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.

Brain Image Compression Techniques

Abstract: This paper presents a survey of different methods in the prospect of medical and brain image compression. Compressing an image is basically a process of encoding the image to reduce the size of image as a number of bytes for storage and transmission purposes. This should be done while preserving as much as possible the quality of the image. Hence, image compression has been founded necessary in different medical imaging systems, where there are too much number of bytes of the reconstructed images. There are basically two categories of compression techniques; namely lossless and lossy compression techniques. As the name indicates, in the lossless technique the image is encoded without any loss of information. But in the lossy one, it is allowed that some information is missed. The lossy compression techniques are commonly applied to multimedia data such as audio, video, and still images. Several lossless and lossy compression approaches have been applied to medical images. Different compression approaches belonging to the two categories are discussed and Brain images compression techniques are highlighted. Furthermore, quantitative comparisons between different compression methods are given.as well as advantages and disadvantages of each method. Keywords— Medical image compression, Brain image compression, EEG, CT, Run length encoding, Huffman encoding,

New Technique for Mobile User's Location Detection, Future Prediction and their

Abstract: Positioning of mobile users has received growing attention and has potential for applications and services to enhance both Location Based Services (LBS) and cellular network performance. So, several researches are carried out to develop methods and algorithms which enhances the positioning accuracy and execution time (1)-(7). This paper presents a new technique for current location detection with high accuracy and accepted execution time (8). Combination of cellular network and Global Positioning System (GPS) positioning techniques provide a higher accuracy of mobile location than positions based on a standalone GPS or mobile network platform. The proposed hybrid Uplink Time Difference of Arrival and Assisted GPS technique (UTDOA-AGPS) for mobile user's location detection utilizes Universal Mobile Telecommunication System (UMTS) network, Mobile Station (MS) and GPS positioning characteristics. Due to flexibility of the proposed technique, many positioning sub-techniques are chosen according to positioning parameters. As a result, the required number of GPS satellites is reduced and many drawbacks are overcome. The paper also presents a novel algorithm for future location prediction of mobile subscriber over mobile network platform (9) which results in great enhancement in LBS applications and mobile network performance. In the proposed algorithm, Intra Cell Movement Prediction (ICMP), for mobile user's future location prediction is carried out to benefit from both intra and inter cell based techniques to enhance both network and services. The proposed ICMP algorithm depends on map based intra-cell prediction and utilizes the network database and hybrid (UTDoA-AGPS) positioning technique in extracting user trajectories and movement rules to predict the next movement of mobile user. The performance of the proposed algorithm is evaluated through computer simulation and compared with that of (10) and (11). The simulation results indicate that the proposed ICMP algorithm shows a comparable precession, accuracy, execution time and it can be adapted according to the needed application characteristics and the surrounding environment. Many public and commercial location applications are regenerated based on the proposed current location detection and future location prediction techniques.

Model Predictive Control for an Active Magnetic Bearing System

Abstract: Active magnetic bearing (AMB) systems have attracted much attention in the high speed rotating machinery industry. This paper presents an application of discrete-time model predictive control (MPC) subject to input/states constraints to control an AMB system based on linear time-invariant (LTI) model. The main control objectives are to levitate the rotor shaft of the AMB system while tracking a reference trajectory and to reject possible disturbances without violating the input and state constraints. A nonlinear (NL) model of the AMB system is considered; at each sampling instant, a finite horizon MPC problem is solved to compute the optimal control input. The performance and the efficiency of the proposed MPC is validated via simulation and comparison with another classical PID controller.

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.