Make the most of the common architectures used for deriving meaningful data from sensors. This book provides you with the tools to understand how sensor data is converted into actionable knowledge and provides tips for in-depth work in this field. Making Sense of Sensors starts with an overview of the general pipeline to extract meaningful data from sensors. It then dives deeper into some commonly used sensors and algorithms designed for knowledge extraction. Practical examples and pointers to more information are used to outline the key aspects of Multimodal recognition. The book concludes with a discussion on relationship extraction, knowledge representation, and management. In today’s world we are surrounded by sensors collecting various types of data about us and our environments. These sensors are the primary input devices for wearable computers, IoT, and other mobile devices. The information is presented in way that allows readers to associate the examples with their daily lives for better understanding of the concepts. What You'll Learn Look at the general architecture for sensor based data Understand how data from common domains such as inertial, visual and audio is processed Master multi-modal recognition using multiple heterogeneous sensors Transition from recognition to knowledge through relationship understanding between entities Leverage different methods and tools for knowledge representation and management Who This Book Is For New college graduates and professionals interested in acquiring knowledge and the skills to develop innovative solutions around today's sensor-rich devices.

Make: Sensors is the definitive introduction and guide to the sometimes-tricky world of using sensors to monitor the physical world. With dozens of projects and experiments for you to build, this book shows you how to build sensor projects with both Arduino and Raspberry Pi. Use Arduino when you need a low-power, low-complexity brain for your sensor, and choose Raspberry Pi when you need to perform additional processing using the Linux operating system running on that device.You'll learn about touch sensors, light sensors, accelerometers, gyroscopes, magnetic sensors, as well as temperature, humidity, and gas sensors.

An investigation of how-to guides for sensor technologies Sensors are increasingly common within citizen-sensing and DIY projects, but these devices often require the use of a how-to guide. From online instructional videos for troubleshooting sensor installations to handbooks for using and abusing the Internet of Things, the how-to genres and formats of digital instruction continue to expand and develop. As the how-to proliferates, and instructions unfold through multiple aspects of technoscientific practices, Jennifer Gabrys asks why the how-to has become one of the prevailing genres of the digital. How to Do Things with Sensors explores the ways in which things are made do-able with and through sensors and further considers how worlds are made sense-able and actionable through the instructional mode of citizen-sensing projects. Forerunners: Ideas First Short books of thought-in-process scholarship, where intense analysis, questioning, and speculation take the lead

A practical, step-by-step approach to making sense out of data Making Sense of Data educates readers on the steps and issues that need to be considered in order to successfully complete a data analysis or data mining project. The author provides clear explanations that guide the reader to make timely and accurate decisions from data in almost every field of study. A step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. With a comprehensive collection of methods from both data analysis and data mining disciplines, this book successfully describes the issues that need to be considered, the steps that need to be taken, and appropriately treats technical topics to accomplish effective decision making from data. Readers are given a solid foundation in the procedures associated with complex data analysis or data mining projects and are provided with concrete discussions of the most universal tasks and technical solutions related to the analysis of data, including: * Problem definitions * Data preparation * Data visualization * Data mining * Statistics * Grouping methods * Predictive modeling * Deployment issues and applications Throughout the book, the author examines why these multiple approaches are needed and how these methods will solve different problems. Processes, along with methods, are carefully and meticulously outlined for use in any data analysis or data mining project. From summarizing and interpreting data, to identifying non-trivial facts, patterns, and relationships in the data, to making predictions from the data, Making Sense of Data addresses the many issues that need to be considered as well as the steps that need to be taken to master data analysis and mining.

Through a genealogy of photosensitive elements in media devices and artworks, this book investigates three dichotomies that impoverish debates and proposals in media art: material/immaterial, organic/machinic, and theory/practice. It combines historical and analytical approaches, through new materialism, media archaeology, cultural techniques and second-order cybernetics. Known media stories are reframed from an alternative perspective, elucidating photosensitivity as a metonymy to provide guidelines to art students, artists, curators and theoreticians - especially those who are committed to critical views of scientific and technological knowledge in aesthetic experimentations.

To build electronic projects that can sense the physical world, you need to build circuits based around sensors: electronic components that react to physical phenomena by sending an electrical signal. Even with only basic electronic components, you can build useful and educational sensor projects. But if you incorporate Arduino or Raspberry Pi into your project, you can build much more sophisticated projects that can react in interesting ways and even connect to the Internet. This book starts by teaching you the basic electronic circuits to read and react to a sensor. It then goes on to show how to use Arduino to develop sensor systems, and wraps up by teaching you how to build sensor projects with the Linux-powered Raspberry Pi.

Increasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources. The book explains how a myriad of societal applications can be derived from this massive amount of data collected and shared by average individuals. The title offers theoretical foundations to support emerging data-driven cyber-physical applications and touches on key issues such as privacy. The authors present solutions based on recent research and novel ideas that leverage techniques from cyber-physical systems, sensor networks, machine learning, data mining, and information fusion. Offers a unique interdisciplinary perspective bridging social networks, big data, cyber-physical systems, and reliability Presents novel theoretical foundations for assured social sensing and modeling humans as sensors Includes case studies and application examples based on real data sets Supplemental material includes sample datasets and fact-finding software that implements the main algorithms described in the book