Alam Shwab | Qatar University (original) (raw)

Papers by Alam Shwab

5th European Conference on Speech Communication and Technology (Eurospeech 1997)

Temporal Decomposition (TD) is an efficient technique for modeling speech spectral evolution thro... more Temporal Decomposition (TD) is an efficient technique for modeling speech spectral evolution through orthogonalization of the matrix of spectral parameters which reduces the amount of spectral information in TD-based speech coding. We have shown in earlier ...

TENCON '97 Brisbane - Australia. Proceedings of IEEE TENCON '97. IEEE Region 10 Annual Conference. Speech and Image Technologies for Computing and Telecommunications (Cat. No.97CH36162)

Fingerprints have been used as unique identi ers of individuals for a very long time. The identi ... more Fingerprints have been used as unique identi ers of individuals for a very long time. The identi cation of ngerprint (FP) images is based on matching the features of a query FP, against those stored in a database. As FP databases are characterized by their large size and may contain noisy and distorted query images, an e cient and robust representation of FP images is essential for a reliable identi cation. Assuming FPs to be sample images from non-stationary processes of textured images of ow patterns, we propose here a new technique for preprocessing FP images for the purpose of identi cation. In the proposed algorithm, enhancement a s w ell as ridge extraction processes are based on local dominant ridge directions. The obtained thinned image is then smoothed using morphological operations to detect FP structural features. The proposed algorithm results in an e cient, robust and fast representation of FPs, which accurately retains the delity i n m i n utiae (ridge endings and bifurcations).

Proceedings of ICICS, 1997 International Conference on Information, Communications and Signal Processing. Theme: Trends in Information Systems Engineering and Wireless Multimedia Communications (Cat. No.97TH8237)

Neurocomputing, 2018

Structural damage detection has been an interdisciplinary area of interest for various engineerin... more Structural damage detection has been an interdisciplinary area of interest for various engineering fields. While the available damage detection methods have been in the process of adapting machine learning concepts, most machine learning based methods extract "hand-crafted" features which are fixed and manually selected in advance. Their performance varies significantly among various patterns of data depending on the particular structure under analysis. Convolutional neural networks (CNNs), on the other hand, can fuse and simultaneously optimize two major sets of an assessment task (feature extraction and classification) into a single learning block during the training phase. This ability not only provides an improved classification performance but also yields a superior computational efficiency. 1D CNNs have recently achieved state-of-the-art performance in vibration-based structural damage detection; however, it has been reported that the training of the CNNs requires significant amount of measurements especially in large structures. In order to overcome this limitation, this paper presents an enhanced CNN-based approach that requires only two measurement sets regardless of the size of the structure. This approach is verified using the experimental data of the Phase II benchmark problem of structural health monitoring which had been introduced by IASC-ASCE Structural Health Monitoring Task Group. As a result, it is shown that the enhanced CNN-based approach successfully estimated the actual amount of damage for the nine damage scenarios of the benchmark study.

SoftwareX, 2018

The Multisensor Time-Frequency Signal Processing (MTFSP) Matlab package is an analysis tool for m... more The Multisensor Time-Frequency Signal Processing (MTFSP) Matlab package is an analysis tool for multichannel non-stationary signals collected from an array of sensors. By combining array signal processing for non-stationary signals and multichannel high resolution time-frequency methods, MTFSP enables applications such as cross-channel causality relationships, automated component separation and direction of arrival estimation, using multisensor time-frequency distributions (MTFDs). MTFSP can address old and new applications such as: abnormality detection in biomedical signals, source localization in wireless communications or condition monitoring and fault detection in industrial plants. It allows e.g. the reproduction of the results presented in Boashash and Aïssa-El-Bey (in press) [2].

Knowledge-Based Systems, 2016

2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA), 2013

ABSTRACT Separation of different signal components, produced by one or more sources, is a problem... more ABSTRACT Separation of different signal components, produced by one or more sources, is a problem encountered in many signal processing applications. This paper proposes a fully automatic undetermined blind source separation method, based on a peak detection and extraction technique from a signal time-frequency distribution (TFD). Information on the local number of components is obtained from the TFD Short-term Rényi entropy. It also allows to detect components time supports in the time-frequency plane, with no need for predefined thresholds on the components amplitude. This approach allows to extract different signal components without prior knowledge about the signal. The method is also used as a quality criterion to compare Quadratic TFDs. Results for synthetic and real data are reported for different TFDs, including the recently introduced Extended Modified B distribution.

Abstract Multimedia and streaming applications are getting more and more important in 3G networks... more Abstract Multimedia and streaming applications are getting more and more important in 3G networks and will be an important part of the future 4G networks. In this article, after introducing 3G and 4G concepts and architectures, multimedia support in these network ...

EURASIP Journal on Advances in Signal Processing, 2012

This article presents a novel algorithm based on the cross-Wigner-Ville Distribution (XWVD) for o... more 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 signal's frequency. On the average, the difference in the phase estimate error between the Stransform 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.

International Symposium on Signal Processing and Its Applications, 1996

One of the features of a radio monitoring system is the estimation of modulation parameters for d... more One of the features of a radio monitoring system is the estimation of modulation parameters for digital modulated signals. For low signal-to-noise ratio conditions, time-frequency signal analysis is an attractive method to use. However, analysis of digital modulated signals using existing time-frequency dishibutions have proven that none of these distributions are suitable for this task. To improve the time-frequency representation

Page 1. MI3 Mobile Communication III 116 An New Joint Channel Estimation and Detection Algorithm ... more Page 1. MI3 Mobile Communication III 116 An New Joint Channel Estimation and Detection Algorithm for MlMO Channels Ebrahim Karami, Mohsen Shiva University of Tehran, IRAN 117 PIC Multiuser DS-CDMA Detection together with EM Channel Estimation GR Mohammad-Khani, JP Cances, MJ Syed, V. Meghdadi University of Limoges, FRANCE 1 18 Maintaining QoS by Utilizing Hierarchical Wireless Systems Henrik Persson, Johan M Karlsson Lund University, SWEDEN ...

Circuits, Systems and Computers, 1977. Conference Record. 1977 11th Asilomar Conference on

Abstract The paper considers the problem of spectral estimation of noisy non-stationary signals w... more Abstract The paper considers the problem of spectral estimation of noisy non-stationary signals with application to electroencephalogram (EEG) data. Four well known methods for estimating the time-varying spectrum of a non-stationary signal are first reviewed and their performance compared. These methods which work well when the signal-to-noise ratio (SNR) is high, are shown to fail with varying degrees as SNR decreases. A technique for preprocessing noisy EEG data called time-frequency peak filtering (TFPF) is then ...

Time-frequency (TF) based machine learning methodologies can improve the design of classification... more Time-frequency (TF) based machine learning methodologies can improve the design of classification systems for non-stationary signals. Using selected TF distributions (TFDs), TF feature extraction is performed on multi-channel recordings using channel fusion and feature fusion approaches. Following the findings of previous studies, a TF feature set is defined to include three complementary categories: signal related features, statistical features and image features. Multi-class strategies are then used to improve the classification algorithm robustness to artifacts. The optimal subset of TF features is selected using the wrapper method with sequential forward feature selection (SFFS). In addition, a new proposed measure for TF feature selection is shown to improve the sensitivity of the classifier (while slightly reducing total accuracy and specificity). As an illustration, the TF approach is applied to the design of a system for detection of seizure activity in real newborn EEG signals. Experimental results indicate that: (1) The compact kernel distribution (CKD) outperforms other TFDs in classification accuracy; (2) a feature fusion strategy gives better classification than a channel fusion strategy; e.g. using all TF features, the CKD achieves a classification accuracy of 82% with feature fusion, which is 4% more than the channel fusion approach; (3) the SFFS wrapper feature selection method improves the classification performance for all TFDs; e.g. total accuracy is improved by 4.6%; (4) the multi-class strategy improves the seizure detection accuracy in the presence of artifacts; e.g. a total accuracy of 86.61% with one vs. one multi-class approach is achieved i.e. 0.91% more than the binary classification approach. The results obtained on a large practical real data set confirm the improved performance capability of TF features for knowledge based systems.

Sahmoudi, M, Abed-Meraim, K., Linh-Trung, N., Sucic, V., Tupin, F. and Boashash, B. (2003). An im... more Sahmoudi, M, Abed-Meraim, K., Linh-Trung, N., Sucic, V., Tupin, F. and Boashash, B. (2003). An image and time-frequency processing methods for blind separation of non-stationary sources. In: , Proceedings of Journée d&amp;amp;amp;amp;amp;amp;#x27;Etude sur les Méthodes pour les Signaux Complexes en Traïtement d&amp;amp;amp;amp;amp;amp;#x27;Image. Journée d&amp;amp;amp;amp;amp;amp;#x27;Etude sur les Méthodes pour les Signaux Complexes en Traïtement d&amp;amp;amp;amp;amp;amp;#x27;Image, Paris, France, (). 9-10 December 2003. ... Sahmoudi, M Abed-Meraim, K. Linh-Trung, N. Sucic, V. Tupin, F. Boashash, B.

Time-frequency based methods have been shown to outperform other methods in dealing with newborn ... more Time-frequency based methods have been shown to outperform other methods in dealing with newborn EEG. This is due to the fact that newborn EEG is nonstationary and multicomponent. This paper presents a new time-frequency based EEG seizure detection method. It uses the distribution of the interspike intervals of a high frequency slice of the time-frequency representation of an EEG epoch to discriminate between seizure and non-seizure activities. The seizure detected through this method is then tracked throughout all the available EEG channels by cross-correlating the binary encoded signals of both the detected seizure and the subsequent EEG epochs in all channels. This approach allows the study of the migrating behavior of seizure using EEG signals.}

Most IF estimation techniques, such as those presented in the previous articles of this chapter, ... more Most IF estimation techniques, such as those presented in the previous articles of this chapter, assume that the signal of interest has a constant amplitude. While this is a valid assumption in a wide range of scenarios, there are several important applications in which this assumption does not hold. Indeed, in many situations the signal may be subjected to a random amplitude modulation which behaves as multiplicative noise. Examples include fading in wireless communications [1], fluctuating targets in radar [2], and structural vibration of a spacecraft during launch and atmospheric turbulence . In this article, we focus on non-parametric methods. In particular, we show that the Wigner-Ville distribution (defined in Section 2.1.4) is able to display the IF of a signal affected by multiplicative noise, and that this representation is optimal in the sense of maximum energy concentration for a linear FM signal. For higher-order polynomial FM signals, the use of the polynomial Wigner-Ville distribution (PWVD), presented in Article 5.4, is shown to give optimal representations. Statistical performance of each case will be presented here.

A separable kernel gives separate control of the frequency-smoothing and time-smoothing of the WV... more A separable kernel gives separate control of the frequency-smoothing and time-smoothing of the WVD: the lag-dependent factor causes a convolution in the fre-quency direction in the (t, f) plane, while the Doppler-dependent factor causes a convolution in the time direction. A Doppler-independent (DI) kernel smoothes the WVD in the frequency direction only, reducing the inner artifacts and preserving the time marginal. A lag-independent (LI) kernel smoothes the WVD in the time direction only, reducing the cross-terms and preserving the ...

A key characteristic of a nonstationary signal, when analyzed in the time-frequency domain, is th... more A key characteristic of a nonstationary signal, when analyzed in the time-frequency domain, is the signal complexity, quan-tified as the number of components in the signal. This paper describes a method for the estimation of this number of com-ponents of a signal using the short-term Rényi entropy of its time-frequency distribution (TFD). We focus on the charac-teristics of TFDs that make them suitable for such a task. The performance of the proposed algorithm is studied with respect to the parameters of the S-method TFD, which combines the virtues of both the spectrogram and the Wigner-Ville distri-bution. Once the optimal parameters of the TFD have been determined, the applicability of the method in the analysis of signals in low SNRs and real life signals is assessed.

Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005., 2005

The aim of this paper is to investigate the effect of sampling on the empirical mode decompositio... more The aim of this paper is to investigate the effect of sampling on the empirical mode decomposition (EMD). To this end, an experiment utilising linear frequency modulated (LFM) signals was used to simulate different sampling rates. This experiment showed that as the frequency content of the signal (fc) approached the sampling frequency (fs) the EMD performed poorly due to poor amplitude resolution. This led to a definition of a sampling limit that was 5 times the Nyqvist rate (fs/10) to improve the performance of the EMD. ...

This chapter is intended to relate recent advances in the field of time–frequency signal processi... more This chapter is intended to relate recent advances in the field of time–frequency signal processing (TFSP) to the need for further capacity of wireless communications systems. It first presents, in a brief and heuris-tic approach, the fundamentals of TFSP. It then describes the TFSP-based methodologies that are used in wireless communications with special emphasis on spread-spectrum techniques and time–frequency array processing. Topics discussed include channel modeling and identification, estimation of scattering func-tion, interference mitigation, direction of arrival estimation, time–frequency MUSIC, and time–frequency source separation. Finally, other emerging applications of TFSP to wireless communications are discussed.