Alessandro Massaro - Electronics in Advanced Research Industries

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Electronics in Advanced Research Industries: краткое содержание, описание и аннотация

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A one-of-a-kind examination of the latest developments in machine control 
 
In 
, accomplished electronics researcher and engineer Alessandro Massaro delivers a comprehensive exploration of the latest ways in which people have achieved machine control, including automated vision technologies, advanced electronic and micro-nano sensors, advanced robotics, and more. 
The book is composed of nine chapters, each containing examples and diagrams designed to assist the reader in applying the concepts discussed within to common issues and problems in the real-world. Combining electronics and mechatronics to show how they can each be implemented in production line systems, the book presents insightful new ways to use artificial intelligence in production line machines. The author explains how facilities can upgrade their systems to an Industry 5.0 environment. 
Electronics in Advanced Research Industries: Industry 4.0 to Industry 5.0 Advances A thorough introduction to the state-of-the-art in a variety of technological areas, including flexible technologies, scientific approaches, and intelligent automatic systems Comprehensive explorations of information technology infrastructures that support Industry 5.0 facilities, including production process simulation Practical discussions of human-machine interfaces, including mechatronic machine interface architectures integrating sensor systems and machine-to-machine (M2M) interfaces In-depth examinations of internet of things (IoT) solutions in industry, including cloud computing IoT Perfect for professionals working in electrical industry sectors in manufacturing, production line manufacturers, engineers, and members of R&D industry teams, 
 will also earn a place in libraries of technicians working in the process industry.

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12 12 Massaro, A. and Galiano, A. (2020). Image processing and post‐data mining processing for security in industrial applications: security in industry. In: Handbook of Research on Intelligent Data Processing and Information Security Systems (eds. S.M. Bilan and S.I. Al‐Zoubi), 117–146. Hershey, PA: IGI Global.

13 13 Massaro, A. and Galiano, A. (2020). Infrared thermography for intelligent robotic systems in research industry inspections: thermography in industry processes. In: Handbook of Research on Advanced Mechatronic Systems and Intelligent Robotics (ed. M.K. Habib), 98–125. Hershey, PA: IGI Global.

14 14 Tan, C.L. and Mohseni, H. (2018). Emerging technologies for high performance infrared detectors. Nanophotonics 7 (1): 167–197.

15 15 Kaufmann, R., Isella, G., Sanchez‐Amores, A. et al. (2011). Near infrared image sensor with integrated germanium photodiodes. Journal of Applied Physics 110 (2): 1–6.

16 16 Kumar, V., Hallqvist, C., and Ekwall, D. (2017). Developing a framework for traceability implementation in the textile supply chain. Systems 5 (33): 1–21.

17 17 Tzoulis, I. and Andreopoulou, Z. (2013). Emerging traceability technologies as a tool for quality wood trade. Procedia Technology 8 (1): 606–611.

18 18 Agrawal, T.K., Koehl, L., and Campagne, C. (2018). A secured tag for implementation of traceability in textile and clothing supply chain. The International Journal of Advanced Manufacturing Technology 99 (1): 2563–2577.

19 19 Chen, R.‐S., Chen, C.‐C., Yeh, K.C. et al. (2008). Using RFID technology in food produce traceability. WSEAS Transactions on Information Science and Applications 5 (11): 1551–1560.

20 20 Kelepouris, T., Pramatari, K., and Doukidis, G. (2007). RFID‐enabled traceability in the food supply chain. 107 (2): 183–200.

21 21 Sethi, P., Sarangi, S., and R. (2017). Internet of Things: architectures, protocols, and applications. Journal of Electrical and Computer Engineering 2017 (9324035): 1–25.

22 22 Ke, C.K., Wu, M.Y., Chan, Y.W., and Lu, K.C. (2018). Developing a BLE Beacon‐based location system using location fingerprint positioning for smart home power management. Energies 11 (3464): 1–18.

23 23 Lin, Y.‐W. and Lin, C.‐Y. (2018). An interactive real‐time locating system based on Bluetooth low‐energy beacon network. Sensors 18 (1637): 1–17.

24 24 Triantafyllou, A., Sarigiannidis, P., and Lagkas, T.D. (2018). Network protocols, schemes, and mechanisms for Internet of Things (IoT): features, open challenges, and trends. Wireless Communications and Mobile Computing https://doi.org/10.1155/2018/5349894.

25 25 Cilfone, A., Davoli, L., Belli, L., and Ferrari, G. (2019). Wireless mesh networking: an IoT‐oriented perspective survey on relevant technologies. Future Internet 11 (99): 1–35.

26 26 Froiz‐Míguez, I., Fernández‐Caramés, T.M., Fraga‐Lamas, P., and Castedo, L. (2018). Design, implementation and practical evaluation of an IoT home automation system for fog computing applications based on MQTT and ZigBee‐WiFi sensor nodes. Sensors 18 (2660): 1–42.

27 27 Amelia, A., Julham, Sundawa, B.V. et al. (2017). Implementation of the RS232 communication trainer using computers and the ATMEGA microcontroller for interface engineering courses. Journal of Physics: Conference Series 890 (012095): 1–6.

28 28 Dey, M. (2012). Comparision of data transfer protocols over USB. International Journal of Engineering Research & Technology 1 (9): 1–6.

29 29 Riberio, F.M., Costa, T.S., Baratella, A. et al. (2014). Comparative analysis of industrial network profinet, ethernet/IP, and HSE. International Journal of Innovative Computing, Information and Control 10 (5): 1931–1945.

30 30 Jaloudi, S. (2019). Communication protocols of an industrial Internet of Things environment: a comparative study. Future Internet 11 (66): 1–18.

31 31 Savaglio, C., Ganzha, M., Paprzycki, M. et al. (2019). Agent‐based Internet of Things: state‐of‐the‐art and research challenges. Future Generation Computer Systems 102 (1): 1038–1053.

32 32 Massaro, A., Calicchio, A., Maritati, V. et al. (2018). A case study of innovation of an information communication system and upgrade of the knowledge base in industry by ESB, artificial intelligence, and big data system integration. International Journal of Artificial Intelligence and Applications (IJAIA) 9 (5): 27–43.

33 33 Burhan, M., Asif Rehman, R., Khan, B., and Kim, B.‐S. (2018). IoT elements, layered architectures and security issues: a comprehensive survey. Sensors 18 (2796): 1–37.

34 34 Arsan, T., Günay, F., and Kaya, E. (2014). Implementation of application for huge data file transfer. International Journal of Wireless & Mobile Networks (IJWMN) 6 (4): 27–46.

35 35 Riabov, V.V. and SMTP (Simple Mail Transfer Protocol) (2007). The Handbook of Computer Networks, Volume 2, LANs, MANs, WANs, the Internet, and Global, Cellular, and Wireless Networks (ed. H. Bidgoli), 388–406. Hoboken, NJ: Wiley.

36 36 Nguyen, T.S. and Huynh, T.‐H. (eds.) (2015). Design and implementation of Modbus slave based on ARM Platform and FreeRTOS environment. Proceedings of International Conference on Advanced Technologies for Communications (ATC), Ho Chi Minh City, Vietnam (14–16 October 2015). Piscataway, NJ: IEEE.

37 37 Rajinder, S. and Satish, K. (2016). An overview of world wide web protocol (Hypertext Transfer Protocol and Hypertext Transfer Protocol Secure). International Journal of Advanced Research in Computer Science and Software Engineering 6 (5): 396–399.

38 38 Ansari, D.B., Rehman, A.U., and Mughal, R.A. (2018). Internet of Things (IoT) protocols: a brief exploration of MQTT and CoAP. International Journal of Computer Applications 179 (27): 9–14.

39 39 Tukade, T.M. and Banakar, R.M. (2018). Data transfer protocols in IoT – an overview. International Journal of Pure and Applied Mathematics 118 (16): 121–138.

40 40 Kastner, W., Neugschwandtner, G., and Kogler, M. (eds.) (2006). An open approach to Eib/Knx software development. Proceedings of 6th IFAC International Conference, Puebla, Mexico (14–25 November 2005). Elsevier Ltd.

41 41 Massaro, A., Manfredonia, I., Galiano, A., and Contuzzi, N. (eds.) (2019). Inline image vision Technique for tires industry 4.0: quality and defect monitoring in tires assembly. Proceedings of 2019 IEEE International Workshop on Metrology for Industry 4.0 and IoT, Naples, Italy (4–6 June 2019). Piscataway, NJ: IEEE.

42 42 Sathya, R. and Abraham, A. (2013). Comparison of supervised and unsupervised learning algorithms for pattern classification. International Journal of Advanced Research in Artificial Intelligence 2 (2): 34–38.

43 43 Massaro, A., Manfredonia, I., Galiano, A. et al. (2019). Sensing and quality monitoring facilities designed for pasta industry including traceability, image vision and predictive maintenance. Proceeding of 2019 IEEE International Workshop on Metrology for Industry 4.0 and IoT, Naples, Italy (4–6 June 2019). Piscataway, NJ: IEEE.

44 44 Massaro, A., Manfredonia, I., Galiano, A., and Xhaysa, B. (2019). Advanced process defect monitoring model and prediction improvement by artificial neural network in kitchen manufacturing industry: a case of study. Proceeding of IEEE International Workshop on Metrology for Industry 4.0 and IoT, Naples, Italy (4–6 June 2019). Piscataway, NJ: IEEE.

45 45 Massaro, A., Vitti, V., and Galiano, A. (2018). Automatic image processing engine oriented on quality control of electronic boards. Signal & Image Processing: An International Journal (SIPIJ) 9 (2): 1–14.

46 46 Kiran, B.R., Thomas, D.M., and Parakkal, R. (2018). An overview of deep learning based methods for unsupervised and semi‐supervised anomaly detection in videos. Journal of Imaging 4 (36): 1–25.

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