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.

This volume covers the various sensors related to automotive and aerospace sectors, discussing their properties as well as how they are realized, calibrated and deployed. Written by experts in the field, it provides a ready reference to product developers, researchers and students working on sensor design and fabrication, and provides perspective on both current and future research.

Biological sensors are usually remarkably small, sensitive and efficient. It is highly desirable to design corresponding artificial sensors for scientific, industrial and commercial purposes. This book is designed to fill an urgent need for interdisciplinary exchange between biologists studying sensors in the natural world and engineers and physical scientists developing artificial sensors. The main topics cover mechanical sensors, e.g. waves and sounds, visual sensors and vision and chemosensors. Readers will obtain a fuller understanding of the nature and performance of natural sensors as well as enhanced appreciation for the current status and the potential applicability of artificial microsensors.

As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing.

Over the last twenty years there has been tremendous growth in the research and development of sensors and sensor signal processing methods. Advances in materials and fabrication techniques have led to a departure from traditional sensor types and the development of novel sensing techniques and devices, many of which are now finding favor in indust

The rapid advancement of IoT and smart technologies poses significant challenges for academic scholars, including meeting new requirements such as reliability, quality of service, and energy efficiency. AIoT and Smart Sensing Technologies for Smart Devices, edited by Fadi ALTURJMAN, offers an invaluable solution by serving as a knowledge hub that brings together diverse research and professional contributions. This comprehensive book enables scholars to delve into topics like IoT-oriented applications, sustainable development, machine learning, and wireless networks, providing the necessary insights to navigate the complexities of IoT and smart technologies. Targeted at experts, researchers, and students, this book equips readers with the understanding needed to tackle the challenges in this dynamic field. Covering areas such as IoT paradigms, network security, machine learning approaches, and wireless network protocols, it serves as a guide for unlocking the potential of AIoT. With this resource, scholars can stay informed, explore innovative approaches, and actively contribute to the advancement of AIoT and smart technologies, making it a compelling solution for those grappling with the complexities of this transformative field.

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.

Welcome to "Sensing the Future: An Exploration of Industrial Sensors." This book takes you on a captivating journey into the world of industrial sensors, shedding light on their significance, applications, challenges, and the exciting prospects they hold for the future. As technology rapidly advances, industrial sensors have become integral components of modern industries, revolutionizing the way we manufacture, transport, provide healthcare, and harness energy. With their ability to detect and measure physical properties, industrial sensors serve as the eyes and ears of various sectors, providing critical data for decision-making and control. In this book, we delve into the fundamentals of industrial sensors, exploring the principles behind their operation and the diverse types available. We delve into real-world case studies showcasing the profound impact of sensors on manufacturing, healthcare, transportation, and energy sectors. Additionally, we discuss the ethical considerations and privacy concerns related to sensor data, ensuring responsible and secure deployment. Moreover, we explore the future prospects of industrial sensors, examining emerging trends such as AI integration, sensor fusion, quantum sensing, and the expansion of IoT. The book delves into the potential for sensors to shape smart cities, support sustainable practices, and transform the delivery of healthcare services. We believe that understanding the transformative potential of industrial sensors is crucial for researchers, professionals, policymakers, and enthusiasts alike. By comprehending the intricacies of these remarkable technologies, we can harness their power to drive innovation, improve efficiency, and contribute to a more sustainable and connected world. We invite you to embark on this enlightening journey through the world of industrial sensors. Our hope is that this book sparks your curiosity, inspires new ideas, and fosters a deeper appreciation for the significance of sensors in shaping the future of technology and human progress. Let us embark on this adventure together, as we "Sense the Future" and explore the boundless possibilities that lie ahead. Happy reading! Charles Nehme

In the last few years the scientific community has realized that obtaining a better understanding of interactions between natural systems and the man-made environment across different scales demands more research efforts in remote sensing. An integrated Earth system observatory that merges surface-based, air-borne, space-borne, and even underground sensors with comprehensive and predictive capabilities indicates promise for revolutionizing the study of global water, energy, and carbon cycles as well as land use and land cover changes. The aim of this book is to present a suite of relevant concepts, tools, and methods of integrated multisensor data fusion and machine learning technologies to promote environmental sustainability. The process of machine learning for intelligent feature extraction consists of regular, deep, and fast learning algorithms. The niche for integrating data fusion and machine learning for remote sensing rests upon the creation of a new scientific architecture in remote sensing science that is designed to support numerical as well as symbolic feature extraction managed by several cognitively oriented machine learning tasks at finer scales. By grouping a suite of satellites with similar nature in platform design, data merging may come to help for cloudy pixel reconstruction over the space domain or concatenation of time series images over the time domain, or even both simultaneously. Organized in 5 parts, from Fundamental Principles of Remote Sensing; Feature Extraction for Remote Sensing; Image and Data Fusion for Remote Sensing; Integrated Data Merging, Data Reconstruction, Data Fusion, and Machine Learning; to Remote Sensing for Environmental Decision Analysis, the book will be a useful reference for graduate students, academic scholars, and working professionals who are involved in the study of Earth systems and the environment for a sustainable future. The new knowledge in this book can be applied successfully in many areas of environmental science and engineering.