E-Systems for the 21st Century
E-based systems and computer networks are becoming standard practice across all sectors, including health, engineering, business, education, security, and citizen interaction with local and national government. With contributions from researchers and practitioners from around the world, this two-volume book discusses and reports on new and important developments in the field of e-systems, covering a wide range of current issues in the design, engineering, and adoption of e-systems.
Research Infrastructures for Hardware Accelerators
Hardware acceleration in the form of customized datapath and control circuitry tuned to specific applications has gained popularity for its promise to utilize transistors more efficiently. Historically, the computer architecture community has focused on general-purpose processors, and extensive research infrastructure has been developed to support research efforts in this domain. Envisioning future computing systems with a diverse set of general-purpose cores and accelerators, computer architects must add accelerator-related research infrastructures to their toolboxes to explore future heterogeneous systems. This book serves as a primer for the field, as an overview of the vast literature on accelerator architectures and their design flows, and as a resource guidebook for researchers working in related areas.
Power-Efficient Computer Architectures
As Moore's Law and Dennard scaling trends have slowed, the challenges of building high-performance computer architectures while maintaining acceptable power efficiency levels have heightened. Over the past ten years, architecture techniques for power efficiency have shifted from primarily focusing on module-level efficiencies, toward more holistic design styles based on parallelism and heterogeneity. This work highlights and synthesizes recent techniques and trends in power-efficient computer architecture. Table of Contents: Introduction / Voltage and Frequency Management / Heterogeneity and Specialization / Communication and Memory Systems / Conclusions / Bibliography / Authors' Biographies
Resilient Architecture Design for Voltage Variation
Shrinking feature size and diminishing supply voltage are making circuits sensitive to supply voltage fluctuations within the microprocessor, caused by normal workload activity changes. If left unattended, voltage fluctuations can lead to timing violations or even transistor lifetime issues that degrade processor robustness. Mechanisms that learn to tolerate, avoid, and eliminate voltage fluctuations based on program and microarchitectural events can help steer the processor clear of danger, thus enabling tighter voltage margins that improve performance or lower power consumption. We describe the problem of voltage variation and the factors that influence this variation during processor design and operation. We also describe a variety of runtime hardware and software mitigation techniques that either tolerate, avoid, and/or eliminate voltage violations. We hope processor architects will find the information useful since tolerance, avoidance, and elimination are generalizable constructsthat can serve as a basis for addressing other reliability challenges as well. Table of Contents: Introduction / Modeling Voltage Variation / Understanding the Characteristics of Voltage Variation / Traditional Solutions and Emerging Solution Forecast / Allowing and Tolerating Voltage Emergencies / Predicting and Avoiding Voltage Emergencies / Eliminiating Recurring Voltage Emergencies / Future Directions on Resiliency
Security Basics for Computer Architects
Design for security is an essential aspect of the design of future computers. However, security is not well understood by the computer architecture community. Many important security aspects have evolved over the last several decades in the cryptography, operating systems, and networking communities. This book attempts to introduce the computer architecture student, researcher, or practitioner to the basic concepts of security and threat-based design. Past work in different security communities can inform our thinking and provide a rich set of technologies for building architectural support for security into all future computers and embedded computing devices and appliances. I have tried to keep the book short, which means that many interesting topics and applications could not be included. What the book focuses on are the fundamental security concepts, across different security communities, that should be understood by any computer architect trying to design or evaluate security-awarecomputer architectures.
Multithreading Architecture
Multithreaded architectures now appear across the entire range of computing devices, from the highest-performing general purpose devices to low-end embedded processors. Multithreading enables a processor core to more effectively utilize its computational resources, as a stall in one thread need not cause execution resources to be idle. This enables the computer architect to maximize performance within area constraints, power constraints, or energy constraints. However, the architectural options for the processor designer or architect looking to implement multithreading are quite extensive and varied, as evidenced not only by the research literature but also by the variety of commercial implementations. This book introduces the basic concepts of multithreading, describes a number of models of multithreading, and then develops the three classic models (coarse-grain, fine-grain, and simultaneous multithreading) in greater detail. It describes a wide variety of architectural and software design tradeoffs, as well as opportunities specific to multithreading architectures. Finally, it details a number of important commercial and academic hardware implementations of multithreading. Table of Contents: Introduction / Multithreaded Execution Models / Coarse-Grain Multithreading / Fine-Grain Multithreading / Simultaneous Multithreading / Managing Contention / New Opportunities for Multithreaded Processors / Experimentation and Metrics / Implementations of Multithreaded Processors / Conclusion
Single-Instruction Multiple-Data Execution
Having hit power limitations to even more aggressive out-of-order execution in processor cores, many architects in the past decade have turned to single-instruction-multiple-data (SIMD) execution to increase single-threaded performance. SIMD execution, or having a single instruction drive execution of an identical operation on multiple data items, was already well established as a technique to efficiently exploit data parallelism. Furthermore, support for it was already included in many commodity processors. However, in the past decade, SIMD execution has seen a dramatic increase in the set of applications using it, which has motivated big improvements in hardware support in mainstream microprocessors. The easiest way to provide a big performance boost to SIMD hardware is to make it wider--i.e., increase the number of data items hardware operates on simultaneously. Indeed, microprocessor vendors have done this. However, as we exploit more data parallelism in applications, certain challenges can negatively impact performance. In particular, conditional execution, non-contiguous memory accesses, and the presence of some dependences across data items are key roadblocks to achieving peak performance with SIMD execution. This book first describes data parallelism, and why it is so common in popular applications. We then describe SIMD execution, and explain where its performance and energy benefits come from compared to other techniques to exploit parallelism. Finally, we describe SIMD hardware support in current commodity microprocessors. This includes both expected design tradeoffs, as well as unexpected ones, as we work to overcome challenges encountered when trying to map real software to SIMD execution.
Customizable Computing
Since the end of Dennard scaling in the early 2000s, improving the energy efficiency of computation has been the main concern of the research community and industry. The large energy efficiency gap between general-purpose processors and application-specific integrated circuits (ASICs) motivates the exploration of customizable architectures, where one can adapt the architecture to the workload. In this Synthesis lecture, we present an overview and introduction of the recent developments on energy-efficient customizable architectures, including customizable cores and accelerators, on-chip memory customization, and interconnect optimization. In addition to a discussion of the general techniques and classification of different approaches used in each area, we also highlight and illustrate some of the most successful design examples in each category and discuss their impact on performance and energy efficiency. We hope that this work captures the state-of-the-art research and development oncustomizable architectures and serves as a useful reference basis for further research, design, and implementation for large-scale deployment in future computing systems.
A Primer on Hardware Prefetching
Since the 1970's, microprocessor-based digital platforms have been riding Moore's law, allowing for doubling of density for the same area roughly every two years. However, whereas microprocessor fabrication has focused on increasing instruction execution rate, memory fabrication technologies have focused primarily on an increase in capacity with negligible increase in speed. This divergent trend in performance between the processors and memory has led to a phenomenon referred to as the "Memory Wall." To overcome the memory wall, designers have resorted to a hierarchy of cache memory levels, which rely on the principal of memory access locality to reduce the observed memory access time and the performance gap between processors and memory. Unfortunately, important workload classes exhibit adverse memory access patterns that baffle the simple policies built into modern cache hierarchies to move instructions and data across cache levels. As such, processors often spend much time idling upon a demand fetch of memory blocks that miss in higher cache levels. Prefetching--predicting future memory accesses and issuing requests for the corresponding memory blocks in advance of explicit accesses--is an effective approach to hide memory access latency. There have been a myriad of proposed prefetching techniques, and nearly every modern processor includes some hardware prefetching mechanisms targeting simple and regular memory access patterns. This primer offers an overview of the various classes of hardware prefetchers for instructions and data proposed in the research literature, and presents examples of techniques incorporated into modern microprocessors.
A Primer on Compression in the Memory Hierarchy
This synthesis lecture presents the current state-of-the-art in applying low-latency, lossless hardware compression algorithms to cache, memory, and the memory/cache link. There are many non-trivial challenges that must be addressed to make data compression work well in this context. First, since compressed data must be decompressed before it can be accessed, decompression latency ends up on the critical memory access path. This imposes a significant constraint on the choice of compression algorithms. Second, while conventional memory systems store fixed-size entities like data types, cache blocks, and memory pages, these entities will suddenly vary in size in a memory system that employs compression. Dealing with variable size entities in a memory system using compression has a significant impact on the way caches are organized and how to manage the resources in main memory. We systematically discuss solutions in the open literature to these problems. Chapter 2 provides the foundations of data compression by first introducing the fundamental concept of value locality. We then introduce a taxonomy of compression algorithms and show how previously proposed algorithms fit within that logical framework. Chapter 3 discusses the different ways that cache memory systems can employ compression, focusing on the trade-offs between latency, capacity, and complexity of alternative ways to compact compressed cache blocks. Chapter 4 discusses issues in applying data compression to main memory and Chapter 5 covers techniques for compressing data on the cache-to-memory links. This book should help a skilled memory system designer understand the fundamental challenges in applying compression to the memory hierarchy and introduce him/her to the state-of-the-art techniques in addressing them.
E-Systems for the 21st Century
E-based systems and computer networks are becoming standard practice across all sectors, including health, engineering, business, education, security, and citizen interaction with local and national government. With contributions from researchers and practitioners from around the world, this two-volume book discusses and reports on new and important developments in the field of e-systems, covering a wide range of current issues in the design, engineering, and adoption of e-systems.
Shared-Memory Synchronization
This book offers a comprehensive survey of shared-memory synchronization, with an emphasis on "systems-level" issues. It includes sufficient coverage of architectural details to understand correctness and performance on modern multicore machines, and sufficient coverage of higher-level issues to understand how synchronization is embedded in modern programming languages.The primary intended audience for this book is "systems programmers"--the authors of operating systems, library packages, language run-time systems, concurrent data structures, and server and utility programs. Much of the discussion should also be of interest to application programmers who want to make good use of the synchronization mechanisms available to them, and to computer architects who want to understand the ramifications of their design decisions on systems-level code.
Analyzing Analytics
This book aims to achieve the following goals: (1) to provide a high-level survey of key analytics models and algorithms without going into mathematical details; (2) to analyze the usage patterns of these models; and (3) to discuss opportunities for accelerating analytics workloads using software, hardware, and system approaches. The book first describes 14 key analytics models (exemplars) that span data mining, machine learning, and data management domains. For each analytics exemplar, we summarize its computational and runtime patterns and apply the information to evaluate parallelization and acceleration alternatives for that exemplar. Using case studies from important application domains such as deep learning, text analytics, and business intelligence (BI), we demonstrate how various software and hardware acceleration strategies are implemented in practice. This book is intended for both experienced professionals and students who are interested in understanding core algorithms behind analytics workloads. It is designed to serve as a guide for addressing various open problems in accelerating analytics workloads, e.g., new architectural features for supporting analytics workloads, impact on programming models and runtime systems, and designing analytics systems.
Datacenter Design and Management
An era of big data demands datacenters, which house the computing infrastructure that translates raw data into valuable information. This book defines datacenters broadly, as large distributed systems that perform parallel computation for diverse users. These systems exist in multiple forms--private and public--and are built at multiple scales. Datacenter design and management is multifaceted, requiring the simultaneous pursuit of multiple objectives. Performance, efficiency, and fairness are first-order design and management objectives, which can each be viewed from several perspectives. This book surveys datacenter research from a computer architect's perspective, addressing challenges in applications, design, management, server simulation, and system simulation. This perspective complements the rich bodies of work in datacenters as a warehouse-scale system, which study the implications for infrastructure that encloses computing equipment, and in datacenters as distributed systems, which employ abstract details in processor and memory subsystems. This book is written for first- or second-year graduate students in computer architecture and may be helpful for those in computer systems. The goal of this book is to prepare computer architects for datacenter-oriented research by describing prevalent perspectives and the state-of-the-art.
On-Chip Photonic Interconnects
As the number of cores on a chip continues to climb, architects will need to address both bandwidth and power consumption issues related to the interconnection network. Electrical interconnects are not likely to scale well to a large number of processors for energy efficiency reasons, and the problem is compounded by the fact that there is a fixed total power budget for a die, dictated by the amount of heat that can be dissipated without special (and expensive) cooling and packaging techniques. Thus, there is a need to seek alternatives to electrical signaling for on-chip interconnection applications. Photonics, which has a fundamentally different mechanism of signal propagation, offers the potential to not only overcome the drawbacks of electrical signaling, but also enable the architect to build energy efficient, scalable systems. The purpose of this book is to introduce computer architects to the possibilities and challenges of working with photons and designing on-chip photonic interconnection networks.
Non-Volatile In-Memory Computing by Spintronics
Exa-scale computing needs to re-examine the existing hardware platform that can support intensive data-oriented computing. Since the main bottleneck is from memory, we aim to develop an energy-efficient in-memory computing platform in this book. First, the models of spin-transfer torque magnetic tunnel junction and racetrack memory are presented. Next, we show that the spintronics could be a candidate for future data-oriented computing for storage, logic, and interconnect. As a result, by utilizing spintronics, in-memory-based computing has been applied for data encryption and machine learning. The implementations of in-memory AES, Simon cipher, as well as interconnect are explained in details. In addition, in-memory-based machine learning and face recognition are also illustrated in this book.
Optimization and Mathematical Modeling in Computer Architecture
In this book we give an overview of modeling techniques used to describe computer systems to mathematical optimization tools. We give a brief introduction to various classes of mathematical optimization frameworks with special focus on mixed integer linear programming which provides a good balance between solver time and expressiveness. We present four detailed case studies -- instruction set customization, data center resource management, spatial architecture scheduling, and resource allocation in tiled architectures -- showing how MILP can be used and quantifying by how much it outperforms traditional design exploration techniques. This book should help a skilled systems designer to learn techniques for using MILP in their problems, and the skilled optimization expert to understand the types of computer systems problems that MILP can be applied to.
Architectural and Operating System Support for Virtual Memory
This book provides computer engineers, academic researchers, new graduate students, and seasoned practitioners an end-to-end overview of virtual memory. We begin with a recap of foundational concepts and discuss not only state-of-the-art virtual memory hardware and software support available today, but also emerging research trends in this space. The span of topics covers processor microarchitecture, memory systems, operating system design, and memory allocation. We show how efficient virtual memory implementations hinge on careful hardware and software cooperation, and we discuss new research directions aimed at addressing emerging problems in this space. Virtual memory is a classic computer science abstraction and one of the pillars of the computing revolution. It has long enabled hardware flexibility, software portability, and overall better security, to name just a few of its powerful benefits. Nearly all user-level programs today take for granted that they will have beenfreed from the burden of physical memory management by the hardware, the operating system, device drivers, and system libraries. However, despite its ubiquity in systems ranging from warehouse-scale datacenters to embedded Internet of Things (IoT) devices, the overheads of virtual memory are becoming a critical performance bottleneck today. Virtual memory architectures designed for individual CPUs or even individual cores are in many cases struggling to scale up and scale out to today's systems which now increasingly include exotic hardware accelerators (such as GPUs, FPGAs, or DSPs) and emerging memory technologies (such as non-volatile memory), and which run increasingly intensive workloads (such as virtualized and/or "big data" applications). As such, many of the fundamental abstractions and implementation approaches for virtual memory are being augmented, extended, or entirely rebuilt in order to ensure that virtual memory remains viable and performant in the years to come.
Machine Learning on Kubernetes
Build a Kubernetes-based self-serving, agile data science and machine learning ecosystem for your organization using reliable and secure open source technologiesKey Features: Build a complete machine learning platform on KubernetesImprove the agility and velocity of your team by adopting the self-service capabilities of the platformReduce time-to-market by automating data pipelines and model training and deploymentBook Description: MLOps is an emerging field that aims to bring repeatability, automation, and standardization of the software engineering domain to data science and machine learning engineering. By implementing MLOps with Kubernetes, data scientists, IT professionals, and data engineers can collaborate and build machine learning solutions that deliver business value for their organization.You'll begin by understanding the different components of a machine learning project. Then, you'll design and build a practical end-to-end machine learning project using open source software. As you progress, you'll understand the basics of MLOps and the value it can bring to machine learning projects. You will also gain experience in building, configuring, and using an open source, containerized machine learning platform. In later chapters, you will prepare data, build and deploy machine learning models, and automate workflow tasks using the same platform. Finally, the exercises in this book will help you get hands-on experience in Kubernetes and open source tools, such as JupyterHub, MLflow, and Airflow.By the end of this book, you'll have learned how to effectively build, train, and deploy a machine learning model using the machine learning platform you built.What You Will Learn: Understand the different stages of a machine learning projectUse open source software to build a machine learning platform on KubernetesImplement a complete ML project using the machine learning platform presented in this bookImprove on your organization's collaborative journey toward machine learningDiscover how to use the platform as a data engineer, ML engineer, or data scientistFind out how to apply machine learning to solve real business problemsWho this book is for: This book is for data scientists, data engineers, IT platform owners, AI product owners, and data architects who want to build their own platform for ML development. Although this book starts with the basics, a solid understanding of Python and Kubernetes, along with knowledge of the basic concepts of data science and data engineering will help you grasp the topics covered in this book in a better way.
Design of Internet of Things
The text focuses on practical design aspects such as how to finalize a processor integrated circuit, which operating system to use, etc. in a single volume.
CMake Best Practices
Explore a compendium of tips, tricks, and techniques for leveraging CMake and empowering your software development workflow.Key Features: Understand what CMake is, how it works, and how to interact with itDiscover how to properly create and maintain well-structured CMake projectsExplore tools and techniques to get the most out of your CMake projectBook Description: CMake is a powerful tool used to perform a wide variety of tasks, so finding a good starting point for learning CMake is difficult. This book cuts to the core and covers the most common tasks that can be accomplished with CMake without taking an academic approach. While the CMake documentation is comprehensive, it is often hard to find good examples of how things fit together, especially since there are lots of dirty hacks and obsolete solutions available on the internet. This book focuses on helping you to tie things together and create clean and maintainable projects with CMake.You'll not only get to grips with the basics but also work through real-world examples of structuring large and complex maintainable projects and creating builds that run in any programming environment. You'll understand the steps to integrate and automate various tools for improving the overall software quality, such as testing frameworks, fuzzers, and automatic generation of documentation. And since writing code is only half of the work, the book also guides you in creating installers and packaging and distributing your software. All this is tailored to modern development workflows that make heavy use of CI/CD infrastructure.By the end of this CMake book, you'll be able to set up and maintain complex software projects using CMake in the best way possible.What You Will Learn: Get to grips with architecting a well-structured CMake projectModularize and reuse CMake code across projectsIntegrate various tools for static analysis, linting, formatting, and documentation into a CMake projectGet hands-on with performing cross-platform buildsDiscover how you can easily use different toolchains with CMakeGet started with crafting a well-defined and portable build environment for your projectWho this book is for: This book is for software engineers and build system maintainers working with C or C++ on a regular basis and trying to use CMake to better effect for their everyday tasks. Basic C++ and general programming knowledge will help you to better understand the examples covered in the book.
Machine Learning for Edge Computing
This book divides edge intelligence into AI for edge (intelligence-enabled edge computing) and AI on edge (artificial intelligence on edge). It focuses on providing optimal solutions to the key concerns in edge computing through effective AI technologies, and it also discusses how to build AI models, i.e., model training and inference, on edge.
Green Internet of Things
This book focusses on both theoretical and implementation aspects in green computing, next generation networks or networks that can be utilized in providing green systems through IoT enabling technologies i.e. technology behind its architecture and building components. It also encompasses design concepts and related advanced computing.
Metaheuristic Computation with Matlab(r)
The main purpose of this book is to provide a unified view of the most popular metaheuristic methods. Under this perspective, it has presented the fundamental design principles as well as the operators of metaheuristic approaches which are considered essential.
Internet of Things
This book examines IoT as applied to real-life organizations and systems. It considers IoT as a disruptive technology that requires examination and consideration in multiple domains like CPS, ubiquitous distributed computing, sensor technologies, wireless sensor networks, data security and privacy, big data, machine learning, blockchain, and so on.
Network Evolution and Applications
The text gives an understanding and concepts of different types of Internet of things and sensor networks along with respective protocols to the new learners in the field of networking. It further covers foundational information of artificial intelligence for telecommunications.
Blockchain, Big Data and Machine Learning
Present book covers new paradigms in Blockchain, Big Data and Machine Learning concepts including applications and case studies. It explains dead fusion in realizing the privacy and security of blockchain based data analytic environment. Recent research of security based on big data, blockchain and machine learning has been explained.
Dataset Shift in Machine Learning
An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions. Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift. The chapters offer a mathematical and philosophical introduction to the problem, place dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning, provide theoretical views of dataset and covariate shift (including decision theoretic and Bayesian perspectives), and present algorithms for covariate shift. Contributors Shai Ben-David, Steffen Bickel, Karsten Borgwardt, Michael Br羹ckner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Choon Hui Teo, Takafumi Kanamori, Klaus-Robert M羹ller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Sch繹lkopf Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama
Telepresence: Actual and Virtual
1. EARLY HISTORY OF ROBOTIC TELEPRESENCE AND VIRTUAL REALITY. 2. ELEMENTS OF THE TECHNOLOGY. 3. WHAT IS TELEPRESENCE? WHAT IS REALITY?. 4. APPLICATIONS. 5. CHALLENGES FOR ROBOTIC TELEPRESENCE AND VIRTUAL REALITY. APPENDIX 1. RESPONSES TO POLLING. APPENDIX 2. BIBLIOGRAPHY.
Introduction to the Cyber Ranges
With the rising cybercrimes, a well-trained cybersecurity workforce in an organization has become a necessity. This book aims to provide substantial theoretical knowhow on cyber ranges, their architectural design, along with a case study of existing cyber ranges in leading urban sectors like military, academic and commercial.
Supervised Machine Learning
AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. It comprises of bootstrapping to create multiple training and testing data sets, design and analysis of statistical experiments and optimal hyper-parameters for ML methods.
Designing Secure Iot Devices with the Arm Platform Security Architecture and Cortex-M33
Designing Secure IoT devices with the Arm Platform Security Architecture and Cortex-M33 explains how to design and deploy secure IoT devices based on the Cortex-M23/M33 processor. The book is split into three parts. First, it introduces the Cortex-M33 and its architectural design and major processor peripherals. Second, it shows how to design secure software and secure communications to minimize the threat of both hardware and software hacking. And finally, it examines common IoT cloud systems and how to design and deploy a fleet of IoT devices. Example projects are provided for the Keil MDK-ARM and NXP LPCXpresso tool chains. Since their inception, microcontrollers have been designed as functional devices with a CPU, memory and peripherals that can be programmed to accomplish a huge range of tasks. With the growth of internet connected devices and the Internet of Things (IoT), "plain old microcontrollers" are no longer suitable as they lack the features necessary to create both a secure and functional device. The recent development by ARM of the Cortex M23 and M33 architecture is intended for today's IoT world.
Mobile Microservices
In the 5G era, edge computing and new ecosystems of mobile microservices enable new business models, strategies, and competitive advantage. Focusing on microservices, this book introduces the essential concepts, technologies, and trade-offs in the edge computing architectural stack, providing for widespread adoption and dissemination. The book elucidates the concepts, architectures, well-defined building blocks, and prototypes for mobile microservice platforms and pervasive application development, as well as the implementation and configuration of service middleware and AI-based microservices. A goal-oriented service composition model is then proposed by the author, allowing for an economic assessment of connected, smart mobile services. Based on this model, costs can be minimized through statistical workload aggregation effects or backhaul data transport reduction, and customer experience and safety can be enhanced through reduced response times.This title will be a useful guide for students and IT professionals to get started with microservices and when studying the use of microservices in pervasive applications. It will also appeal to researchers and students studying software architecture and service-oriented computing, and especially those interested in edge computing, pervasive computing, the Internet of Things, and mobile microservices.
Blockchain for Iot
This book provides the basic concepts of blockchain technology and its applications to varied domains catering to socio-technical fields. It also introduces intelligent blockchain platforms by way of infusing elements of computational intelligence into blockchain technology.
Object Detection with Deep Learning Models
The book discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision.
Internet of Things
This book examines IoT as applied to real-life organizations and systems. It considers IoT as a disruptive technology that requires examination and consideration in multiple domains like CPS, ubiquitous distributed computing, sensor technologies, wireless sensor networks, data security and privacy, big data, machine learning, blockchain, and so on.
Artificial Intelligence in a Throughput Model
Physical and behavioral biometric technologies such as fingerprinting, facial recognition, voice identification, etc. have enhanced the level of security substantially in recent years. Governments and corporates have employed these technologies to achieve better customer satisfaction. However, biometrics faces major challenges in reducing criminal, terrorist activities and electronic frauds, especially in choosing appropriate decision-making algorithms. To face this challenge, new developments have been made, that amalgamate biometrics with artificial intelligence (AI) in decision-making modeling. Advanced software algorithms of AI, processing information offered by biometric technology, achieve better results. This has led to growth in the biometrics technology industry, and is set to increase the security and internal control operations manifold. This book provides an overview of the existing biometric technologies, decision-making algorithms and the growth opportunity in biometrics. The book proposes a throughput model, which draws on computer science, economics and psychology to model perceptual, informational sources, judgmental processes and decision choice algorithms. It reviews how biometrics might be applied to reduce risks to individuals and organizations, especially when dealing with digital-based media.
Deep Learning in Computer Vision
This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community.
Big Data Management in Sensing - Applications in AI and Iot
The book is centrally focused on human computer Interaction and how sensors within small and wide groups of Nano-robots employ Deep Learning for applications in industry. It covers a wide array of topics that are useful for researchers and students to gain knowledge about AI and sensors in nanobots. Furthermore, the book explores Deep Learning approaches to enhance the accuracy of AI systems applied in medical robotics for surgical techniques. Secondly, we plan to explore bio-nano-robotics, which is a field in nano-robotics, that deals with automatic intelligence handling, self-assembly and replication, information processing and programmability.
What Every Engineer Should Know About the Internet of Things
Internet of Things (IoT) products and cyber-physical systems (CPS) are being utilized in almost every discipline and there continues to be significant increases in spending on design, development, and deployment of IoT applications and analytics within every domain, from our homes, schools, government, and industry. This practical text provides an introduction to IoT that can be understood by every engineering discipline and discusses detailed applications of IoT. Developed to help engineers navigate this increasingly important and cross-disciplinary topic, this work: Offers research-based examples and case studies to facilitate the understanding of each IoT primitive Highlights IoT's connection to blockchain Provides and understanding of benefits and challenges of IoT and its importance to a variety of engineering disciplines Written to be accessible to non-experts in the subject, What Every Engineer Should Know About the Internet of Things communicates the importance of this technology and how it can support and challenge all interrelated actors as well as all involved assets across many domains.
Computational Engineering
Computational engineering is a promising and emerging field that deals with the development of models for providing high performance computing, to analyse designs and fix complex problems. Its framework includes data science for developing algorithms and mathematical foundations like fourier analysis and discrete fourier transforms. This book integrates physical and experimental approaches applied in the development of the discipline. It includes comprehensive techniques and applications to fabricate structures and networks. It focuses upon applied mathematics, computer modelling and other related fields. This text is an asset for anyone who is interested in the field of computational engineering.
Computer Engineering
Computer engineering is a rapidly evolving field that integrates computer science and electrical engineering. Some of the diverse topics covered in this book address the varied branches that fall within the scope of this subject by discussing concepts like multimedia, embedded systems, computer networking and language programming, microprocessors, etc. It is a compilation of valuable researches and case-studies by eminent experts from around the world that aim to explain the most significant concepts and advancements in the above mentioned fields. It will help the readers in keeping pace with the rapid changes in this discipline.
Innovations in Computer Science and Engineering
Rapid technological changes have led to new innovations in computer science and engineering. The ever growing need for advanced technology has fueled the research in the fields of computing, signal processing and embedded systems. This book examines various studies that are constantly contributing towards advancing technologies and brings forth new areas for future research. This book is an attempt to provide in-depth knowledge about the theory and practice of mobile computing, robotics and industrial electronics. It will provide comprehensive knowledge to the readers.
Fundamentals of Machine Learning
The scientific study of statistical models and algorithms that computer systems use in order to perform a specific task without any explicit instructions is referred to as machine learning. It relies on patterns and inference. Machine learning is a subset of artificial intelligence. The study of mathematical optimization contributes significantly to the methods, applications and theory of machine learning. Some of the different models, which are used within this field are artificial neural networks, decision trees and Bayesian networks. Machine learning is applied in various other fields such as in machine perception, agriculture, adaptive websites, bioinformatics, optimization, sentiment analysis, etc. The topics included in this book on machine learning are of utmost significance and bound to provide incredible insights to readers. It unfolds the innovative aspects of this field, which will be crucial for the progress of this field in the future. Those in search of information to further their knowledge will be greatly assisted by this book.
Handbook of Automated Scoring
Overview of the innovative automated scoring theory, latest development of computational methodologies, real world large-scale applications for automated scoring for complex tasks. Provides a scientifically grounded description of the key research and development efforts that it takes to move automated scoring systems into operational practice.
Introduction to Wavelet Transforms
This book introduces the basics of wavelet transforms, including continuous and discrete wavelet transforms, and Daubechies wavelets. It also includes elementary examples of wavelets, and certain well-known applications.
Machine Learning in Healthcare
Machine Learning in Healthcare discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. It covers fundamental concepts including mathematical requisites and traditional machine-learning framework followed by advanced machine-learning methods and their applications in medical fields.
Software Architecture for Busy Developers
A quick start guide to learning essential software architecture tools, frameworks, design patterns, and best practicesKey Features: Apply critical thinking to your software development and architecture practices and bring structure to your approach using well-known IT standardsUnderstand the impact of cloud-native approaches on software architectureIntegrate the latest technology trends into your architectural designsBook Description: Are you a seasoned developer who likes to add value to a project beyond just writing code? Have you realized that good development practices are not enough to make a project successful, and you now want to embrace the bigger picture in the IT landscape? If so, you're ready to become a software architect; someone who can deal with any IT stakeholder as well as add value to the numerous dimensions of software development.The sheer volume of content on software architecture can be overwhelming, however. Software Architecture for Busy Developers is here to help. Written by St矇phane Eyskens, author of The Azure Cloud Native Mapbook, this book guides you through your software architecture journey in a pragmatic way using real-world scenarios. By drawing on over 20 years of consulting experience, St矇phane will help you understand the role of a software architect, without the fluff or unnecessarily complex theory.You'll begin by understanding what non-functional requirements mean and how they concretely impact target architecture. The book then covers different frameworks used across the entire enterprise landscape with the help of use cases and examples. Finally, you'll discover ways in which the cloud is becoming a game changer in the world of software architecture.By the end of this book, you'll have gained a holistic understanding of the architectural landscape, as well as more specific software architecture skills. You'll also be ready to pursue your software architecture journey on your own - and in just one weekend!What You Will Learn: Understand the roles and responsibilities of a software architectExplore enterprise architecture tools and frameworks such as The Open Group Architecture Framework (TOGAF) and ArchiMateGet to grips with key design patterns used in software developmentExplore the widely adopted Architecture Tradeoff Analysis Method (ATAM)Discover the benefits and drawbacks of monoliths, service-oriented architecture (SOA), and microservicesStay on top of trending architectures such as API-driven, serverless, and cloud nativeWho this book is for: This book is for developers who want to move up the organizational ladder and become software architects by understanding the broader application landscape and discovering how large enterprises deal with software architecture practices. Prior knowledge of software development is required to get the most out of this book.
