Prof. Dr. Lorena Barona López | Sensors | Best Researcher Award

Prof. Dr. Lorena Barona López | Sensors | Best Researcher Award 

Prof. Dr. Lorena Barona López, EPN, Ecuador 

Lorena Isabel Barona López is a researcher and professor specializing in computer science and telecommunications, currently affiliated with the Escuela Politécnica Nacional in Quito, Ecuador. She holds a Ph.D. in Computer Science from the Universidad Complutense de Madrid, Spain, where she developed a situational awareness model for 5G mobile networks within the SELFNET architecture. Her academic background also includes a Master’s in Telematic Services and Networking Engineering from Universidad Politécnica de Madrid and a Bachelor’s degree in Electronic and Information Network Engineering from Escuela Politécnica Nacional. Dr. Barona’s research interests encompass 5G networks, software-defined networking (SDN), virtualization, and data analysis. She is an accredited Level 1 researcher by the Ecuadorian Secretariat for Higher Education, Science, and Technology and a member of various international research networks, including Ciencia Digital and Indra Digital Lab. With extensive teaching experience at both national and international universities, she has lectured in areas such as cybersecurity, network security, embedded systems, and system architecture. Dr. Barona also serves as the Executive Editor of the Ciencia Digital journal and has professional experience in systems administration and IT support. She is proficient in both Spanish and English and actively pursues professional development in artificial intelligence, machine learning, programming, and digital pedagogy.

Professional Profile:

GOOGLE SCHOLAR

ORCID

Summary of Suitability for Best Researcher Award

Dr. Lorena Isabel Barona López is a distinguished academic and researcher in the fields of Computer Science, 5G networks, Data Analysis, Software-Defined Networks (SDN), and Virtualization. Her extensive research background, innovative contributions to next-generation networking technologies, and commitment to both education and scientific advancement make her an exceptionally qualified candidate for the Best Researcher Award.

👩‍🎓 Education

  • 🎓 Ph.D. in Computer Science
    Universidad Complutense de Madrid, Spain
    (Nov 2013 – Sep 2017)
    📘 Thesis: Modelo de Conciencia Situacional para el Análisis de Datos en Redes Móviles 5G
    🔍 Research Areas: 5G, Data Analysis, SDN, Virtualization

  • 🎓 Master’s in Telematic Services & Networking Engineering
    Universidad Politécnica de Madrid, Spain
    (Sep 2012 – Jul 2013)
    📘 Thesis: Propuesta de Escenarios Virtuales con la Herramienta VNX para Pruebas del Protocolo OpenFlow
    🔍 Focus: OpenFlow, Networks, SDN, Virtualization

  • 🎓 Bachelor’s in Electronic and Information Network Engineering
    Escuela Politécnica Nacional, Ecuador
    (Oct 2004 – Oct 2010)
    📘 Project: IP-based Surveillance System for Condominiums in Ambato
    🔍 Focus: IP, Networks

💼 Work & Teaching Experience

  • 🧠 Researcher, GASS Group
    Universidad Complutense de Madrid
    (Nov 2013 – Jul 2017)
    🧪 Focus on cybersecurity, 5G, and data analysis.

  • 📝 Executive Editor, Ciencia Digital Journal
    (Sep 2017 – Present)

  • 👩‍🏫 Teaching Roles (2011–2025):
    Courses in:

    • Communication Systems, Embedded Systems, Cybersecurity 🔐

    • Network Security, Software Engineering 💻

    • Extended Databases, Operating Systems 🖥️

    • MOOCs and online teaching platforms 🎓

    📍 Institutions:

    • Escuela Politécnica Nacional

    • Universidad Internacional de la Rioja (Spain)

    • Universidad Técnica de Ambato

    • Universidad Metropolitana

    • Universidad de las Américas

  • 🖥️ System Administrator, COMDECSA
    (May 2011 – Sep 2012)

  • 🧪 Lab Assistant, Escuela Politécnica Nacional
    (Sep 2009 – Aug 2010)

🏅 Achievements, Accreditations, and Honors

  • 🥇 Aggregated Researcher Level 1, Ecuador’s Secretaría de Educación Superior, Ciencia y Tecnología (2018)

  • 🌐 Member of:

    • Ciencia Digital Network (2019)

    • International Network Indra Digital Lab, Spain

  • 📜 Extensive professional certifications in:

    • AI & Machine Learning 🤖

    • ICT in Education 💡

    • Cloud Dashboards ☁️

    • Programming (Python 🐍, Java ☕, R 📊)

    • Educational tools and Moodle platforms 🎓

  • 🎖️ Expert in Pedagogical Innovation: flipped classrooms, digital teaching tools, and virtual course design.

Publication Top Notes:

A Systematic Literature Review of Machine Unlearning Techniques in Neural Networks

Heterogeneity Challenges of Federated Learning for Future Wireless Communication Networks

A comparison of EMG-based hand gesture recognition systems based on supervised and reinforcement learning

Recognition of Hand Gestures Based on EMG Signals with Deep and Double-Deep Q-Networks

Hand Gesture and Arm Movement Recognition for Multimodal Control of a 3-DOF Helicopter

Hand Gesture Recognition Using EMG-IMU Signals and Deep Q-Networks

A Hand Gesture Recognition System Using EMG and Reinforcement Learning: A Q-Learning Approach

Assoc Prof Dr Zhongxiang Liu | Applications of Sensors | Best Researcher Award

Assoc Prof Dr Zhongxiang Liu | Applications of Sensors | Best Researcher Award 

 Assoc Prof Dr Zhongxiang Liu,Associate Researcher, Southeast University,China

Zhongxiang Liu is an Associate Professor at the School of Transportation, Southeast University, Nanjing, China. He has a strong background in structural engineering, with substantial experience in academia and research. His international exposure includes postdoctoral research at Columbia University and a visiting scholarship at Virginia Tech in the USA. With multiple editorial roles and contributions to international journals, Liu has established himself as a distinguished researcher in structural health monitoring and smart city infrastructure. He has received prestigious awards for his contributions to civil and structural engineering and is dedicated to advancing resilient and sustainable engineering solutions.

Professional Profile:

Summary of Suitability for the Best Researcher Award:

Zhongxiang Liu is an Associate Professor at the School of Transportation, Southeast University, with a Ph.D. in Structural Engineering from Southeast University, China. He has a robust academic background, having completed postdoctoral research at Columbia University and held a visiting scholar position at Virginia Tech. His research expertise lies in structural health monitoring, resilient structures for smart cities, and dynamic analysis of offshore structures. Liu is recognized for his prolific contributions as a guest editor for multiple high-impact journals and has received several prestigious awards, including the Science and Technology Progress Award of China and the Gold Medal at the International Exhibition of Inventions of Geneva.

Education

Zhongxiang Liu earned his Ph.D. in Structural Engineering from Southeast University, Nanjing, China, in 2019. Prior to that, he completed his M.S. in Structural Engineering at the same institution in Chongqing, China, in 2015. He holds a B.S. in Civil Engineering from Chongqing University, Chongqing, China, awarded in 2012. His strong academic foundation has provided him with the necessary expertise to excel in structural health monitoring, offshore structure dynamics, and novel sensing technologies for smart cities.

Work Experience

Liu has been serving as an Associate Professor at Southeast University, Nanjing, China, since February 2021. Before this, he was a Postdoctoral Research Fellow at Columbia University, USA, from November 2019 to November 2020. He also gained valuable international experience as a Visiting Scholar at Virginia Tech, USA, from November 2016 to November 2017. His work in these roles has focused on structural health monitoring, dynamic analysis of offshore structures, and the application of resilient engineering technologies in urban infrastructure.

Skills

Zhongxiang Liu specializes in structural health monitoring, offshore structure analysis, and resilient urban infrastructure. His skills extend to advanced hydro- and aero-dynamic analysis, condition assessment of bridges, and novel sensing technologies for smart city applications. He has editorial and review expertise for high-impact journals, as well as experience in conducting performance evaluations of structural systems. His proficiency in combining these skills to address practical engineering challenges makes him a versatile researcher and educator in the field of civil engineering.

Awards and Honors

Liu has received numerous prestigious awards, including the First Prize in the Science and Technology Progress Award of Jiangsu, China (2022) and the Grand Prize from the China Highway and Transportation Society (2022). His research was recognized internationally, earning the Gold Award with congratulations from the Jury at the 48th International Exhibition of Inventions of Geneva in 2023. Liu was also honored with the Liu Huixian Earthquake Engineering Scholarship Award (2019), as well as several National Scholarships from the Ministry of Education of China during his doctoral and master’s studies.

Membership

Zhongxiang Liu is actively involved in the academic community, serving as a guest editor for multiple journals including Materials (MDPI), Applied Sciences (MDPI), and Modelling (MDPI). He is a Youth Editorial member for the International Journal of Structural Integrity and Journal of Disaster Prevention and Mitigation Engineering. In addition, he reviews for more than 20 international journals in the fields of structural engineering and performance evaluation, further establishing his expertise and influence in the field.

Teaching Experience

As an Associate Professor at Southeast University, Liu is involved in teaching and mentoring students in advanced structural engineering concepts. His international experience at prestigious institutions such as Columbia University and Virginia Tech enriches his teaching, allowing him to incorporate a global perspective into his lectures. He guides students in the fields of structural health monitoring, offshore structures, and smart city technologies, preparing the next generation of engineers to tackle modern infrastructure challenges with innovative solutions.

Research Focus

Liu’s research primarily focuses on structural health monitoring and the condition assessment of bridges, along with hydro- and aero-dynamic analysis of offshore structures. His work emphasizes resilient infrastructure solutions, particularly in the context of smart cities. By developing novel sensing technologies and resilient structural designs, Liu aims to enhance urban infrastructure’s longevity and safety. His research also explores the control and evaluation of dynamic responses in offshore structures, contributing to advancements in both civil engineering and sustainable urban development.

Publication top Notes:

 

Sensors for high energy physics applications

Introduction of Sensors for high energy physics applications

Sensors for high energy physics applications are at the forefront of scientific discovery, enabling the detection and measurement of subatomic particles and phenomena in particle accelerators and detectors.

Particle Detectors:

Investigating the development of particle Detectors including silicon strip detectors calorimeters and time-of-flight detectors used to identify and track particles produced in high-energy collisions.

Radiation-Hard Sensors:

Focusing on sensors and materials that can withstand the intense radiation Environments found in particle Physics experiments ensuring long-term reliability and accuracy.

Fast Timing Detectors:

Addressing the need for sensors with high temporal Resolution for time-of-flight Measurements particle identification, and the study of short-lived particles.

Gas and Liquid Detectors:

Analyzing gas and liquid detectors. such as drift chambers and time projection Chambers, used for precise particle tracking and momentum measurement.

Trigger and Data Acquisition Systems:

Investigating sensor technologies integrated into Trigger and data Acquisition systems to efficiently select and record relevant collision events in real-time from the vast data generated in high-energy physics experiments.