Doctoral Researcher at University of Dschang, Cameroon.
Koagne Longpa Tamo Silas is a Ph.D. student in Physics at Dschang State University, Cameroon, with a specialization in Medical Physics and Embedded Systems. He holds an M.Sc. in Physics (Electronics) from Dschang State University and a DIPET 2 in Electronics from the Higher Technical Teacher Training College, University of Bamenda. With a strong foundation in artificial neural networks, analog electronics, and microcontroller programming, his research focuses on integrating automation and AI in medical physics. In addition to his research, he has extensive teaching experience in electronics and computer science at various technical institutions in Cameroon. His industrial expertise includes electronic circuit design, electrical network maintenance, and embedded system applications.
Publication Profile
Google Scholar
Educational Details
Mr. Koagne Longpa Tamo Silas is currently a Ph.D. student in Physics at Dschang State University, Cameroon, specializing in Medical Physics, where he has been enrolled since December 2022. He previously obtained an M.Sc. in Physics (Electronics) from Dschang State University in July 2022, with a thesis on the Specification and Implementation of Multilayer Perceptron Analog Artificial Neural Networks, under the supervision of Dr. Djimeli Tsajio Alain B. His B.Sc. in Physics was completed in August 2021 at the same university.
Before his graduate studies, he pursued technical education at the Higher Technical Teacher Training College, University of Bamenda, earning:
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DIPET 2 in Electronics (July 2020) with a dissertation on Digital Breath Alcohol Detection System with SMS Alert and Vehicle Tracking on Google Maps, supervised by Prof. Nfah Mbaka Eutace and Dr. Kamdem Kuate Paul Didier.
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DIPET 1 in Electronics (August 2018) with a dissertation on Electronic Attendance System Based on RFID with Automatic Door Unit, supervised by Mr. Kouam Jules.
He also holds a GCE Advanced Level (5 papers, 2015) and a GCE Ordinary Level (8 papers, 2013) from Government Bilingual High School Mbouda. His academic journey began at École Primaire Bilingue de la Promotion Mbouda, where he obtained his First School Leaving Certificate (FSLC) in 2008.
Professional Experience
Mr. Silas has extensive experience in academia and industry. He has been an Electronics Teacher at Government Technical College Ngombo-ku, Cameroon, since January 2021, where he instructs students in circuit design, microcontrollers, and automation. He previously served as a Junior Lecturer in Computer Science at Higher Technical Teacher Training College, Bambili (2019-2020) and an Electronics Teacher at Government Technical High School Bambui (2017-2018).
His industrial experience includes:
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HYTECHS-Yaoundé, Cameroon (2019) – Worked on maintenance of HP INKJET and RICOH printers, electronic printing device repairs, and installation of printing equipment. Supervised by Mr. Nkuimen Tankeu Cedric.
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MEECH CAM Sarl-Yaoundé, Cameroon (2016) – Focused on underground electric cable installation, maintenance of high-voltage network devices, and electrical network line installation. Supervised by Mr. Ndjegnia Franck Enrico.
Research Interest
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Analog Artificial Neural Networks
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Digital and Analog Electronics
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Embedded Systems and Microcontroller Programming
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Circuit Simulation (SPICE, Cadence Virtuoso)
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Electronic Design Automation (EDA)
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Analog Signal Processing
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Electronics and Communication Systems
Author Metrics
Mr. Silas is an emerging researcher in Medical Physics and Electronics, contributing to research on artificial neural networks, embedded systems, and medical automation. His work has been supervised by esteemed faculty at Dschang and Bamenda universities. As he advances in his Ph.D. studies, his publications and contributions to the field are expected to grow in impact within scientific and engineering communities.
Top Noted Publication
1. A High-Resolution Non-Volatile Floating Gate Transistor Memory Cell for On-Chip Learning in Analog Artificial Neural Networks
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Authors: KLT Silas, DTA Bernard, FT Bernard, L Jean-Pierre, GW Ejuh
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Year: 2025
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Research Focus:
This paper presents the design and implementation of a high-resolution, non-volatile floating gate transistor memory cell, optimized for on-chip learning in analog artificial neural networks (ANNs). The study focuses on developing efficient, low-power, and high-precision memory architectures tailored for ANN applications, particularly in medical diagnostics and real-time data processing.
2. Breast Cancer Diagnosis with Machine Learning Using Feed-Forward Multilayer Perceptron Analog Artificial Neural Network
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Authors: B Djimeli-Tsajio Alain, KLT Silas, LT Jean-Pierre, N Thierry, GW Ejuh
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Year: 2024
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Research Focus:
This study explores the application of feed-forward multilayer perceptron (MLP) analog artificial neural networks (ANNs) for breast cancer diagnosis. The model utilizes machine learning techniques to enhance diagnostic accuracy, reducing false positives and false negatives in mammography analysis. The findings demonstrate the potential of ANN-based medical imaging solutions in early cancer detection and precision medicine.
3. Design and Implementation of a Digital Breath Alcohol Detection System with SMS Alert and Vehicle Tracking on Google Map
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Author: KLT Silas
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Year: 2020
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Research Focus:
This project details the development of a digital breath alcohol detection system that integrates an SMS alert mechanism and real-time vehicle tracking using Google Maps. The system is designed to improve road safety by detecting alcohol levels in drivers and alerting authorities or emergency contacts. The integration of embedded systems, microcontrollers, and GPS technology makes it a valuable tool for transportation safety enforcement.
4. Design and Realization of an Electronic Attendance System Based on RFID with an Automatic Door Unit
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Author: MK Jules
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Contributor: KLT Silas
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University: University of Bamenda
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Year: 2018
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Research Focus:
This paper presents an RFID-based electronic attendance system with an automatic door control unit, aimed at enhancing security and automation in institutional environments. The system automatically logs student or staff attendance and grants access based on RFID authentication, improving accuracy and eliminating manual attendance tracking.
Conclusion
Mr. Koagne Longpa Tamo Silas is a strong candidate for the Innovative Research Award due to his pioneering work in analog artificial neural networks, medical AI applications, and embedded systems. His contributions to AI-driven medical diagnostics and ANN memory design demonstrate a forward-thinking approach to AI and electronics integration.
To further strengthen his candidacy, publishing in high-impact journals, increasing citations, pursuing patents, and collaborating on interdisciplinary AI projects would enhance the global impact of his work. Nonetheless, his innovative research in analog ANNs and automation technologies makes him a deserving nominee for this award.