Yuan Yao – författare

This book addresses the fundamentals and practical implementations of antennas for Global Navigation Satellite Systems (GNSS)

In this book, the authors discuss the various aspects of GNSS antennas, including fundamentals of GNSS, design approaches for the GNSS terminal and satellite antennas, performance enhancement techniques and effects of user’s presence and surrounding environment on these antennas.  In addition, the book will provide the reader with an insight into the most important aspects of the GNSS antenna technology and lay the foundations for future advancements.  It also includes a number of real case studies describing the ways in which antenna design can be adapted to conform to the design constraints of practical user devices, and also the management of potential adverse interactions between the antenna and its platform.

Key Features:

Covers the fundamentals and practical implementations of antennas for Global Navigation Satellite Systems (GNSS) Describes technological advancements for GPS, Glonass, Galileo and Compass Aims to address future issues such as multipath interference, in building operation, RF interference in mobile Includes a number of real case studies to illustrate practical implementation of GNSS

This book will be an invaluable guide for antenna designers, system engineers, researchers for GNSS systems and postgraduate students (antennas, satellite communication technology). R&D engineers in mobile handset manufacturers, spectrum engineers will also find this book of interest.

This SpringerBrief presents a typical life-cycle of mobile data mining applications, including:data capturing and processing which determines what data to collect, how to collect these data, and how to reduce the noise in the data based on smartphone sensorsfeature engineering which extracts and selects features to serve as the input of algorithms based on the collected and processed datamodel and algorithm designIn particular, this brief concentrates on the model and algorithm design aspect, and explains three challenging requirements of mobile data mining applications: energy-saving, personalization, and real-timeEnergy saving is a fundamental requirement of mobile applications, due to the limited battery capacity of smartphones. The authors  explore the existing practices in the methodology level (e.g. by designing hierarchical models) for saving energy. Another fundamental requirement of mobile applications is personalization.  Most of the existing methods tend to train generic models for all users, but the authors provide existing personalized treatments for mobile applications, as the behaviors may differ greatly from one user to another in many mobile applications. The third requirement is real-time. That is, the mobile application should return responses in a real-time manner, meanwhile balancing effectiveness and efficiency.This SpringerBrief targets data mining and machine learning researchers and practitioners working in these related fields. Advanced level students studying computer science and electrical engineering will also find this brief useful as a study guide.

This SpringerBrief presents a typical life-cycle of mobile data mining applications, including:

data capturing and processing which determines what data to collect, how to collect these data, and how to reduce the noise in the data based on smartphone sensors feature engineering which extracts and selects features to serve as the input of algorithms based on the collected and processed data model and algorithm designIn particular, this brief concentrates on the model and algorithm design aspect, and explains three challenging requirements of mobile data mining applications: energy-saving, personalization, and real-time

 Energy saving is a fundamental requirement of mobile applications, due to the limited battery capacity of smartphones. The authors  explore the existing practices in the methodology level (e.g. by designing hierarchical models) for saving energy. Another fundamental requirement of mobile applications is personalization.  Most of the existing methods tend to train generic models for all users, but the authors provide existing personalized treatments for mobile applications, as the behaviors may differ greatly from one user to another in many mobile applications. The third requirement is real-time. That is, the mobile application should return responses in a real-time manner, meanwhile balancing effectiveness and efficiency.

 This SpringerBrief targets data mining and machine learning researchers and practitioners working in these related fields. Advanced level students studying computer science and electrical engineering will also find this brief useful as a study guide.