Sabah M. Ahmed

Bio: Sabah M. Ahmed is an academic researcher from Egypt-Japan University of Science and Technology. The author has contributed to research in topics: Wavelet & Wavelet packet decomposition. The author has an hindex of 19, co-authored 73 publications receiving 1435 citations. Previous affiliations of Sabah M. Ahmed include Jordan University of Science and Technology & Assiut University.

A New Energy-Efficient Adaptive Clustering Protocol Based on Genetic Algorithm for Improving the Lifetime and the Stable Period of Wireless Sensor Networks

Abstract: This paper presents a new Genetic Algorithm-based Energy-Efficient adaptive clustering hierarchy Protocol (GAEEP) to efficiently maximize the lifetime and to improve the stable period of Wireless Sensor Networks (WSNs). The new protocol is aimed at prolonging the lifetime of WSNs by finding the optimum number of cluster heads (CHs) and their locations based on minimizing the energy consumption of the sensor nodes using genetic algorithm. The operation of the GAEEP is broken up into rounds, where each round begins with a set-up phase, when the base station finds the optimum number of CHs and assigns members nodes of each CH, followed by a steady-state phase, when the sensed data are transferred to CHs and collected in frames; then these frames are transferred to the base station. The performance of the GAEEP is compared with previous protocols using Matlab simulation. Simulation results show that GAEEP protocol improves the network lifetime and stability period over previous protocols in both homogeneous and heterogeneous cases. Moreover, GAEEP protocol increases the reliability of clustering process because it expands the stability period and compresses the instability period.

A New EEG Acquisition Protocol for Biometric Identification Using Eye Blinking Signals

Abstract: In this paper, a new acquisition protocol is adopted for identifying individuals from electroencephalogram signals based on eye blinking waveforms. For this purpose, a database of 10 subjects is collected using Neurosky Mindwave headset. Then, the eye blinking signal is extracted from brain wave recordings and used for the identification task. The feature extraction stage includes fitting the extracted eye blinks to auto- regressive model. Two algorithms are implemented for auto- regressive modeling namely; Levinson-Durbin and Burg algorithms. Then, discriminant analysis is adopted for classification scheme. Linear and quadratic discriminant functions are tested and compared in this paper. Using Burg algorithm with linear discriminant analysis, the proposed system can identify subjects with best accuracy of 99.8%. The obtained results in this paper confirm that eye blinking waveform carries discriminant information and is therefore appropriate as a basis for person identification methods.

A Novel Biometric Approach for Human Identification and Verification Using Eye Blinking Signal

Abstract: In this letter, a novel technique is adopted for human recognition based on eye blinking waveform extracted from electro-oculogram signals. For this purpose, a database of 25 subjects is collected using Neurosky Mindwave headset. Then, the eye blinking signal is extracted and applied for identification and verification tasks. The pre-processing stage includes empirical mode decomposition to isolate electro-oculogram signal from brainwaves. Then, time delineation of the eye blinking waveform is utilized for feature extraction. Finally, linear discriminant analysis is adopted for classification. Based on the achieved results, the proposed system can identify subjects with best accuracy of 97.3% and verify them with an equal error rate of 3.7%. The obtained results in this letter confirm that eye blinking waveform carries discriminant information and is therefore appropriate as a basis for human recognition task.

Genomic Analysis and Classification of Exon and Intron Sequences Using DNA Numerical Mapping Techniques

Abstract: Using digital signal processing in genomic field is a key of solving most problems in this area such as prediction of gene locations in a genomic sequence and identifying the defect regions in DNA sequence. It is found that, using DSP is possible only if the symbol sequences are mapped into numbers. In literature many techniques have been developed for numerical representation of DNA sequences. They can be classified into two types, Fixed Mapping (FM) and Physico Chemical Property Based Mapping (PCPBM ( . The open question is that, which one of these numerical representation techniques is to be used? The answer to this question needs understanding these numerical representations considering the fact that each mapping depends on a particular application. This paper explains this answer and introduces comparison between these techniques in terms of their precision in exon and intron classification. Simulations are carried out using short sequences of the human genome (GRch37/hg19). The final results indicate that the classification performance is a function of the numerical representation method.

The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

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.

A smart home energy management system using IoT and big data analytics approach

Abstract: Increasing cost and demand of energy has led many organizations to find smart ways for monitoring, controlling and saving energy. A smart Energy Management System (EMS) can contribute towards cutting the costs while still meeting energy demand. The emerging technologies of Internet of Things (IoT) and Big Data can be utilized to better manage energy consumption in residential, commercial, and industrial sectors. This paper presents an Energy Management System (EMS) for smart homes. In this system, each home device is interfaced with a data acquisition module that is an IoT object with a unique IP address resulting in a large mesh wireless network of devices. The data acquisition System on Chip (SoC) module collects energy consumption data from each device of each smart home and transmits the data to a centralized server for further processing and analysis. This information from all residential areas accumulates in the utility’s server as Big Data. The proposed EMS utilizes off-the-shelf Business Intelligence (BI) and Big Data analytics software packages to better manage energy consumption and to meet consumer demand. Since air conditioning contributes to 60% of electricity consumption in Arab Gulf countries, HVAC (Heating, Ventilation and Air Conditioning) Units have been taken as a case study to validate the proposed system. A prototype was built and tested in the lab to mimic small residential area HVAC systems1.