Ahmed Yahya

@azhar.edu.eg

Prof of electronics, Department of electrical engineering, Faculty of engineering,
Al-Azhar University

Ahmed Yahya


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https://researchid.co/profahmedyahya

RESEARCH INTERESTS

Distributed Cloud Edge Computing, Vertical Testbed Architctures, LoRaWAN Networks, V2X Communications, Area-efficient instruction set synthesis for reconfigurable system-on-chip designs, and Cognitive IOT Networks
20

Scopus Publications

527

Scholar Citations

14

Scholar h-index

18

Scholar i10-index

Scopus Publications

  • Multiple Targets CFAR Detection Performance Based on an Intelligent Clustering Algorithm in K-Distribution Sea Clutter
    Mansoor M. Al-dabaa, Eugen Laslo, Ahmed A. Emran, Ahmed Yahya, Ashraf Aboshosha
    Sensors, 2025
    Maintaining a Constant False Alarm Rate (CFAR) in the presence of K-distributed sea clutter is vital due to the dynamic and unpredictable nature of maritime environments. However, conventional CFAR detectors suffer significant performance degradation in multi-target scenarios, primarily due to the masking effect caused by interfering targets. To address this challenge, this paper introduces an advanced detection scheme that integrates Linear Density-Based Spatial Clustering for Applications with Noise (Lin-DBSCAN) with CFAR processing. Lin-DBSCAN is specifically tailored to efficiently identify and isolate interfering targets and sea spikes, which typically manifest as outliers in the symmetric reference windows surrounding the Cell Under Test (CUT). By leveraging Lin-DBSCAN, the proposed Lin-DBSCAN-CFAR method effectively filters out anomalous signals from the background clutter, resulting in enhanced detection accuracy and robustness, especially under complex sea clutter conditions. Extensive simulations under varying conditions, including multiple target environments, varying false alarm rates, and different clutter shape parameters, demonstrate that Lin-DBSCAN-CFAR significantly outperforms conventional CFAR approaches. It is noteworthy that the proposed method achieves detection performance comparable to the more computationally intensive DBSCAN-CFAR while significantly reducing computational complexity. Simulation results reveal that Lin-DBSCAN-CFAR requires a 1 to 2 dB lower SNR to reach a detection probability of 0.8 compared with the nearest traditional CFAR techniques, confirming its superiority in both accuracy and efficiency.
  • A Survey on Machine Learning Techniques in Smart Grids Based on Wireless Sensor Networks
    Ashraf M. Etman, Mohamed S. Abdalzaher, Ahmed A. Emran, Ahmed Yahya, Mostafa Shaaban
    IEEE Access, 2025
    Smart grids (SGs) are crucial to the efficiency and sustainability of modern energy systems. As the world’s population continues to increase, so does the need for energy, and traditional energy systems are struggling to keep up. In this context, this study reviews the possibilities of deploying machine learning (ML) on wireless sensor networks (WSNs) in smart grid systems. In several ways, SGs may gain from combining WSNs with ML, including enhance system reliability, sustainability, improve fault detection, and increase energy efficiency. This paper offers an extensive review of pertinent research emphasizing the use of supervised, unsupervised, and reinforcement learning approaches. The evaluation contains 234 peer reviewed articles from highly regarded academic journals and conferences covering the years 2017 through 2024 which depict the effectiveness of supervised techniques on WSNs in the field of SGs. In addition the paper presents set of the most usable datasets in the field of WSNs and SGs, and introduces a comparison between our paper and relevant surveys. The study also analyses the opportunities and challenges related to the application of WSNs and ML in SGs and offers possible research directions. Overall, the study makes it clear that combining WSNs with ML may significantly contribute to the creation of smart grid systems that are more effective, dependable, and sustainable.
  • DEEP LEARNING MITIGATION OF SEA CLUTTER FOR ENHANCED RADAR TARGET DETECTION
    Mansoor M Al-dabaa, Ahmed A. Emran, Ahmed Yahya, Ashraf Aboshosha
    Journal of Al Azhar University Engineering Sector, 2024
  • Spectrum Sensing Optimization Using De-noising and Energy Detection
    Iranian Journal of Electrical and Electronic Engineering, 2024
  • SENSING TIME IMPROVEMENT USING TWO STAGE DETECTORS FOR COGNITIVE RADIO SYSTEM
    Mohamed Khalaf, Ahmed Fawzi, Ahmed Yahya
    International Journal of Computer Networks and Communications, 2024
    Cognitive radio (CR) is a promising technology for both present and future telecommunications to satisfy the demand of the next generation due to inefficient use of the allocated spectrum. Due to its ability to utilize the available bandwidth of other wireless communication networks and so enhance its occupancy. Spectrum sensing (SS) is the key characteristic of the CR system that helps it identify the empty spectrum. SS has gained a lot of interest recently and it is an active research area since it offers additional opportunities to secondary users. A broad variety of analytical methods to identify the Primary User's (PUs) presence have emerged as a result of SS techniques for CRs. Although each approach has its own benefits, the drawbacks attached to them make an individual implementation of the technique impractical for usage. To mitigate the drawbacks and maximize the benefits offered by the individual methods, a two stage detector can be employed for SS. However, the stages method lengthens the time needed to sense the spectrum and produce a definitive result. In this paper, we propose a two-stage sensing approach, where the first stage is an Interval Dependent Denoising detector (IDD) and followed by a second stage is Energy Detection (ED) which provides a large decrease in the mean sensing time compared to different related two-stage SS approaches The ED method is simple and has a short sensing time, but it performs poorly when the Signal to Noise Ratio (SNR) is low, Hence it is thought that separating PU activity from noise is essential for accurate SS. Therefore, we use IDD as a noise reduction technique in the first stage before the ED stage. The simulation results have been utilized to demonstrate that this technique leads to a large savings in sensing time as compared to an existing two-stage detection approaches.
  • A Distributed Cloud Architecture Based on General De Bruijn Overlay Network
    Osama R. S. Ramadan, Mohamed Yasin I. Afifi, Ahmed Yahya
    International Journal of Cloud Applications and Computing, 2024
    Distributed cloud systems enable the distribution of computing resources across various geographical locations. While offering benefits like accelerated content delivery, the scalability and coherence maintenance of these systems pose significant challenges. Recent studies reveal shortcomings in existing distributed system schemes to meet modern cloud application demands and maintain coherence among heterogeneous system elements. This paper proposes a service-oriented network architecture for distributed cloud computing networks. Using a De Bruijn network as a software-defined overlay network, the architecture ensures scalability and coherence. Through service-based addressing, requests are issued to designated service address bands, streamlining service discovery. The architecture's evaluation through extensive simulations showcases sustainable scalability and inherent load-balancing properties. The paper concludes with insights into future research directions, emphasizing the extension of the proposed architecture to emerging distributed cloud use cases and decentralized security.
  • A framework for classifying breast cancer based on deep features integration and selection
    Abdallah M. Hassan, Ahmed Yahya, Ashraf Aboshosha
    Neural Computing and Applications, 2023
    Deep convolutional neural networks (DCNNs) are one of the most advanced techniques for classifying images in a range of applications. One of the most prevalent cancers that cause death in women is breast cancer. For survival rates to increase, early detection and treatment of breast cancer is essential. Deep learning (DL) can help radiologists diagnose and classify breast cancer lesions. This paper proposes a computer-aided system based on DL techniques for automatically classify breast cancer tumors in histopathological images. There are nine DCNN architectures used in this work. Four schemes are performed in the proposed framework to find the best approach. The first scheme consists of pre-trained DCNNs based on the transfer learning concept. The second scheme performs feature extraction of the DCNN architectures and uses a support vector machine (SVM) classifier for evaluation. The third one performs feature integration to show how the integrated deep features may enhance the SVM classifiers' accuracy. Finally, in the fourth scheme, the Chi-square (χ2) feature selection method is applied to reduce the large feature size in the feature integration step. The results of the proposed system present a promising performance for breast cancer classification with an accuracy of 99.24%. The system performance shows that the proposed tool is suitable to assist radiologists in diagnosing breast cancer tumors.
  • A decision support healthcare system based on IoT and neural network technique
    Khadeja Al_Sayed Fahmy, Ahmed Yahya, M. Zorkany
    Journal of Engineering Design and Technology, 2022
    Purpose The purpose of this paper is to develop e-health and patient monitoring systems remotely to overcome the difficulty of patients going to hospitals especially in times of epidemics such as virus disease (COVID-19). Artificial intelligence (AI) technology will be combined Internet of Things (IoT) in this research to overcome these challenges. The research aims to select the most appropriate, best-hidden layers numbers and the activation function types for the neural network (NN). Then, define the patient data sent through protocols of the IoT. NN checks the patient’s medical sensors data to make the appropriate decision. Then it sends this diagnosis to the doctor. Using the proposed solution, the patients can diagnose and expect the disease automatically and help physicians to discover and analyze the disease remotely without the need for patients to go to the hospital. Design/methodology/approach AI technology will be combined with the IoT in this research. The research aims to select the most appropriate’ best-hidden layers numbers’ and the activation function types for the NN. Findings Decision support health-care system based on IoT and deep learning techniques was proposed. The authors checked out the ability to integrate the deep learning technique in the automatic diagnosis and IoT abilities for speeding message communication over the internet has been investigated in the proposed system. The authors have chosen the appropriate structure of the NN (best-hidden layers numbers and the activation function types) to build the e-health system is performed in this work. Also, depended on the data from expert physicians to learn the NN in the e-health system. In the verification mode, the overall evaluation of the proposed diagnosis health-care system gives reliability under different patient’s conditions. From evaluation and simulation results, it is clear that the double hidden layer of feed-forward NN and its neurons contain Tanh function preferable than other NN. Originality/value AI technology will be combined IoT in this research to overcome challenges. The research aims to select the most appropriate, best-hidden layers numbers and the activation function types for the NN.
  • Re-Learning EXP3 Multi-Armed Bandit Algorithm for Enhancing the Massive IoT-LoRaWAN Network Performance
    Samar Adel Almarzoqi, Ahmed Yahya, Zaki Matar, Ibrahim Gomaa
    Sensors, 2022
    Long-Range Wide Area Network (LoRaWAN) is an open-source protocol for the standard Internet of Things (IoT) Low Power Wide Area Network (LPWAN). This work’s focal point is the LoRa Multi-Armed Bandit decentralized decision-making solution. The contribution of this paper is to study the effect of the re-learning EXP3 Multi-Armed Bandit (MAB) algorithm with previous experts’ advice on the LoRaWAN network performance. LoRa smart node has a self-managed EXP3 algorithm for choosing and updating the transmission parameters based on its observation. The best parameter choice needs previously associated distribution advice (expert) before updating different choices for confidence. The paper proposes a new approach to study the effects of combined expert distribution for each transmission parameter on the LoRaWAN network performance. The successful transmission of the packet with optimized power consumption is the pivot of this paper. The validation of the simulation result has proven that combined expert distribution improves LoRaWAN network’s performance in terms of data throughput and power consumption.
  • Enhancement of spectrum sensing performance via cooperative cognitive radio networks at low snr
    Kotb A. Kotb, Ahmed S. Shalaby, Ahmed Yahya
    Iraqi Journal of Science, 2021
    The inefficient use of spectrum is the key subject to overcome the upcoming spectrum crunch issue. This paper presents a study of performance of cooperative cognitive network via hard combining of decision fusion schemes. Simulation results presented different cooperative hard decision fusion schemes for cognitive network. The hard-decision fusion schemes provided different discriminations for detection levels. They also produced small values of Miss-Detection Probability at different values of Probability of False Alarm and adaptive threshold levels. The sensing performance was investigated under the influence of channel condition for proper operating conditions. An increase in the detection performance was achieved for cognitive users (secondary users) of the authorized unused dynamic spectrum holes (primary users) while operating in a very low signal-to-noise ratio with the proper condition of minimum total error rate.
  • Design of Multi-band Microstrip Patch Antennas for Mid-band 5G Wireless Communication
    Karima Mazen, Ahmed Emran, Ahmed S. Shalaby, Ahmed Yahya
    International Journal of Advanced Computer Science and Applications, 2021
  • A Modified Particle Swarm Optimization Approach for Latency of Wireless Sensor Networks
    Jannat H. Elrefaei, Ahmed Yahya, Mouhamed K. Shaat, Ahmed H. Madian, Refaat M. Fikry
    International Journal of Advanced Computer Science and Applications, 2021
  • Performance evaluation of IoT messaging protocol implementation for e-health systems
    M Zorkany, K.Fahmy -, Ahmed Yahya
    International Journal of Advanced Computer Science and Applications, 2019
  • Comparison of different backpropagation training algorithms using robust M-estimators performance functions
    Ali R. Abd Ellah, Mohamed H. Essai, Ahmed Yahya
    Proceedings 2015 10th International Conference on Computer Engineering and Systems Icces 2015, 2016
  • Performance comparison of linear multiuser detectors and neural network detector for DS/CDMA systems in AWGN
    Hassan A. Hassan, Mohamed H. Essai, Ahmed Yahya
    Proceedings 2015 10th International Conference on Computer Engineering and Systems Icces 2015, 2016
  • Multi-Path approach for real-time service delivery optimization over HetNets
    Anwar Fouad, Emad Abd-Elrahman, Ahmed Yehya
    Iwcmc 2015 11th International Wireless Communications and Mobile Computing Conference, 2015
  • Deterministic UWB channel modeling using ray tracing approach
    A. Fathy, A. Yahya, Hani Ragai
    ICET 2014 2nd International Conference on Engineering and Technology, 2015
  • Performance analysis for new call bounding scheme with SFR in LTE-advanced networks
    Mahammad A. Safwat, Hesham M. El Badawy, Ahmad Yehya, H. El Motaafy
    Proceedings 16th IEEE International Conference on High Performance Computing and Communications Hpcc 2014 11th IEEE International Conference on Embedded Software and Systems Icess 2014 and 6th International Symposium on Cyberspace Safety and Security Css 2014, 2014
  • Performance Evaluation of Blackhole Attack on VANET's Routing Protocols
    International Journal of Software Engineering and Its Applications, 2014
  • Energy-aware architecture for multi-rate ad hoc networks
    Ahmed Yahya
    Egyptian Informatics Journal, 2010

RECENT SCHOLAR PUBLICATIONS

  • Klein–Gordon (KG)–MGT thermoelasticity in Pasternak nanobeams
    A Yahya, A Zakria, E Salih
    Journal of Thermal Stresses, 1-23 , 2026
    2026
  • Sustainable ceramic tile production: Thermochemical, microstructural, and physico-mechanical characterization of valorized granite sawing waste
    A Yahya, N Abdel-Kader, W Ogila, A Moustafa, AA Hegazy, ...
    Ceramics International , 2026
    2026
  • Fractional modeling of nonlinear dispersive systems: on the comparative study of Whitham–Broer–Kaup equations using various derivatives
    SA Ahmed, R Shah, A Mohamed, AA Hassan, HE Dargail, HM Barakat, ...
    Scientific Reports , 2026
    2026
  • Magneto-Photo-Thermoelastic Disturbances in a Generalized Rotating Semiconductor Medium with Variable Thermal Properties
    A Saidi, AE Abouelregal, A Yahya, A Zakria
    Iranian Journal of Science and Technology, Transactions of Mechanical … , 2026
    2026
  • Sumudu Decomposition for the Fractional Dual Phase-Lag of Thermoelastic Nano-Beams Supported by Pasternak Foundations for Nonlocal Vibrations
    A Yahya, A Zakria, SA Ahmed, AA Hassan, IE Ahmed, IO Ahmed
    Journal of Vibration Engineering & Technologies 14 (3), 127 , 2026
    2026
  • Thermoelastic Oscillations of a Solid Medium with Voids via the Influence of Atangana-Baleanu-Caputo Fractional Derivative
    AA Hassan, A Yahya, A Zakria, SA Ahmed, IE Ahmed, IO Ahmed, E Salih, ...
    Symmetry 18 (2), 359 , 2026
    2026
  • Vibrational Analysis of Thermoelastic Beams on Dual-Parameter Foundations via the Fractional Three-Phase-Lag Approach
    A Zakria, A Yahya, IE Ahmed, IO Ahmed, AA Hassan, M Suhail, E Salih
    Micromachines 17 (2), 241 , 2026
    2026
  • Fractional thermoelastic behavior of nanoscale beams on a generalized elastic substrate
    A Zakria, A Yahya
    Journal of Engineering Mathematics 156 (1), 2 , 2026
    2026
  • Sumudu decomposition method with non-singular kernel operators for solving time fractional Whitham-Broer-Kaup equations
    SA Ahmed, MGS Al-Safi, R Shah, A Mohamed, AA Hassan, A Zakria, ...
    Boundary Value Problems , 2026
    2026
    Citations: 1
  • Moore–Gibson–Thompson heat conduction model under the Klein–Gordon (KG) nonlocality for a thermoelastic solid cylinder with voids
    A Yahya, A Saidi, AE Abouelregal, A Zakria
    Mechanics Based Design of Structures and Machines 54 (1), 2518497 , 2026
    2026
    Citations: 2
  • Generalized thermoelastic responses in an infinite solid cylinder under a thermoelastic diffusion model including the Soret and Dufour effects
    A Saidi, A Yahya, AE Abouelregal, A Zakria
    Journal of Thermal Stresses, 1-30 , 2026
    2026
  • Microstructural, physico-mechanical, optical and photocatalytic characteristics of kaolin-based eucryptite glass ceramics
    A Yahya, S Allam, ON Almasarawi, SAM Abdel-Hameed, B Raab, ...
    Applied Clay Science 279, 108024 , 2026
    2026
  • Dynamic Response of Saturated Soil to Anisotropic Thermal Conductivity Impacts Under the Moore‒Gibson‒Thompson Thermoelastic Model
    FA Mohammed, A Yahya, A Saidi, A Zakria, HE Dargail
    Iranian Journal of Science and Technology, Transactions of Civil Engineering … , 2025
    2025
  • A Study on the Impact of Metamaterials on Performance of Antennas in Millimeter-Wave Networks
    I Osama, M Elhefnawy, A Yahya
    International Journal of Engineering and Applied Sciences-October 6 … , 2025
    2025
  • Response of nonlocal thermoelastic nanobeams supported by Pasternak foundations to the effect of generalized fractional theory with three-phase lags
    A Zakria, A Yahya, A Saidi, M Suhail, MNA Rabih, OAA Osman
    Scientific Reports 15 (1), 22317 , 2025
    2025
    Citations: 4
  • Fractional dual phase-lag Model for Vibration analysis of a generalized nonlocal thermoelastic microbeam based on a Pasternak foundation
    A Zakria, A Yahya, E Salih, AA Hassan, SA Ahmed, IE Ahmed, ...
    International Journal of Thermofluids 28, 101314 , 2025
    2025
    Citations: 3
  • Investigation of the mechanical, microstructure, thermal and gamma-ray attenuation characteristics of serpentinite and ilmenite-based concrete as bio-shielding materials
    A Yahya, A Mounir, S Al-Motori, A Moustafa, M Kohail, B Raab, AM Soltan, ...
    Materials Today Communications 47, 113107 , 2025
    2025
    Citations: 8
  • Vibration analysis of a generalized thermoelastic microbeam based on a Pasternak foundation with dual phase-lag model
    A Zakria, A Yahya, A Saidi, M Suhail, MNA Rabih, ...
    Engineering Research Express 7 (2), 025108 , 2025
    2025
    Citations: 4
  • Response of generalized thermoelastic for free vibration of a solid cylinder with voids under a dual-phase lag model
    A Yahya, A Saidi
    Iranian Journal of Science and Technology, Transactions of Mechanical … , 2025
    2025
    Citations: 7
  • Multiple Targets CFAR Detection Performance Based on an Intelligent Clustering Algorithm in K-Distribution Sea Clutter
    AYAA Mansoor M. Al-dabaa 1, Eugen Laslo , Ahmed A. Emran
    sensor 25 (2613), 14 , 2025
    2025
    Citations: 5

MOST CITED SCHOLAR PUBLICATIONS

  • Mesoporous silica nanoparticles: Their potential as drug delivery carriers and nanoscavengers in Alzheimer's and Parkinson's diseases
    MS Attia, A Yahya, NA Monaem, SA Sabry
    Saudi Pharmaceutical Journal 31 (3), 417-432 , 2023
    2023
    Citations: 43
  • Design of Multi-band Microstrip Patch Antennas for Mid-band 5G Wireless Communication
    ahmed yahya karima mazen, ahmed emran, ahmed salah shalaby
    International Journal of Advanced Computer Science and Applications (IJACSA … , 2021
    2021
    Citations: 31
  • A Survey on Machine Learning Techniques in Smart Grids Based on Wireless Sensor Networks
    MS ASHRAF M. ETMAN1 , MOHAMED S. ABDALZAHER , AHMED A. EMRAN , AHMED YAHYA
    IEEE ACCESS 13, 2604-2627 , 2025
    2025
    Citations: 30
  • Generalized thermoelastic responses in an infinite solid cylinder under the thermoelastic-diffusion model with four lags
    AE Abouelregal, H Ahmad, AMH Yahya, A Saidi, H Alfadil
    Chinese Journal of Physics 76, 121-134 , 2022
    2022
    Citations: 30
  • Pharmaceutical polymers and P-glycoprotein: Current trends and possible outcomes in drug delivery
    MS Attia, MT Elsebaey, G Yahya, H Chopra, MA Marzouk, A Yahya, ...
    Materials Today Communications 34, 105318 , 2023
    2023
    Citations: 28
  • Re-Learning EXP3 Multi-Armed Bandit Algorithm for Enhancing The Massive IoT-LoRaWAN Network Performance
    G samar adel almarzoqi, Ahmed Yahya, Zaki Matar
    Sensor, communication 22 (1603) , 2022
    2022
    Citations: 27
  • Comparison of different backpropagation training algorithms using robust M-estimators performance functions
    AR Abd Ellah, MH Essai, A Yahya
    2015 Tenth International Conference on Computer Engineering & Systems (ICCES … , 2015
    2015
    Citations: 27
  • Optimization of microstructure of basalt-based fibers intended for improved thermal and acoustic insulations
    M Farouk, AM Soltan, S Schlüter, E Hamzawy, A Farrag, A El-Kammar, ...
    Journal of Building Engineering 34, 101904 , 2021
    2021
    Citations: 26
  • A decision support healthcare system based on IoT and neural network technique
    KAS Fahmy, A Yahya, M Zorkany
    Journal of Engineering, Design and Technology 20 (3), 727-748 , 2022
    2022
    Citations: 22
  • Performance Evaluation of IoT Messaging Protocol Implementation for E-Health Systems
    a yahya zorkany, fahmy
    International Journal of Advanced Computer Science and Applications (IJACSA … , 2019
    2019
    Citations: 20
  • Thermoelastic responses in rotating nanobeams with variable physical properties due to periodic pulse heating
    AMH Yahya, AE Abouelregal, KM Khalil, D Atta
    Case Studies in Thermal Engineering 28, 101443 , 2021
    2021
    Citations: 19
  • Pulse Shape Discrimination Techniques based on Cross-correlation and Principal Component Analysis.
    MA A yahya, H saleh, M sayed
    International Journal of Computer Applications (0975–8887) 38 (5), 6-11 , 2012
    2012
    Citations: 17
  • Keterangan dokumen dalam bentuk digital di Mahkamah Syariah: Analisis berkaitan definisi serta kebolehterimaannya di sisi prinsip Syariah di Malaysia
    A Yahya, A Azam, A Hassan
    UKM Journal Article Repository 1, 1-12 , 2017
    2017
    Citations: 15
  • Performance evaluation of blackhole attack on vanet's routing protocols
    EF Ahmed, RA Abouhogail, A Yahya
    International Journal of Software Engineering and Its Applications 8 (9), 39-54 , 2014
    2014
    Citations: 15
  • A framework for classifying breast cancer based on deep features integration and selection
    AM Hassan, A Yahya, A Aboshosha
    Neural Computing and Applications, 9 , 2023
    2023
    Citations: 13
  • Robust backpropagation learning algorithm study for feed forward neural networks
    ARA Ellah, MH Essai, A Yahya
    Master’s Thesis , 2016
    2016
    Citations: 12
  • Energy-aware architecture for multi-rate ad hoc networks
    A Yahya
    Egyptian Informatics Journal 11 (1), 33-38 , 2010
    2010
    Citations: 12
  • Optimized Image Compression Techniques for the Embedded Processors
    AA Al-hamid, A Yahya, RA El-Khoribi
    International Journal of Hybrid Information Technology 9 (1), 319-328 , 2016
    2016
    Citations: 11
  • Investigation of the mechanical, microstructure, thermal and gamma-ray attenuation characteristics of serpentinite and ilmenite-based concrete as bio-shielding materials
    A Yahya, A Mounir, S Al-Motori, A Moustafa, M Kohail, B Raab, AM Soltan, ...
    Materials Today Communications 47, 113107 , 2025
    2025
    Citations: 8
  • Electrical, microstructural and physical characteristics of talc-based cordierite ceramics
    A Yahya, AM Soltan, R Mahani, B El-Kaliouby, S Kenawy, EMA Hamzawy
    Silicon 15 (6), 2901-2919 , 2023
    2023
    Citations: 8