Assist. Prof. Dr. Lechen Li | Signal Processing | Best Researcher Award

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Assist. Prof. Dr. Lechen Li | Signal Processing | Best Researcher Award 

Assist. Prof. Dr. Lechen Li, Hohai University, China

Lechen Li is an Assistant Professor at the College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, China. He received his Ph.D. in Engineering Mechanics from Columbia University in 2023, where his research focused on smart grid development, data-driven system control, and computational structural dynamics. Prior to that, he earned an M.S. in Data Science from Columbia University, supported by the Robert A.W. and Christine S. Carleton Scholarship, and a B.S. in Engineering Mechanics from Sichuan University, where he won the First Prize in the Zhou Peiyuan National Mechanics Modeling Contest. His current research spans infrastructure intelligent monitoring, data-driven seepage analysis, computational dynamics, and renewable energy optimization. Dr. Li has presented award-winning work at major international conferences such as the 8th World Conference on Structural Control and Monitoring and has collaborated on interdisciplinary projects involving smart electricity networks and structural health monitoring.

Professional Profile:

SCOPUS

🏅 Summary of Suitability for Best Researcher Award

Dr. Lechen Li is a highly promising and accomplished early-career researcher whose interdisciplinary work spans Engineering Mechanics, Data Science, and Infrastructure Monitoring. His exceptional academic background, innovative research in intelligent structural control, and impactful contributions to renewable energy systems make him an excellent candidate for the Best Researcher Award.

🎓 Education

  • Ph.D. in Engineering Mechanics
    Columbia University, USA (09/2019 – 06/2023)
    🔍 Research: Smart Grid, Structural Dynamics, Signal Processing
    📊 GPA: 3.889/4.0

  • M.S. in Data Science
    Columbia University, USA (09/2018 – 08/2019)
    💡 Focus: Neural Networks, Dynamical Systems
    📚 Robert A.W. and Christine S. Carleton Scholarship
    📊 GPA: 3.917/4.0

  • B.S. in Engineering Mechanics
    Sichuan University, China (09/2014 – 06/2018)
    🧠 Strong foundation in Mechanics & Modelling
    📊 GPA: 3.6/4.0
    🥇 First Prize in Zhou Peiyuan National Mechanics Modeling Contest (2017)
    🏅 First Prize Scholarship (Twice between 2014–2016)

🧑‍🏫 Current Position

  • Assistant Professor
    College of Water Conservancy and Hydropower Engineering, Hohai University, China (06/2023 – Present)
    🌊 Research: Infrastructure Intelligent Monitoring, Seepage Control, Computational Dynamics, Renewable Energy.

🧪 Research & Projects

  • Structural Control & Health Monitoring
    Columbia University (2021 – 2023)
    🤖 Developed a Generalized Auto-Encoder (GAE) for damage detection
    🎙️ Presented at 8WCSCM, awarded Best Conference Paper (2022) 🏆

  • Smart Grid for Residential Buildings
    Columbia University (2019 – 2020)
    ⚡ Built ConvLSTM neural network for intelligent load control
    📈 Improved forecasting accuracy by 16%

💼 Industry Experience

  • Data Research Analyst
    CICT, Colombo, Sri Lanka (12/2017 – 03/2018)
    🚢 Applied ML for port logistics & road paving optimization
    🔁 Designed reinforcement learning system for transportation planning

  • CAE Analyst
    National Institute of Water, Energy & Transportation, China (06/2016 – 08/2016)
    🏗️ Simulated pile-soil stress using XFEM
    🔍 Assessed 70% lateral pressure effects on platform-supported pile groups

🏅 Achievements, Awards & Honors

  • 🥇 Best Conference Paper Award, 8WCSCM (2022)

  • 📚 Robert A.W. and Christine S. Carleton Scholarship (2018)

  • 🧠 First Prize, Zhou Peiyuan National Mechanics Modeling Contest (2017)

  • 🎖️ Sichuan University First Prize Scholarship (2014–2016, twice)

Publication Top Notes:

Experimental Study on Dynamic Characteristics of Coarse-Grained Materials and Its Application on Numerical Analysis for Permanent Deformation of Rockfll Dams

Dr. Xinxin Ouyang | Passive Location Awards | Best Researcher Award

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Dr. Xinxin Ouyang | Passive Location Awards | Best Researcher Award 

Dr. Xinxin Ouyang, National Key Laboratory on Blind Signal Processing, China

Dr. Xinxin Ouyang is a distinguished researcher specializing in signal processing and geolocation techniques. He earned his B.S. (2010), M.S. (2013), and Ph.D. (2017) degrees from the University of Electronic Science and Technology of China (UESTC). Dr. Ouyang has made significant contributions to the field of Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) estimation algorithms, particularly for frequency-hopping signals in challenging environments such as flat fading channels. His work has been published in prominent journals, including IET Signal Processing, IET Communications, and Electronics, and he has presented his findings at esteemed conferences like IEEE ChinaSIP. Dr. Ouyang’s research addresses cutting-edge challenges in coherent integration methods and their applications in geolocation and signal analysis, positioning him as a key contributor to advancements in modern signal processing.

Professional Profile:

SCOPUS

Summary of Suitability for the Best Researcher Award

Dr. Xinxin Ouyang is a highly qualified researcher with a robust academic and professional background in signal processing, TDOA/FDOA estimation methods, and coherent integration techniques. Below are the key points supporting his suitability for the Best Researcher Award.

📚 Education

  • B.S. in Electronic Science and Technology, University of Electronic Science and Technology of China (2010)
  • M.S. in Electronic Science and Technology, University of Electronic Science and Technology of China (2013)
  • Ph.D. in Electronic Science and Technology, University of Electronic Science and Technology of China (2017)

💼 Work Experience

  • Specialized researcher in signal processing and geolocation techniques, with expertise in TDOA/FDOA estimation methods for frequency-hopping and frequency-shift keying signals.
  • Contributor to advancements in geolocation in flat fading channels and coherent integration for improved signal estimation techniques.

🏆 Achievements and Contributions

  • Published 6 peer-reviewed articles in prestigious journals and conferences, including:
    • IET Signal Processing (2017)
    • Electronics (2022, 2023, 2025)
    • IET Communications (2020)
    • IEEE ChinaSIP (2015)
  • Developed innovative algorithms for geolocation and signal processing, significantly improving TDOA/FDOA estimation accuracy and reliability.
  • Contributed to the foundational understanding of the Cramer-Rao Bound for TDOA estimation in challenging environments.

🏅 Awards and Honors

  • Recognized for excellence in research contributions in signal processing by leading academic organizations.
  • Honored for presenting groundbreaking findings at the IEEE ChinaSIP Conference (2015).

Publication Top Notes:

Multiple Signal TDOA/FDOA Joint Estimation with Coherent Integration

A Coherent Integrated TDOA Estimation Method for Target and Reference Signals

TDOA/FDOA estimation algorithm of frequency-hopping signals based on CAF coherent integration

A Phase Delay Estimation Algorithm of Frequency Hopping Signal Based on Chinese Reminder Theorem

A Novel Method for Unambiguous DFO Estimation of Radar Signals in LEO Dual-Satellite Geolocation System

Dr. Longbin Jin | Signal Processing Awards | Best Researcher Award

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Dr. Longbin Jin | Signal Processing Awards | Best Researcher Award 

Dr. Longbin Jin, Konkuk University, South Korea

Longbin Jin, is a Ph.D. candidate in Computer Science at Konkuk University, Korea, with an expected graduation in February 2025. His research focuses on adaptive visual prompting for video action recognition in vision-language models under the guidance of Professor Eun Yi Kim. He holds a Master’s degree in Smart ICT Convergence and a Bachelor’s degree in Mechanical Engineering & Automation from Shanghai University, China. Throughout his academic career, Longbin has received numerous accolades, including winning the ICASSP 2023 SPGC Challenge and multiple Excellence and Encouragement Prizes at the Korea Software Congress. Currently, he serves as an AI Researcher at Voinosis in Seoul, where he develops AI models for early detection of hearing loss and cognitive impairment in the elderly. He is also an instructor at Konkuk University, teaching courses on Artificial Intelligence, Computer Vision, and Machine Learning. His project experience includes collaborations on medical imaging and virtual reality, demonstrating his expertise in applying AI technologies across diverse fields. Longbin is proficient in English, Chinese, and Korean, reflecting his international background and commitment to advancing technology in healthcare and education.

Professional Profile:

GOOGLE SCHOLAR

Research for Community Impact Award: Longbin Jin’s Suitability

Longbin Jin is a highly qualified candidate for the Research for Community Impact Award due to his significant contributions in the fields of artificial intelligence and healthcare, particularly in projects that directly benefit the community.

📚 Education

  • Ph.D. in Computer Science
    Konkuk University, Korea
    Expected: February 2025
    Thesis: Adaptive Visual Prompting for Video Action Recognition in Vision-Language Models
    Advisor: Prof. Eun Yi Kim
  • M.S. in Smart ICT Convergence
    Konkuk University, Korea
    Graduated: August 2020
    Thesis: E-EmoticonNet: EEG-based Emotion Recognition with Context Information
    Advisor: Prof. Eun Yi Kim
  • B.S. in Mechanical Engineering & Automation
    Shanghai University, China
    Graduated: August 2018

💼 Work Experience

  • AI Researcher
    Voinosis, Seoul, Korea
    December 2022 – Present

    • Researcher on AI models for early detection of hearing loss and cognitive impairment based on voice analysis for the elderly (VoiceCheck & BrainGuardDoctor Apps).
  • Instructor
    Konkuk University, Seoul, Korea
    March 2022 – Present

    • Teaching courses on Computer Vision, Artificial Intelligence, and Machine Learning.
  • AI Engineer
    Lulla, Seoul, Korea
    October 2022 – November 2022

    • Main researcher for an AI model for a child face-matching system to assist kindergarten teachers (Lulla App).

🏆 Achievements, Awards, and Honors

  • Winner of ICASSP 2023 SPGC Challenge: Multilingual Alzheimer’s Dementia Recognition through Spontaneous Speech (First Author) 🥇
  • Excellence Prize, Korea Software Congress 2023 🥇
  • Encouragement Prize, ACM Student Research Competition, Computer Human Interaction 2020 (First Author) 🎖️
  • Excellence Prize, Korea Software Congress 2019 (First Author) 🏅
  • Encouragement Prize, Korea Software Congress 2019 (First Author) 🎖️
  • Excellent Presentation, International Conference on Culture Technology 2018 🌟

Publication Top Notes:

Interpretable Cross-Subject EEG-Based Emotion Recognition Using Channel-Wise Features†

CITED:29

Consen: Complementary and simultaneous ensemble for alzheimer’s disease detection and mmse score prediction

CITED:15

Eeg-based user identification using channel-wise features

CITED:7

E-EmotiConNet: EEG-based emotion recognition with context information

CITED:2

Emotion Recognition based BCI using Channel-wise Features

CITED:1

 

Kim Bjerge | Signal Processing | Best Researcher Award

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Kim Bjerge | Signal Processing | Best Researcher Award

Mr. Kim Bjerge, Aarhus University, Denmark.

Kim Bjerge is an Associate Professor at Aarhus University in the Department of Electrical and Computer Engineering, specializing in Signal Processing and Machine Learning. With a Ph.D. focused on Computer Vision and Deep Learning for Insect Monitoring, Kim combines academic expertise with significant industry experience. He has held various teaching and leadership positions at Aarhus University and has contributed to research projects in computer vision. His work has resulted in a notable H-index of 14 and 1080 citations on Google Scholar. Kim is dedicated to advancing technology in engineering education and research. 🎓💻📈

Publication Profiles 

Googlescholoar

Education and Experience

  • Ph.D. in Computer Vision and Deep Learning for Insect Monitoring (Aarhus University, 2022 – present) 📚
  • M.Sc. Eng. in Information Technology (Aarhus University, 2013) 📖
  • B. Eng. in Electronics Engineering (Engineering College of Aarhus, 1989) 🔧
  • Associate Professor and Group Leader (Aarhus University, 2021 – present) 🎓
  • Associate Professor and Group Leader, Signal Processing (Aarhus University, 2009 – 2020) 📊
  • Senior Consultant, IT-Development (Danish Technological Institute, 2007 – 2009) 🛠️
  • Software Development Manager (TC Electronic A/S, 1999 – 2007) 🎶
  • System Developer (Crisplant A/S, 1996 – 1999) 📦
  • System Manager (Sam-system A/S, 1989 – 1996) 💼

Suitability For The Award

Mr. Kim Bjerge, Associate Professor at Aarhus University’s Department of Electrical and Computer Engineering, is an exemplary candidate for the Best Researcher Award due to his outstanding contributions to computer vision, deep learning, and signal processing. With a remarkable career spanning academia and industry, he has made groundbreaking advancements in the fields of artificial intelligence, embedded systems, and digital signal processing, impacting both research and application development globally.

Professional Development

Kim Bjerge has pursued extensive professional development through various programs. He completed the Pedagogical Programme in Engineering at the Center for Engineering Education Research and Development, earning 10 ECTS credits. Additionally, he participated in project management training at Provinu and various management courses at Aarhus Business College, enhancing his skills in human resources, organizational strategy, and software engineering. His commitment to ongoing learning ensures that he remains at the forefront of engineering education and technology. 📚🔧🌱

Research Focus

Kim Bjerge’s research focuses on the intersection of computer vision, deep learning, and machine learning, particularly in the context of insect monitoring. His work aims to develop innovative solutions that enhance the understanding and management of ecological systems through advanced image analysis and artificial intelligence techniques. By leveraging his expertise in signal processing, he contributes to the development of cutting-edge technologies that have practical applications in various fields, including agriculture and environmental science. 🌱🔍🤖

Publication Top Notes 

  • Deep learning and computer vision will transform entomology – Cited by: 362, Year: 2021 📖
  • Towards the fully automated monitoring of ecological communities – Cited by: 141, Year: 2022 🌱
  • An automated light trap to monitor moths (Lepidoptera) using computer vision-based tracking and deep learning – Cited by: 119, Year: 2021 🦋
  • Real-time insect tracking and monitoring with computer vision and deep learning – Cited by: 110, Year: 2021 📹
  • A computer vision system to monitor the infestation level of Varroa destructor in a honeybee colony – Cited by: 85, Year: 2019 🐝
  • Accurate detection and identification of insects from camera trap images with deep learning – Cited by: 61, Year: 2023 🔍
  • A living laboratory exploring mobile support for everyday life with diabetes – Cited by: 40, Year: 2010 📱
  • Hierarchical classification of insects with multitask learning and anomaly detection – Cited by: 26, Year: 2023 📊
  • Enhancing non-technical skills by a multidisciplinary engineering summer school – Cited by: 19, Year: 2017 🎓

Prof. Shing-Tai Pan | Signal Processing Awards | Best Researcher Award

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Prof. Shing-Tai Pan | Signal Processing Awards | Best Researcher Award 

Prof. Shing-Tai Pan, National University of Kaohsiung, Taiwan

Shing-Tai Pan, is a distinguished academic in the field of computer science and engineering. He earned his M.S. degree in Electrical Engineering from National Sun Yat-Sen University, Kaohsiung, Taiwan, in 1992, followed by a Ph.D. from National Chiao Tung University, Hsinchu, Taiwan, in 1996. Since 2006, he has been a Professor in the Department of Computer Science and Information Engineering at the National University of Kaohsiung, Taiwan. Prof. Pan is an active member of several professional organizations, including the Taiwanese Association for Artificial Intelligence (TAAI), the Chinese Automatic Control Society (CACS), and The Association for Computational Linguistics and Chinese Language Processing (ACLCLP). His research interests encompass biomedical signal processing, digital signal processing, speech recognition, evolutionary computations, artificial intelligence applications, and intelligent control system design.

Professional Profile:

SCOPUS

ORCID

Summary of Suitability for the Best Researcher Award: Shing-Tai Pan

Shing-Tai Pan is a distinguished academic and researcher whose extensive contributions to the fields of biomedical signal processing, speech recognition, and artificial intelligence make him a highly suitable candidate for the Best Researcher Award. With a career spanning over two decades, his work reflects innovation, collaboration, and a commitment to advancing technology for societal benefits.

Education

  1. M.S. in Electrical Engineering
    • Institution: National Sun Yat-Sen University, Kaohsiung, Taiwan
    • Year: 1992
  2. Ph.D. in Electrical Engineering
    • Institution: National Chiao Tung University, Hsinchu, Taiwan
    • Year: 1996

Work Experience

  1. Department of Computer Science and Information Engineering
    • Position: Professor
    • Institution: National University of Kaohsiung, Kaohsiung, Taiwan
    • Joined: 2006

Professional Memberships

  • Taiwanese Association for Artificial Intelligence (TAAI)
  • Chinese Automatic Control Society (CACS)
  • The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)

Research Interests

  • Biomedical Signal Processing
  • Digital Signal Processing
  • Speech Recognition
  • Evolutionary Computations
  • Artificial Intelligence Applications
  • Intelligent Control Systems Design

Publication Top Notes:

Fuzzy‐HMM modeling for emotion detection using electrocardiogram signals

Performance Improvement of Speech Emotion Recognition Systems by Combining 1D CNN and LSTM with Data Augmentation

Editorial for special issue entitled “CACS2020: Applications of emerging intelligent techniques on modeling and control of modern systems”

Editorial for special section “CACS18: Modelling and control for practical systems”

Efficient robust speech recognition with empirical mode decomposition using an FPGA chip with dual core

 

Mr. Rifa Asyari | Signal Processing Awards | Best Researcher Award

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Mr. Rifa Asyari | Signal Processing Awards | Best Researcher Award

Mr. Rifa Asyari, University of Southern Denmark, Denmark.

Rifa Atul Izza Asyari is a highly skilled RF Engineer with over four years of hands-on experience in designing, analyzing, and optimizing advanced RF systems such as radar, RF front-end modules, metasurfaces, and antennas. He is currently pursuing a Ph.D. in Biomedical Engineering at the University of Southern Denmark, with his research focused on radar technology for vital sign monitoring, and is set to graduate in December 2024. He holds an M.Sc. in Telecommunication Engineering from National Sun Yat-Sen University, Taiwan, where he developed high-gain array antennas with frequency-selective surfaces, and a B.Sc. in Electrical Engineering from Universitas Islam Indonesia, Indonesia, with a thesis on optical network design for 4G LTE systems.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award

Rifa Atul Izza Asyari demonstrates an exemplary profile as a highly skilled RF Engineer and researcher with significant contributions in academia and industry. Here’s why Rifa is an excellent candidate for the Best Researcher Award

🎓 Education:

  • PhD in Biomedical Engineering 🏫
    University of Southern Denmark (Odense, Denmark)
    📅 Expected Completion: Dec 2024
    📑 Thesis: Radar for Vital Sign Monitoring
  • MSc in Telecommunication Engineering 📡
    National Sun Yat-Sen University (Kaohsiung, Taiwan)
    📅 June 2019
    📑 Thesis: High Gain Array Antenna with Frequency Selective Surface for Vital Sign Monitoring
  • BSc in Electrical Engineering ⚡
    Universitas Islam Indonesia (Yogyakarta, Indonesia)
    📅 Feb 2016
    📑 Thesis: Optical Network Design for 4G Long Term Evolution Sleman

💼 Work Experience:

  • Senior Hardware R&D Engineer ⚙️ at Pegatron (Batam, Indonesia)
    📅 Sept 2019 – Jan 2022

    • Designed and validated RF front-end WiFi modules and IP cameras.
    • Improved broadband performance by 40% and optimized key metrics like EVM and BER.
    • Conducted DVT, OTA measurements, and troubleshooting for WiFi and 5G communication standards.
  • Fibre Optic Engineer 🌐 at Biznet Networks (Yogyakarta, Indonesia)
    📅 May 2016 – Jun 2017

    • Planned and designed fiber-optic network architectures.
    • Conducted fusion splicing and troubleshooting using OTDR.
  • Heavy Dump Truck Operator 🚛 at Pamapersada Nusantara (Tabalong, Indonesia)
    📅 Jan 2010 – Sept 2012

    • Operated Komatsu trucks for open-cast mining operations.

🏆 Achievements:

  • 🏅 Best Student Paper Award at Taiwan Telecommunication Annual Symposium (2020)
  • 🌟 Top 50 Online Global Startup Weekend Unite to Fight COVID-19 (2020)
  • 📜 IMPTE Scholarship Award at National Sun Yat-Sen University (2017)

🔧 Technical Skills:

  • Programming: 🖥️ MATLAB, Python, C/C++, R
  • RF Tools: 📡 CST, Ansys, ADS, Spectrum Analyzer
  • Networking: 🌐 LAN/WAN, TCP/IP, VPN
  • Soft Skills: 🗣️ Leadership, Problem-solving, Presentation

Publication Top Notes:

High Gain Array Antenna With FSS for Vital Sign Monitoring Through the Wall

Mr. Qiang Yu | Signal Theory Award | Best Researcher Award

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Mr. Qiang Yu | Signal Theory Award | Best Researcher Award

Mr. Qiang Yu, Shanxi Normal University, China

Dr. Qiang Yu is a distinguished professor in the Department of Mathematical Sciences at Shanxi Normal University, China. With a solid academic foundation, Dr. Yu obtained his B.S. in Mathematics Education from Shi He Zi University in 2003, followed by an M.S. in Applied Mathematics and a Ph.D. in Basic Mathematics from Shaanxi Normal University in 2009 and 2014, respectively. His doctoral research, under the guidance of Professor Baowei Wu, focused on nonlinear systems, stability, stabilization, and control. Dr. Yu’s professional career began as a mathematics teacher at Fukang No.1 Senior School in Xinjiang. He then advanced to academic positions at Heng Shui University and Shaanxi Normal University, where he steadily rose through the ranks from Lecturer to Associate Professor, and ultimately to Professor in December 2023. He also served as a visiting professor at the Shibaura Institute of Technology in Japan from 2019 to 2020, collaborating with Prof. Guisheng Zhai.

Professional Profile:

GOOGLE SCHOLAR

Summary of Suitability for Best Researcher Award

Dr. Qiang Yu demonstrates an exceptional academic and professional background that makes him highly suitable for the Best Researcher Award. His educational foundation in mathematics (B.S., M.S., and Ph.D. from Shi He Zi University and Shaanxi Normal University) underpins his rigorous work in complex areas like control theory, dynamical systems, and nonlinear systems. He has further specialized in switched systems, robust control, and time-delay systems—highly relevant research areas with widespread applications in engineering and mathematics.

📚 Academic Background

  • 🎓 B.S. in Mathematics Education (1999-2003) – Shi He Zi University
  • 🎓 M.S. in Applied Mathematics (2006-2009) – Shaanxi Normal University
  • 🎓 Ph.D. in Basic Mathematics (2011-2014) – Shaanxi Normal University
    • 📑 Advisor: Professor Baowei Wu
    • 🔍 Research Theme: Nonlinear Systems, Stability, Stabilization, and Control

🏫 Professional Experience

  • 🧑‍🏫 Math Teacher – Fukang No.1 Senior School, Xinjiang (2003-2006)
  • 📈 Lecturer – Heng Shui University, Hebei (2009-2011)
  • 🎓 Lecturer, Associate Professor, and Professor – Shaanxi Normal University, Shanxi (2014-present)
  • 🌏 Visiting Professor – Shibaura Institute of Technology, Saitama, Japan (2019-2020)

🔬 Research Interests

  • 🛠️ Switched Systems
  • 📏 Robust Control
  • ⏳ Time-Delay Systems
  • 📊 Control Theory and Engineering
  • 🧮 Applied Mathematics
  • 🔄 Dynamical Systems
  • 🤖 Neural Networks

🌐 Professional Memberships and Activities

  • 🔍 Reviewer for Mathematical Reviews
  • 🏅 Member, IEEE Control Systems Society
  • 🧩 Member, Chinese Mathematical Society
  • 🤖 Member, Chinese Association of Automation
  • 🇨🇳 Member, Society of Industry and Applied Mathematics of China
  • ✒️ Editorial Board Member, American Journal of Applied Mathematics (Since 2020)

🏆 Honors and Awards

  • 🥇 First Prize Gardener’s Scholarship, Shaanxi Normal University (2013)
  • 🥈 Second Prize in National College Student Academic Competitions (2013)
  • 🎖️ National Scholarship for Doctoral Students, Ministry of Education of China (2013)
  • 🏅 Excellent Postgraduate Student Award, Shaanxi Normal University (2014)
  • 🥉 Third Prize in Teachers’ Skills Competition, Shanxi Normal University (2015)
  • 🥈 Second Prize in National Mathematics Micro Course Design Competition (2016)
  • 🌟 Advanced Individual for Educational Excellence, Shanxi Normal University (2017)
  • 🥉 Third Prize for Excellent Academic Paper, Shanxi Science and Technology Department (2018)
  • 🥇 First Prize Science and Technology Award, Shaanxi Higher Education Institutions (2019)
  • 🏆 Second Prize of the Natural Science Award, Shaanxi Science and Technology (2022)

Publication top Notes:

Sampled-data synchronization of delayed multi-agent networks and its application to coupled circuit

CITED:62

Analysis of mixed convection flow in an inclined lid-driven enclosure with Buongiorno’s nanofluid model

CITED:56

Stability analysis for discrete-time switched systems with stable and unstable modes based on a weighted average dwell time approach

CITED:45

Coiflets solutions for Föppl-von Kármán equations governing large deflection of a thin flat plate by a novel wavelet-homotopy approach

CITED:39

Robust stability analysis of uncertain switched linear systems with unstable subsystems

CITED:38

Stability analysis of discrete-time switched linear systems with unstable subsystems

CITED:36

Dr. B. Omkar Lakshmi Jagan | Signal Estimation Award | Best Researcher Award

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Dr. B. Omkar Lakshmi Jagan | Signal Estimation Award | Best Researcher Award 

Dr. B. Omkar Lakshmi Jagan, Vignan’s Institute of Information Technology, India

Dr. Banana Omkar Lakshmi Jagan is an accomplished academic and researcher in the field of Statistical Signal Processing, with a Ph.D. from Koneru Lakshmaiah Education Foundation. Currently serving as an Assistant Professor in the Department of Computer Science Engineering at Vignan’s Institute of Information Technology, Dr. Jagan has a diverse teaching and research background. His previous roles include Assistant Professor in Artificial Intelligence and Machine Learning at Malla Reddy University and research positions with the NRB-DRDO projects focused on submarine target motion analysis and performance evaluation of algorithms. With over five years of research experience and nearly two years in teaching, Dr. Jagan has specialized in Deep Learning, Machine Learning, Linux Programming, and IoT. He has also earned additional certifications in Deep Learning and IoT from NPTEL. His commitment to both academic excellence and innovative research drives his career in exploring advanced technologies and methodologies in his field.

Professional Profile:

Suitability for the Best Researcher Award:

Dr. Banana Omkar Lakshmi Jagan has demonstrated significant achievements in research, teaching, and contributions to multiple domains, particularly in Statistical Signal Processing, Machine Learning, Deep Learning, and Target Tracking. Based on his extensive academic background, research projects, and publications, he is a strong candidate for the Best Researcher Award.

Education 

  • Ph.D. in Statistical Signal Processing
    2023
    Koneru Lakshmaiah Education Foundation (Deemed to be University), Andhra Pradesh
  • M.Tech. in Power Systems
    2016
    Koneru Lakshmaiah Education Foundation (Deemed to be University), Andhra Pradesh
  • B.Tech. in Electrical and Electronics Engineering
    2014
    Sri Sivani College of Engineering, JNTU Kakinada, Andhra Pradesh
  • Intermediate (M.P.C)
    2008
    Board of Intermediate Education, Andhra Pradesh
  • Xth Grade
    2006
    Council for the Indian School Examinations, Delhi

Work Experience

  1. Assistant Professor
    Department of Computer Science Engineering
    Vignan’s Institute of Information Technology (A), Duvvada, Visakhapatnam, Andhra Pradesh, India
    May 22, 2024 – Present
  2. Assistant Professor
    Department of Artificial Intelligence and Machine Learning, Department of Computer Science Engineering
    School of Engineering, Malla Reddy University, Hyderabad, Telangana, India
    December 28, 2022 – May 22, 2024
  3. Research Associate (RA)
    NSTL-DRDO Project, Department of Electronics and Communication Engineering
    Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India
    July 17, 2022 – December 27, 2022
    Project Title: Performance Evaluation of all TMA Algorithms for Bot & Calculation of MLA & SOA for Identified Zigging Targets
  4. Senior Research Fellow (SRF)
    NRB-DRDO Project, Department of Electronics and Communication Engineering
    Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India
    July 9, 2021 – January 8, 2022
    Project Title: State of Art Submarine Target Motion Analysis
  5. Junior Research Fellow (JRF)
    NRB-DRDO Project, Department of Electronics and Communication Engineering
    Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India
    July 9, 2019 – July 8, 2021
    Project Title: State of Art Submarine Target Motion Analysis
  6. Junior Research Fellow (JRF)
    NRB-DRDO Project, Department of Electronics and Communication Engineering
    Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India
    July 12, 2016 – July 11, 2018
    Project Title: Advance Submarine Target Motion Analysis

Publication top Notes:

CITED:56
CITED:26
CITED:21
CITED:18
CITED:13
CITED:11

Dr. Zhengjia Xu | Signal Analysis Award | Best Researcher Award

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Dr. Zhengjia Xu | Signal Analysis Award | Best Researcher Award

Dr. Zhengjia Xu, Cranfield University, United Kingdom

Dr. Zhengjia Xu is an accomplished Electronic and Embedded Software Engineer with a rich blend of academic and industrial experience. He holds a Ph.D. in Aerospace from Cranfield University, where his research focused on cognitive communication and intelligent DSP for drone applications. Dr. Xu has a strong track record of over 25 peer-reviewed publications, including influential journal articles and conference papers in fields such as intelligent signal processing and aerospace engineering. Currently, he serves as a Research Fellow at Cranfield University, specializing in position, navigation, and timing systems, and has led several high-profile projects funded by ESA and EPSRC. His prior roles include Senior RF Engineer at Drone Defense Services Ltd, where he made significant advancements in passive RF radar and SDR-based receivers, and Electronic and Embedded Software Engineer at ASH Wireless (Captec LTD), where he developed advanced NB-IoT products and embedded firmware.

Professional Profile:

Summary of Suitability for the Best Researcher Award

Zhengjia Xu has a comprehensive technical and research background, demonstrating expertise in both industrial and academic settings. His experience spans a range of areas including embedded software development, digital system design, RF analysis, and intelligent signal processing.

Education

Ph.D. in Aerospace Engineering
Cranfield University, Cranfield, UK
September 2017 – March 2021

  • Research Area: Cognitive communication, intelligent DSP, drone communication
  • Thesis: “Cognitive Communication for UAV Applications”
  • Proposed a DSP algorithm enabled by deep learning for RF fingerprint identification, simulated air-to-ground communication performance, and proposed system architectures for UAV communications.

M.Sc. in Vehicle Operation Design
Nanjing University of Aeronautics and Astronautics, Nanjing, China
September 2014 – June 2017

  • Research Area: Aircraft modeling and control, fault-tolerant control
  • Simulated aircraft aerodynamic models, developed aircraft control algorithms, and analyzed real flight data. Involved in PCB layout design and the development of flight simulators.

Bachelor in Electrical and Electronics Engineering
Nanjing University of Aeronautics and Astronautics, Nanjing, China
September 2010 – June 2014

  • Thesis: “Research on Stability and Control of Quadratic Aircraft Based on STM32F407”
  • Designed PID-based flight control software on a self-designed PCB board with STM32 MCU.

Work Experience

Research Fellow in Position, Navigation, and Timing
Cranfield University, Bedford, UK
March 2023 – Present

  • Managed projects with stakeholders including Telespazio – Thales UK, European Space Agency, and others.
  • Co-supervised over 10 MSc and PhD student projects.
  • Main researcher for two ESA-funded projects and one EPSRC-funded 6G project.
  • Delivered lectures and developed course materials for 13 hours across four modules.

Senior RF Engineer
Drone Defense Services Ltd, Retford, UK
March 2022 – March 2023

  • Developed passive RF radar products, including software refactoring and hardware integration.
  • Improved radar detection range significantly and led the design of an SDR-based OFDM receiver.
  • Proficient in SDR developments and GPU platform optimization.

Electronic and Embedded Software Engineer
ASH Wireless (Captec LTD), Southampton, UK
March 2021 – March 2022

  • Developed an NB-IoT product for air-to-ground communication.
  • Customized IoT protocol suite and performed schematic design and RF validation.
  • Experienced in STM ARM Cortex-based embedded application developments.

Volunteer, IET Aerospace TN Committee
Institution of Engineering and Technology (IET)
September 2022 – Present

  • Participated in strategic planning and board meetings for the aerospace technical network.

Publication top Notes:

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Dr. Sangyeop Lee | Signal Processing | Best Researcher Award

Posted on by ScienceFather

Dr. Sangyeop Lee | Signal Processing | Best Researcher Award

Dr. Sangyeop Lee, LG Electronics, South Korea

Sangyeop Lee, Ph.D., is a seasoned Senior Researcher and Data Scientist at LG Electronics, currently based at the Life Data Fusion Laboratory within the B2B Advanced Technology Center in Seoul, Republic of Korea. With a robust academic background, including a Ph.D. in Computer Science from Yonsei University, Sangyeop has been actively involved in both research and academia. His research interests span various domains, notably including LLM fine-tuning, artificial neural networks for biomedical signal processing, and context-awareness in the clinical domain using machine learning techniques. Throughout his career, he has contributed significantly to cutting-edge projects such as Smartcare in Kindergarten and neptuNE, addressing critical issues like child behavior monitoring and home healthcare. Sangyeop’s expertise extends to teaching and mentoring, evident from his engagements as a lecturer and teaching assistant at Yonsei University. His dedication to advancing technology and solving real-world problems underscores his commitment to innovation in the fields of data science and healthcare.

Professional Profile

Orcid

 

Affiliation:

Sangyeop is currently affiliated with the LEAD technology task at the Life Data Fusion Laboratory within the B2B Advanced Technology Center at LG Electronics, located in Seocho R&D Campus, Seoul, Republic of Korea.

Research Interests:

His research interests include LLM fine-tuning, artificial neural networks for biomedical signal processing, and context-awareness using machine learning techniques in clinical settings.

Teaching Experience:

Sangyeop has contributed to education as a lecturer and teaching assistant at Yonsei University, covering subjects like AI for Medical Problems and Engineering Information Processing, where he taught Python practice.

Projects:

  1. Smartcare in Kindergarten: Collaborated with DNX Kidsnote and Severance Hospital to utilize AI technology in studying children’s behavior and location in kindergartens using wearables/radars.
  2. neptuNE: Developed sensors and mobile devices for home monitoring, addressing nocturnal enuresis in children, in collaboration with Samsung Electronics and Severance Hospital.
  3. Ready-Made Implant: Conducted a confidential study on mass production with pre-made implants and recommending customized implant models through dental data analysis, in collaboration with Ostem Implant and Yonsei University.

Publications:

Sangyeop has several publications in prestigious conferences and journals, including IEEE Radar Conference and Sensors, focusing on topics like artificial intelligence, biomedical engineering, and healthcare.

Application:

Sangyeop has contributed to the development of in-home monitoring with wearables and NE Diary Application, enhancing healthcare solutions through technology.

Sangyeop’s dedication to advancing data-driven solutions in healthcare underscores his commitment to innovation and improving patient outcomes. 🌟

Publications Notes:📄

Wearable-Based Integrated System for In-Home Monitoring and Analysis of Nocturnal Enuresis

Continuous body impedance measurement to detect bladder volume changes during urodynamic study: A prospective study in pediatric patients