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In-air gesture control extends a touch screen and enables contactless interaction, thus has become a popular research direction in the past few years. Prior work has implemented this functionality based on cameras, acoustic signals, and Wi-Fi via existing hardware on commercial devices. However, these methods have low user acceptance. Solutions based on cameras and acoustic signals raise privacy concerns, while WiFi-based solutions are vulnerable to background noise. As a result, these methods are not commercialized and recent flagship smartphones have implemented in-air gesture recognition by adding extra hardware on-board, such as mmWave radar and depth camera. The question is, can we support in-air gesture control on legacy devices without any hardware modifications? To answer this question, in this work, we propose SMART, an in-air gesture recognition system leveraging the screen and ambient light sensor (ALS), which are ordinary modalities on mobile devices. For the transmitter side, we design a screen display mechanism to embed spatial information and preserve the viewing experience; for the receiver side, we develop a framework to recognize gestures from low-quality ALS readings. We implement and evaluate SMART on both a tablet and several smartphones. Results show that SMART can recognize 9 types of frequently used in-air gestures with an average accuracy of 96.1%.

Federated Learning (FL) requires frequent exchange of model parameters, which leads to long communication delay, especially when the network environments of clients vary greatly. Moreover, the parameter server needs to wait for the slowest client (i.e., straggler, which may have the largest model size, lowest computing capability or worst network condition) to upload parameters, which may significantly degrade the communication efficiency. Commonly-used client selection methods such as partial client selection would lead to the waste of computing resources and weaken the generalization of the global model. To tackle this problem, along a different line, in this paper, we advocate the approach of model parameter dropout instead of client selection, and accordingly propose a novel framework of Federated Learning scheme with Differential parameter Dropout (FedDD). FedDD consists of two key modules: dropout rate allocation and uploaded parameter selection, which will optimize the model parameter uploading ratios tailored to different clients’ heterogeneous conditions and also select the proper set of important model parameters for uploading subject to clients’ dropout rate constraints. Specifically, the dropout rate allocation is formulated as a convex optimization problem, taking system heterogeneity, data heterogeneity, and model heterogeneity among clients into consideration. The uploaded parameter selection strategy prioritizes on eliciting important parameters for uploading to speedup convergence. Furthermore, we theoretically analyze the convergence of the proposed FedDD scheme. Extensive performance evaluations demonstrate that the proposed FedDD scheme can achieve outstanding performances in both communication efficiency and model convergence, and also possesses a strong generalization capability to data of rare classes.

Spectrum sensing plays a crucial role in spectrum monitoring and management. However, due to the expensive cost of high-speed ADCs, wideband spectrum sensing is a long-standing challenge. In this paper, we present how to transform Ultra-wideband (UWB) devices into a spectrum sensor which can provide wideband spectrum monitoring at a low cost. Compared with the expensive high-speed ADCs which cost at least hundreds of dollars, a UWB device is only several dollars. As the low-cost UWB technology is not originally designed for spectrum sensing, we address the inherent limitations of low-cost devices such as limited memory, low SPI speed and low accuracy, and show how to obtain spectrum occupancy information from the noisy and spurious UWB channel impulse response. In this paper, we present WISE, which not only can give accurate channel occupancy information, but also can precisely estimate the signal power and bandwidth. WISE can also detect fleeting radar signals. We implement WISE and perform extensive evaluations with both controlled experiments and field tests. Results show that WISE can sense up to 900MHz bandwidth with frequency range from 0.5GHz to 7GHz, and the power estimation error is less than 3dB. WISE can also accurately detect busy 5G channels and can classify the encrypted traffic from the channel usage patterns. We believe that WISE provides a new paradigm for low-cost wideband spectrum sensing, which is critical for large-scale fine-grained spectrum monitoring.

Ingestion is a major exposure route for hydrophobic organic pollutants in fish, but the microbial transformation and estrogenic modification of the novel plastic additives by the gut microbiota of fish remain obscure. Using an in vitroapproach, we provide evidence that structure-related transformation of various plastic additives by the gastric and intestinal (GI) microbiota from crucian carp, with the degradation ratio of bisphenols and triphenyl phosphate faster than those of brominated compounds. The degradation kinetics for these pollutants could be limited by oxygen and cometabolic substrates (i.e., glucose). The fish GI microbiota could utilize the vast majority of carbon sources in a Biolog EcoPlate, suggesting their high metabolic potential and ability to transform various organic compounds. Unique microorganisms associated with transformation of the plastic additives including genera of Citrobacter, Klebsiella, and some unclassified genera in Enterobacteriaceaewere identified by combining high-throughput genetic analyses and metagenomic analyses. Through identification of anaerobic transformation products by high-resolution mass spectrometry, alkyl-cleavage was found the common transformation mechanism, and hydrolysis was the major pathway for ester-containing pollutants. After anaerobic incubation, the estrogenic activities of triphenyl phosphate and bisphenols A, F, and AF declined, whereas that of bisphenol AP increased.

The increasing discharge and ubiquitous occurrence of novel brominated flame retardants (NBFRs) in aquatic environments have initiated intense global concerns; however, little information is available regarding their structure-related trophodynamics in marine food webs. In this study, a tropical marine food web including 29 species (18 fish and 11 invertebrate species) was collected from coral reef waters of the Xisha Islands, the South China Sea, for an analysis of 11 representative NBFRs. The mean ∑NBFR concentrations generally increased in the following sequence: sea cucumbers (0.330 ng/g lw) < crabs (0.380 ng/g lw) < shells (2.10 ng/g lw) < herbivorous fishes (2.30 ng/g lw) < carnivorous fishes (4.13 ng/g lw), with decabromodiphenyl ethane (DBDPE) and hexabromobenzene (HBB) as the predominant components. Trophic magnification was observed for all of the investigated NBFRs, with trophic magnification factors (TMFs) ranging from 1.53 (tetrabromobisphenol A bis(dibromopropyl ether)) to 5.32 (HBB). Significant negative correlations were also found between the TMFs and the tested in vitrotransformation clearance rates (CLin vitro) for the target NBFRs except for bis(2-ethylhexyl)-3,4,5,6-tetrabromo-phthalate (TBPH) (p< 0.05). Multiple linear regression analysis confirmed that the transformation rate is a more powerful predictor for TMFs than the hydrophobicity of NBFRs in this marine food web.

Recent years have witnessed the prevalence of wearable devices. Wearable devices are intelligent and multifunctional, but they rely heavily on batteries. This greatly limits their application scope, where replacement of battery or recharging is challenging or inconvenient. We note that wearable devices have the opportunity to harvest energy from human motion, as they are worn by the users as long as being functioning. In this article, we propose a battery-free sensing platform for wearable devices in the form factor of shoes. It harvests the kinetic energy from walking or running to supply devices with power for sensing, processing, and wireless communication, covering all the functionalities of commercial wearable devices. We achieve this goal by enabling the whole system running on the harvested energy from two feet. Each foot performs separate tasks and two feet are coordinated by ambient backscatter communication. We instantiate this idea by building a prototype, containing energy harvesting insoles, power management circuits, and ambient backscatter module. Evaluation results demonstrate that the system can wake up shortly after several seconds’ walk and have sufficient Bluetooth throughput for supporting many applications. We believe that our framework can stir a lot of useful applications that were infeasible previously.

Mobile devices, including laptops, smartphones, wearables, etc., have become essential tools in modern life. We rely on them for social activities, document processing, and health status monitoring. As these devices contain sensitive personal information, various security mechanisms, such as firewalls, traffic monitors, and information flow control systems [1], have been developed for mobile devices to prevent unauthorized data leakage.

The accumulation and trophodynamics of organophosphate flame retardants (OPFRs) and their metabolites were investigated in the estuarine food web of the Pearl River, China. The mean ∑OPFR concentration among the investigated species increased in the following order: fish [431 ± 346 ng/g lipid weight (lw)] < snail (1310 ± 621 ng/g lw) < shrimp (1581 ± 1134 ng/g lw) < crab (1744 ± 1397 ng/g lw). The di-alkyl phosphates (DAPs) of di-(n-butyl) phosphate (DNBP), bis(2-butoxyethyl) phosphate (BBOEP), and diphenyl phosphate (DPHP) were the most abundant metabolites, with concentrations same as or even higher than their corresponding parent compounds. The log bioaccumulation factors for most OPFRs were lower than 3.70, and significant biomagnification was only found for trisphenyl phosphate [TPHP, with the trophic magnification factors (TMFs) > 1]. The TMFs of OPFRs, except for TPHP and tributyl phosphate had a positive correlation with lipophilicity (log KOW, p≤ 0.05) and a negative correlation with the biotransformation rate (log KM, p≤ 0.05). The mean TMF > 1 was observed for all of the OPFR metabolites based on the bootstrap regression method. The “pseudo-biomagnification” of OPFR metabolites might be attributed to the biotransformation of OPFRs in organisms at high trophic levels.