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Signal Processing and Networking for Big Data Applications

Signal Processing and Networking for Big Data Applications
Author: Zhu Han
Publisher: Cambridge University Press
Total Pages: 375
Release: 2017-04-27
Genre: Technology & Engineering
ISBN: 1108155944
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This unique text helps make sense of big data in engineering applications using tools and techniques from signal processing. It presents fundamental signal processing theories and software implementations, reviews current research trends and challenges, and describes the techniques used for analysis, design and optimization. Readers will learn about key theoretical issues such as data modelling and representation, scalable and low-complexity information processing and optimization, tensor and sublinear algorithms, and deep learning and software architecture, and their application to a wide range of engineering scenarios. Applications discussed in detail include wireless networking, smart grid systems, and sensor networks and cloud computing. This is the ideal text for researchers and practising engineers wanting to solve practical problems involving large amounts of data, and for students looking to grasp the fundamentals of big data analytics.

Sublinear Algorithms for Big Data Applications

Sublinear Algorithms for Big Data Applications
Author: Dan Wang
Publisher: Springer
Total Pages: 85
Release: 2015-07-16
Genre: Computers
ISBN: 3319204483
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The brief focuses on applying sublinear algorithms to manage critical big data challenges. The text offers an essential introduction to sublinear algorithms, explaining why they are vital to large scale data systems. It also demonstrates how to apply sublinear algorithms to three familiar big data applications: wireless sensor networks, big data processing in Map Reduce and smart grids. These applications present common experiences, bridging the theoretical advances of sublinear algorithms and the application domain. Sublinear Algorithms for Big Data Applications is suitable for researchers, engineers and graduate students in the computer science, communications and signal processing communities.

Cooperative and Graph Signal Processing

Cooperative and Graph Signal Processing
Author: Petar Djuric
Publisher: Academic Press
Total Pages: 866
Release: 2018-07-04
Genre: Computers
ISBN: 0128136782
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Cooperative and Graph Signal Processing: Principles and Applications presents the fundamentals of signal processing over networks and the latest advances in graph signal processing. A range of key concepts are clearly explained, including learning, adaptation, optimization, control, inference and machine learning. Building on the principles of these areas, the book then shows how they are relevant to understanding distributed communication, networking and sensing and social networks. Finally, the book shows how the principles are applied to a range of applications, such as Big data, Media and video, Smart grids, Internet of Things, Wireless health and Neuroscience. With this book readers will learn the basics of adaptation and learning in networks, the essentials of detection, estimation and filtering, Bayesian inference in networks, optimization and control, machine learning, signal processing on graphs, signal processing for distributed communication, social networks from the perspective of flow of information, and how to apply signal processing methods in distributed settings. Presents the first book on cooperative signal processing and graph signal processing Provides a range of applications and application areas that are thoroughly covered Includes an editor in chief and associate editor from the IEEE Transactions on Signal Processing and Information Processing over Networks who have recruited top contributors for the book

Signal and Information Processing, Networking and Computers

Signal and Information Processing, Networking and Computers
Author: Songlin Sun
Publisher: Springer
Total Pages: 620
Release: 2019-04-16
Genre: Technology & Engineering
ISBN: 9811371237
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This proceedings book presents selected papers from the 5th Conference on Signal and Information Processing, Networking and Computers (ICSINC), held in Yuzhou, China, from November 29 to December 1, 2018. It focuses on the current research in a wide range of areas in the fields of information theory, communication systems, computer science, signal processing, aerospace technologies, and other related technologies. With contributions from experts from both academia and industry, it is a valuable resource for anyone who is interested in this field.

Big Data and Computational Intelligence in Networking

Big Data and Computational Intelligence in Networking
Author: Yulei Wu
Publisher: CRC Press
Total Pages: 530
Release: 2017-12-14
Genre: Computers
ISBN: 1498784879
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This book presents state-of-the-art solutions to the theoretical and practical challenges stemming from the leverage of big data and its computational intelligence in supporting smart network operation, management, and optimization. In particular, the technical focus covers the comprehensive understanding of network big data, efficient collection and management of network big data, distributed and scalable online analytics for network big data, and emerging applications of network big data for computational intelligence.

Signal and Information Processing, Networking and Computers

Signal and Information Processing, Networking and Computers
Author: Yue Wang
Publisher: Springer Nature
Total Pages: 1104
Release: 2020-12-17
Genre: Technology & Engineering
ISBN: 9813341025
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This book collects selected papers from the 7th Conference on Signal and Information Processing, Networking and Computers held in Rizhao, China, on September 21-23, 2020. The 7th International Conference on Signal and Information Processing, Networking and Computers (ICSINC) was held in Rizhao, China, on September 21-23, 2020.

Big Data over Networks

Big Data over Networks
Author: Shuguang Cui
Publisher: Cambridge University Press
Total Pages: 459
Release: 2016-01-14
Genre: Technology & Engineering
ISBN: 1316445275
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Utilising both key mathematical tools and state-of-the-art research results, this text explores the principles underpinning large-scale information processing over networks and examines the crucial interaction between big data and its associated communication, social and biological networks. Written by experts in the diverse fields of machine learning, optimisation, statistics, signal processing, networking, communications, sociology and biology, this book employs two complementary approaches: first analysing how the underlying network constrains the upper-layer of collaborative big data processing, and second, examining how big data processing may boost performance in various networks. Unifying the broad scope of the book is the rigorous mathematical treatment of the subjects, which is enriched by in-depth discussion of future directions and numerous open-ended problems that conclude each chapter. Readers will be able to master the fundamental principles for dealing with big data over large systems, making it essential reading for graduate students, scientific researchers and industry practitioners alike.

Pattern Recognition and Data Analysis with Applications

Pattern Recognition and Data Analysis with Applications
Author: Deepak Gupta
Publisher: Springer Nature
Total Pages: 816
Release: 2022-09-01
Genre: Technology & Engineering
ISBN: 9811915202
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This book covers latest advancements in the areas of machine learning, computer vision, pattern recognition, computational learning theory, big data analytics, network intelligence, signal processing and their applications in real world. The topics covered in machine learning involves feature extraction, variants of support vector machine (SVM), extreme learning machine (ELM), artificial neural network (ANN) and other areas in machine learning. The mathematical analysis of computer vision and pattern recognition involves the use of geometric techniques, scene understanding and modelling from video, 3D object recognition, localization and tracking, medical image analysis and so on. Computational learning theory involves different kinds of learning like incremental, online, reinforcement, manifold, multi-task, semi-supervised, etc. Further, it covers the real-time challenges involved while processing big data analytics and stream processing with the integration of smart data computing services and interconnectivity. Additionally, it covers the recent developments to network intelligence for analyzing the network information and thereby adapting the algorithms dynamically to improve the efficiency. In the last, it includes the progress in signal processing to process the normal and abnormal categories of real-world signals, for instance signals generated from IoT devices, smart systems, speech, videos, etc., and involves biomedical signal processing: electrocardiogram (ECG), electroencephalogram (EEG), magnetoencephalography (MEG) and electromyogram (EMG).

Big Data and Networks Technologies

Big Data and Networks Technologies
Author: Yousef Farhaoui
Publisher: Springer
Total Pages: 372
Release: 2019-07-18
Genre: Computers
ISBN: 9783030236717
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This book reviews the state of the art in big data analysis and networks technologies. It addresses a range of issues that pertain to: signal processing, probability models, machine learning, data mining, databases, data engineering, pattern recognition, visualization, predictive analytics, data warehousing, data compression, computer programming, smart cities, networks technologies, etc. Data is becoming an increasingly decisive resource in modern societies, economies, and governmental organizations. In turn, data science inspires novel techniques and theories drawn from mathematics, statistics, information theory, computer science, and the social sciences. All papers presented here are the product of extensive field research involving applications and techniques related to data analysis in general, and to big data and networks technologies in particular. Given its scope, the book will appeal to advanced undergraduate and graduate students, postdoctoral researchers, lecturers and industrial researchers, as well general readers interested in big data analysis and networks technologies.

Big Data Analytics for Cyber-Physical Systems

Big Data Analytics for Cyber-Physical Systems
Author: Guido Dartmann
Publisher: Elsevier
Total Pages: 396
Release: 2019-07-15
Genre: Law
ISBN: 0128166371
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Cyber-physical systems (CPS) and the Internet of Things (IoT) are rapidly developing technologies that are transforming our society. The disruptive transformation of the economy and society is expected due to the data collected by these systems, rather than the technological aspects of such as networks, embedded systems, and cloud technology. However, to create value out of the data, it must be transformed into information and therefore, expertise in data analytics and machine learning is the key component of future smart systems in cities and other applications. Big Data Analytics in Cyber-Physical Systems examines sensor signal processing, IoT gateways, optimization and decision making, intelligent mobility, and implementation of machine learning algorithms in embedded systems. This book focuses on the interaction between IoT technology and the mathematical tools to evaluate the extracted data of those systems. Each chapter provides different tools and applications in order to present a broad list of data analytics and machine learning tools in multiple IoT applications. Additionally, this volume addresses the education transfer needed to incorporate these technologies into our society by examining new platforms for IoT in schools, new courses and concepts for universities and adult education on IoT and data science. Fills the gap between IoT, CPS, and mathematical modeling Numerous use cases that discuss how concepts are applied in different domains and applications Provides "best practices," "real developments", and "winning stories" to complement technical information Uniquely covers contents within the context of mathematical foundations of signal processing and machine learning in CPS and IoT