“[Ada Lovelace], like Steve Jobs, stands at the intersection of arts and technology."—Walter Isaacson, author of The Innovators Over 150 years after her death, a widely-used scientific computer program was named “Ada,” after Ada Lovelace, the only legitimate daughter of the eighteenth century’s version of a rock star, Lord Byron. Why? Because, after computer pioneers such as Alan Turing began to rediscover her, it slowly became apparent that she had been a key but overlooked figure in the invention of the computer. In Ada Lovelace, James Essinger makes the case that the computer age could have started two centuries ago if Lovelace’s contemporaries had recognized her research and fully grasped its implications. It’s a remarkable tale, starting with the outrageous behavior of her father, which made Ada instantly famous upon birth. Ada would go on to overcome numerous obstacles to obtain a level of education typically forbidden to women of her day. She would eventually join forces with Charles Babbage, generally credited with inventing the computer, although as Essinger makes clear, Babbage couldn’t have done it without Lovelace. Indeed, Lovelace wrote what is today considered the world’s first computer program—despite opposition that the principles of science were “beyond the strength of a woman’s physical power of application.” Based on ten years of research and filled with fascinating characters and observations of the period, not to mention numerous illustrations, Essinger tells Ada’s fascinating story in unprecedented detail to absorbing and inspiring effect.

Autonomous Driving and Advanced Driver-Assistance Systems (ADAS): Applications, Development, Legal Issues, and Testing outlines the latest research related to autonomous cars and advanced driver-assistance systems, including the development, testing, and verification for real-time situations of sensor fusion, sensor placement, control algorithms, and computer vision. Features: Co-edited by an experienced roboticist and author and an experienced academic Addresses the legal aspect of autonomous driving and ADAS Presents the application of ADAS in autonomous vehicle parking systems With an infinite number of real-time possibilities that need to be addressed, the methods and the examples included in this book are a valuable source of information for academic and industrial researchers, automotive companies, and suppliers.

An emerging trend in the automobile industry is its convergence with information technology (IT). Indeed, it has been estimated that almost 90% of new automobile technologies involve IT in some form. Smart driving technologies that improve safety as well as green fuel technologies are quite representative of the convergence between IT and automobiles. The smart driving technologies include three key elements: sensing of driving environments, detection of objects and potential hazards and the generation of driving control signals including warning signals. Although radar-based systems are primarily used for sensing the driving environments, the camera has gained importance in advanced driver assistance systems (ADAS). This book covers system-on-a-chip (SoC) designs—including both algorithms and hardware—related with image sensing and object detection by using the camera for smart driving systems. It introduces a variety of algorithms such as lens correction, super resolution, image enhancement and object detections from the images captured by low-cost vehicle camera. This is followed by implementation issues such as SoC architecture, hardware accelerator, software development environment and reliability techniques for automobile vision systems. This book is aimed for the new and practicing engineers in automotive and chip-design industries to provide some overall guidelines for the development of automotive vision systems. It will also help graduate students understand and get started for the research work in this field.

This book constitutes the proceedings of the 17th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2017, held in Helsinki, Finland, in August 2017. The 25 full papers presented were carefully reviewed and selected from 117 submissions. They cover topics such as parallel and distributed architectures; software systems and programming models; distributed and network-based computing; big data and its applications; parallel and distributed algorithms; applications of parallel and distributed computing; service dependability and security in distributed and parallel systems; service dependability and security in distributed and parallel systems; performance modeling and evaluation.This volume also includes 41 papers of four workshops, namely: the 4th International Workshop on Data, Text, Web, and Social Network Mining (DTWSM 2017), the 5th International Workshop on Parallelism in Bioinformatics (PBio 2017), the First International Workshop on Distributed Autonomous Computing in Smart City (DACSC 2017), and the Second International Workshop on Ultrascale Computing for Early Researchers (UCER 2017).

With the rapid development of artificial intelligence and the emergence of various new sensors, autonomous driving has grown in popularity in recent years. The implementation of autonomous driving requires new sources of sensory data, such as cameras, radars, and lidars, and the algorithm processing requires a high degree of parallel computing. In this regard, traditional CPUs have insufficient computing power, while DSPs are good at image processing but lack sufficient performance for deep learning. Although GPUs are good at training, they are too “power-hungry,” which can affect vehicle performance. Therefore, this book looks to the future, arguing that custom ASICs are bound to become mainstream. With the goal of ICs design for autonomous driving, this book discusses the theory and engineering practice of designing future-oriented autonomous driving SoC chips. The content is divided into thirteen chapters, the first chapter mainly introduces readers to the current challenges and research directions in autonomous driving. Chapters 2–6 focus on algorithm design for perception and planning control. Chapters 7–10 address the optimization of deep learning models and the design of deep learning chips, while Chapters 11-12 cover automatic driving software architecture design. Chapter 13 discusses the 5G application on autonomous drving. This book is suitable for all undergraduates, graduate students, and engineering technicians who are interested in autonomous driving.

This book contains the latest research on machine learning and embedded computing in advanced driver assistance systems (ADAS). It encompasses research in detection, tracking, LiDAR and camera processing, ethics, and communications. Several new datasets are also provided for future research work. Researchers and others interested in these topics will find important advances contained in this book.

This book provides a comprehensive reference for both academia and industry on the fundamentals, technology details, and applications of Advanced Driver-Assistance Systems (ADAS) and autonomous driving, an emerging and rapidly growing area. The book written by experts covers the most recent research results and industry progress in the following areas: ADAS system design and test methodologies, advanced materials, modern automotive technologies, artificial intelligence, reliability concerns, and failure analysis in ADAS. Numerous images, tables, and didactic schematics are included throughout. This essential book equips readers with an in-depth understanding of all aspects of ADAS, providing insights into key areas for future research and development. • Provides comprehensive coverage of the state-of-the-art in ADAS • Covers advanced materials, deep learning, quality and reliability concerns, and fault isolation and failure analysis • Discusses ADAS system design and test methodologies, novel automotive technologies • Features contributions from both academic and industry authors, for a complete view of this important technology

This text, extensively class-tested over a decade at UC Berkeley and UC San Diego, explains the fundamentals of algorithms in a story line that makes the material enjoyable and easy to digest. Emphasis is placed on understanding the crisp mathematical idea behind each algorithm, in a manner that is intuitive and rigorous without being unduly formal. Features include:The use of boxes to strengthen the narrative: pieces that provide historical context, descriptions of how the algorithms are used in practice, and excursions for the mathematically sophisticated. Carefully chosen advanced topics that can be skipped in a standard one-semester course but can be covered in an advanced algorithms course or in a more leisurely two-semester sequence.An accessible treatment of linear programming introduces students to one of the greatest achievements in algorithms. An optional chapter on the quantum algorithm for factoring provides a unique peephole into this exciting topic. In addition to the text DasGupta also offers a Solutions Manual which is available on the Online Learning Center."Algorithms is an outstanding undergraduate text equally informed by the historical roots and contemporary applications of its subject. Like a captivating novel it is a joy to read." Tim Roughgarden Stanford University

The AVEC symposium is a leading international conference in the fields of vehicle dynamics and advanced vehicle control, bringing together scientists and engineers from academia and automotive industry. The first symposium was held in 1992 in Yokohama, Japan. Since then, biennial AVEC symposia have been established internationally and have considerably contributed to the progress of technology in automotive research and development. In 2016 the 13th International Symposium on Advanced Vehicle Control (AVEC’16) was held in Munich, Germany, from 13th to 16th of September 2016. The symposium was hosted by the Munich University of Applied Sciences. AVEC’16 puts a special focus on automatic driving, autonomous driving functions and driver assist systems, integrated control of interacting control systems, controlled suspension systems, active wheel torque distribution, and vehicle state and parameter estimation. 132 papers were presented at the symposium and are published in these proceedings as full paper contributions. The papers review the latest research developments and practical applications in highly relevant areas of vehicle control, and may serve as a reference for researchers and engineers.

Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing. Summary As a software engineer, you’ll encounter countless programming challenges that initially seem confusing, difficult, or even impossible. Don’t despair! Many of these “new” problems already have well-established solutions. Advanced Algorithms and Data Structures teaches you powerful approaches to a wide range of tricky coding challenges that you can adapt and apply to your own applications. Providing a balanced blend of classic, advanced, and new algorithms, this practical guide upgrades your programming toolbox with new perspectives and hands-on techniques. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Can you improve the speed and efficiency of your applications without investing in new hardware? Well, yes, you can: Innovations in algorithms and data structures have led to huge advances in application performance. Pick up this book to discover a collection of advanced algorithms that will make you a more effective developer. About the book Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing. You’ll discover cutting-edge approaches to a variety of tricky scenarios. You’ll even learn to design your own data structures for projects that require a custom solution. What's inside Build on basic data structures you already know Profile your algorithms to speed up application Store and query strings efficiently Distribute clustering algorithms with MapReduce Solve logistics problems using graphs and optimization algorithms About the reader For intermediate programmers. About the author Marcello La Rocca is a research scientist and a full-stack engineer. His focus is on optimization algorithms, genetic algorithms, machine learning, and quantum computing. Table of Contents 1 Introducing data structures PART 1 IMPROVING OVER BASIC DATA STRUCTURES 2 Improving priority queues: d-way heaps 3 Treaps: Using randomization to balance binary search trees 4 Bloom filters: Reducing the memory for tracking content 5 Disjoint sets: Sub-linear time processing 6 Trie, radix trie: Efficient string search 7 Use case: LRU cache PART 2 MULTIDEMENSIONAL QUERIES 8 Nearest neighbors search 9 K-d trees: Multidimensional data indexing 10 Similarity Search Trees: Approximate nearest neighbors search for image retrieval 11 Applications of nearest neighbor search 12 Clustering 13 Parallel clustering: MapReduce and canopy clustering PART 3 PLANAR GRAPHS AND MINIMUM CROSSING NUMBER 14 An introduction to graphs: Finding paths of minimum distance 15 Graph embeddings and planarity: Drawing graphs with minimal edge intersections 16 Gradient descent: Optimization problems (not just) on graphs 17 Simulated annealing: Optimization beyond local minima 18 Genetic algorithms: Biologically inspired, fast-converging optimization