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Biomedical Circuits and Systems
Integrated circuit design for biomedical applications requires an interdisciplinary background, ranging from electrical engineering to material engineering to computer science. This book is written to help build the foundation for researchers, engineers, and students to further develop their interest and knowledge in this field. This book provides an overview of various biosensors by introducing fundamental building blocks for integrated biomedical systems. State-of-the-art projects for various applications and experience in developing these systems are explained in detail. Future design trends in this field is also discussed in this book.
Deep Learning for Robot Perception and Cognition
Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. - Presents deep learning principles and methodologies - Explains the principles of applying end-to-end learning in robotics applications - Presents how to design and train deep learning models - Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more - Uses robotic simulation environments for training deep learning models - Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis
Artificial Intelligence and Human Rights
The scope of Artificial Intelligence's (AI) hold on modern life is only just beginning to be fully understood. Academics, professionals, policymakers, and legislators are analysing the effects of AI in the legal realm, notably in human rights work. Artificial Intelligence technologies and modern human rights have lived parallel lives for the last sixty years, and they continue to evolve with one another as both fields take shape. Human Rights and Artificial Intelligence explores the effects of AI on both the concept of human rights and on specific topics, including civil and political rights, privacy, non-discrimination, fair procedure, and asylum. Second- and third-generation human rights are also addressed. By mapping this relationship, the book clarifies the benefits and risks for human rights as new AI applications are designed and deployed. Its granular perspective makes Human Rights and Artificial Intelligence a seminal text on the legal ramifications of machine learning. This expansive volume will be useful to academics and professionals navigating the complex relationship between AI and human rights.
Complex Systems
The National Academies Keck Futures Initiative was launched in 2003 to stimulate new modes of scientific inquiry and break down the conceptual and institutional barriers to interdisciplinary research. At the Conference on Complex Systems, participants were divided into twelve interdisciplinary working groups. The groups spent nine hours over two days exploring diverse challenges at the interface of science, engineering, and medicine. The groups included researchers from science, engineering, and medicine, as well as representatives from private and public funding agencies, universities, businesses, journals, and the science media. The groups needed to address the challenge of communicating and working together from a diversity of expertise and perspectives as they attempted to solve complicated, interdisciplinary problems in a relatively short time. The summaries contained in this volume describe the problem and outline the approach taken, including what research needs to be done to understand the fundamental science behind the challenge, the proposed plan for engineering the application, the reasoning that went into it and the benefits to society of the problem solution.
Data Science Quick Reference Manual – Deep Learning
This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises. Part in a series of texts, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. As this text uses Orange for the application aspects, it describes its installation and widgets. The data modeling phas...
Renewable Energy: Generation and Application
The book covers the current status of renewable energy technology, such as solar, wind, hydro and geothermal power engineering and biomass conversion. It focusses on technical challenges and potential future developments in electricity generation. electrical vehicles, heating and cooling, industrial processes and rural electrification. Keywords: Solar Energy, Wind Energy, Wind Farms. Hydropower, Hydroelectric Dams, Geothermal Energy, Biomass Energy, Agricultural Residues, Organic Waste, Electricity Transportation, Global Energy Systems.
Deep Learning for Computer Architects
Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware. This text serves as a primer for computer architects in a new and rapidly evolving field. We review how machine learning has evolved since its inception in the 1960s and track the ke...
Collaborations of Consequence
This publication represents the culmination of the National Academies Keck Futures Initiative (NAKFI), a program of the National Academy of Sciences, the National Academy of Engineering, and the National Academy of Medicine supported by a 15-year, $40 million grant from the W. M. Keck Foundation to advance the future of science through interdisciplinary research. From 2003 to 2017, more than 2,000 researchers and other professionals across disciplines and sectors attended an annual "think-tank" style conference to contemplate real-world challenges. Seed grants awarded to conference participants enabled further pursuit of bold, new research and ideas generated at the conference.
Scaling Up Machine Learning
This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.
Practical Deep Learning for Cloud, Mobile, and Edge
Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users
