Ubiquitous WiFi and Acoustic Sensing: Principles, Technologies, and Applications.

With the increasing pervasiveness of mobile devices such as smartphones, smart TVs, and wearables, smart sensing, transforming the physical world into digital information based on various sensing medias, has drawn researchers' great attention. Among different sensing medias, WiFi and acoustic signals stand out due to their ubiquity and zero hardware cost. Based on different basic principles, researchers have proposed different technologies for sensing applications with WiFi and acoustic signals covering human activity recognition, motion tracking, indoor localization, health monitoring, and the like. To enable readers to get a comprehensive understanding of ubiquitous wireless sensing, we conduct a survey of existing work to introduce their underlying principles, proposed technologies, and practical applications. Besides we also discuss some open issues of this research area. Our survey reals that as a promising research direction, WiFi and acoustic sensing technologies can bring about fancy applications, but still have limitations in hardware restriction, robustness, and applicability. Supplementary Information: The online version contains supplementary material available at 10.1007/s11390-023-3073-5.

HXPY: A High-Performance Data Processing Package for Financial Time-Series Data.

A tremendous amount of data has been generated by global financial markets everyday, and such time-series data needs to be analyzed in real time to explore its potential value. In recent years, we have witnessed the successful adoption of machine learning models on financial data, where the importance of accuracy and timeliness demands highly effective computing frameworks. However, traditional financial time-series data processing frameworks have shown performance degradation and adaptation issues, such as the outlier handling with stock suspension in Pandas and TA-Lib. In this paper, we propose HXPY, a high-performance data processing package with a C++/Python interface for financial time-series data. HXPY supports miscellaneous acceleration techniques such as the streaming algorithm, the vectorization instruction set, and memory optimization, together with various functions such as time window functions, group operations, down-sampling operations, cross-section operations, row-wise or column-wise operations, shape transformations, and alignment functions. The results of benchmark and incremental analysis demonstrate the superior performance of HXPY compared with its counterparts. From MiBs to GiBs data, HXPY significantly outperforms other in-memory dataframe computing rivals even up to hundreds of times. Supplementary Information: The online version contains supplementary material available at 10.1007/s11390-023-2879-5.