How we are tracked, surveilled, tantalized, and seduced by machines ranging from smart watches and Roombas to immersive art installations. Sensing machines are everywhere in our world. As we move through the day, electronic sensors and computers adjust our thermostats, guide our Roombas, count our steps, change the orientation of an image when we rotate our phones. There are more of these electronic devices in the world than there are people—in 2020, thirty to fifty billion of them (versus 7.8 billion people), with more than a trillion expected in the next decade. In Sensing Machines, Chris Salter examines how we are tracked, surveilled, tantalized, and seduced by machines ranging from smart watches and mood trackers to massive immersive art installations. Salter, an artist/scholar who has worked with sensors and computers for more than twenty years, explains that the quantification of bodies, senses, and experience did not begin with the surveillance capitalism practiced by Facebook, Amazon, Netflix, and Google but can be traced back to mathematical and statistical techniques of the nineteenth century. He describes the emergence of the “sensed self,” investigating how sensor technology has been deployed in music and gaming, programmable and immersive art environments, driving, and even eating, with e-tongues and e-noses that can taste and smell for us. Sensing technology turns our experience into data; but Salter’s story isn’t just about what these machines want from us, but what we want from them—new sensations, the thrill of the uncanny, and magic that will transport us from our daily grind.

How we are tracked, surveilled, tantalized, and seduced by machines ranging from smart watches and Roombas to immersive art installations. Sensing machines are everywhere in our world. As we move through the day, electronic sensors and computers adjust our thermostats, guide our Roombas, count our steps, change the orientation of an image when we rotate our phones. There are more of these electronic devices in the world than there are people—in 2020, thirty to fifty billion of them (versus 7.8 billion people), with more than a trillion expected in the next decade. In Sensing Machines, Chris Salter examines how we are tracked, surveilled, tantalized, and seduced by machines ranging from smart watches and mood trackers to massive immersive art installations. Salter, an artist/scholar who has worked with sensors and computers for more than twenty years, explains that the quantification of bodies, senses, and experience did not begin with the surveillance capitalism practiced by Facebook, Amazon, Netflix, and Google but can be traced back to mathematical and statistical techniques of the nineteenth century. He describes the emergence of the “sensed self,” investigating how sensor technology has been deployed in music and gaming, programmable and immersive art environments, driving, and even eating, with e-tongues and e-noses that can taste and smell for us. Sensing technology turns our experience into data; but Salter’s story isn’t just about what these machines want from us, but what we want from them—new sensations, the thrill of the uncanny, and magic that will transport us from our daily grind.

“Visual Sensing and its Applications: Integration of Laser Sensors to Industrial Robots” provides comprehensive and up-to-date coverage of research and development on this robotic vision system. A laser-structured light is the main concern in discussions of visual sensing. Also addressed in this book are all components of the robotic vision system and an emphasis on how to increase the accuracy of the system using three levels of calibration. This includes calibration of the vision system (eye calibration), calibration of eye-to-hand configuration and calibration of robot kinematics (hand calibration). With the integration of the laser sensors to industrial robots numerous applications in the field of robotic welding, grinding, machining, inspection, and palletizing are illustrated based on practical engineering projects in order to demonstrate how the visual sensing is performed. The book will serve as a valuable resource for researchers and engineers in the areas of robotics and machine vision. Dr. Zhongxue Gan is a vice chairman and chief scientist of the ENN Group, China. He serves as a member of the National Energy Expert Consultation Committee of China and member of the National Coal Council of the USA. He is also a co-founder of Intersmart Robotic Systems Co. Ltd., China. He was a research fellow in flexible automation systems at ABB and a founding director of ABB Corporate Research Robot Laboratories, both in the USA and in China. Dr. Qing Tang is a co-founder and CEO of Intersmart Robotic Systems Co. Ltd., China and an adjunct professor in Physics at Sichuan University, China. He was a principle consulting engineer and project manager at the ABB Corporate Research Robot Laboratory in the USA.

This book is devoted to Slime mould Physarum polycephalum, which is a large single cell capable for distributed sensing, concurrent information processing, parallel computation and decentralized actuation. The ease of culturing and experimenting with Physarum makes this slime mould an ideal substrate for real-world implementations of unconventional sensing and computing devices The book is a treatise of theoretical and experimental laboratory studies on sensing and computing properties of slime mould, and on the development of mathematical and logical theories of Physarum behavior. It is shown how to make logical gates and circuits, electronic devices (memristors, diodes, transistors, wires, chemical and tactile sensors) with the slime mould. The book demonstrates how to modify properties of Physarum computing circuits with functional nano-particles and polymers, to interface the slime mould with field-programmable arrays, and to use Physarum as a controller of microbial fuel cells. A unique multi-agent model of slime is shown to serve well as a software slime mould capable for solving problems of computational geometry and graph optimization. The multiagent model is complemented by cellular automata models with parallel accelerations. Presented mathematical models inspired by Physarum include non-quantum implementation of Shor's factorization, structural learning, computation of shortest path tree on dynamic graphs, supply chain network design, p-adic computing and syllogistic reasoning. The book is a unique composition of vibrant and lavishly illustrated essays which will inspire scientists, engineers and artists to exploit natural phenomena in designs of future and emergent computing and sensing devices. It is a 'bible' of experimental computing with spatially extended living substrates, it spanstopics from biology of slime mould, to bio-sensing, to unconventional computing devices and robotics, non-classical logics and music and arts.

Intelligent Data Sensing and Processing for Health and Well-being Applications uniquely combines full exploration of the latest technologies for sensor-collected intelligence with detailed coverage of real-case applications for healthcare and well-being at home and in the workplace. Forward-thinking in its approach, the book presents concepts and technologies needed for the implementation of today's mobile, pervasive and ubiquitous systems, and for tomorrow's IoT and cyber-physical systems. Users will find a detailed overview of the fundamental concepts of gathering, processing and analyzing data from devices disseminated in the environment, as well as the latest proposals for collecting, processing and abstraction of data-sets. In addition, the book addresses algorithms, methods and technologies for diagnosis and informed decision-making for healthcare and well-being. Topics include emotional interface with ambient intelligence and emerging applications in detection and diagnosis of neurological diseases. Finally, the book explores the trends and challenges in an array of areas, such as applications for intelligent monitoring in the workplace for well-being, acquiring data traffic in cities to improve the assistance of first aiders, and applications for supporting the elderly at home. - Examines the latest applications and future directions for mobile data sensing in an array of health and well-being scenarios - Combines leading computing paradigms and technologies, development applications, empirical studies, and future trends in the multidisciplinary field of smart sensors, smart sensor networks, data analysis and machine intelligence methods - Features an analysis of security, privacy and ethical issues in smart sensor health and well-being applications - Equips readers interested in interdisciplinary projects in ubiquitous computing or pervasive computing and ambient intelligence with the latest trends and developments

Technological advancements in recent years have enabled the development of tiny, cheap disposable and self contained battery powered computers, known as sensor nodes or “motes”, which can accept input from an attached sensor, process this input and transmit the results wirelessly to some interested device(s). When a number of these nodes work together, conceivably up to hundreds of thousands, a Wireless Sensor Network (WSN) is formed. Research in the area of wireless sensor networks has become increasingly wid- pread in recent years, partly due to their wide range of potential uses and also partly due to the fact that the technology enabling such networks is now widely available from many di?erent suppliers, such as: Crossbow, MoteIV, Intel and SUN (java based motes). These wireless sensor networks have the potential to allow a level of integration between computers and the physical world that, to date, has been virtually impos- ble. The uses for such networks is almost limitless and include such diverse app- cations as a counter sniper system for urban warfare [1] tracking the path of a forest re [2], determining the structural stability of a building after an earthquake [3], or tracking people or objects inside a building [4], etc.

Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, including compressive sensing and sampling, distributed signal processing, and intelligent signal learning. Presenting recent research results of world-renowned sensing experts, the book is organized into three parts: Machine Learning—describes the application of machine learning and other AI principles in sensor network intelligence—covering smart sensor/transducer architecture and data representation for intelligent sensors Signal Processing—considers the optimization of sensor network performance based on digital signal processing techniques—including cross-layer integration of routing and application-specific signal processing as well as on-board image processing in wireless multimedia sensor networks for intelligent transportation systems Networking—focuses on network protocol design in order to achieve an intelligent sensor networking—covering energy-efficient opportunistic routing protocols for sensor networking and multi-agent-driven wireless sensor cooperation Maintaining a focus on "intelligent" designs, the book details signal processing principles in sensor networks. It elaborates on critical platforms for intelligent sensor networks and illustrates key applications—including target tracking, object identification, and structural health monitoring. It also includes a paradigm for validating the extent of spatiotemporal associations among data sources to enhance data cleaning in sensor networks, a sensor stream reduction application, and also considers the use of Kalman filters for attack detection in a water system sensor network that consists of water level sensors and velocity sensors.