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Advanced Data Mining and Applications: 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings (Lecture Notes in Computer Science #10086)
by Jinyan Li, Xue Li, Shuliang Wang, Jianxin Li and Quan Z. ShengThis book constitutes the proceedings of the 12th International Conference on Advanced Data Mining and Applications, ADMA 2016, held in Gold Coast, Australia, in December 2016. The 70 papers presented in this volume were carefully reviewed and selected from 105 submissions. The selected papers covered a wide variety of important topics in the area of data mining, including parallel and distributed data mining algorithms, mining on data streams, graph mining, spatial data mining, multimedia data mining, Web mining, the Internet of Things, health informatics, and biomedical data mining.
Advanced Data Mining and Applications: 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2–4, 2022, Proceedings, Part II (Lecture Notes in Computer Science #13088)
by Bohan Li Lin Yue Jing Jiang Weitong Chen Xue Li Guodong Long Fei Fang Han YuThis book constitutes the proceedings of the 17th International Conference on Advanced Data Mining and Applications, ADMA 2021, held in Sydney, Australia in February 2022.*The 26 full papers presented together with 35 short papers were carefully reviewed and selected from 116 submissions. The papers were organized in topical sections in Part II named: Pattern mining; Graph mining; Text mining; Multimedia and time series data mining; and Classification, clustering and recommendation. * The conference was originally planned for December 2021, but was postponed to 2022.
Advanced Data Mining and Applications: 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2–4, 2022, Proceedings, Part I (Lecture Notes in Computer Science #13087)
by Bohan Li Lin Yue Jing Jiang Weitong Chen Xue Li Guodong Long Fei Fang Han YuThis book constitutes the proceedings of the 17th International Conference on Advanced Data Mining and Applications, ADMA 2021, held in Sydney, Australia in February 2022.*The 26 full papers presented together with 35 short papers were carefully reviewed and selected from 116 submissions. The papers were organized in topical sections in Part I, including: Healthcare, Education, Web Application and On-device application. * The conference was originally planned for December 2021, but was postponed to 2022.
Advanced Data Mining and Applications: 15th International Conference, ADMA 2019, Dalian, China, November 21–23, 2019, Proceedings (Lecture Notes in Computer Science #11888)
by Shuliang Wang Xue Li Jianxin Li Sen Wang Shaowen QinThis book constitutes the proceedings of the 15th International Conference on Advanced Data Mining and Applications, ADMA 2019, held in Dalian, China in November 2019. The 39 full papers presented together with 26 short papers and 2 demo papers were carefully reviewed and selected from 170 submissions. The papers were organized in topical sections named: Data Mining Foundations; Classification and Clustering Methods; Recommender Systems; Social Network and Social Media; Behavior Modeling and User Profiling; Text and Multimedia Mining; Spatial-Temporal Data; Medical and Healthcare Data/Decision Analytics; and Other Applications.
Advanced Data Mining and Applications: 19th International Conference, ADMA 2023, Shenyang, China, August 21–23, 2023, Proceedings, Part V (Lecture Notes in Computer Science #14180)
by Xiaochun Yang Heru Suhartanto Guoren Wang Bin Wang Jing Jiang Bing Li Huaijie Zhu Ningning CuiThis book constitutes the refereed proceedings of the 19th International Conference on Advanced Data Mining and Applications, ADMA 2023, held in Shenyang, China, during August 21–23, 2023.The 216 full papers included in this book were carefully reviewed and selected from 503 submissions. They were organized in topical sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining.
Advanced Data Mining and Applications: 19th International Conference, ADMA 2023, Shenyang, China, August 21–23, 2023, Proceedings, Part IV (Lecture Notes in Computer Science #14179)
by Xiaochun Yang Heru Suhartanto Guoren Wang Bin Wang Jing Jiang Bing Li Huaijie Zhu Ningning CuiThis book constitutes the refereed proceedings of the 19th International Conference on Advanced Data Mining and Applications, ADMA 2023, held in Shenyang, China, during August 21–23, 2023.The 216 full papers included in this book were carefully reviewed and selected from 503 submissions. They were organized in topical sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining.
Advanced Data Mining and Applications: 19th International Conference, ADMA 2023, Shenyang, China, August 21–23, 2023, Proceedings, Part III (Lecture Notes in Computer Science #14178)
by Xiaochun Yang Heru Suhartanto Guoren Wang Bin Wang Jing Jiang Bing Li Huaijie Zhu Ningning CuiThis book constitutes the refereed proceedings of the 19th International Conference on Advanced Data Mining and Applications, ADMA 2023, held in Shenyang, China, during August 21–23, 2023.The 216 full papers included in this book were carefully reviewed and selected from 503 submissions. They were organized in topical sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining.
Advanced Data Mining and Applications: 19th International Conference, ADMA 2023, Shenyang, China, August 21–23, 2023, Proceedings, Part II (Lecture Notes in Computer Science #14177)
by Xiaochun Yang Heru Suhartanto Guoren Wang Bin Wang Jing Jiang Bing Li Huaijie Zhu Ningning CuiThis book constitutes the refereed proceedings of the 19th International Conference on Advanced Data Mining and Applications, ADMA 2023, held in Shenyang, China, during August 21–23, 2023.The 216 full papers included in this book were carefully reviewed and selected from 503 submissions. They were organized in topical sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining.
Advanced Data Mining and Applications: 19th International Conference, ADMA 2023, Shenyang, China, August 21–23, 2023, Proceedings, Part I (Lecture Notes in Computer Science #14176)
by Xiaochun Yang Heru Suhartanto Guoren Wang Bin Wang Jing Jiang Bing Li Huaijie Zhu Ningning CuiThis book constitutes the refereed proceedings of the 19th International Conference on Advanced Data Mining and Applications, ADMA 2023, held in Shenyang, China, during August 21–23, 2023.The 216 full papers included in this book were carefully reviewed and selected from 503 submissions. They were organized in topical sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining.
Advanced Data Mining and Applications: 16th International Conference, ADMA 2020, Foshan, China, November 12–14, 2020, Proceedings (Lecture Notes in Computer Science #12447)
by Xiaochun Yang Chang-Dong Wang Md. Saiful Islam Zheng ZhangThis book constitutes the proceedings of the 16th International Conference on Advanced Data Mining and Applications, ADMA 2020, held in Foshan, China in November 2020.The 35 full papers presented together with 14 short papers papers were carefully reviewed and selected from 96 submissions. The papers were organized in topical sections named: Machine Learning; Text Mining; Graph Mining; Predictive Analytics; Recommender Systems; Privacy and Security; Query Processing; Data Mining Applications.
Advanced Data Science and Analytics with Python (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
by Jesus Rogel-SalazarAdvanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications. Features: Targets readers with a background in programming, who are interested in the tools used in data analytics and data science Uses Python throughout Presents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needs Focuses on the practical use of the tools rather than on lengthy explanations Provides the reader with the opportunity to use the book whenever needed rather than following a sequential path The book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book. Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences – in this case, literally to the users’ fingertips in the form of an iPhone app. About the Author Dr. Jesús Rogel-Salazar is a lead data scientist in the field, working for companies such as Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.
Advanced Data Structures: Theory and Applications
by Suman Saha Shailendra ShuklaAdvanced data structures is a core course in Computer Science which most graduate program in Computer Science, Computer Science and Engineering, and other allied engineering disciplines, offer during the first year or first semester of the curriculum. The objective of this course is to enable students to have the much-needed foundation for advanced technical skill, leading to better problem-solving in their respective disciplines. Although the course is running in almost all the technical universities for decades, major changes in the syllabus have been observed due to the recent paradigm shift of computation which is more focused on huge data and internet-based technologies. Majority of the institute has been redefined their course content of advanced data structure to fit the current need and course material heavily relies on research papers because of nonavailability of the redefined text book advanced data structure. To the best of our knowledge well-known textbook on advanced data structure provides only partial coverage of the syllabus. The book offers comprehensive coverage of the most essential topics, including: Part I details advancements on basic data structures, viz., cuckoo hashing, skip list, tango tree and Fibonacci heaps and index files. Part II details data structures of different evolving data domains like special data structures, temporal data structures, external memory data structures, distributed and streaming data structures. Part III elucidates the applications of these data structures on different areas of computer science viz, network, www, DBMS, cryptography, graphics to name a few. The concepts and techniques behind each data structure and their applications have been explained. Every chapter includes a variety of Illustrative Problems pertaining to the data structure(s) detailed, a summary of the technical content of the chapter and a list of Review Questions, to reinforce the comprehension of the concepts. The book could be used both as an introductory or an advanced-level textbook for the advanced undergraduate, graduate and research programmes which offer advanced data structures as a core or an elective course. While the book is primarily meant to serve as a course material for use in the classroom, it could be used as a starting point for the beginner researcher of a specific domain.
Advanced Decision-Making Methods and Applications in System Safety and Reliability Problems: Approaches, Case Studies, Multi-criteria Decision-Making, Multi-objective Decision-Making, Fuzzy Risk-Based Models (Studies in Systems, Decision and Control #211)
by He Li Mohammad YazdiThis book reviews and presents several approaches to advanced decision-making models for safety and risk assessment. Each introduced model provides case studies indicating a high level of efficiency, robustness, and applicability, which allow readers to utilize them in their understudy risk-based assessment applications. The book begins by introducing a novel dynamic DEMATEL for improving safety management systems. It then progresses logically, dedicating a chapter to each approach, including advanced FMEA with probabilistic linguistic preference relations, Bayesian Network approach and interval type-2 fuzzy set, advanced TOPSIS with spherical fuzzy set, and advanced BWM with neutrosophic fuzzy set and evidence theory. This book will be of interest to professionals and researchers working in the field of system safety and reliability and postgraduate and undergraduate students studying applications of decision-making tools and expert systems.
Advanced Deep Learning for Engineers and Scientists: A Practical Approach (EAI/Springer Innovations in Communication and Computing)
by Ramani Kannan G. R. Kanagachidambaresan S. Albert Alexander Kolla Bhanu PrakashThis book provides a complete illustration of deep learning concepts with case-studies and practical examples useful for real time applications. This book introduces a broad range of topics in deep learning. The authors start with the fundamentals, architectures, tools needed for effective implementation for scientists. They then present technical exposure towards deep learning using Keras, Tensorflow, Pytorch and Python. They proceed with advanced concepts with hands-on sessions for deep learning. Engineers, scientists, researches looking for a practical approach to deep learning will enjoy this book.Presents practical basics to advanced concepts in deep learning and how to apply them through various projects;Discusses topics such as deep learning in smart grids and renewable energy & sustainable development;Explains how to implement advanced techniques in deep learning using Pytorch, Keras, Python programming.
Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more
by Rowel AtienzaUnderstanding and coding advanced deep learning algorithms with the most intuitive deep learning library in existenceKey FeaturesExplore the most advanced deep learning techniques that drive modern AI resultsImplement deep neural networks, autoencoders, GANs, VAEs, and deep reinforcement learningA wide study of GANs, including Improved GANs, Cross-Domain GANs, and Disentangled Representation GANsBook DescriptionRecent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Deep Reinforcement Learning (DRL) are creating impressive AI results in our news headlines - such as AlphaGo Zero beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like. Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques. The journey begins with an overview of MLPs, CNNs, and RNNs, which are the building blocks for the more advanced techniques in the book. You’ll learn how to implement deep learning models with Keras and TensorFlow 1.x, and move forwards to advanced techniques, as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You then learn all about GANs, and how they can open new levels of AI performance. Next, you’ll get up to speed with how VAEs are implemented, and you’ll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of advanced techniques, you'll learn how to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.What you will learnCutting-edge techniques in human-like AI performanceImplement advanced deep learning models using KerasThe building blocks for advanced techniques - MLPs, CNNs, and RNNsDeep neural networks – ResNet and DenseNetAutoencoders and Variational Autoencoders (VAEs)Generative Adversarial Networks (GANs) and creative AI techniquesDisentangled Representation GANs, and Cross-Domain GANsDeep reinforcement learning methods and implementationProduce industry-standard applications using OpenAI GymDeep Q-Learning and Policy Gradient MethodsWho this book is forSome fluency with Python is assumed. As an advanced book, you'll be familiar with some machine learning approaches, and some practical experience with DL will be helpful. Knowledge of Keras or TensorFlow 1.x is not required but would be helpful.
Advanced Deep Learning with Python: Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch
by Ivan VasilevGain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key Features Get to grips with building faster and more robust deep learning architectures Investigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorch Apply deep neural networks (DNNs) to computer vision problems, NLP, and GANs Book Description In order to build robust deep learning systems, you'll need to understand everything from how neural networks work to training CNN models. In this book, you'll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You'll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you'll focus on variational autoencoders and GANs. You'll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You'll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you'll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you'll understand how to apply deep learning to autonomous vehicles. By the end of this book, you'll have mastered key deep learning concepts and the different applications of deep learning models in the real world. What you will learn Cover advanced and state-of-the-art neural network architectures Understand the theory and math behind neural networks Train DNNs and apply them to modern deep learning problems Use CNNs for object detection and image segmentation Implement generative adversarial networks (GANs) and variational autoencoders to generate new images Solve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence models Understand DL techniques, such as meta-learning and graph neural networks Who this book is for This book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.
Advanced Deep Learning with R: Become an expert at designing, building, and improving advanced neural network models using R
by Bharatendra RaiThis book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to extend their skills and knowledge to implement deep learning techniques and algorithms using the power of R. With this book, you will learn to build advanced neural networks using extensive libraries from R ecosystem. Strong understanding of machine learning and working knowledge of R programming language is expected.
Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition
by Rowel AtienzaUpdated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras Key Features Explore the most advanced deep learning techniques that drive modern AI results New coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentation Completely updated for TensorFlow 2.x Book Description Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you'll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI. What you will learn Use mutual information maximization techniques to perform unsupervised learning Use segmentation to identify the pixel-wise class of each object in an image Identify both the bounding box and class of objects in an image using object detection Learn the building blocks for advanced techniques - MLPss, CNN, and RNNs Understand deep neural networks - including ResNet and DenseNet Understand and build autoregressive models – autoencoders, VAEs, and GANs Discover and implement deep reinforcement learning methods Who this book is for This is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be helpful. Knowledge of Keras or TensorFlow 2.0 is not required but is recommended.
Advanced Design and Implementation of Virtual Machines
by Xiao-Feng LiAlong with the increasingly important runtime engines pervasive in our daily-life computing, there is a strong demand from the software community for a solid presentation on the design and implementation of modern virtual machines, including the Java virtual machine, JavaScript engine and Android execution engine. The community expects to see not only formal algorithm description, but also pragmatic code snippets; to understand not only research topics, but also engineering solutions. This book meets these demands by providing a unique description that combines high level design with low level implementations and academic advanced topics with commercial solutions. This book takes a holistic approach to the design of VM architecture, with contents organized into a consistent framework, introducing topics and algorithms in an easily understood step by step process. It focuses on the critical aspects of VM design, which are often overlooked in other works, such as runtime helpers, stack unwinding and native interface. The algorithms are fully illustrated in figures and implemented in easy to digest code snippets, making the abstract concepts tangible and programmable for system software developers.
Advanced Digital Auditing: Theory and Practice of Auditing Complex Information Systems and Technologies (Progress in IS)
by Egon Berghout Rob Fijneman Lennard Hendriks Mona De Boer Bert-Jan ButijnThis open access book discusses the most modern approach to auditing complex digital systems and technologies. It combines proven auditing approaches, advanced programming techniques and complex application areas, and covers the latest findings on theory and practice in this rapidly developing field. Especially for those who want to learn more about novel approaches to testing complex information systems and related technologies, such as blockchain and self-learning systems, the book will be a valuable resource. It is aimed at students and practitioners who are interested in contemporary technology and managerial implications.
Advanced Digital Image Processing and Its Applications in Big Data
by Ankur Dumka Alaknanda Ashok Parag Verma Poonam VermaThis book covers the technology of digital image processing in various fields with big data and their applications. Readers will understand various technologies and strategies used in digital image processing as well as handling big data, using machine-learning techniques. This book will help to improve the skills of students and researchers in such fields as engineering, agriculture, and medical imaging. There is a need to be able to understand and analyse the latest developments of digital image technology. As such, this book will cover: · Applications such as biomedical science and biometric image processing, content-based image retrieval, remote sensing, pattern recognition, shape and texture analysis · New concepts in color interpolation to produce the full color from the sub-pattern bare pattern color prevalent in today's digital cameras and other imaging devices · Image compression standards that are needed to serve diverse applications · Applications of remote sensing, medical science, traffic management, education, innovation, and analysis in agricultural design and image processing · Both soft and hard computing approaches at great length in relation to major image processing tasks · The direction and development of current and future research in many areas of image processing · A comprehensive bibliography for additional research (integrated within the framework of the book) This book focuses not only on theoretical and practical knowledge in the field but also on the traditional and latest tools and techniques adopted in image processing and data science. It also provides an indispensable guide to a wide range of basic and advanced techniques in the fields of image processing and data science.
The Advanced Digital Photographer's Workbook: Professionals Creating and Outputting World-Class Images
by Editor Yvonne J. ButlerTHE ADVANCED DIGITAL PHOTOGRAPHER'S WORKBOOK is packed full of real-world yet incredibly practical and effective solutions to move digital photographers to a new level of performance. Contributors include twelve world-class professional digital photographers who share their tips and tricks. The authors provide details to move you beyond the basics of capture, processing and output to more sophisticated workflow functions and techniques that will help you create world-class images. They cover rigorous yet easy-to-understand approaches to: capture a great image in black-and-white and color, correct color, calibrate and set up systems properly, creatively manipulate and enhance the image, and produce an excellent print or output of the image. Contributors: Steve Anchell, Stephen Burns, Yvonne Butler, Eric Cheng, Joe Farace, Lou Jones, Rick Sammon, George Schaub, Jeremy Sutton, Tony Sweet, Taz Tally, Eddie Tapp
Advanced Digital Preservation
by David GiarettaThere is growing recognition of the need to address the fragility of digital information, on which our society heavily depends for smooth operation in all aspects of daily life. This has been discussed in many books and articles on digital preservation, so why is there a need for yet one more? Because, for the most part, those other publications focus on documents, images and webpages - objects that are normally rendered to be simply displayed by software to a human viewer. Yet there are clearly many more types of digital objects that may need to be preserved, such as databases, scientific data and software itself. David Giaretta, Director of the Alliance for Permanent Access, and his contributors explain why the tools and techniques used for preserving rendered objects are inadequate for all these other types of digital objects, and they provide the concepts, techniques and tools that are needed. The book is structured in three parts. The first part is on theory, i.e., the concepts and techniques that are essential for preserving digitally encoded information. The second part then shows practice, i.e., the use and validation of these tools and techniques. Finally, the third part concludes by addressing how to judge whether money is being well spent, in terms of effectiveness and cost sharing. Various examples of digital objects from many sources are used to explain the tools and techniques presented. The presentation style mainly aims at practitioners in libraries, archives and industry who are either directly responsible for preservation or who need to prepare for audits of their archives. Researchers in digital preservation and developers of preservation tools and techniques will also find valuable practical information here. Researchers creating digitally encoded information of all kinds will also need to be aware of these topics so that they can help to ensure that their data is usable and can be valued by others now and in the future. To further assist the reader, the book is supported by many hours of videos and presentations from the CASPAR project and by a set of open source software.
Advanced Digital System Design: A Practical Guide to Verilog Based FPGA and ASIC Implementation
by Shirshendu RoyThe book is designed to serve as a textbook for courses offered to undergraduate and graduate students enrolled in electrical, electronics, and communication engineering. The objective of this book is to help the readers to understand the concepts of digital system design as well as to motivate the students to pursue research in this field. Verilog Hardware Description Language (HDL) is preferred in this book to realize digital architectures. Concepts of Verilog HDL are discussed in a separate chapter and many Verilog codes are given in this book for better understanding. Concepts of system Verilog to realize digital hardware are also discussed in a separate chapter. The book covers basic topics of digital logic design like binary number systems, combinational circuit design, sequential circuit design, and finite state machine (FSM) design. The book also covers some advanced topics on digital arithmetic like design of high-speed adders, multipliers, dividers, square root circuits, and CORDIC block. The readers can learn about FPGA and ASIC implementation steps and issues that arise at the time of implementation. One chapter of the book is dedicated to study the low-power design techniques and another to discuss the concepts of static time analysis (STA) of a digital system. Design and implementation of many digital systems are discussed in detail in a separate chapter. In the last chapter, basics of some advanced FPGA design techniques like partial re-configuration and system on chip (SoC) implementation are discussed. These designs can help the readers to design their architecture. This book can be very helpful to both undergraduate and postgraduate students and researchers.
Advanced Digital System Design using SoC FPGAs: An Integrated Hardware/Software Approach
by Ross K. SniderThis textbook teaches students techniques for the design of advanced digital systems using System-on-Chip (SoC) Field Programmable Gate Arrays (FPGAs). The author demonstrates design of custom hardware components for the FPGA fabric using VHDL, with implementation of custom hardware-software interfaces. Readers gain hands-on experience by writing programs and Linux device drivers in C to interact with custom hardware. This textbook enables laboratory experience in the design of custom digital systems using SoC FPGAs, emphasizing computational tasks such as digital signal processing, audio, or video processing.