Water and Thermal Management of Proton Exchange Membrane Fuel Cells introduces the main research methods and latest advances in the water and thermal management of PEMFCs. The book introduces the transport mechanism of each component, including modeling methods at different scales, along with practical exercises. Topics include PEMFC fundamentals, working principles and transport mechanisms, characterization tests and diagnostic analysis, the simulation of multiphase transport and electrode kinetics, cell-scale modeling, stack-scale modeling, and system-scale modeling. This volume offers a practical handbook for researchers, students and engineers in the fields of proton exchange membrane fuel cells. Proton exchange membrane fuel cells (PEMFCs) are high-efficiency and low-emission electrochemical energy conversion devices. Inside the PEMFC complex, physical and chemical processes take place, such as electrochemical reaction, multiphase flow and heat transfer. This book explores these topics, and more.

This book provides a comprehensive introduction to applying compressive sensing to improve data quality in the context of mobile crowdsensing. It addresses the following main topics: recovering missing data, efficiently collecting data, preserving user privacy, and detecting false data. Mobile crowdsensing, as an emerging sensing paradigm, enables the masses to take part in data collection tasks with the aid of powerful mobile devices. However, mobile crowdsensing platforms have yet to be widely adopted in practice, the major concern being the quality of the data collected. There are numerous causes: some locations may generate redundant data, while others may not be covered at all, since the participants are rarely systematically coordinated; privacy is a concern for some people, who don't wish to share their real-time locations, and therefore some key information may be missing; further, some participants may upload fake data in order to fraudulently gain rewards. To address these problematic aspects, compressive sensing, which works by accurately recovering a sparse signal using very few samples, has proven to offer an effective solution.

This book provides a comprehensive introduction to applying compressive sensing to improve data quality in the context of mobile crowdsensing. It addresses the following main topics: recovering missing data, efficiently collecting data, preserving user privacy, and detecting false data. Mobile crowdsensing, as an emerging sensing paradigm, enables the masses to take part in data collection tasks with the aid of powerful mobile devices. However, mobile crowdsensing platforms have yet to be widely adopted in practice, the major concern being the quality of the data collected. There are numerous causes: some locations may generate redundant data, while others may not be covered at all, since the participants are rarely systematically coordinated; privacy is a concern for some people, who don't wish to share their real-time locations, and therefore some key information may be missing; further, some participants may upload fake data in order to fraudulently gain rewards. To address these problematic aspects, compressive sensing, which works by accurately recovering a sparse signal using very few samples, has proven to offer an effective solution.