Abdelhay Ali

Abdelhay Ali

Assiut University

H-index: 8

Africa-Egypt

About Abdelhay Ali

Abdelhay Ali, With an exceptional h-index of 8 and a recent h-index of 7 (since 2020), a distinguished researcher at Assiut University, specializes in the field of Machine Learning, Digital Design, ASIC FPGA, Human Body Communication, Wireless Sensor Networks.

Abdelhay Ali Information

University

Assiut University

Position

Assistant Professor

Citations(all)

248

Citations(since 2020)

188

Cited By

139

hIndex(all)

8

hIndex(since 2020)

7

i10Index(all)

8

i10Index(since 2020)

7

Email

University Profile Page

Assiut University

Abdelhay Ali Skills & Research Interests

Machine Learning

Digital Design

ASIC FPGA

Human Body Communication

Wireless Sensor Networks

Top articles of Abdelhay Ali

EQS-Band Human Body Communication through frequency hopping and MCU-Based transmitter

Human Body Communication (HBC) is an emerging technology that uses the human body as a communication channel. It offers significant advantages over traditional RF techniques in terms of power consumption and security. In recent developments, Electro-quasistatic HBC (EQS-HBC) in the frequency band below 1 MHz has been employed to enable communication without signal radiation beyond the body, effectively turning the body into a wired communication medium. This paper delves into the application of the EQS band for HBC. Experimental results show the determinantal effect of intermittent noise that sporadically disrupts communications across the band of interest. To address this challenge, we introduce an innovative frequency-hopping transceiver system, which allows the transmitter to seamlessly adapt to different frequencies. In addition, we present a miniature transmitter design, incorporating a …

Authors

Abdelhay Ali,Amr N Abdelrahman,Abdulkadir Celik,Ahmed M Eltawil

Journal

Smart Health

Published Date

2024/6/1

Performance Evaluation and Analysis of Deep Learning Autoencoder-Based Wireless Communication System

Recent advancements in deep learning have led to the emergence of autoencoder-based (AE) wireless communication systems, presenting a promising approach to tackle the challenges posed by conventional mathematical models. In this research, a thorough evaluation and analysis of the performance of deep learning AE-based wireless communication systems is conducted. Specifically, our investigation focuses on employing the additive white Gaussian noise (AWGN) channel and explores the impact of varying signal-to-noise ratio (SNR) conditions during the AE training process on system performance. The results show that training the autoencoder under diverse SNR conditions, particularly with an extended number of epochs, surpasses the performance of a fixed-trained autoencoder regarding block error rate (BLER). Additionally, a comparison of BLER between multiple AE models and traditional …

Authors

Eman Ismail,Abdelhay Ali,Omar AM Aly,Mohammed Abo-Zahhad

Published Date

2023/10/7

Channel Modeling and Characterization of EQS Capacitive Coupling Human Body Communication

Human body communication (HBC) has emerged as a key alternative to radio frequency (RF) communication, with path loss (PL) evaluation being crucial for HBC system development. Despite the existence of various PL measuring techniques in the quasi-static electric (EQS) band, the obtained results exhibit significant variance that tends to make overly optimistic PL estimates for HBCs. Additionally, these methods have displayed inconsistencies in comparison to simulation results, primarily because of the lack of an accurate simulation model, which fails to provide a complete characterization of capacitive coupling HBC (CC-HBC) operation. To address these issues, this study proposes a simple, battery-powered transceiver to accurately measure PL. Furthermore, a comprehensive lumped circuit model is introduced to verify measurements and support the characterization and development of CC-HBC systems …

Authors

Qi Huang,Abdelhay Ali,Abdulkadir Celik,Ahmed Eltawil

Published Date

2023/8/6

Deep learning-based Human Body Communication baseband transceiver for WBAN IEEE 802.15. 6

Recently, Wireless Body Area Network (WBAN) has revolutionized e-health-care. WBAN boosts monitoring vital signs utilizing tiny wireless sensors implanted in or around the human body. In February 2012, the IEEE 802.15.6 WBAN standard was released for low-power and short-range communication around the human body. The standard defines one medium access control layer and three different physical layers: narrow band , ultra-wideband, and Human Body Communication (HBC) layers. We are motivated by exploiting the human body as a communication medium. We propose a novel optimized architecture for the HBC baseband transceiver based on deep learning. The receiver utilizes two deep neural networks: one for frame synchronization to recover data and timing precisely and the other for the channel decoder to improve transceiver performance and reduce power consumption. In addition, low …

Authors

Abdelhay Ali,Sabah M Ahmed,Mohammed S Sayed,Ahmed Shalaby

Journal

Engineering Applications of Artificial Intelligence

Published Date

2022/10/1

See List of Professors in Abdelhay Ali University(Assiut University)

Abdelhay Ali FAQs

What is Abdelhay Ali's h-index at Assiut University?

The h-index of Abdelhay Ali has been 7 since 2020 and 8 in total.

What are Abdelhay Ali's top articles?

The articles with the titles of

EQS-Band Human Body Communication through frequency hopping and MCU-Based transmitter

Performance Evaluation and Analysis of Deep Learning Autoencoder-Based Wireless Communication System

Channel Modeling and Characterization of EQS Capacitive Coupling Human Body Communication

Deep learning-based Human Body Communication baseband transceiver for WBAN IEEE 802.15. 6

are the top articles of Abdelhay Ali at Assiut University.

What are Abdelhay Ali's research interests?

The research interests of Abdelhay Ali are: Machine Learning, Digital Design, ASIC FPGA, Human Body Communication, Wireless Sensor Networks

What is Abdelhay Ali's total number of citations?

Abdelhay Ali has 248 citations in total.

What are the co-authors of Abdelhay Ali?

The co-authors of Abdelhay Ali are Prof. Mohammed Abo-Zahhad Abo-Zeid, koji inoue, Mohammed S. Sayed, Mohammed Farrag.

    Co-Authors

    H-index: 31
    Prof. Mohammed Abo-Zahhad Abo-Zeid

    Prof. Mohammed Abo-Zahhad Abo-Zeid

    Egypt-Japan University of Science and Technology

    H-index: 23
    koji inoue

    koji inoue

    Kyushu University

    H-index: 17
    Mohammed S. Sayed

    Mohammed S. Sayed

    Egypt-Japan University of Science and Technology

    H-index: 13
    Mohammed Farrag

    Mohammed Farrag

    Assiut University

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