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Books in Computer science

The Computing collection presents a range of foundational and applied content across computer and data science, including fields such as Artificial Intelligence; Computational Modelling; Computer Networks, Computer Organization & Architecture, Computer Vision & Pattern Recognition, Data Management; Embedded Systems & Computer Engineering; HCI/User Interface Design; Information Security; Machine Learning; Network Security; Software Engineering.

  • Modeling and Simulation of Computer Networks and Systems

    Methodologies and Applications
    • 1st Edition
    • Faouzi Zarai + 1 more
    • English
    Modeling and Simulation of Computer Networks and Systems: Methodologies and Applications introduces you to a broad array of modeling and simulation issues related to computer networks and systems. It focuses on the theories, tools, applications and uses of modeling and simulation in order to effectively optimize networks. It describes methodologies for modeling and simulation of new generations of wireless and mobiles networks and cloud and grid computing systems. Drawing upon years of practical experience and using numerous examples and illustrative applications recognized experts in both academia and industry, discuss: Important and emerging topics in computer networks and systems including but not limited to; modeling, simulation, analysis and security of wireless and mobiles networks especially as they relate to next generation wireless networks Methodologies, strategies and tools, and strategies needed to build computer networks and systems modeling and simulation from the bottom up Different network performance metrics including, mobility, congestion, quality of service, security and more... Modeling and Simulation of Computer Networks and Systems is a must have resource for network architects, engineers and researchers who want to gain insight into optimizing network performance through the use of modeling and simulation.

  • Entity Information Life Cycle for Big Data

    Master Data Management and Information Integration
    • 1st Edition
    • John R. Talburt + 1 more
    • English
    Entity Information Life Cycle for Big Data walks you through the ins and outs of managing entity information so you can successfully achieve master data management (MDM) in the era of big data. This book explains big data’s impact on MDM and the critical role of entity information management system (EIMS) in successful MDM. Expert authors Dr. John R. Talburt and Dr. Yinle Zhou provide a thorough background in the principles of managing the entity information life cycle and provide practical tips and techniques for implementing an EIMS, strategies for exploiting distributed processing to handle big data for EIMS, and examples from real applications. Additional material on the theory of EIIM and methods for assessing and evaluating EIMS performance also make this book appropriate for use as a textbook in courses on entity and identity management, data management, customer relationship management (CRM), and related topics.

  • Advances in Independent Component Analysis and Learning Machines

    • 1st Edition
    • Ella Bingham + 3 more
    • English
    In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining. Examples of topics which have developed from the advances of ICA, which are covered in the book are: A unifying probabilistic model for PCA and ICA Optimization methods for matrix decompositions Insights into the FastICA algorithm Unsupervised deep learning Machine vision and image retrieval

  • Hacking Web Intelligence

    Open Source Intelligence and Web Reconnaissance Concepts and Techniques
    • 1st Edition
    • Sudhanshu Chauhan + 1 more
    • English
    Open source intelligence (OSINT) and web reconnaissance are rich topics for infosec professionals looking for the best ways to sift through the abundance of information widely available online. In many cases, the first stage of any security assessment—that is, reconnaissance—is not given enough attention by security professionals, hackers, and penetration testers. Often, the information openly present is as critical as the confidential data. Hacking Web Intelligence shows you how to dig into the Web and uncover the information many don't even know exists. The book takes a holistic approach that is not only about using tools to find information online but also how to link all the information and transform it into presentable and actionable intelligence. You will also learn how to secure your information online to prevent it being discovered by these reconnaissance methods. Hacking Web Intelligence is an in-depth technical reference covering the methods and techniques you need to unearth open source information from the Internet and utilize it for the purpose of targeted attack during a security assessment. This book will introduce you to many new and leading-edge reconnaissance, information gathering, and open source intelligence methods and techniques, including metadata extraction tools, advanced search engines, advanced browsers, power searching methods, online anonymity tools such as TOR and i2p, OSINT tools such as Maltego, Shodan, Creepy, SearchDiggity, Recon-ng, Social Network Analysis (SNA), Darkweb/Deepweb, data visualization, and much more.

  • Power and Performance

    Software Analysis and Optimization
    • 1st Edition
    • Jim Kukunas
    • English
    Power and Performance: Software Analysis and Optimization is a guide to solving performance problems in modern Linux systems. Power-efficient chips are no help if the software those chips run on is inefficient. Starting with the necessary architectural background as a foundation, the book demonstrates the proper usage of performance analysis tools in order to pinpoint the cause of performance problems, and includes best practices for handling common performance issues those tools identify.

  • Ecological Design of Smart Home Networks

    Technologies, Social Impact and Sustainability
    • 1st Edition
    • N. Saito + 1 more
    • English
    This book provides an authoritative guide for postgraduate students and academic researchers in electronics, computer and network engineering, telecommunications, energy technology and home automation, as well as R&D managers in industrial sectors such as wireless technology, consumer electronics, telecommunications and networking, information technology, energy technology and home automation. Part One outlines the key principles and technologies needed for ecological smart home networks. Beginning with a thorough overview of the concept behind ecological smart home network design, the book reviews such important areas as power line communications, hybrid systems and middleware platforms. Part Two then goes on to discuss some important applications of this technology, with wireless smart sensor networks for home and telecare, and smart home networking for content and energy management (including the intelligent Zero Emission Urban System), all explored in detail.

  • Industrial Tomography

    Systems and Applications
    • 1st Edition
    • Mi Wang
    • English
    Industrial Tomography: Systems and Applications thoroughly explores the important tomographic techniques of industrial tomography, also discussing image reconstruction, systems, and applications. The text presents complex processes, including the way three-dimensional imaging is used to create multiple cross-sections, and how computer software helps monitor flows, filtering, mixing, drying processes, and chemical reactions inside vessels and pipelines. Readers will find a comprehensive discussion on the ways tomography systems can be used to optimize the performance of a wide variety of industrial processes.

  • Machine Learning

    A Bayesian and Optimization Perspective
    • 1st Edition
    • Sergios Theodoridis
    • English
    This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques – together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models.The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models.

  • Social Sensing

    Building Reliable Systems on Unreliable Data
    • 1st Edition
    • Dong Wang + 2 more
    • English
    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.

  • Learning-Based Local Visual Representation and Indexing

    • 1st Edition
    • Rongrong Ji + 4 more
    • English
    Learning-Based Local Visual Representation and Indexing, reviews the state-of-the-art in visual content representation and indexing, introduces cutting-edge techniques in learning based visual representation, and discusses emerging topics in visual local representation, and introduces the most recent advances in content-based visual search techniques.

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