Electronics in Advanced Research Industries. Alessandro Massaro

Electronics in Advanced Research Industries - Alessandro Massaro


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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.

      47 47 Xu, X., Zheng, H., Guo, Z. et al. (2019). SDD‐CNN: small data‐driven convolution neural networks for subtle roller defect inspection. Applied Sciences 9 (1364): 1–16.

      48 48 Perez, H., Tah, J.H.M., and Mosavi, A. (2019). Deep learning for detecting building defects using convolutional neural networks. Sensors 19 (3556): 1–22.

      49 49 Lin, C.‐S., Huang, Y.‐C., Chen, S.‐H. et al. (2018). The application of deep learning and image processing technology in laser positioning. Applied Sciences 8 (1542): 1–13.

      50 50 Iskra, P. and Hernàndez, R.E. (2012). Toward a process monitoring of CNC wood router. Sensor selection and surface roughness prediction. Wood Science and Technology 46 (1): 115–128.

      51 51 Iskra, P. and Hernàndez, R.E. (2009). The influence of cutting parameters on the surface quality of routed paper birch and surface roughness prediction modeling. Wood and Fiber Science 41 (1): 28–37.

      52 52 Contuzzi, N., Massaro, A., Manfredonia, I. et al. (2019). A decision making process model based on a multilevel control platform suitable for Industry 4.0. Proceeding of 2019 IEEE International Workshop on Metrology for Industry 4.0 and IoT, Naples, Italy (4–6 June 2019). Piscataway, NJ: IEEE.

      53 53 De Smedt, J., Hasić, F., Vanden Broucke, S.K.L.M., and Vanthienen, J. (2017). Towards a holistic discovery of decisions in process‐aware information systems. Proceedings of the International Conference on Business Process Management, Barcelona, Spain (10–15 September 2017). Cham: Springer Nature.

      54 54 Tirgul, C.S. and Naik, M.R. (2016). Artificial intelligence and robotics. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 5 (6): 1787–1793.

      55 55 Kaura, H., Honrao, V.K., Patil, S., and Shetty, P. (2013). Gesture controlled robot using image processing. International Journal of Advanced Research in Artificial Intelligence 2 (5): 69–77.

      56 56 Moulianitis, V.C. and Aspragathos, N.A. (2015). IT and mechatronics in industrial robotic workcell design and operation. In: Encyclopedia of Information Science and Technology, 3e (ed. M. Khosrow‐Pour), 440–455. Hershey, PA: IGI Global.

      57 57 Birglen, L. (2019). Design of a partially‐coupled self‐adaptive robotic finger optimized for collaborative robots. Autonomous Robots 43 (2): 523–538.

      58 58 Jamshidi, P., Cámara, J., Schmerl, B. et al. (2019). Machine learning meets quantitative planning: enabling self‐adaptation in autonomous robots. Proceedings of the 14th International Symposium on Software Engineering for Adaptive and Self‐Managing Systems, Montreal, Canada (25–26 May 2019). Piscataway, NJ: IEEE.

      59 59 Khalid, A., Kirisci, P., Ghrairi, Z. et al. (2016). A methodology to develop collaborative robotic cyber physical systems for production environments. Logistics Research 9 (23): 1–15.

      60 60 Alcácer, V. and Cruz‐Machado, V. (2019). Scanning the Industry 4.0: a literature review on technologies for manufacturing systems. Engineering Science and Technology, an International Journal 22 (1): 899–919.

      61 61 Vidal, F., Álvarez, M., González, R. et al. (2011). Development of a flexible and adaptive robotic cell for small batch manufacturing. Contemporary Materials 2 (1): 1–12.

      62 62 Wang, S., Wan, J., Li, D., and Zhang, C. (2016). Implementing smart factory of industry 4.0: an outlook. International Journal of Distributed Sensor Networks 12 (1): 1–10.

      63 63 Mehrpouya, M., Dehghanghadikolaei, A., Fotovvati, B. et al. (2019).


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