Component-Based Software Development for Embedded Systems : An Overview of Current Research Trends
Embedded systems are ubiquitous. They appear in cell phones, microwave ovens, refrigerators, consumer electronics, cars, and jets. Some of these embedded s- tems are safety- or security-critical such as in medical equipment, nuclear plants, and X-by-wire control systems in naval, ground and aerospace transportation - hicles. With the continuing shift from hardware to software, embedded systems are increasingly dominated by embedded software. Embedded software is complex. Its engineering inherently involves a mul- disciplinary interplay with the physics of the embedding system or environment. Embedded software also comes in ever larger quantity and diversity. The next generation of premium automobiles will carry around one gigabyte of binary code. The proposed US DDX submarine is e?ectively a ?oating embedded so- ware system, comprising 30 billion lines of code written in over 100 programming languages. Embedded software is expensive. Cost estimates are quoted at around US$15– 30 per line (from commencement to shipping). In the defense realm, costs can range up to $100, while for highly critical applications, such as the Space Shuttle, the cost per line approximates $1,000. In view of the exponential increase in complexity, the projected costs of future embedded software are staggering.
كتب مشابهة
New challenges in software engineering ; Vol 1
Explores the key challenges shaping the future of software development, including automation, AI-driven development, security-focused engineering, resilient and autonomous architectures, business process optimization, cloud computing, microservices, high-performance distributed systems, and sustainable technologies. Software engineering is undergoing a constant transformation, driven by rapid technological advances and evolving market demands. additionally, it delves into the ethical considerations of AI, the evolution of intuitive user interfaces, and the importance of multidisciplinary collaboration.
Fundamentals of manufacturing engineering using digital visualization
Offers a guide to core principles and practices of manufacturing engineering. It covers the design of, together with technological and measurement issues for, technical systems. Locating charts and setup schemes describing different machining processes are included. Concepts of product quality, with a focus on accuracy indicators, machining accuracy, roughness, and the impact of surface quality on exploitation properties are also explained. Furthermore, key machining methods, including turning, milling, hole machining, grinding, and gear machining, are analyzed in depth, covering their principles, applications, and techniques. The book is enriched by QR codes, linking to a mobile application presenting additional information about the content, for an interactive and extended learning experience. It also uses illustrations visualized with digital tools to promote a better understanding of the concepts.
AI in drug discovery
Constitutes the refereed proceedings of the First international workshop on ai in Drug Discovery, AIDD 2024, held as a part of the 33rd International Conference on Artificial Neural Networks, ICANN 2024, in Lugano, Switzerland, on September 19, 2024. These papers focus on various aspects of the rapidly evolving field of Artificial Intelligence (AI)-driven drug discovery in chemistry, including Big Data and advanced Machine Learning, eXplainable AI (XAI), Chemoinformatics, Use of deep learning to predict molecular properties, Modeling and prediction of chemical reaction data and Generative models.
AI in banking : Practical applications and case studies
Delves into the application of AI from theory to practice, offering detailed insights into AI project design and code implementation across eleven business scenarios in four major sectors: retail banking, e-banking, bank credit, and tech operations. it provides hands-on examples of various technologies, including automatic machine learning, integrated learning, graph computation, recommendation systems, causal inference, generative adversarial networks, supervised learning, unsupervised learning, computer vision, reinforcement learning, fuzzy control, automatic control, speech recognition, semantic understanding, bayesian networks, edge computing, and more. this book stands as a rare and practical guide to AI projects in the banking industry.
