Shakti Prasad Sethi | Instrumenation | Young Scientist Award

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Dr. Shakti Prasad Sethi | Instrumentation| Young Scientist Award

University of Liverpool India | India

Dr. Shakti Prasad Sethi is an Assistant Professor at the School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, India, and an Honorary Research Fellow at the Department of Physics, University of Liverpool, U.K. He specializes in signal processing, instrumentation, and machine learning, with extensive experience in plasma diagnostics, beam instrumentation, and data-driven modeling. His research focuses on monitoring and fault diagnosis of transferred arc plasma using advanced signal processing and deep learning techniques, implementing optical emission spectroscopy for plasma characterization, and developing AI-based solutions for high-intensity particle beams. He completed his PhD at the Academy of Scientific and Innovative Research (AcSIR), with laboratory work at CSIR-Institute of Minerals and Materials Technology, Bhubaneswar, India, and holds a master’s degree in Communication System Engineering and a bachelor’s in Electronics and Communication Engineering. Shakti has held positions as Postdoctoral Research Associate at the University of Liverpool, Assistant Professor at Chandigarh University, and CSIR research fellow, contributing to projects involving machine learning for sensor failure accommodation, plasma parameter monitoring, and classification of operating conditions. He has authored multiple publications in international journals including IEEE Transactions on Plasma Science, Review of Scientific Instruments, and preprints in Vacuum, and has presented at international conferences and workshops. Skilled in Python, MATLAB, SQL, LabVIEW, COMSOL, and CST Microwave Studio, he also has experience in cloud platforms and data acquisition systems. Shakti is a reviewer for reputed journals, a STEM ambassador, and a life member of the Plasma Science Society of India and the Power Beam Society of India. His professional endeavors bridge theoretical research with practical instrumentation solutions, advancing AI-driven diagnostics and monitoring in plasma science and beam instrumentation. Outside academia, he enjoys playing cricket, cycling, cooking, and music, and has collaborated with institutions including CERN and the University of Liverpool.

Featured Publications

  • Sethi, S. P., Das, D. P., & Behera, S. K. (2023). Cathode position detection in a transferred arc plasma using artificial neural network. IEEE Transactions on Plasma Science.

  • Sethi, S. P., Das, D. P., Behera, S. K., & Ray, N. (2023). Instability and fault analysis of arc plasma using advanced signal processing methods. Review of Scientific Instruments.

  • Sethi, S. P., Das, D. P., & Behera, S. K. (2023). Monitoring of arc plasma process parameter using CNN-based deep learning algorithm to accommodate sensor failure. IEEE Transactions on Plasma Science.

  • Singh, A. P., Rahi, P., & Sethi, S. P. (2023). Cell counting based on image processing for the detection of cancer clumps. In 2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS).

 

Emanuele Buchicchio | Instrumentation | Best Researcher Award

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Mr. Emanuele Buchicchio | Instrumentation | Best Researcher Award

👨‍🏫Emanuele Buchicchio – Short Biography Emanuele Buchicchio, an Italian software engineer and dedicated Ph.D. student, resides in Todi, Italy. With a profound passion for science and technology, Emanuele has amassed extensive experience in diverse software projects and products. His expertise spans web application design, .Net, C#, Java, machine learning, cybersecurity, and more. Holding positions of Chief Technology Officer and Chief Information Security Officer at Smartpeg, he spearheads AI-powered solutions for human resource management. Emanuele is currently pursuing a Ph.D. at the University of Perugia, focusing on artificial intelligence, machine learning, and measurement tools. His multifaceted skills encompass industrial IoT, embedded systems, and cloud computing.. 📚

🌐 Professional Profiles:

📚 Education

Ph.D. Winter/Summer Schools (2020-2022) Machine Learning Engineer with Microsoft Azure Nanodegree (Oct. 2020 – Jan. 2021) Master of Business Administration (MBA, 2011) MEng Computer Engineering (2006) BS Information Technology Engineering (2004)

 

🌟 key Skills

Web application design and development .Net, .Net Core, C#, Java, JavaEE, Typescript, Angular, Python Industrial IoT, Embedded Systems, Measurements, and data acquisition Machine learning, deep learning, cybersecurity ISO 27001, Cloud (Microsoft Azure, Google Cloud), Agile software development GDPR compliance

 

👩‍💼 Work Experience

Ph.D. Student (Since Nov. 2020): University of Perugia – Instrumentation & Measurement Chief Technology Officer (Since July 2016): Smartpeg srl, Perugia Senior Software Developer (June 2014 – July 2016): Pegaso200 srl, Corciano Consultant / Software Developer (Feb. 2011 – Jun. 2014): Gianos Consulting S.r.l., Orvieto Product Manager (Oct. 2008 – Jan. 2011): Eles Semiconductor Equipment, Todi Software Engineer (Jan. 2007 – Oct. 2008): Eles Semiconductor Equipment, Todi

Research Focus:

Emanuele Buchicchio’s research revolves around innovative applications in instrumentation, measurement, and artificial intelligence, contributing significantly to battery technology and beyond. His work spans diverse areas such as: Battery Systems: Specializing in Electrochemical Impedance Spectroscopy (EIS), Emanuele explores uncertainty characterization and state-of-charge estimation. His contributions to battery SOC estimation using machine learning and equivalent circuit models are noteworthy. Gesture Recognition: Emanuele delves into novel realms with a magnetic positioning system, employing Convolutional Neural Networks (CNNs) for accurate gesture recognition in sign language, showcasing the

Top Noted Publications 📚:

Uncertainty Characterization of a Practical System for Broadband Measurement of Battery EIS,” in IEEE Transactions on Instrumentation and Measurement., Paper Published in 2022

 

Gesture recognition of sign language alphabet with a convolutional neural network using a magnetic positioning system, Paper Published in 2021

 

EasyEIS: A software tool to perform electrochemical impedance spectroscopy using a source measure unit, Paper Published in 2022

Battery SOC estimation from EIS data based on machine learning and equivalent circuit model. Paper Published in 2023