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

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

 

Dr. Fahman Saeed | Signal Distortion Awards | Best Researcher Award

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Dr. Fahman Saeed | Signal Distortion Awards | Best Researcher Award 

Dr. Fahman Saeed, Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia

Dr. Fahman Saeed is an Assistant Professor in the College of Computer and Information Sciences at Imam Mohammad Ibn Saud Islamic University (IMSIU) in Riyadh, Saudi Arabia. With a Ph.D. in Computer Science from King Saud University, his research focuses on deep learning models, particularly for automatic diabetic retinopathy screening. He has contributed significantly to various research projects, including the development of fingerprint interoperability solutions and privacy-protected breast cancer screening systems, earning multiple ISI papers, patents, and conference presentations. Dr. Saeed also has extensive experience in machine learning, specializing in PyTorch, TensorFlow, and large language models. In addition to his academic achievements, he actively participates in professional activities, such as curriculum development and leading workshops on AI, NLP, and generative AI. His dedication to education and research, coupled with his expertise in artificial intelligence, continues to influence both his academic institution and the broader scientific community.

Professional Profile:

ORCID

Suitability for Best Researcher Award: Fahman Saeed

Fahman Saeed is exceptionally suited for the Best Researcher Award due to his outstanding contributions to the field of computer science, particularly in the areas of deep learning, machine learning, and artificial intelligence. With a robust academic background and extensive experience in both research and teaching, Dr. Saeed has demonstrated leadership in advancing the application of machine learning technologies in critical areas like medical diagnostics and data security.

Education 🎓

  • Ph.D. in Computer Science
    • Institution: King Saud University, Saudi Arabia 🎓
    • Graduation: November 2021 📅
    • Dissertation: Developing an auto deep learning model with less complexity and high performance for automatic diabetic retinopathy screening 🧠💻
  • M.Sc. in Computer Science
    • Institution: King Saud University, Saudi Arabia 🎓
    • Graduation: May 2014 📅
  • B.Sc. in Computer Science
    • Institution: King Saud University, Saudi Arabia 🎓
    • Graduation: February 2007 📅

Academic Experience 📚

  • Assistant Professor
    • Institution: College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia 🏫
    • Duration: 2022 to Present ⏳
    • Responsibilities: Teaching courses in Artificial Intelligence 🤖, Natural Language Processing 💬, Algorithm Design and Analysis 💻, Image Processing 🖼️, and Computer Vision 👀
  • Lecturer (Part-time)
    • Institution: King Saud University, Riyadh, Saudi Arabia 🎓
    • Duration: 2017 to 2021 ⏳
  • Researcher
    • Institution: King Saud University, Riyadh, Saudi Arabia 🧪
    • Duration: March 2015 to 2021 ⏳
    • Projects:
      • Automatic Diabetic Retinopathy Screening 🩺👁️
        • Achievements: Two ISI papers 📄
      • Identification of Fingerprint Interoperability 🧑‍⚖️
        • Achievements: One patent, one ISI paper, two conference papers 📑
      • Cloud-Based Privacy-Protected Computer-Aided Diagnosis System for Breast Cancer Screening 🩻
        • Achievements: One ISI paper 📄

Publication Top Notes

Adaptive Renewable Energy Forecasting Utilizing a Data-Driven PCA-Transformer Architecture

Blockchain-Based Quality Assurance System for Academic Programs
Optimal Sizing and Placement of Distributed Generation under N-1 Contingency Using Hybrid Crow Search–Particle Swarm Algorithm
A Data-Driven Convolutional Neural Network Approach for Power Quality Disturbance Signal Classification (DeepPQDS-FKTNet)

Designing the Architecture of a Convolutional Neural Network Automatically for Diabetic Retinopathy Diagnosis

 

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

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:

CITED:15
CITED:13
CITED:12
CITED:8
CITED:8

Mr. Yeonjae Park | Signal Cleaning Award | Best Scholar Award

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Mr. Yeonjae Park | Signal Cleaning Award | Best Scholar Award

Mr. Yeonjae Park, The Graduate School of Yonsei University, South Korea

Yeonjae Park is a Master’s student at Yonsei University in the Department of Medical Informatics and Biostatistics, under the guidance of Professor Dae Ryong Kang. With a strong foundation in Computer and Telecommunication Engineering as well as Information and Statistics, Park obtained dual B.S. degrees from Yonsei University, where they were mentored by Professors Cho Young-rae and Na Seongyong. Their research interests span machine learning, deep learning, generative models, multi-modal data analysis, and time series forecasting. Park has gained valuable research experience through various positions, including as a researcher intern at the Artificial Intelligence-Information Retrieval Lab, a researcher at the Applied Data Science Lab, and their current role at the National Health BigData Clinical Research Institute. Their projects encompass a range of topics, from text extraction and OCR recognition to complex analyses in genomics, disease correlations, and the effectiveness of medical treatments.

Professional Profile:

Summary of Suitability for Best Scholar Award:

Yeonjae Park has a strong academic foundation, holding dual Bachelor’s degrees in Computer and Telecommunication Engineering and Information and Statistics from Yonsei University, one of South Korea’s most prestigious institutions. Currently, Yeonjae is pursuing a Master’s degree in Medical Informatics and Biostatistics at the same university, under the guidance of a notable advisor, Dae Ryong Kang.

Education 📚

  • Samseon Middle School, Seoul, Korea (Mar. 2010 ~ Jul. 2010)
  • SungSan Middle School, Seoul, Korea (Jul. 2010 ~ Feb. 2013)
  • Kwangsung High School, Seoul, Korea (Mar. 2013 ~ Feb. 2016)
  • Yonsei University, Department of Computer and Telecommunication Engineering 🖥️ (Mar. 2016 ~ Aug. 2021)
    • B.S. in Computer and Telecommunication Engineering
    • Advisor: Prof. Cho Young-rae
  • Yonsei University, Department of Information and Statistics 📊 (Feb. 2016 ~ Aug. 2021)
    • B.S. in Information and Statistics
    • Advisor: Prof. Na Seongyong
  • Yonsei University, Department of Medical Informatics and Biostatistics 🧬 (Aug. 2021 ~ Present)
    • Master Student
    • Advisor: Prof. Dae Ryong Kang

Research Interests 🔍

  • Machine Learning / Deep Learning 🤖
  • Generative Models 🌀
  • Multi Modal 🧠
  • Time Series Forecasting ⏳

Research Experiences 💼

  • Researcher Intern at Artificial Intelligence-Information Retrieval Lab, Yonsei University, Korea (May. 2019 ~ Apr. 2020)
  • Researcher at Applied Data Science Lab, Yonsei University, Korea (May. 2020 ~ Jan. 2021)
  • Researcher at National Health BigData Clinical Research Institute, Korea (Jan. 2021 ~ Present)

 

Publication top Notes:

Development and Validation of a Real-Time Service Model for Noise Removal and Arrhythmia Classification Using Electrocardiogram Signals

Intracardiac Echocardiogram: Feasibility, Efficacy, and Safety for Guidance of Transcatheter Multiple Atrial Septal Defects Closure

 

 

 

Mrs. Ainhoa Osa Sanchez | Signal processing | Best Researcher Award

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Mrs. Ainhoa Osa Sanchez | Signal processing | Best Researcher Award 

Mrs. Ainhoa Osa Sanchez, EVIDA Research Group, University of Deusto, Spain

Ainhoa Osa Sánchez is a dedicated researcher specializing in sensors for biological applications. Born on April 11, 1999, she completed her degree in Industrial Electronics and Automation Engineering from the University of Deusto in 2021 and earned a Master’s degree in Industry 4.0 from the International University of La Rioja in 2022. Since 2022, she has been an active member of the eVIDA group, where she initially joined as a researcher in 2020.During her academic and professional journey, Ainhoa has collaborated in various capacities, including internships and research positions, focusing on advanced technological solutions. Her master’s thesis revolved around telemonitoring vital signs at home for the elderly, utilizing IoT and 3D design with a serverless architecture for data storage and visualization via Amazon Web Services.Currently, Ainhoa is pursuing her doctorate at the University of Deusto. Her research is centered on using portable EEG and NIR sensor signals for pain detection case studies, incorporating artificial intelligence models. This work has significant implications for elderly care, chronic pain, and fibromyalgia. She is also engaged in a collaborative project with the University of Louisville and Alamein International University to develop a neural network aimed at identifying the degree of macular degeneration through image analysis.

Professional Profile:

GOOGLE SCHOLAR

Education

Degree: Master’s Degree in Industry 4.0
University / Country: International University of La Rioja
Year: 2022

Degree: Degree in Industrial Electronics and Automatic Engineering
University / Country: University of Deusto
Year: 2021

Skills and Abilities:

  • Advanced understanding of computing, IoT, and artificial intelligence
  • Ability to use data structures to improve programming results
  • Excellent knowledge of several programming languages, including Java, C, and Python
  • Very good knowledge of big data and cybersecurity
  • Experience using and analyzing data from wearable biomedical devices such as EEG, fNIRS, and EMG

 Relevant Accomplishments

C.1. Most Important Publications in National or International Peer-Reviewed Journals, Books, and Conferences

Scientific Papers:

  1. Ainhoa Osa-Sanchez, Oscar Jossa-Bastidas, Amaia Mendez-Zorrilla, Ibon Oleagordia-Ruiz, Begonya Garcia-Zapirain. 2023. “Design of intelligent monitoring of loneliness in the elderly using a serverless architecture with real-time communication API.” Technology and Health Care, IOS Press. 31-6, pp. 2401-2409.
  2. Jossa-Bastidas, Oscar, Osa Sanchez, Ainhoa, Bravo-Lamas, Leire, Garcia-Zapirain, Begonya. 2023. “IoT System for Gluten Prediction in Flour Samples Using NIRS Technology, Deep and Machine Learning Techniques.” Electronics, 12-8. ISSN 2079-9292.

Publication top Notes:

IoT system for gluten prediction in flour samples using nirs technology, Deep and Machine Learning Techniques

CITED : 1

Design and implementation of food quality system using a Serverless Architecture: case study of gluten intolerance

CITED : 1

Gluten Analysis Composition Using Nir Spectroscopy and Artificial Intelligence Techniques

CITED : 1

Design of intelligent monitoring of loneliness in the elderly using a serverless architecture with real-time communication API

 

 

Dr. Sangyeop Lee | Signal Processing | Best Researcher Award

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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

 

 

 

Fengshou Gu | Signal Processing Award | Best Researcher Award

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Prof Dr. Fengshou Gu | Signal Processing Award | Best Researcher Award

Professor at University of Huddersfield – The Institute of Railway Research (IRR) – Huddersfield, United Kingdom

Professor Fengshou Gu is a highly accomplished researcher and academic with a distinguished career in the field of condition monitoring and diagnostics. With over 30 years of experience, he has made significant contributions to developing advanced monitoring and diagnostic techniques, numerical simulation methods, and signal processing techniques. His research has focused on various areas, including machine modeling, fault diagnosis, energy harvesting, and wireless sensor networks. Professor Gu’s work has been published in numerous prestigious journals, and he has presented his research at international conferences. He has also supervised over 100 PhD students and examined many more worldwide. Overall, Professor Gu’s expertise, innovative research, and dedication to advancing the field of condition monitoring and diagnostics make him a highly respected figure in the academic and research community.

Professional Profile

Education:

Professor Fengshou Gu’s academic journey began at Taiyuan University of Technology in Shanxi, China, where he earned his Bachelor of Science (B.S.) in Mechanical Engineering, graduating in September 1979. He continued his studies at the same institution, completing his Master of Science (M.Sc.) in the Mechanical Department from January 1981 to March 1985. Professor Gu pursued his doctoral studies at the University of Manchester, United Kingdom, where he obtained his Doctorate (Dr.) from the School of Mechanical Engineering from August 2004 to September 2008.

Work Experiences:

Professor Fengshou Gu has accumulated a wealth of experience throughout his career, starting with his tenure as a Lecturer in Vibration and Acoustics at Taiyuan University of Technology, China, from January 1985 to June 1991. Following this, he served as a Research Engineer at the University of Manchester, U.K., from July 1991 to October 1996. His role evolved to Senior Research Engineer at the same institution, where he continued his impactful work until September 2007. Since then, Professor Gu has held the positions of Principal Research Fellow, Professor, Head of MDARG (Machine Diagnostics, Dynamics, and Artificial Intelligence Research Group), and Deputy Director of CEPE (Centre of Excellence for Precision Engineering), solidifying his reputation as a leading expert in condition monitoring and diagnostics.

Skills:

Professor Fengshou Gu possesses a diverse range of skills that have been instrumental in his research and academic endeavors. He is proficient in numerical analysis, particularly in the context of friction stir welding, as evidenced by his review publications in this area. His expertise also extends to predictive modeling for biodiesel properties and their impact on engine performance, highlighting his strong background in engineering analysis and modeling. Additionally, Professor Gu has a deep understanding of machine condition monitoring, demonstrated by his work on energy harvesting technologies for self-powered wireless sensor networks and his research on diesel engine combustion characteristics. His skills also encompass signal processing techniques, including acoustic measurements and independent component analysis for fault diagnosis in mechanical equipment. Professor Gu’s proficiency in thermal imaging enhancement and modal analysis further underlines his expertise in machinery fault diagnosis. Overall, his skills in numerical analysis, predictive modeling, condition monitoring, and signal processing have contributed significantly to his impactful research contributions.

Achievements:

Professor Fengshou Gu has achieved numerous milestones in the field of condition monitoring and diagnostics, showcasing his exceptional expertise and innovative contributions. He has developed groundbreaking techniques such as single-channel Blind Source Separation (BSS) for acoustic source separation and the MSB-SE nonlinear modulation analysis theory, which have significantly advanced the field. His pioneering work on On-Rotor Sensing (ORS) based dynamic measurement and analysis theory has revolutionized dynamic measurement approaches. Professor Gu’s research has also led to the establishment of vibro-acoustic models (AAC, FAS) for tribological systems and diagnostic approaches, enhancing the understanding and diagnosis of complex machinery. Additionally, he has made significant contributions to online Operational Modal Analysis (OMA) with his Correlation Signal Cluster-based Stochastic Subspace Identification (CSC-SSI) method, applicable to both linear and nonlinear systems. Professor Gu’s innovative work extends to the development of instantaneous electric signature analysis for motor-driven system monitoring, nonlinear dynamic-based energy harvesting concepts, and thermal energy-based self-powered wireless sensor networks, showcasing his commitment to advancing sustainable and efficient monitoring technologies. His research on the nonlinear temperature field distribution of infrared thermal images for machine condition and performance monitoring has further demonstrated his pioneering approach to condition monitoring. Furthermore, Professor Gu has developed remote modal identification techniques based on photogrammetry analysis, highlighting his multidisciplinary and innovative research efforts.

Publications:

A review of numerical analysis of friction stir welding

Authors: X He, F Gu, A Ball

Citations: 542

Year: 2014

Prediction models for density and viscosity of biodiesel and their effects on fuel supply system in CI engines

Authors: B Tesfa, R Mishra, F Gu, N Powles

Citations: 278

Year: 2010

The measurement of instantaneous angular speed

Authors: Y Li, F Gu, G Harris, A Ball, N Bennett, K Travis

Citations: 230

Year: 2005

Energy harvesting technologies for achieving self-powered wireless sensor networks in machine condition monitoring: A review

Authors: X Tang, X Wang, R Cattley, F Gu, AD Ball

Citations: 216

Year: 2018

Detecting the crankshaft torsional vibration of diesel engines for combustion related diagnosis

Authors: P Charles, JK Sinha, F Gu, L Lidstone, AD Ball

Citations: 205

Year: 2009

A review on online state of charge and state of health estimation for lithium-ion batteries in electric vehicles

Authors: Z Wang, G Feng, D Zhen, F Gu, A Ball

Citations: 197

Year: 2021

Numerical simulation and experimental study of a two-stage reciprocating compressor for condition monitoring

Authors: M Elhaj, F Gu, AD Ball, A Albarbar, M Al-Qattan, A Naid

Citations: 196

Year: 2008

Combustion and performance characteristics of CI (compression ignition) engine running with biodiesel

Authors: B Tesfa, R Mishra, C Zhang, F Gu, AD Ball

Citations: 185

Year: 2013

Water injection effects on the performance and emission characteristics of a CI engine operating with biodiesel

Authors: B Tesfa, R Mishra, F Gu, AD Ball

Citations: 185

Year: 2012

A study of the noise from diesel engines using the independent component analysis

Authors: W Li, F Gu, AD Ball, AYT Leung, CE Phipps

Citations: 183

Year: 2001