Proceedings of the International Conference on Computer Science, Electronics and Industrial Engineering (CSEI 2024)
Volume 2: Emerging Technologies in Communication, Manufacturing, and Renewable Energy Systems
This book captures the dynamic spirit of technological advancement and interdisciplinary collaboration showcased at the sixth edition of the conference. This second volume of the proceedings from the VI International Conference on Computer Science, Electronics and Industrial Engineering (CSEI 2024) presents a wide-ranging collection of innovative research under the theme “Emerging Technologies in Communication, Manufacturing, and Renewable Energy Systems.”
The contributions span several key areas where digital transformation is reshaping traditional practices. Educational informatics features prominently, demonstrating how virtual reality, augmented reality, and artificial intelligence are creating more inclusive and personalized learning environments. These developments are particularly impactful in areas such as chemistry education, traffic safety training, and the support of learners with special educational needs.
Further chapters explore the integration of cyber-physical systems and Internet of Things technologies in applications ranging from smart manufacturing to precision agriculture. Healthcare innovation also finds its place, with research on early detection of neurodegenerative diseases and advanced physiological signal processing, reflecting the convergence of digital intelligence and human wellbeing.
The book also delves into transformative approaches in production and industrial engineering. From sustainable agro-industrial processes to occupational safety under the Industry 5.0 paradigm, authors explore how human-centered and environmentally conscious design is becoming a cornerstone of technological development. Software innovations and digital tools highlight the importance of usability and efficiency, with contributions such as gamified rehabilitation systems and agricultural management platforms.
Finally, advances in automated manufacturing and control systems reveal how robotics and real-time monitoring are driving efficiency and resilience across sectors—from agricultural automation to aerospace training systems.
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| Veröffentlichung: | 21.10.2025 |
| Höhe/Breite/Gewicht | H 23,5 cm / B 15,5 cm / - |
| Seiten | 1060 |
| Art des Mediums | Buch [Taschenbuch] |
| Preis DE | EUR 235.39 |
| Preis AT | EUR 241.99 |
| Reihe | Lecture Notes in Networks and Systems |
| ISBN-13 | 978-3-031-98889-9 |
| ISBN-10 | 3031988892 |
Über den Autor
Marcelo V. Garcia, PhD. (Scopus ID: 56597269000, Web of Science ResearcherID: J 3195-2019) is a researcher from INTELIA research group. He studied electronics and instrumentation engineering at the University of the Armed Forces-ESPE. In 2013 he obtained his Master's Degree in Control, Automation, and Robotics Engineering and in 2018 he obtained his doctorate at the University of the Basque Country (UPV / EHU). His studies were carried out thanks to a grant from the Ecuadorian government. From 2008 to 2013 he worked as an engineer in different companies in the area of oil and gas in Ecuador such as Schlumberger, Petrobras and Petroamazonas EP. His research interest is focused on the design of next-generation architectures based on Industry 4.0 in various domains such as automation and smart manufacturing.
Rommel Santiago Velastegui Hernández, Mg. (Scopus ID: 57212198255) He obtained the degree of Industrial Engineer in Automation Processes and the Master in Operations Management at the Technical University of Ambato (UTA) Ecuador in 2013 and 2017, respectively. He is currently studying the PhD Programme in Industrial Engineering and Production at the Polytechnic University of Valencia, Spain since 2021, under the supervision of Prof. Raúl Poler Escoto and Prof. Francisco Manuel Diaz-Madroñero Boluda. He has written more than thirteen SCOPUS scientific articles. His current research interests include industrial production, multi-agent robotic systems, lean management, supply chain management (SCM), machine learning (ML) and quality management systems (QMS) for production and industrial process control, education and social sciences.
