Making Sense of Sensors: End-to-End Algorithms and Infrastructure Design from Wearable-Devices to Data Centers

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