Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

A compact high-energy particle detector for low-cost deep space missions

Over the last few decades particle physics has led to many new discoveries, laying the foundation for modern science. However, there are still many unanswered questions which the next generation of particle detectors could address, potentially expanding our knowledge and understanding of the Universe. Owing to recent technological advancements, electronic sensors are now able to acquire measurements previously unobtainable, creating opportunities for new deep-space high-energy particle missions. Consequently, a new compact instrument was developed capable of detecting gamma rays, neutrons and charged particles. This instrument combines the latest in FPGA System-on-Chip technology as the central processor and a 3x3 array of silicon photomultipliers coupled with an organic plastic scintillator as the detector. Using modern digital pulse shape discrimination and signal processing techniques, the scintillator and photomultiplier combination has been shown to accurately discriminate between the di_erent particle types and provide information such as total energy and incident direction. The instrument demonstrated the ability to capture 30,000 particle events per second across 9 channels - around 15 times that of the U.S. based CLAS detector. Furthermore, the input signals are simultaneously sampled at a maximum rate of 5 GSPS across all channels with 14-bit resolution. Future developments will include FPGA-implemented digital signal processing as well as hardware design for small satellite based deep-space missions that can overcome radiation vulnerability

Proceedings of the Caltech Conference on Very Large Scale Integration, held at the California Institute of Technology 22 - 24 January 1979 ; Organized by the Caltech Computer Science Department and the Caltech Industrial Associates Office

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Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering

Topical Workshop on Electronics for Particle Physics

The purpose of the workshop was to present results and original concepts for electronics research and development relevant to particle physics experiments as well as accelerator and beam instrumentation at future facilities; to review the status of electronics for the LHC experiments; to identify and encourage common efforts for the development of electronics; and to promote information exchange and collaboration in the relevant engineering and physics communities

Best Environmental Management Practice for the Waste Management Sector

The way communities generate and manage their waste plays an absolutely key role in their ability to use resources efficiently. While making European economy more resource efficient and circular requires a large spectrum of actions, a huge potential for saving resources lays in improving waste management at local level in Europe. On the basis of an in-depth analysis of the actions implemented by frontrunner organisations in the waste management sector, this report describes a set of best practices with high potential for broad uptake. They are called Best Environmental Management Practices (BEMPs) and aim to help local authorities in charge of waste management and waste management companies move towards circular economy. The BEMPs, identified in close cooperation with a technical working group comprising experts from the sector, cover the waste management areas which determine the most the overall waste management performance: setting a waste management strategy, promoting waste prevention, establishing an efficient waste collection that supports re-use and recycling, and stimulating waste and product re-use. Certain areas of waste treatment are also covered. The BEMPs address mainly the management of municipal solid waste, but also of construction and demolition waste and healthcare waste. Additionally, the report provides a set of environmental performance indicators that organisations can use to assess their waste management performance and monitor progress as well as benchmarks of excellence that give an indication of the levels achieved by best performers. The report presents a wide range of information (environmental benefits, economics, case studies, references, etc.) for each of the best practices and aims to provide inspiration and guidance to organisations of the sector. In addition, the report will be the technical basis for the development of an EMAS (EU Eco-Management and Audit Scheme) Sectoral Reference Document on Best Environmental Management Practice for the Waste Management sector according to Article 46 of Regulation (EC) No 1221/2009 (EMAS Regulation).JRC.B.5-Circular Economy and Industrial Leadershi