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

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.

About the Authors

Omesh Tickoo is a research manager at Intel Labs. His team is currently active in the area of knowledge extraction from multi-modal sensor data centered around vision and speech. In his research career Omesh has made contributions to computer systems evolution in areas like wireless networks, platform partitioning and QoS, SoC architecture, Virtualization and Machine Learning. Omesh is also very active in fostering academic research with contributions toward organizing conferences, academic project mentoring and joint industry-academic research projects. He has authored 30+ conference and journal papers and filed 15+ patent applications. Omesh received his PhD from Rensselaer Polytechnic Institute in 2015.

Ravi Iyer is a Senior Principal Engineer, CTO, and Director in Intel's New Business Initiatives (NBI). He leads technology innovation/incubation efforts and has made significant contributions from low-power system-on-chip wearable/IOT devices to high performance multi-core server architectures including novel cores, innovative cache/memory hierarchies, QoS, accelerators, algorithms/workloads and performance/power analysis. Ravi has published over 150 papers, filed over 50 patents and actively participates in conferences and journals. Ravi is an IEEE Fellow.