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.

The Effect of Coil Geometry and Winding Method on the Electromagnetic Launcher Performance

Abstract: Electromagnetic launchers become spread in many applications. This type of launcher is used to accelerate a slug made of ferromagnetic material. Many researchers compete to enhance the performance of this type. The launcher consists of simple components such as a capacitor bank, electronic switches, launcher coil, and charging circuit. In this paper, the authors have physically built the launcher system and reinforced the launcher performance by changing the winding method and making the trigger coil in a conical shape to reduce the deceleration effect of the last half of the trigger, this modification has proven its superiority over the straight coil.

Predicting the Motion of the End Effector in a Flexible Interconnected Manipulator with Neural Networks

Abstract: The estimation of the position of flexible robotic manipulators is a challenging task, especially for parallel and interconnected robots. This paper proposes a method to estimate the position of the end-effector of a flexible interconnected manipulator based on a virtual sensor principle. By using MSC ADAMS software, we developed a virtual prototype of the flexible interconnected manipulator and devised all feasible neural networks that map nonlinear relationships between angles of the active/passive joints to the position of the end-effector. The results indicated that it is possible to use the neural network estimate the position of the end-effector with a single passive joint and with high accuracy in both training-testing, with Mean Squared Error in the scale of 10−3 m, and unseen environments, with error bounded by less than 0.2 mm. The obtained results show the proficiency of the nonlinear relationships based on neural networks to predict the motion/position of the flexible manipulator with a promising and desirable capability.

C19. Immune node deployment algorithm for mobile wireless sensor networks with limited mobility based on probabilistic sensing model

Abstract: Coverage has direct effect on the network performance, thus it considered as the measure of quality of service in WSNs. The deployment strategy of sensor nodes in the sensor field is the most critical factor related to the network coverage. So, in this paper a centralized deployment algorithm based on immune optimization algorithm is proposed to improve the coverage of mobile sensor networks. The proposed algorithm redeploys the random deployed sensor nodes to maximize the coverage area based on a probabilistic sensing model. Moreover, the proposed algorithm limits the moving distance of mobile sensor nodes to reduce the dissipated energy in mobility and to ensure the connectivity among the sensor nodes. The performance of the proposed algorithm is compared with the CSAPO algorithm using MATLAB simulation. Simulation results show that the proposed algorithm outperforms the CSAPO algorithm in terms of the network coverage, mobility cost and convergence speed.

End-to-End Mobile Robot Navigation using a Residual Deep Reinforcement Learning in Dynamic Human Environments

Abstract: Safe navigation through human crowds is key to enabling practical mobility ubiquitously. The Deep Reinforcement Learning (DRL) and the End-to-End (E2E) approaches to goal-oriented robot navigation have the potential to render policies able to tackle localization, path planning, obstacle avoidance, and adaptation to change in unison. In this paper, we report an architecture based on convolutional units and residual blocks being able to enhance adaptability to unseen and dynamic human environments. In particular, our scheme outperformed the state-of-the-art baselines SOADRL and NAVREP by about 13% and 18% on average success rate, respectively, throughout 27 unseen and dynamic navigation instances. Furthermore, our approach avoids the explicit encoding of positions and trajectories of moving humans compared to the standard models. Our results show the potential to render adaptive and generalizable policies for unknown and dynamic human environments.

Enhancement of Gabor Directional Wavelet Edge Detection Method

Abstract: This paper presents an approach for the enhancement of the performance of conventional Gabor directional wavelet edge detection method. The approach involves preand post-processing techniques. In the preprocessing technique, the input image is segmented by an optimal threshold method based on Shannon and Tsallis entropy. The segmented image is then applied to the Gabor directional wavelet edge detection method. In the postprocessing, the output of the Gabor directional wavelet edge detection method is hysteresis thresholded. The lower and higher threshold values are computed using Otsu’s method. The output greater than the higher threshold is taken as edge and, the lower than low threshold level is rejected. The output between the lower and higher is threshold using a single threshold value also computed by Otsu’s method. Simulations results have been presented to show the impact of the proposed preand post-processing techniques in enhancing the performance of the Gabor directional wavelet edge detection method. The performance of the presented pre-, post-processed and simultaneous preand postprocessed Gabor directional wavelet edge detection method are compared with the conventional Gabor and the commonly used Sobel and Canny methods. The results have shown that images with different edges can be best handled by the presented simultaneous preand post-processed Gabor directional wavelet edge detection 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.