quality analysis using linear time-frequency distribution

This paper presents the implementation of linear time-frequency distribution (TFD) techniques to analyze power quality signals. The power quality signals are swell, sag, interruption, transient, harmonic, interharmonic, notching and normal voltage based on IEEE Std. 1159-1995. The time-frequency analysis techniques selected are spectrogram and Gabor transform. From the time-frequency representation (TFR) obtained, the signal parameters are estimated to identify the signal characteristics. Identified signal characteristics that are used as input to a classifier includes the average of root mean square (RMS) voltage, total waveform distortion, total harmonic distortion and total nonharmonic distortion and duration of swell, sag, interruption and transient. From the linear time-frequency analysis techniques used, the optimal technique is chosen in terms of accuracy, computation complexity and memory size.

Adaptive optimal kernel smooth-windowed Wigner-Ville bispectrum for digital communication signals

Higher-order time-frequency distribution (HO-TFD) outperforms the bilinear TFD in noisy conditions but suffers more severely from cross-terms when used to analyze multi-component signals. Various kernel functions have been introduced to suppress cross-terms in bilinear TFD but in general TFD with a fixed kernel do not give accurate TFR for all type of signals. In this paper, adaptive optimal TFR is obtained by extending the separable kernel design in bilinear TFD to the third-order TFD and is able to achieve accurate time-frequency representation at SNR as low as -2dB. This globally adaptive optimal kernel smooth-windowed Wigner-Ville bispectrum (AOK-SWWVB) is designed where its separable kernel is determined automatically from the input signal, without prior knowledge of the signal parameters. It is shown that this system performance is comparable to the system when priori knowledge of the signal is known.

Detection and Classification of Power Quality Disturbances Using Time-Frequency Analysis Technique

This paper presents the detection and classifications of power quality disturbances using time-frequency signal analysis. The method used is based on the pattern recognition approach. It consists of parameter estimation followed classification. Based on the spectrogram time-frequency analysis, a set of signal parameters are estimated as input to a classifier network. The power quality events that are analyzed are swell, sag, interruption, harmonic, interharmonic, transient, notching and normal voltage. The parameter estimation is characterized by voltage signal in rms per unit, waveform distortion, harmonic distortion and interharmonic distortion. A rule based system is developed to detect and classify the various types of power quality disturbances. The system has been tested with 100 data for each power quality event at SNR from OdB to 50dB to verify its performance. The results show that the system gives 100 percent accuracy of power quality signals at 30 dB of SNR.

Identification of Different Types of Partial Discharge Sources from Acoustic Emission Signals in the Time-Frequency Representation

The subject matter of this paper is to introduce the time-frequency representation in analyzing the acoustic emission signals emitted by partial discharge sources. Three different types of partial discharge sources used to generate the acoustic emission signals during the partial discharge (PD) occurrences are created in an experimental tank filled with transformer oil. These partial discharge sources are the plain pressboard, the floating metal in the pressboard and the bubble in the pressboard. The acoustic emission (AE) signals are detected and stored as time-frequency representation, in the form of spectrogram, by utilizing the short-time Fourier transform (STFT). Finally, seven descriptors are introduced in order to extract the features from each of the spectrogram. The obtained results also confirmed the ability of the proposed technique to discriminate between different types of PD sources.

Power quality analysis using spectrogram and gabor transformation

This paper discusses the implementation of time-frequency analysis techniques to analyze power quality disturbances. The approached methods are spectrogram and Gabor transform algorithms. Signal parameters such as time marginal and frequency marginal are extracted from the time-frequency distributions. The parameters are analyzed in terms of correctness measurement of root mean square (RMS), total harmonic distortion (THD), total waveform distortion (TWD) and total interharmonic distortion (TnHD) values. Power quality events that are analyzed are swell, sag, interruption, harmonic, interharmonic, transient, notching and normal voltage. The results show that Gabor transform provides better performance in terms of correctness of parameters measurement, window length, frequency resolution and memory size.

Implementation of pipelined data encryption standard (DES) using Altera CPLD

The paper presents a pipelined data encryption standard (DES) architecture design implemented in Altera CPLD. The architecture contains three main parts, DES module, pipeline module and control unit module. A four-segment pipeline is used in this architecture to burst the throughput of the DES. Although the processing time for a single encryption operation is still the same; but with more encryption operations, this pipelined DES can increase significantly the throughput. Altera Hardware Description Language (AHDL) is used to implement the pipelined DES design.

Power quality analysis using smooth-windowed wigner-ville distribution

Bilinear time-frequency distributions (TFDs) are developed to represent time-varying signal jointly in time and frequency representation (TFR). Since the TFDs offer good time and frequency resolution, they are appropriate to analyze power quality signals that consist of magnitude variation and multiple frequencies. However, the TFD suffer from cross-terms interferences due to their bilinear structures. In this paper, smooth-windowed Wigner-Ville distribution (SWWVD) is used to analyze power quality signals. The power quality signals are swell, sag, interruption, harmonic, interharmonic and transient. To get accurate TFR, the parameters of the separable kernel are estimated from the signal. A set of performance measures is defined and used to compare the TFR for various kernel parameters. The comparison shows that signals with different parameters require different kernel settings to get the optimal TFR.

Efficient phase estimation for the classification of digitally phase modulated signals using the cross-WVD: a performance evaluation and comparison with the S-transform

This article presents a novel algorithm based on the cross-Wigner-Ville Distribution (XWVD) for optimum phase estimation within the class of phase shift keying signals. The proposed method is a special case of the general class of cross time-frequency distributions, which can represent the phase information for digitally phase modulated signals, unlike the quadratic time-frequency distributions. An adaptive window kernel is proposed where the window is adjusted using the localized lag autocorrelation function to remove most of the undesirable duplicated terms. The method is compared with the S-transform, a hybrid between the short-time Fourier transform and wavelet transform that has the property of preserving the phase of the signals as well as other key signal characteristics. The peak of the time-frequency representation is used as an estimator of the instantaneous information bearing phase. It is shown that the adaptive windowed XWVD (AW-XWVD) is an optimum phase estimator as it meets the Cramer-Rao Lower Bound (CRLB) at signal-to-noise ratio (SNR) of 5 dB for both binary phase shift keying and quadrature phase shift keying. The 8 phase shift keying signal requires a higher threshold of about 7 dB. In contrast, the S-transform never meets the CRLB for all range of SNR and its performance depends greatly on the signals frequency. On the average, the difference in the phase estimate error between the S-transform estimate and the CRLB is approximately 20 dB. In terms of symbol error rate, the AW-XWVD outperforms the S-transform and it has a performance comparable to the conventional detector. Thus, the AW-XWVD is the preferred phase estimator as it clearly outperforms the S-transform.