Mohammed Abo-Zahhad

Bio: Mohammed Abo-Zahhad is an academic researcher from Egypt-Japan University of Science and Technology. The author has contributed to research in topics: Wireless sensor network & Wavelet. The author has an hindex of 24, co-authored 125 publications receiving 1917 citations. Previous affiliations of Mohammed Abo-Zahhad include Assiut University & Jordan University of Science and Technology.

Coverage maximization in mobile Wireless Sensor Networks utilizing immune node deployment algorithm

Abstract: A Wireless Sensor Network (WSN) consists of spatially distributed autonomous sensors with sensing, computation and wireless communication capabilities. Each sensor generally has the task to monitor, measure ambient conditions, and disseminate the collected data towards a base station. One of the key points in the design stage of a WSN that is related to the sensing attribute is the coverage of the sensing field. The coverage issue in WSNs depends on many factors, such as the network topology, sensor sensing model, and the most important one is the deployment strategy. The sensor nodes can be deployed either deterministically or randomly. Random deployment of the sensor nodes can cause coverage holes formulation; therefore, in most cases, random deployment is not guaranteed to be efficient for achieving the required coverage. In this case, the mobility feature of the nodes can be utilized in order to maximize the coverage. This is Non-deterministic Polynomial-time hard (NP-hard) problem. So in this paper, the Immune Algorithm (IA) is used to relocate the mobile sensor nodes after the initial configuration to maximize the coverage area with the moving dissipated energy minimized. The performance of the proposed algorithm is compared with the previous algorithms using Matlab simulation. Simulation results show that the proposed algorithm improves the network coverage and the redundant covered area with minimum moving consumption energy.

Design and implementation of building energy monitoring and management system based on wireless sensor networks

Abstract: Wireless sensor networks (WSNs) play a key role in extending the smart grid implementation towards residential premises and energy management applications. Efficient supply and demand balance, and consequently reducing the electricity expenses and carbon emissions, is an immediate benefit of implementing smart grids. In this paper, design and implementation of an energy management system (EMS) for efficient load management are proposed. The EMS reduces the consumption of the consumers at the peak load hours and thus reduces the carbon emissions of the household. The proposed system consists of two main parts. The first part is an Energy Management Unit (EMU) which has a graphical user interface for runtime monitoring and control. The second part is sensor nodes which measure the power consumption of the different loads and transfer it to the EMU via multi-hop network. The EMU is implemented using NI LABVIEW software and XBee-PRO ZigBee module to communicate with sensor nodes. Hardware model is implemented using Arduino Uno microcontroller, XBee-PRO ZigBee module and the ACS712 current sensor. The EMS is applied to building of Electrical Engineering Department at Assiut University as a case study.

Efficient vehicle detection and tracking strategy in aerial videos by employing morphological operations and feature points motion analysis

Abstract: Real-time automatic detection and tracking of moving vehicles in videos acquired by airborne cameras is a challenging problem due to vehicle occlusion, camera movement, and high computational cost. This paper presents an efficient and robust real-time approach for automatic vehicle detection and tracking in aerial videos that employ both detections and tracking features to enhance the final decision. The use of Top-hat and Bottom-hat transformation aided by the morphological operation in the detection phase has been adopted. After detection, background regions are eliminated by motion feature points’ analysis of the obtained object regions using a combined technique between KLT tracker and K-means clustering. Obtained object features are clustered into separate objects based on their motion characteristic. Finally, an efficient connecting algorithm is introduced to assign the vehicle labels with their corresponding cluster trajectories. The proposed method was tested on videos taken in different scenarios. The experimental results showed that the recall, precision, and tracking accuracy of the proposed method were about 95.1 %, 97.5%, and 95.2%, respectively. The method also achieves a fast processing speed. Thus, the proposed approach has superior overall performance compared to newly published approaches.

A hybrid ECG compression algorithm based on singular value decomposition and discrete wavelet transform.

Abstract: Increasing use of computerized ECG processing systems requires effective electrocardiogram (ECG) data compression techniques which aim to enlarge storage capacity and improve data transmission over phone and internet lines. This paper presents a compression technique for ECG signals using the singular value decomposition (SVD) combined with discrete wavelet transform (DWT). The central idea is to transform the ECG signal to a rectangular matrix, compute the SVD, and then discard small singular values of the matrix. The resulting compressed matrix is wavelet transformed, thresholded and coded to increase the compression ratio. The number of singular values and the threshold level adopted are based on the percentage root mean square difference (PRD) and the compression ratio required. The technique has been tested on ECG signals obtained from MIT-BIH arrhythmia database. The results showed that data reduction with high signal fidelity can thus be achieved with average data compression ratio of 25.2:1 and av...

Wavelet transforms and the ECG: a review.

Abstract: The wavelet transform has emerged over recent years as a powerful time-frequency analysis and signal coding tool favoured for the interrogation of complex nonstationary signals. Its application to biosignal processing has been at the forefront of these developments where it has been found particularly useful in the study of these, often problematic, signals: none more so than the ECG. In this review, the emerging role of the wavelet transform in the interrogation of the ECG is discussed in detail, where both the continuous and the discrete transform are considered in turn.

Coded Communication over Fading Channels

Abstract: In studying the performance of coded communications over memoryless channels (with or without fading), the results are given as upper bounds on the average bit error probability (BEP). In principle, there are three different approaches to arriving at these bounds, all of which employ obtaining the so-called pairwise error probability , or the probability of choosing one symbol sequence over another for a given pair of possible transmitted symbol sequences, followed by a weighted summation over all pairwise events. In this chapter, we will focus on the results obtained from the third approach since these provide the tightest upper bounds on the true performance. The first emphasis will be placed on evaluating the pairwise error probability with and without CSI, following which we shall discuss how the results of these evaluations can be used via the transfer bound approach to evaluate average BEP of coded modulation transmitted over the fading channel.