Papers by Zahir M Hussain
An adaptive deep neural network is used in an inverse system identification setting to approximat... more An adaptive deep neural network is used in an inverse system identification setting to approximate the inverse of a nonlinear plant with the aim of constituting the plant controller by copying to the latter the weights and architecture of the converging deep neural network. This deep learning (DL) approach to the adaptive inverse control (AIC) problem is shown to outperform adaptive filtering techniques and algorithms normally used in adaptive control, especially when the plant is nonlinear. The deeper the controller the better the inverse function approximation, provided that the nonlinear plant have an inverse and that this inverse can be approximated. Simulation results prove the feasibility of this DL-based adaptive inverse control scheme. The DL-based AIC system is robust to parameter change of the nonlinear plant in that, under such change, the plant output reassumes the value of the reference signal considerably faster than with the adaptive filter counterpart of the deep neu...
Wireless Sensor Networks - Insights and Innovations
Meaningful information sharing between the sensors of a wireless sensor network (WSN) necessitate... more Meaningful information sharing between the sensors of a wireless sensor network (WSN) necessitates node localization, especially if the information to be shared is the location itself, such as in warehousing and information logistics. Trilateration and multilateration positioning methods can be employed in two-dimensional and threedimensional space respectively. These methods use distance measurements and analytically estimate the target location; they suffer from decreased accuracy and computational complexity especially in the three-dimensional case. Iterative optimization methods, such as gradient descent (GD), offer an attractive alternative and enable moving target tracking as well. This chapter focuses on positioning in three dimensions using time-of-arrival (TOA) distance measurements between the target and a number of anchor nodes. For centralized localization, a GD-based algorithm is presented for localization of moving sensors in a WSN. Our proposed algorithm is based on systematically replacing anchor nodes to avoid local minima positions which result from the moving target deviating from the convex hull of the anchors. We also propose a GD-based distributed algorithm to localize a fixed target by allowing gossip between anchor nodes. Promising results are obtained in the presence of noise and link failures compared to centralized localization. Convergence factor issues are discussed, and future work is outlined.
This work analyses the performance of linear and different quadratic interpolators (in terms of e... more This work analyses the performance of linear and different quadratic interpolators (in terms of estimation error) for FFT frequency estimation of single tones under the effects of multiplicative noise. This method finds a quadratic fit in the neighborhood of the maximum of FFT with the three points, then apply different approximation methods: maximum of FFT, barycentric, and Quinn's Estimator. Numerical results showed that barycentric method is the best estimator under Gaussian multiplicative noise in terms of minimum mean squared estimation error, especially at high signal-to-noise ratios.
Indonesian Journal of Electrical Engineering and Computer Science, 2022
Recent research has demonstrated the effectiveness of utilizing neural networks for detect tamper... more Recent research has demonstrated the effectiveness of utilizing neural networks for detect tampering in images. However, because accessing a database is complex, which is needed in the classification process to detect tampering, reference-free steganalysis attracted attention. In recent work, an approach for least significant bit (LSB) steganalysis has been presented based on analyzing the derivatives of the histogram correlation. In this paper, we further examine this strategy for other steganographic methods. Detecting image tampering in the spatial domain, such as image steganography. It is found that the above approach could be applied successfully to other kinds of steganography with different orders of histogram-correlation derivatives. Also, the limits of the ratio stego-image to cover are considered, where very small ratios can escape this detection method unless modified.
This work combines compressive sensing and short word-length techniques to achieve localization a... more This work combines compressive sensing and short word-length techniques to achieve localization and target tracking in wireless sensor networks with energy-efficient communication between the network anchors and the fusion center. Gradient descent localization is performed using time-of-arrival (TOA) data which are indicative of the distance between anchors and the target thereby achieving range-based localization. The short word-length techniques considered are delta modulation and sigma-delta modulation. The energy efficiency is due to the reduction of the data volume transmitted from anchors to the fusion center by employing any of the two delta modulation variants with compressive sensing techniques. Delta modulation allows the transmission of one bit per TOA sample. The communication energy efficiency is increased by RⱮ, R≥1, where R is the sample reduction ratio of compressive sensing and Ɱ is the number of bits originally present in a TOA-sample word. It is found that the loc...
Frequency estimation of a single sinusoid in colored noise has received a considerable amount of ... more Frequency estimation of a single sinusoid in colored noise has received a considerable amount of attention in the research community. Taking into account the recent emergence and advances in compressive covariance sensing (CCS), the aim of this work is to combine the two disciplines by studying the effects of compressed measurements of a single sinusoid in moving-average (MA) colored noise on its frequency estimation accuracy. CCS techniques can recover the second-order statistics of the original uncompressed signal from the compressed measurements, thereby enabling correlation-based frequency estimation of single tones in colored noise using higher-order lags. Acceptable accuracy is achieved for moderate compression ratios and for a sufficiently large number of available compressed signal samples. It is expected that the proposed method would be advantageous in applications involving resource-limited systems such as wireless sensor networks.
International Journal of Advanced Research in Artificial Intelligence, 2014
Automatic recognition of people faces is a challenging problem that has received significant atte... more Automatic recognition of people faces is a challenging problem that has received significant attention from signal processing researchers in recent years. This is due to its several applications in different fields, including security and forensic analysis. Despite this attention, face recognition is still one among the most challenging problems. Up to this moment, there is no technique that provides a reliable solution to all situations. In this paper a novel technique for face recognition is presented. This technique, which is called ISSIM, is derived from our recently published information-theoretic similarity measure HSSIM, which was based on joint histogram. Face recognition with ISSIM is still based on joint histogram of a test image and a database images. Performance evaluation was performed on MATLAB using part of the well-known AT&T image database that consists of 49 face images, from which seven subjects are chosen, and for each subject seven views (poses) are chosen with different facial expressions. The goal of this paper is to present a simplified approach for face recognition that may work in realtime environments. Performance of our information-theoretic face recognition method (ISSIM) has been demonstrated experimentally and is shown to outperform the well-known, statistical-based method (SSIM).
Journal of Computer Science, 2019
A comparative study is presented to evaluate the performance of three important Blind Source Sepa... more A comparative study is presented to evaluate the performance of three important Blind Source Separation (BSS) techniques under noisy conditions. The ability of FastICA, SOBI and JadeR is tested in separating several kinds of signals under noisy conditions, including human speech and frequency-modulated (quadratic and linear FM) signals. Additionally, different mixing matrices are used to inspect the effect of the mixing process. The influence of two types of noise (semi-white Gaussian and uniform) has been investigated under different Signal to Noise Ratios (SNR). The Pearson correlation coefficient (versus signal to noise ratio) between original and recovered signals is used as a performance metric. Despite the wide use of BSS techniques, there has been no extensive study in these directions. It is found that JadeR out performs other BSS techniques under semi-white Gaussian and uniformly-distributed noise.
Journal of Computer Science, 2018
In this study, two techniques are introduced for image steganography in the spatial domain. These... more In this study, two techniques are introduced for image steganography in the spatial domain. These systems employ chaos theory to track the addresses of shuffled bits in steganography. The first system is based on the well-known LSB technique, while the second system is based on a recent approach that searches for the identical bits between the secret message and the cover image. A modified logistic map is employed in the chaotic map to generate integer chaotic series to extract the shuffled addresses bits. Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), histogram analysis and correlative analysis are used for testing and evaluating the new levels of security for the proposed techniques. The results show that the proposed methods outperform existing systems.
Journal of Kufa for Mathematics and Computer, 2018
Modified version of Joint Approximation Diagonalization Estimation of Real Signals algorithm (JAD... more Modified version of Joint Approximation Diagonalization Estimation of Real Signals algorithm (JADER) is proposed to enhance efficiency and speed of Blind Signal Separation (BSS). MJADER based on the mixture's dimensions minimization step, where the cumulant matrices have been estimated using a reduced-dimension observed mixture. The approach (M-JADER) is based on a threshold step, it is easy to implement, computationally efficient and faster than standard JADER about 50% where it has less running time. The comparison done under tow types of niose(semi-white Gaussian noise and Uniform noise).
International Journal of Advanced Computer Science and Applications, 2014
Face recognition is an interesting field of computer vision with many commercial and scientific a... more Face recognition is an interesting field of computer vision with many commercial and scientific applications. It is considered as a very hot topic and challenging problem at the moment. Many methods and techniques have been proposed and applied for this purpose, such as neural networks, PCA, Gabor filtering, etc. Each approach has its weaknesses as well as its points of strength. This paper introduces a highly efficient method for the recognition of human faces in digital images using a new feature extraction method that combines the global and local information in different views (poses) of facial images. Feature extraction techniques are applied on the images (faces) based on Zernike moments and structural similarity measure (SSIM) with local and semi-global blocks. Preprocessing is carried out whenever needed, and numbers of measurements are derived. More specifically, instead of the usual approach for applying statistics or structural methods only, the proposed methodology integrates higher-order representation patterns extracted by Zernike moments with a modified version of SSIM (M-SSIM). Individual measurements and metrics resulted from mixed SSIM and Zernike-based approaches give a powerful recognition tool with great results. Experiments reveal that correlative Zernike vectors give a better discriminant compared with using 2D correlation of the image itself. The recognition rate using ORL Database of Faces reaches 98.75%, while using FEI (Brazilian) Face Database we got 96.57%. The proposed approach is robust against rotation and noise.
Discrete Wavelet Transforms - Algorithms and Applications, 2011
2007 Australasian Telecommunication Networks and Applications Conference, ATNAC 2007, 2008
Exhibition, 2009
Page 1. Circular 16-QAM Modulation Scheme for Wavelet and Fourier Based OFDM Systems Khaizuran Ab... more Page 1. Circular 16-QAM Modulation Scheme for Wavelet and Fourier Based OFDM Systems Khaizuran Abdullah, Noura Al-Hinai, Amin Z. Sadik ∗ , MIEEE, and Zahir M. Hussain, SMIEEE School of Electrical & Computer Engineering ...
International Journal of Communications, Network and System Sciences, 2009
The BER performance for an optimal circular 16-QAM constellation is theoretically derived and app... more The BER performance for an optimal circular 16-QAM constellation is theoretically derived and applied in wavelet based OFDM system in additive white Gaussian noise channel. Signal point constellations have been discussed in much literature. An optimal circular 16-QAM is developed. The calculation of the BER is based on the four types of the decision boundaries. Each decision boundary is determined based on the space distance d following the pdf Gaussian distribution with respect to the in-phase and quadrature components nI and nQ with the assumption that they are statistically independent to each other. The BER analysis for other circular M-ary QAM is also analyzed. The system is then applied to wavelet based OFDM. The wavelet transform is considered because it offers a better spectral containment feature compared to conventional OFDM using Fourier transform. The circular schemes are slightly better than the square schemes in most SNR values. All simulation results have met the theoretical calculations. When applying to wavelet based OFDM, the circular modulation scheme has also performed slightly less errors as compared to the square modulation scheme.
International Conference on Communication, …, 2009
Page 1. INTERNATIONAL CONFERENCE ON COMMUNICATION, COMPUTER AND POWER (ICCCP&#x27... more Page 1. INTERNATIONAL CONFERENCE ON COMMUNICATION, COMPUTER AND POWER (ICCCP'09) MUSCAT, FEBRUARY 15-18, 2009 Studies on DWT-OFDM and FFT-OFDM Systems Khaizuran Abdullah 1 and Zahir M. Hussain1, SMIEEE ...
European Journal of Remote Sensing, 2019
Image similarity or distortion assessment is fundamental to a wide range of applications througho... more Image similarity or distortion assessment is fundamental to a wide range of applications throughout the field of image processing and computer vision. Many image similarity measures have been proposed to treat specific types of image distortions. Most of these measures are based on statistical approaches, such as the classic SSIM. In this paper, we present a different approach by interpolating the information theory with the statistic, because the information theory has a high capability to predict the relationship among image intensity values. Our unique hybrid approach incorporates information theory (Shannon entropy) with a statistic (SSIM), as well as a distinctive structural feature provided by edge detection (Canny). Correlative and algebraic structures have also been utilized. This approach combines the best features of Shannon entropy and a joint histogram of the two images under test, and SSIM with edge detection as a structural feature. The proposed method (ISSM) has been tested versus SSIM and FSIM under Gaussian noise, where good results have been obtained even under a wide range of PSNR. Simulation results using the IVC and TID2008 image databases show that the proposed approach outperforms the SSIM and FSIM approaches in similarity and recognition of the image.
Digital Signal Processing, 2011
This chapter introduces the basic theory of Digital Signal Processing, including sampling theory ... more This chapter introduces the basic theory of Digital Signal Processing, including sampling theory and digitization, both in the time domain and in the frequency domain. The core topics covered by this chapter are discrete-time convolution, transforms (Z, Discrete-time Fourier, and Discrete Fourier), design of conventional digital filters, and the treatment of some important DSP applications, including banking and financial applications, audio effects production, design and implementation of specific digital systems (such as integrators, differentiators, resonators and oscillators).
In this work we present a study on the performance of signal similarity measures under non-Gaussi... more In this work we present a study on the performance of signal similarity measures under non-Gaussian noise. Pink noise has been considered, with 1/f power spectral density. This kind of noise has been generated by filtering Gaussian noise through an FIR filter. One-dimensional and two-dimensional signals have been considered. We tested 2D image similarity using the well-known similarity measures: Structural Similarity Index Measure (SSIM), modified Feature-based Similarity Measure (MFSIM), and Histogram-based Similarity Measure (HSSIM). Also, we tested 1D similarity measures: Cosine Similarity, Pearson Correlation, Tanimoto similarity, and Angular similarity. Results show that HSSIM and MFSIM outperform SSIM in low PSNR under pink noise and Gaussian noise. For 1D similarity, it is shown that Cosine Similarity and Pearson Correlation outperform other 1D similarity, especially at low SNR.
Proceedings of the Sixth International Symposium on Signal Processing and its Applications (Cat.No.01EX467)
... Joumal, vol. 53, Feb. 1974. Zahir M. Hussain, Boualem Boashash, Mudhafar Hassan-Ali, and Sale... more ... Joumal, vol. 53, Feb. 1974. Zahir M. Hussain, Boualem Boashash, Mudhafar Hassan-Ali, and Saleh R. AI-Araji, "A time-delay digital tanlock loop," IEEE fiansactions on Signal Processing, in press, August 2001. Peyton Z. Peebles ...
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Papers by Zahir M Hussain