Millimeter-wave (mmWave) has emerged as a promising solution for contact-free sensing due to its high resolution. Although promising, it faces several critical issues, including occlusion from the surrounding environment, unstable orientation-dependent sensing performance, and significant interference when multiple targets are in close proximity. These fundamental issues hinder the widespread adoption of mmWave sensing in the real world. In this paper, we propose SeRadar, the first systematic framework that leverages all useful secondary reflections to significantly enhance reliability and bring mmWave sensing one step closer to real-world adoption. Unlike primary reflections commonly used in wireless sensing, secondary reflections—typically much weaker due to being reflected multiple times—are generally ignored in existing literature. However, we observe that secondary reflections are common in various scenarios and carry valuable information about target movements, which could also contribute to sensing. To effectively utilize secondary reflections for sensing, SeRadar addresses several challenges associated with secondary reflections. Specifically, it boosts weak secondary reflections to improve their sensing capability, identifies useful ones from a large number of secondary reflections captured in the environment, and mitigates primary-secondary interference in multi-target scenarios. We evaluate the performance of SeRadar in various environments, including offices, apartments, and vehicle cabins. Extensive experiments demonstrate SeRadar can enhance accuracy and reliability in diverse sensing scenarios.

Various wireless technologies have been utilized for sensing. Although promising, these wireless sensing technologies have inherent limitations. Prior research mainly focuses on overcoming the limitations of an individual wireless sensing technology, and little attention has been paid to the potential benefits of sensing with more than one wireless technology. In this paper, we introduce the concept of cross-technology sensing for the first time, and propose LoFiSen to enable LoRa-to-WiFi sensing. LoFiSen leverages the strengths of both LoRa and WiFi—combining LoRa's long-range capability with WiFi's pervasiveness. The chirp characteristic of LoRa signal significantly improves the sensing range of WiFi, and the widespread availability of WiFi devices makes LoRa sensing more pervasive. LoFiSen is fully compatible with LoRa and WiFi protocols, and can work on commodity LoRa and WiFi hardware. The key component of our design is enabling the WiFi receiver to capture fine-grained LoRa signal variations for sensing. Real-world experiments demonstrate that LoFiSen improves the WiFi sensing range for respiration monitoring from 8 m to 41 m, and pushes the walking sensing range from 16 m to 73.5 m. Through-wall passive respiration monitoring, previously infeasible with state-of-the-art WiFi sensing, is now possible with LoFiSen.

LoRa-based contact-free wireless sensing has attracted a lot of attention owing to its long sensing range, enabling wide-area sensing for the first time. While promising, existing LoRa sensing assumes there is no communication going on which is usually not true. We observe a severe degradation of sensing performance in real-world settings in the presence of communication. This issue hinders LoRa sensing from being adopted in real life and being integrated into the already established LoRa networking infrastructure. In this paper, we propose LSencom which takes the first step toward making LoRa-based wireless sensing work in the presence of communication. The key design is to employ the reversed chirp, i.e., downchirp, for sensing while keeping the original upchirp for communication. This design smartly leverages the orthogonality between downchirp and upchirp to mitigate the interference between communication and sensing. While the upchirp-downchirp design can remove most of the interference, we further adopt a novel chirp rotation method to deal with the remaining power leakage interference from upchirp to downchirp, enhancing the sensing performance. We implement LSencom on commodity LoRa nodes. Real-world experiments demonstrate that LSencom can reduce the communication-induced interference on sensing by 22.3 dB, and enable LoRa sensing even in the presence of multiple communication links.

LoRa technology holds great promise for wide-area wireless sensing, owing to its long-range connectivity and strong penetration capability. However, existing LoRa sensing systems face a fundamental limitation, i.e., they assume that the gateway only receives signals from the sensing node, without any interference from other communication nodes. This is because transmissions from communication nodes inevitably distort the sensing pattern extracted from the received sensing signal, leading to sensing failure. To address this challenging issue, we propose InterSen, a novel solution specifically designed to address communication interference on LoRa sensing. We conduct an in-depth analysis of how communication interference disrupts LoRa sensing and design innovative signal processing methods to recover the sensing pattern corrupted by interference. We evaluate the performance of InterSen across three real-world sensing applications, i.e., respiration monitoring, walking sensing, and gesture recognition. Comprehensive experiments demonstrate that InterSen achieves accurate sensing even in the presence of multiple communication nodes. This brings LoRa sensing one step closer to practical deployment in real-world scenarios.

Wi-Fi technology has emerged as a promising solution for contact-free sensing owing to the pervasiveness of Wi-Fi signals in indoor environments. However, Wi-Fi sensing faces several fundamental issues, including limited sensing range and unstable orientation-dependent sensing performance, hindering the widespread adoption of Wi-Fi sensing in real-life scenarios. In this paper, we propose RISensing, a novel system that leverages Reconfigurable Intelligent Surfaces (RIS) to address these two fundamental issues of Wi-Fi sensing and bring Wi-Fi sensing one step closer to real-world adoption. Unlike prior Wi-Fi sensing works which typically rely on a single target reflection signal to capture the target movement, RISensing utilizes two target reflection signals, i.e., the direct target reflection signal and RIS-based target reflection signal, to boost the sensing capability. RISensing characterizes the RIS-based target reflection signal, and constructively combines it with the direct target reflection. We evaluate the sensing performance of RISensing in various environments, including corridor, office and lab. Extensive experiments demonstrate RISensing can improve the sensing range of Wi-Fi from 4 m to 23 m, and effectively mitigate the orientation-dependent issue.

LoRa technology has become critical in numerous IoT applications, offering long-range connections with low energy consumption. However, their low-power nature makes them vulnerable to cross-technology interference (CTI) from other wireless technologies sharing the same unlicensed frequency bands. Existing solutions address this issue through signal analysis and coding techniques. Despite these efforts, the research on CTI mitigation needs to go one step further - to design a systematic framework for enhanced interference resilience. This article proposes SLoRa+, a systematic framework that integrates symbol recovery with soft decoding to achieve synergic interference resilience for LoRa. SLoRa+’s symbol recovery employs a two-stage analysis to recover corrupted LoRa symbols at a low cost. It also estimates the confidence of the recovered symbols by considering the characteristics of both LoRa and CTI. This confidence information is then utilized in soft decoding to improve error correction capabilities. Supported by theoretical analysis and real testbed evaluations, including commercial LoRa nodes and USRP B210, our experiments under different settings, i.e., various CTI sources and frequency bands, demonstrate SLoRa+’s effectiveness. Compared with state-of-the-art techniques, SLoRa+ enhances CTI protection capability by 1.6× with only 1% of the computation cost.

While out-vehicle sensing has achieved great success with the development of vehicle radar and Lidar systems, invehicle sensing attracts a lot of attention recently. However, the popular camera-based solutions raise privacy concerns and pose requirement on lighting conditions. Researchers recently utilize wireless signals for sensing. However, besides requiring dedicated hardware, the rich multipath in a small cabin space causes severe interference, degrading the sensing reliability. In this paper, we propose a new sensing modality for in-vehicle sensing, leveraging the leaked EM signals from electric vehicles. The key observation is that the human body can capture the leaked signals, and body motions affect the signal variation patterns. Our solution involves designing conductive cloth tags on the seat to effectively collect body-captured signals and adopting a reference tag to deal with interference. Through extensive experiments conducted over 100 hours, covering a driving distance of 4000 kilometers on various real roads, our system, EVLeSen, can achieve over 90% accuracy in recognizing body motions utilizing just the leaked ambient signals.

In recent years, wireless sensing has attracted lots of research attention with a large range of applications enabled. However, several critical issues still hinder wireless sensing from being adopted in daily use: (a) requiring dedicated devices and (or) dedicated signals; (b) limited sensing coverage; and (c) affecting the original function of the wireless technology (e.g., communication). In this work, we propose a new sensing modality, i.e., leveraging the pervasive powerline leakage for sensing. The key observation is that human body can capture such leaked signals, and the received signals vary with body gestures. We design a cheap ring antenna to collect the powerline leaked signals at human body and establish a body-empowered model to sense body motions. We prototype the proposed system with designs spanning both hardware and software. Comprehensive experiments show that the proposed sensing modality can realize a large range of applications in a different way from existing sensing methods. We showcase the powerful capability of this sensing modality using three typical sensing applications: body gesture recognition, sleep posture sensing, and fall detection.

Wireless sensing is capable of capturing rich information of human target without requiring sensors attached to the target. Although promising, two critical issues still exist, i.e., (i) limited sensing range, and (ii) severe interference in real-world settings. Recently, LoRa is employed to improve the sensing range. Although LoRa sensing is able to achieve a longer sensing range than WiFi and acoustic sensing, it is still limited to tens of meters. In this paper, we propose ChirpSen, which fully exploits the property of chirp signal to increase the sensing range. ChirpSen adopts a chirp concentration scheme to concentrate the power of all signal samples in a LoRa chirp at one timestamp, improving the signal power and accordingly boosting the sensing range. With a longer sensing range, the interference issue also becomes more severe. We propose a novel scheme to flexibly control the sensing coverage by tuning the LoRa chirp length in software. Real-world experiments show that ChirpSen is able to increase the detection range of a small size drone (12 cm × 10 cm × 8 cm) from 18 m to 160 m. ChirpSen is capable of monitoring a human's respiration rate at 138 m and tracking a human target's walking trajectory 210 m away.

Keyphrase generation aims to automatically generate short phrases summarizing an input document.The recently emerged ONE2SET paradigm (Ye et al., 2021) generates keyphrases as a set and has achieved competitive performance.Nevertheless, we observe serious calibration errors outputted by ONE2SET, especially in the over-estimation of ∅ token (means "no corresponding keyphrase").In this paper, we deeply analyze this limitation and identify two main reasons behind: 1) the parallel generation has to introduce excessive ∅ as padding tokens into training instances; and 2) the training mechanism assigning target to each slot is unstable and further aggravates the ∅ token over-estimation.To make the model wellcalibrated, we propose WR-ONE2SET which extends ONE2SET with an adaptive instancelevel cost Weighting strategy and a target Reassignment mechanism.The former dynamically penalizes the over-estimated slots for different instances thus smoothing the uneven training distribution.The latter refines the original inappropriate assignment and reduces the supervisory signals of over-estimated slots.Experimental results on commonly-used datasets demonstrate the effectiveness and generality of our proposed paradigm.

While GPS-based outdoor localization has become a norm, very few indoor localization systems have been deployed and used. In this paper, we share our 5-year experience on the design, development and evaluation of a large-scale WiFi indoor localization system. We address practical challenges encountered to bridge the gap between indoor localization research in the laboratory and system deployment in the wild. The system is currently used in 1469 shopping malls, 393 office buildings and 35 hospitals across 35 cities to provide location service to millions of users on a daily basis. We hope the shared experience can benefit the design of real-world indoor localization systems and the practical problems identified can change the focus of indoor localization research. We released our dataset that contains fingerprints collected from 1469 shopping malls and one office building.

Wireless sensing is an emerging technology that can obtain rich context information of human targets in a contact-free manner. Though promising, a missing component of current wireless sensing is sensing under device motions. In this work, we propose to integrate wireless sensing with the mobility of a robot. This is non-trivial because we find that device motions can severely degrade the sensing performance and even completely fail existing wireless sensing systems. In this paper, we propose novel signal processing schemes to address the impact of device motions to enable sensing with device mobility. For the first time, we integrate the robot's mobility with LoRa sensing to enlarge the sensing coverage. Comprehensive experiments demonstrate the effectiveness of the proposed system. We employ two representative sensing applications, i.e., fine-grained respiration monitoring and coarse-grained human walking sensing, to showcase the performance of our system. The proposed system is able to achieve accurate sensing in the presence of device motions, moving wireless sensing one step forward towards truly ubiquitous sensing for real-life adoption.

Code generation aims to automatically generate a piece of code given an input natural language utterance. Currently, among dominant models, it is treated as a sequence-to-tree task, where a decoder outputs a sequence of actions corresponding to the pre-order traversal of an Abstract Syntax Tree. However, such a decoder only exploits the pre-order traversal based preceding actions, which are insufficient to ensure correct action predictions. In this paper, we first throughly analyze the context modeling difference between neural code generation models with different traversals based decodings (preorder traversal vs breadth-first traversal), and then propose to introduce a mutual learning framework to jointly train these models. Under this framework, we continuously enhance both two models via mutual distillation, which involves synchronous executions of two one-to-one knowledge transfers at each training step. More specifically, we alternately choose one model as the student and the other as its teacher, and require the student to fit the training data and the action prediction distributions of its teacher. By doing so, both models can fully absorb the knowledge from each other and thus could be improved simultaneously. Experimental results and in-depth analysis on several benchmark datasets demonstrate the effectiveness of our approach. We release our code at https://github.com/DeepLearnXMU/CGML.

Wireless sensing has become a hot research topic recently, enabling a large range of applications. However, due to the intrinsic nature of employing weak target-reflection signal for sensing, the sensing range is limited. Another issue is the strong interference from surroundings and therefore a lot of wireless sensing systems assume there is no interferer in the environment. One recent work explored the possibility of employing LoRa signal for long range sensing which is a favorable step in addressing the first issue. However, the interference issue becomes even more severe with LoRa due to its larger sensing range. In this paper, we propose Sen-fence - a LoRa-based sensing system - to significantly increase the sensing range and at the same time mitigate the interference. With careful signal processing, Sen-fence is able to maximize the movement-induced signal variation in software to increase the sensing range. To address the interference issue, we propose the concept of "virtual fence" to constrain sensing only within the area of interest. The location and size of virtual fence can be flexibly controlled in software to meet the requirements of different applications. Sen-fence is able to (i) achieve a 50 m sensing range for fine-grained human respiration, which is twice the state-of-the-art; and (ii) efficiently mitigate the interference to make LoRa sensing work in practice.

Monitoring the vibration characteristics of a machine or structure provides valuable information of its health condition and this information can be used to detect problems in their incipient stage. Recently, researchers employ RFID signals for vibration sensing. However, they mainly focus on vibration frequency estimation and still face difficulties in accurately sensing the other important characteristic of vibration which is vibration amplitude in the scale of sub-millimeter. In this paper, we introduce TagSMM, a contactless RFID-based vibration sensing system which can measure vibration amplitude in sub-millimeter resolution. TagSMM employs the signal propagation theory to deeply understand how the signal phase varies with vibration and proposes a coupling-based method to amplify the vibration-induced phase change to achieve sub-millimeter level amplitude sensing for the first time. We design and implement TagSMM with commodity RFID hardware. Our experiments show that TagSMM can detect a 0.5 mm vibration, 10 times better than the state-of-the-arts. Our field studies show TagSMM can sense a drone's abnormal vibration and can also effectively detect a small 0.2 cm screw loose in a motor at a 100% accuracy.

Material sensing is an essential ingredient for many IoT applications. While hyperspectral camera, infrared, X-Ray, and Radar provide potential solutions for material identification, high cost is the major concern limiting their applications. In this paper, we explore the capability of employing RF signals for fine-grained material sensing with commodity RFID device. The key reason for our system to work is that the tag antenna's impedance is changed when it is close or attached to a target. The amount of impedance change is dependent on the target's material type, thus enabling us to utilize the impedance-related phase change available at commodity RFID devices for material sensing. Several key challenges are addressed before we turn the idea into a functional system: (i) the random tag-reader distance causes an additional unknown phase change on top of the phase change caused by the target material; (ii) the tag rotations cause phase shifts and (iii) for conductive liquid, there exists liquid reflection which interferes with the impedance-caused phase change. We address these challenges with novel solutions. Comprehensive experiments show high identification accuracies even for very similar materials such as Pepsi and Coke.

Device-free localization of people and objects indoors not equipped with radios is playing a critical role in many emerging applications. This paper presents an accurate model-based device-free localization system LiFS, implemented on cheap commercial off-the-shelf (COTS) Wi-Fi devices. Unlike previous COTS device-based work, LiFS is able to localize a target accurately without offline training. The basic idea is simple: channel state information (CSI) is sensitive to a target's location and by modelling the CSI measurements of multiple wireless links as a set of power fading based equations, the target location can be determined. However, due to rich multipath propagation indoors, the received signal strength (RSS) or even the fine-grained CSI can not be easily modelled. We observe that even in a rich multipath environment, not all subcarriers are affected equally by multipath reflections. Our pre-processing scheme tries to identify the subcarriers not affected by multipath. Thus, CSIs on the "clean" subcarriers can be utilized for accurate localization.