Zahir M Hussain | University of Kufa - Iraq (original) (raw)

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

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

Journal of Computer Science, 2019

Journal of Computer Science, 2018

Journal of Kufa for Mathematics and Computer, 2018

International Journal of Advanced Computer Science and Applications, 2014

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

International Conference on Communication, …, 2009

Page 1. INTERNATIONAL CONFERENCE ON COMMUNICATION, COMPUTER AND POWER (ICCCP&amp;amp;#x27... more Page 1. INTERNATIONAL CONFERENCE ON COMMUNICATION, COMPUTER AND POWER (ICCCP&amp;amp;#x27;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

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, &amp;quot;A time-delay digital tanlock loop,&amp;quot; IEEE fiansactions on Signal Processing, in press, August 2001. Peyton Z. Peebles ...