ICASISET 2020
ICASISET 2020

Author: Mahalingam Sundhararajan

Publisher: European Alliance for Innovation

Published: 2021-01-27

Total Pages: 891

ISBN-13: 1631902865

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We are delighted to introduce the proceedings of the first edition of the 2020 European Alliance for Innovation (EAI) International Conference on Advanced Scientific Innovation in Science, Engineering and Technology. This conference has brought innovative academics, industrial experts researchers, developers and practitioners around the world in the field of Science, Engineering and Technology to a common forum. The technical program of ICASISET 2020 consisted of 97 full papers, including 6 invited papers in oral presentation sessions at the main conference tracks. The conference tracks were: Innovative Computing, Advanced innovation technology in Communication, Industry automation, hydrogen hybrid machine, computing in medical applications, Image processing and Internet of Things (IoT) and application. Aside from the high-quality technical paper presentations, the technical program also featured two keynote speeches, one invited talk and two technical workshops. The two keynote speeches were Dr. Hoshang Kolivand, Senior Lecturer, Liverpool John moores University, United Kingdom and Dr. Sheldon Williamson from Canada Research Chair in Electric Energy Storage Systems for Transportation Electrification and Professor in the Department of Electrical, Computer and Software Engineering, Ontario Tech University. The two workshops organized were in the topics of Machine learning and Industrial applications. The workshop aimed to gain insights into key challenges, understanding and design criteria of employing recent technologies to develop and implement computational techniques and applications.

Service-Oriented Computing
Service-Oriented Computing

Author: Eleanna Kafeza

Publisher: Springer Nature

Published: 2020-12-08

Total Pages: 611

ISBN-13: 3030653102

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This book constitutes the proceedings of the 18th International Conference on Service-Oriented Computing, ICSOC 2020, which was planned to take place in Dubai, UAE, during December 14-17, 2020. Due to the COVID-19 pandemic the conference was held online. The 23 full, 16 short, and 3 industry papers included in this volume were carefully reviewed and selected from 137 submissions. They were organized in topical sections named: microservices; Internet of Things; services at the edge; machine learning for service oriented computing; smart data and smart services; service oriented technology trends; industry papers.

Compilers and Operating Systems for Low Power
Compilers and Operating Systems for Low Power

Author: Luca Benini

Publisher: Springer Science & Business Media

Published: 2011-06-28

Total Pages: 234

ISBN-13: 1441992928

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Compilers and Operating Systems for Low Power focuses on both application-level compiler directed energy optimization and low-power operating systems. Chapters have been written exclusively for this volume by several of the leading researchers and application developers active in the field. The first six chapters focus on low energy operating systems, or more in general, energy-aware middleware services. The next five chapters are centered on compilation and code optimization. Finally, the last chapter takes a more general viewpoint on mobile computing. The material demonstrates the state-of-the-art work and proves that to obtain the best energy/performance characteristics, compilers, system software, and architecture must work together. The relationship between energy-aware middleware and wireless microsensors, mobile computing and other wireless applications are covered. This work will be of interest to researchers in the areas of low-power computing, embedded systems, compiler optimizations, and operating systems.

TinyML
TinyML

Author: Pete Warden

Publisher: O'Reilly Media

Published: 2019-12-16

Total Pages: 504

ISBN-13: 1492052019

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Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size

Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing
Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing

Author: Management Association, Information Resources

Publisher: IGI Global

Published: 2021-01-25

Total Pages: 2700

ISBN-13: 1799853403

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Distributed systems intertwine with our everyday lives. The benefits and current shortcomings of the underpinning technologies are experienced by a wide range of people and their smart devices. With the rise of large-scale IoT and similar distributed systems, cloud bursting technologies, and partial outsourcing solutions, private entities are encouraged to increase their efficiency and offer unparalleled availability and reliability to their users. The Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing is a vital reference source that provides valuable insight into current and emergent research occurring within the field of distributed computing. It also presents architectures and service frameworks to achieve highly integrated distributed systems and solutions to integration and efficient management challenges faced by current and future distributed systems. Highlighting a range of topics such as data sharing, wireless sensor networks, and scalability, this multi-volume book is ideally designed for system administrators, integrators, designers, developers, researchers, academicians, and students.

Autonomous Control for a Reliable Internet of Services
Autonomous Control for a Reliable Internet of Services

Author: Ivan Ganchev

Publisher: Springer

Published: 2018-05-30

Total Pages: 416

ISBN-13: 3319904159

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This open access book was prepared as a Final Publication of the COST Action IC1304 “Autonomous Control for a Reliable Internet of Services (ACROSS)”. The book contains 14 chapters and constitutes a show-case of the main outcome of the Action in line with its scientific goals. It will serve as a valuable reference for undergraduate and post-graduate students, educators, faculty members, researchers, engineers, and research strategists working in this field. The explosive growth of the Internet has fundamentally changed the global society. The emergence of concepts like SOA, SaaS, PaaS, IaaS, NaaS, and Cloud Computing in general has catalyzed the migration from the information-oriented Internet into an Internet of Services (IoS). This has opened up virtually unbounded possibilities for the creation of new and innovative services that facilitate business processes and improve the quality of life. However, this also calls for new approaches to ensuring the quality and reliability of these services. The objective of this book is, by applying a systematic approach, to assess the state-of-the-art and consolidate the main research results achieved in this area.

Efficient Processing of Deep Neural Networks
Efficient Processing of Deep Neural Networks

Author: Vivienne Sze

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 254

ISBN-13: 3031017668

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This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.

Social Sensing
Social Sensing

Author: Dong Wang

Publisher: Morgan Kaufmann

Published: 2015-04-17

Total Pages: 232

ISBN-13: 0128011319

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Increasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources. The book explains how a myriad of societal applications can be derived from this massive amount of data collected and shared by average individuals. The title offers theoretical foundations to support emerging data-driven cyber-physical applications and touches on key issues such as privacy. The authors present solutions based on recent research and novel ideas that leverage techniques from cyber-physical systems, sensor networks, machine learning, data mining, and information fusion. Offers a unique interdisciplinary perspective bridging social networks, big data, cyber-physical systems, and reliability Presents novel theoretical foundations for assured social sensing and modeling humans as sensors Includes case studies and application examples based on real data sets Supplemental material includes sample datasets and fact-finding software that implements the main algorithms described in the book

Silicon Photonics for High-Performance Computing and Beyond
Silicon Photonics for High-Performance Computing and Beyond

Author: Mahdi Nikdast

Publisher: CRC Press

Published: 2021-11-16

Total Pages: 391

ISBN-13: 1000480143

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Silicon photonics is beginning to play an important role in driving innovations in communication and computation for an increasing number of applications, from health care and biomedical sensors to autonomous driving, datacenter networking, and security. In recent years, there has been a significant amount of effort in industry and academia to innovate, design, develop, analyze, optimize, and fabricate systems employing silicon photonics, shaping the future of not only Datacom and telecom technology but also high-performance computing and emerging computing paradigms, such as optical computing and artificial intelligence. Different from existing books in this area, Silicon Photonics for High-Performance Computing and Beyond presents a comprehensive overview of the current state-of-the-art technology and research achievements in applying silicon photonics for communication and computation. It focuses on various design, development, and integration challenges, reviews the latest advances spanning materials, devices, circuits, systems, and applications. Technical topics discussed in the book include: • Requirements and the latest advances in high-performance computing systems • Device- and system-level challenges and latest improvements to deploy silicon photonics in computing systems • Novel design solutions and design automation techniques for silicon photonic integrated circuits • Novel materials, devices, and photonic integrated circuits on silicon • Emerging computing technologies and applications based on silicon photonics Silicon Photonics for High-Performance Computing and Beyond presents a compilation of 19 outstanding contributions from academic and industry pioneers in the field. The selected contributions present insightful discussions and innovative approaches to understand current and future bottlenecks in high-performance computing systems and traditional computing platforms, and the promise of silicon photonics to address those challenges. It is ideal for researchers and engineers working in the photonics, electrical, and computer engineering industries as well as academic researchers and graduate students (M.S. and Ph.D.) in computer science and engineering, electronic and electrical engineering, applied physics, photonics, and optics.

Federated Learning
Federated Learning

Author: Qiang Yang

Publisher: Springer Nature

Published: 2020-11-25

Total Pages: 291

ISBN-13: 3030630765

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This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”