Zahir M. Hussain

Adaptive instantaneous frequency estimation of multicomponent FM signals using quadratic time-frequency distributions

An adaptive approach to the estimation of the instantaneous frequency (IF) of nonstationary mono- and multicomponent FM signals with additive Gaussian noise is presented. The IF estimation is based on the fact that quadratic time-frequency distributions (TFDs) have maxima around the IF law of the signal. It is shown that the bias and variance of the IF estimate are functions of the lag window length. If there is a bias-variance tradeoff, then the optimal window length for this tradeoff depends on the unknown IF law. Hence, an adaptive algorithm with a time-varying and data-driven window length is needed. The adaptive algorithm can utilize any quadratic TFD that satisfies the following three conditions: First, the IF estimation variance given by the chosen distribution should be a continuously decreasing function of the window length, whereas the bias should be continuously increasing so that the algorithm will converge at the optimal window length for the bias-variance tradeoff, second, the time-lag kernel filter of the chosen distribution should not perform narrowband filtering in the lag direction in order to not interfere with the adaptive window in that direction; third, the distribution should perform effective cross-terms reduction while keeping high resolution in order to be efficient for multicomponent signals. A quadratic distribution with high resolution, effective cross-terms reduction and no lag filtering is proposed. The algorithm estimates multiple IF laws by using a tracking algorithm for the signal components and utilizing the property that the proposed distribution enables nonparametric component amplitude estimation. An extension of the proposed TFD consisting of the use of time-only kernels for adaptive IF estimation is also proposed.

Automatic facial expression recognition: feature extraction and selection

In this paper, we investigate feature extraction and feature selection methods as well as classification methods for automatic facial expression recognition (FER) system. The FER system is fully automatic and consists of the following modules: face detection, facial detection, feature extraction, selection of optimal features, and classification. Face detection is based on AdaBoost algorithm and is followed by the extraction of frame with the maximum intensity of emotion using the inter-frame mutual information criterion. The selected frames are then processed to generate characteristic features using different methods including: Gabor filters, log Gabor filter, local binary pattern (LBP) operator, higher-order local autocorrelation (HLAC) and a recent proposed method called HLAC-like features (HLACLF). The most informative features are selected based on both wrapper and filter feature selection methods. Experiments on several facial expression databases show comparisons of different methods.

Digital Phase Lock Loops

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Higher order orthogonal moments for invariant facial expression recognition

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Multi-component IF estimation

Automatic facial expression recognition (FER) is a sub-area of face analysis research that is based heavily on methods of computer vision, machine learning, and image processing. This study proposes a rotation and noise invariant FER system using an orthogonal invariant moment, namely, Zernike moments (ZM) as a feature extractor and Naive Bayesian (NB) classifier. The system is fully automatic and can recognize seven different expressions. Illumination condition, pose, rotation, noise and others changing in the image are challenging task in pattern recognition system. Simulation results on different databases indicated that higher order ZM features are robust in images that are affected by noise and rotation, whereas the computational rate for feature extraction is lower than other methods.

Time-frequency analysis of normal and abnormal biological signals

An adaptive approach to the estimation of the instantaneous frequency (IF) of non-stationary mono- and multi-component FM signals with additive Gaussian noise is presented. It is shown that the bias and variance of the IF estimate are functions of the lag window length. If there is a bias-variance trade-off, then the optimal window length for this tradeoff depends on the unknown IF law. Hence an adaptive algorithm with a time-varying and data-driven window length is needed. The adaptive algorithm can utilize any quadratic time-frequency distribution that satisfies certain conditions. A quadratic distribution that is most suitable for this approach is proposed. The algorithm estimates multiple IF laws by using a tracking algorithm for the signal components and utilizing the property that the proposed distribution enables non-parametric component amplitudes estimation. An extension of the proposed TFD consisting in the use of time-only kernels for adaptive IF estimation is also proposed.

Adaptive power loading for OFDM-based power line communications impaired by impulsive noise

Abstract Due to the non-stationary, multicomponent nature of biomedical signals, the use of time-frequency analysis can be inevitable for these signals. The choice of the proper time-frequency distribution (TFD) that can reveal the exact multicomponent structure of biological signals is vital in many applications, including the diagnosis of medical abnormalities. In this paper, the instantaneous frequency (IF) estimation using four well-known TFDs is applied for analyzing biological signals. These TFDs are: the Wigner–Ville distribution (WVD), the Choi–Williams distribution (CWD), the Exponential T-distribution (ETD) and the Hyperbolic T-distribution (HTD). Their performance over normal and abnormal biological signals as well as over multicomponent frequency modulation (FM) signals in additive Gaussian noise was compared. Moreover, the feasibility of utilizing the wavelet transform (WT) in IF estimation is also studied. The biological signals considered in this work are the surface electromyogram (SEMG) with the presence of ECG noise and abnormal cardiac signals. The abnormal cardiac signals were taken from a patient with malignant ventricular arrhythmia, and a patient with supraventricular arrhythmia. Simulation results showed that the HTD has a superior performance, in terms of resolution and cross-terms reduction, as compared to other time-frequency distributions.

Joint Time-domain/Frequency-domain Impulsive Noise Reduction in OFDM-based Power Line Communications

Adaptive modulation can improve the performance of OFDM systems significantly. In Power Line Communication systems, impulsive noise has to be considered due to its severe effect in the system performance. This paper deals with the effect of impulsive noise in adaptive power loading in OFDM-based PLC systems. We present a simple power loading algorithm with uniform bit allocation and nonuniform BER distribution and test it by means of computer simulations in a widely-accepted power line channel model impaired with impulsive noise. Closed form expressions for BER and power allocation in the presence of impulsive noise are presented. Simulation results show that the proposed algorithm can achieve a considerable improvement over conventional OFDM with uniform power allocation.

Performance of Fourier-based and wavelet-based OFDM for DVB-T systems

Impulsive noise is one of the major challenges in power line communications and can cause serious problems in OFDM-based PLC systems. In this paper, we propose a combined Time-Domain/Frequency-domain technique for impulsive noise reduction in OFDM-based PLC systems. The performance of the proposed technique is studied against well known time-domain nonlinearities by means of computer simulations. The obtained simulation results show that the Combined TD/FD technique performs better than practically used nonlinearities and can reduce the adverse effect of impulsive noise significantly.

Performance analysis of amplify-forward relay in mixed Nakagami-m and Rician fading channels

We present a comparative study on Fourier-based OFDM (FFT-OFDM) and wavelet-based OFDM (DWT-OFDM) in DVB-T system (DWT being the discrete wavelet transform).We found that the DWT-OFDM outperforms FFT-OFDM in AWGN and Rayleigh fading channels. For AWGN channel, the gain in term of energy per bit to noise ratio Eb/No was improved by about 5 dB when the system used Haar wavelet compared to FFT-OFDM with a cyclic prefix (CP) of 1/4-th the total OFDM symbol period, for the same BER of 0.001. Other members of Daubechies families such as db8, db16 and db32 also outperformed by the gains of 7 dB, 10 dB and 11 dB, respectively, at the same BER. We also considered Daubechies wavelet db32 and FFT-OFDM with a CP of 1/4-th the total OFDM symbol period in the presence of narrowband interference. In terms of Eb/No, DWT-OFDM surpassed FFT-OFDM by 9 dB at 0.02 BER. It is also shown that the DWT-OFDM of Daubechies db8 and db1 outperform FFT-OFDM in Rayleigh fading, both in multipath flat fading and multipath frequency selective fading. The DWT-OFDMpsilas with db8 and Haar outperformed FFT-OFDM by 7 and 2 dB, respectively, at BER of 0.01 in the flat fading channel. In addition, DWT-OFDM and FFT-OFDM showed about the same performance below 10 dB of Eb/No in frequency selective fading. However, the wavelet-based OFDM showed significant improvement of performance at higher than 10 dB of Eb/No.