Recent research have devoted significant efforts on the utilization of WiFi signals to recognize various human activities. An individual's limb motions in the WiFi coverage area could interfere wireless signal propagation, that manifested as unique patterns for activities recognition. Existing approaches though yielding reasonable performance in certain cases, are ignorant of two major challenges. The performed activities of the individual normally have inconsistent speed in different situations and time. Besides that the wireless signal reflected by human bodies normally carry substantial information that is specific to that subject. The activity recognition model trained on a certain individual may not work well when being applied to predict another individual's activities. Since only recording activities of limited subjects in certain speed and scale, recent works commonly have moderate amount of activity data for training the recognition model. The small-size data could often incur the overfitting issue that negative affect the traditional classification model. To address these challenges, we propose a WiFi based human activity recognition system that synthesize variant activities data through 8 CSI transformation methods to mitigate the impact of activity inconsistency and subjectspecific issues, and also design a novel deep learning model that cater to the small-size WiFi activity data. We conduct extensive experiments and show synthetic data improve performance by up to 34.6% and our system achieves around 90% of accuracy with well robustness in adapting to small-size CSI data.

Terahertz spectroscopy has been widely used for investigating the fingerprint spectrum of different substances. For cancerous tissues, the greatest difficulty is the absorption peaks of various substances contained in tissues overlap with each other, which are hard to identify and quantitative analyze. As a result, it is very hard to measure the presence of cancer cell and then to diagnose accurately. In this paper, we select three typical neurotransmitters (γ-aminobutyric acid, L-glutamic acid, dopamine hydrochloride) and two typical metabolites (inositol and creatine) in neurons to measure their terahertz spectra with different mixture ratios. By choosing characteristic absorption peaks, removing baseline and using the least square method, we can identify the components and proportions of each mixture, where the goodness of fit to practical situation is up to 94%. These results provide important evidences for identifying nerve substances and obtaining exact quantitative analysis.

Nanomedicine‐enabled/augmented ultrasound (US) medicine is a unique area of interdisciplinary research that focuses on designing and engineering functional nanosystems to address the challenging issues in US‐based biomedicine, overcoming the shortcomings of traditional microbubbles and optimizing the design of contrast and sonosensitive agents. The single‐faceted summary of available US‐related therapies is still a significant drawback. Here, The proposal of a comprehensive review on the recent advances of sonosensitive nanomaterials in advancing four US‐related biological applications and disease theranostics is aimed. In addition to the mostly explored nanomedicine‐enabled/augmented sonodynamic therapy (SDT), the summary and discussion of other sono‐therapies and progresses/achievements are relatively lacking, including sonomechanical therapy (SMT), sonopiezoelectric therapy (SPT), and sonothermal therapy (STT). The design concepts of the specific sono‐therapies based on nanomedicines are initially introduced. Furthermore, the representative paradigms for nanomedicine‐enabled/enhanced US therapies are elaborated according to therapeutic principles and diversity. This review provides an updated and comprehensive review of the field of nanoultrasonic biomedicine, and comprehensively discusses the progress of versatile ultrasonic disease treatments. Finally, the deep discussion on the facing challenges and prospects is expected to promote the emergence and establishment of a new branch of US biomedicine through the rational combination of nanomedicine and US clinical biomedicine.

Collective perception is a new paradigm to extend the limited horizon of individual vehicles. Incorporating with the recent vehicle-2-x (V2X) technology, connected and autonomous vehicles (CAVs) can periodically share their sensory information, given that traffic management authorities and other road participants can benefit from these information enormously. Apart from the benefits, employing collective perception could result in a certain level of transmission redundancy, because the same object might fall in the visible region of multiple CAVs, hence wasting the already scarce network resources. In this paper, we analytically study the data redundancy issue in highway scenarios, showing that the redundant transmissions could result in heavy loads on the network under medium to dense traffic. We then propose a probabilistic data selection scheme to suppress redundant transmissions. The scheme allows CAVs adaptively adjust the transmission probability of each tracked objects based on the position, vehicular density and road geometry information. Simulation results confirm that our approach can reduce at most 60% communication overhead in the meanwhile maintain the system reliability at desired levels. INDEX TERMS Collective perception, connected and autonomous vehicles, V2X communications, data redundancy.