Browsing by Author "El Nahal, Waleed"

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Efficient Data Compression of ECG Signal Based on Modified Discrete Cosine Transform
(Tech Science Press, 14/01/2022) Hassan, Ashraf Mohamed Ali; Alzaidi, Mohammed S; Ghoneim, Sherif S. M; El Nahal, Waleed
This paper introduced an efficient compression technique that uses the compressive sensing (CS) method to obtain and recover sparse electrocardiography (ECG) signals. The recovery of the signal can be achieved by using sampling rates lower than the Nyquist frequency. A novel analysis was proposed in this paper. To apply CS on ECG signal, the first step is to generate a sparse signal, which can be obtained using Modified Discrete Cosine Transform (MDCT) on the given ECG signal. This transformation is a promising key for other transformations used in this search domain and can be considered as the main contribution of this paper. A small number of wavelet components can describe the ECG signal as related work to obtain a sparse ECG signal. A sensing technique for ECG signal compression, which is a novel area of research, is proposed. ECG signals are introduced randomly between any successive beats of the heart. MIT-BIH database can be represented as the experimental database in this domain of research. The MIT-BIH database consists of various ECG signals involving a patient and standard ECG signals. MATLAB can be considered as the simulation tool used in this work. The proposed method's uniqueness was inspired by the compression ratio (CR) and achieved by MDCT. The performance measurement of the recovered signal was done by calculating the percentage root mean difference (PRD), mean square error (MSE), and peak signal to noise ratio (PSNR) besides the calculation of CR. Finally, the simulation results indicated that this work is one of the most important works in ECG signal compression.
Mobile Multimodal Biometric System for Security
(2014-04) El Nahal, Waleed
Among our huge life requirements, the most important requirement which has a vital role in our daily life is the security. Recently using biometrics in the security systems has a wide range of research interests since they provide systems have more efficient, reliable, and secure than the others. In the present time researches for mobile biometric devices are provided with a fingerprint only which isn’t sufficient for the areas where lawlessness and anarchy cases are existed recently due to the current political situation in some developing countries in Africa and Middle East where the rate of crime increased significantly involving a large number of people getting into the crime scene. Police departments are unable to identify robbers and criminals due to the presence of newly unrecorded ones with the lack of presence of a fixed database holding records of widely known criminals. So in this paper we propose a mobile biometric authentication system (MOBAS) based on Zigbee technology and a multimodal biometric authentication system in which the primary biometric modalities adopted are the fingerprint and the face recognition. The proposed system aims to provide a mobile, light, user friendly, reliable and secure biometric authentication system to the police departments, and that will help the officers anywhere to identify the criminals by taking a fingerprint or capturing an image or both for different scenarios, sending them wirelessly to the server at the appropriate police station and then waiting for a response concerning the person’s criminal record sent wirelessly from the police station and displayed on a mobile biometric authentication device (MOBA). According to the sent criminal record, the police officers will take a suitable action towards this person.
A Modified Hilbert Analysis Method to Improve Voice Stress Analysis Systems
(International Journal of Computer Applications (0975 – 8887), 2019-05) El Nahal, Waleed; Mohamed Ali, Ashraf; Zakaria, Hatem M.
Analyzing the cognitive load generated in the brain is the most important issue for specific applications such as voice stress analysis (VSA) systems in which the detection of stressed speech caused by an act of deception under law enforcement interview questioning or military interrogation. The most widely used algorithm for VSA systems is the empirical mode decomposition (EMD). Currently EMD that uses the cubic spline interpolation technique to find the envelopes of the non-periodic signal takes a long processing time, and to achieve accurate results the process is very time consuming and expensive otherwise some tests tend to produce inaccurate results. On the other hand EMD that uses Hilbert analysis method to speed up the process and provide more accurate results, suffer from finding the envelopes of the non-periodic signal. In this paper, a new algorithm is proposed for VSA, named fast Fourier transform (FFT) with a modified Hilbert analysis method (MH) for EMD algorithm, (FTT_MH_EMD), which provides a new technique that modifies the conventional Hilbert analysis method and combines it with the fast Fourier transform algorithm to overcome the previous limitations of using individually the FTT algorithm, the cubic spline interpolation technique or the conventional Hilbert analysis method and that can speed up the processing time and gives accurate results. Simulations and results witness that the proposed algorithm provides higher accuracy than the other attempts and also the processing time has dropped by 10 times faster than those in the products currently available in the market for VSA The advantages of using FFT for VSA are low cost when implemented in products and fast iterations method, and the disadvantage is the low resolution and therefore low accuracy (60%–70%) [1]. On the other hand the advantage of using Mcquiston Ford for VSA is the high accuracy results which are greater than 80%, and the disadvantage are complex algorithms and expensive implementation [5]. An important, barely audible, frequency is produced by the human vocal cords which could measure in a fairly accurate manner if the subject is being deceitful due to the effect the brain’s cognitive load has on the vocal cords [1 and 5]. This frequency is called the micro-tremor frequency generated by the involuntary micro-tremor muscle fibers in the vocal cords. When the subject is being deceitful, more work is induced on the brain which as a result causes tremor muscles to tense up in a non-voluntary manner [1 and 6]. Different non voluntary muscles would tense up including the micro-tremor muscle fibers found on the vocal cords. This induces a higher micro-tremor frequency range notably showing a large quantity of induced cognitive load and therefore a deceitful subject [7]. The micro-tremor frequency of all muscles in the body, including the vocal chords, vibrates in the 8 to 12 Hz range in the normal case without any stress, and that the VSA vendors claim to be the sole source of detecting if an individual is lying or is under stress [1, 4 and 8]. The paper is organized from seven sections EMD, EMD with Hilbert Analysis, previous work and limitations, the proposed algorithm, the proposed VSA system model, results and simulations and finally the conclusion
Robust and High Accuracy Algorithm for Detection of Pupil Images
(Tech Science Press, 2022-06-17) El Nahal, Waleed; Zaini, Hatim G; Zaini, Raghad H; Ghoneim, Sherif S. M; Hassan, Ashraf Mohamed Ali
Recently, many researchers have tried to develop a robust, fast, and accurate algorithm. This algorithm is for eye-tracking and detecting pupil position in many applications such as head-mounted eye tracking, gaze-based human-computer interaction, medical applications (such as deaf and diabetes patients), and attention analysis. Many real-world conditions challenge the eye appearance, such as illumination, reflections, and occasions. On the other hand, individual differences in eye physiology and other sources of noise, such as contact lenses or make-up. The present work introduces a robust pupil detection algorithm with and higher accuracy than the previous attempts for real-time analytics applications. The proposed circular hough transform with morphing canny edge detection for Pupillometery (CHMCEP) algorithm can detect even the blurred or noisy images by using different filtering methods in the pre-processing or start phase to remove the blur and noise and finally the second filtering process before the circular Hough transform for the center fitting to make sure better accuracy. The performance of the proposed CHM- CEP algorithm was tested against recent pupil detection methods. Simulations and results show that the proposed CHMCEP algorithm achieved detection rates of 87.11, 78.54, 58, and 78 according to Swirski, ExCuSe, Else, and ´ labeled pupils in the wild (LPW) data sets, respectively. These results show that the proposed approach performs better than the other pupil detection methods by a large margin by providing exact and robust pupil positions on challenging ordinary eye pictures.
VLSI Architecture for Optimization Transform Technique based on Compression of ECG Signals
(2019-04) El Nahal, Waleed; Mohamed Ali, Ashraf; Zakaria, Hatem M.
The measurement of electrical activity of the heart via electrodes is named as Electrocardiography (ECG). An efficient compression technique using the compressive sensing method is required. Compressive Sensing (CS) holds the promise to be a key for acquisition and reconstruction of sparse signals. The reconstruction of such signals makes sampling rates below Nyquist rate. In this work, a novel framework was proposed that is based on the idea of CS theory for the compression of mother and fetal heart beats. The proposed scheme is based on the sparse representation of the components derived from the curvelet transform of the original Electrocardiogram (ECG) signal. The ECG signals may be approximated by a few coefficients that can be taken from a wavelet basis. This fact allows a compressed sensing approach for ECG signal compression to be introduced and to be a domain of search. ECG signals illustrate redundancy between adjacent heart beats. This redundancy implies a high fraction of common support between consecutive heart beats. The main contribution of this paper lies in the using of curvelet transform in order to generate sparsity in ECG signal. This transformation is considered an excellent approach as illustrated in this paper. Simulation results represent a better approach than Discrete Wavelet Transform (DWT) that is based on compression of ECG. MIT-BIH database is used for experimentation. The MIT-BIH database contains different kinds of ECG signals that include both abnormal ECG and normal ECG, which have different sampling rates. MATLAB tool is used for simulation purpose. The novelty of the method is that the Compression Ratio (CR) achieved by detail coefficients is better. The performance measure of the reconstructed signal is carried out by Percentage Root Mean Difference (PRD). This paper also introduces the efficient realization of the different transformation techniques using FPGA. Thus the contribution of this paper lies into two main parts. The first part is specialized in determining the proper transformation that is used in the compression of ECG signals. The second part of the contribution is summarized in using suitable hardware to implement this design. Architecture can be based on the ideas of parallelism and pipelining to get the minimum throughput and speed. Architecture is cascade and simple for calculating curvelet coefficients. The reduction of the memory size can be done by splitting ROM table. The description and functionalities of the design are modeled by Verilog HDL. The simulation and synthesis methodology are used on Virtex-II Pro FPGA that uses less number of resources of the FPGA.