This book outlines the common architectures used for deriving meaningful data from sensors. 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. This book provides the reader with the tools to understand how sensor data is converted into actionable knowledge and provides tips for in-depth work in this field. The information is presented in way that allows readers to associate the examples with their daily lives for better understanding of the concepts.
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.
What You’ll Learn:
- General architecture for sensor based data understanding
- Specific examples to understand how data from common domains such as inertial, visual and audio is processed
- Multi-modal recognition using multiple heterogeneous sensors
- How to transition from recognition to knowledge through relationship understanding between entities
- Different methods and tools for knowledge representation and management
- Current state of the art in this rapidly emerging industry
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.