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Nichols C: The Rust Programming
Language, 3rd Edition

The Rust Programming Language, 3rd Edition

Foreword Preface Acknowledgments Introduction Chapter 1: Getting Started Chapter 2: Programming a Guessing Game Chapter 3: Common Programming Concepts Chapter 4: Understanding Ownership Chapter 5: Using Structs to Structure Related Data Chapter 6: Enums and Pattern Matching Chapter 7: Managing Growing Projects with Packages, Crates, and Modules Chapter 8: Common Collections Chapter 9: Error Handling Chapter 10: Generic Types, Traits, and Lifetimes Chapter 11: Writing Automated Tests Chapter 12: An I/O Project: Building a Command Line Program Chapter 13: Functional Language Features: Iterators and Closures Chapter 14: More About Cargo and Crates.io Chapter 15: Smart Pointers Chapter 16: Fearless Concurrency Chapter 17: Fundamentals of Asynchronous Programming: Async, Await, Futures, and Streams Chapter 18: Object-Oriented Programming Features Chapter 19: Patterns and Matching Chapter 20: Advanced Features Chapter 21: Final Project: Building a Multithreaded Web Server Appendix A: Keywords Appendix B: Operators and Symbols Appendix C: Derivable Traits Appendix D: Useful Development Tools Appendix E: Editions Index

An engaging, illustrated collection of insights revealing the practices and principles that expert software designers use to create great software.

66 Ways Experts Think

Software Design Decoded

Embedded Systems in Automotive Applications

Artificial Intelligence and Machine Learning for Safety-Critical Systems: A Comprehensive Guide provides engineers and system designers who are exploring the application of AI/ML methods for safety-critical systems with a dedicated resource capturing the challenges and mitigation strategies involved in designing such systems. Divided into nine sections, the book covers the most important applications of safety-critical systems, helping readers understand how related problems are being solved in different domains/problem settings. The goal of this book is to help ensure that AI-based critical systems better utilize resources, avoid failures, and increase system safety and public safety. The authors present ML techniques in safety-critical systems across multiple domains, including pattern recognition, image processing, edge computing, Internet of Things (IoT), encryption, hardware accelerators, and many others. These applications help readers understand the many challenges that need to be addressed in order to increase the deployment of ML models in critical systems. In addition, the book shows how to improve public trust in ML systems by providing explainable model outputs rather than treating the system as a black box for which the outputs are difficult to explain. Finally, the authors demonstrate how to meet legal certification and regulatory requirements for the appropriate ML models.

A Comprehensive Guide

Artificial Intelligence and Machine Learning for Safety-Critical Systems

Statistical Analysis with Python For Dummies

Applications in R and Python

Essentials of Big Data Analytics

Advanced Topics in Biomaterials

Signal Processing Roadmap: Technologies, Applications, and Future Directions explores cutting-edge and emerging signal-processing techniques across various measurement and monitoring applications, serving as an authoritative reference for engineers, researchers, and technologists. It critically analyses key signal processing considerations like uncertainty modelling that enable more intelligent and reliable next-generation measurement systems, backed by real-world implementation examples in areas ranging from Internet of Things devices to complex biomedical equipment. The book will provide an overview of the latest research in the hybrid information system modelling field, with a particular emphasis on practical applications in various fields. This includes case studies and examples of how these models have been used to solve problems in finance, healthcare, engineering, and other related fields. Additionally, the book reviews the theories and concepts related to non-linear optimization, fuzzy sets, and rough sets. Overall, Signal Processing Roadmap: Technologies, Applications, and Future Directions deals with the dual goals of consolidating the state-of-the-art and charting the future directions across the exciting applications where signal processing will continue playing a pivotal role in building intelligent, reliable, and efficient systems of the future.

Technologies, Applications, and Future Directions

Signal Processing Roadmap

Foundations, Technologies, and Prospects for the Future

Autonomous Vehicle Safety Solutions

A Hands-on Approach

Programming Massively Parallel Processors

A No-Nonsense Guide to the OpenBSD Firewall

The Book of PF, 4th Edition

Quantum Process Algebra introduces readers to the algebraic properties and laws for quantum computing. The book provides readers with all aspects of algebraic theory for quantum computing, including the basis of semantics and axiomatization for quantum computing. With the assumption of a quantum system, readers will learn to solve the modelling of the three main components in a quantum system: unitary operator, quantum measurement, and quantum entanglement, with full support of quantum and classical computing in closed systems. Next, the book establishes the relationship between probabilistic quantum bisimilarity and classical probabilistic bisimilarity, including strong probabilistic bisimilarity and weak probabilistic bisimilarity, which makes an axiomatization of quantum processes possible. With this framework, quantum and classical computing mixed processes are unified with the same structured operational semantics. Finally, the book establishes a series of axiomatizations of quantum process algebras. These process algebras support nearly all main computation properties. Quantum and classical computing in closed quantum systems are unified with the same equational logic and the same structured operational semantics under the framework of ACP-like probabilistic process algebra. This unification means that the mathematics in the book can be used widely for verification of quantum and classical computing mixed systems, for example, most quantum communication protocols. ACP-like axiomatization also inherits the advantages of ACP, for example, and modularity means that it can be extended in an elegant way.

Quantum Process Algebra

Using Computational Thinking in Everyday Life

Problem Solving with Python

Oxford Biology Primers

This primer introduces the elements that distinguish well-written computer code, covering style, clarity, technique, and robustness, to support readers in improving their coding skills. It explains why some approaches are better than others, and shows how good code prevents scientific errors and increases the reproducibility of research.

Good Coding Practice for Science

The Rust Programming Language, 3rd Edition

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