Integrated Sensing and Communications (ISAC) integrates sensing and communication functions through ubiquitous wireless signals, providing a seamless and flexible interaction experience, making it an ideal choice for intelligent Human-Computer Interaction (HCI). Among various interaction methods, gesture recognition has garnered widespread attention. However, current RF-based gesture recognition methods within ISAC are constrained by single-target sensing and insufficient robustness. In this paper, we propose MultiGes, a real-time multi-user gesture recognition system designed to support ISAC-driven scenarios. MultiGes employs dual commercial Impulse Radio Ultra-Wideband (IR-UWB) devices to create multiple wireless links, capturing dynamic motion features from multiple targets. First, a human energy map is constructed based on the reflected signal energy to determine multi-target coordinates. Then, a Differential Human Relative Velocity (DHRV) matrix is extracted to capture fine-grained motion information. Finally, we design a lightweight STNet model to extract spatiotemporal gesture features from the DHRV matrix, enabling real-time multi-target gesture recognition. We implement the MultiGes system prototype and conduct extensive experiments on ten common gestures in HCI scenarios. Experimental results demonstrate that MultiGes achieves efficient recognition for 2 to 5 users, with an average accuracy of over 90%, providing a robust, scalable, and real-time solution for multi-target gesture recognition in ISAC-driven smart environments.

Unauthorized editing of speech recordings poses a significant threat to the security and authenticity of speeches, particularly in the forensic and legal fields. Even worse, the speech is increasingly at risk of being tampered with due to the development of AI techniques (e.g., Audio Deepfake). It is difficult for normal users to guarantee what they say has not been illegally changed. Audio watermark techniques are recognized as an active method against speech forgery. However, such techniques suffer from audio quality degradation and non-real-time insertion. Therefore, they cannot be adopted into real-time voice applications against forgery on remote recordings, e.g., phone calls, live broadcasts, and online meetings. Fortunately, high-definition (HD) audio techniques provide ultrasonic bands without distortion. Therefore, ultrasonic creditable factors can be utilized. We propose an audio tamper-proof system, named Aegis. It provides commodity mobile devices (e.g., smartphones) with an effective method of real-time insertion of inaudible creditable factors. Users can claim that audio with no or mismatched ultrasound is invalid and illegal. In particular, we explore a novel acoustic nonlinear phenomenon where audible signals can be modulated onto the ultrasonic spectrum. By emphasizing the correlation between speech signals and ultrasound, we realize effective defense against various tampering methods. Extensive evaluations demonstrate that Aegis yields a detection accuracy of 99.5% on average even against unseen tampering methods.

Liquid sensing is critical for food safety and public security. Although mmWave-based approaches enable non-invasive and high-accuracy sensing, they are typically limited to single-target and fixed-container scenarios, restricting their applicability in real-world scenarios. In this paper, we present MCLiD, a multi-modal liquid sensing framework that fuses mmWave radar and camera data to achieve simultaneous multi-target and container-independent liquid identification. The basic idea is to leverage camera-captured object positions and container information to guide mmWave data processing, generating robust and discriminative liquid-specific representations for identification. MCLiD addresses a series of practical challenges and integrates three specialized modules for image-mmWave signature construction, liquid-specific feature extraction, and identification. Experimental results show that MCLiD achieves an average accuracy of 96.46 % across all combinations of 10 liquids and 7 container types. In multi-target scenarios, it maintains 96.4% accuracy for two concurrent liquids and 94.02% for five. These results indicate that MCLiD could enable rapid, non-invasive liquid detection for food safety and high-throughput public security applications.

Modern communication tools often suffer quality issues from background noise and other speakers. Existing solutions either face mask blockages or need specialized gear, limiting use on standard earphones. Vibration-based methods, focusing on below 8 kHz speech, lack high frequencies, reducing naturalness. To solve these, we present an innovative multi-sensory speech enhancement framework for commercial earphones. It uses built-in inertial measurement units (IMUs) even with ultra-low 25 Hz sampling as extra input. We developed a mathematical framework linking IMU movement data and vocal signals, incorporating unsupervised domain adaptation to reduce individual differences and guiding intermediate-data integration to connect limited-frequency IMU info with 22 kHz full-range speech.

Daily eating habits shape our long-term health, but most diet apps focus only on calories or macronutrients and overlook deeper issues like chronic inflammation and its effects on cellular aging. Prior medical literature has demonstrated a significant inverse relationship between Dietary Inflammatory Index (DII) and telomere length (TL), a key marker of cellular age. Inspired by this finding, we design ChronoBite, a closed-loop feedback system that pairs real-time inflammation scores with periodic cellular aging insights. In addition to regular calorie tracking, it uses fast-changing DII signals and slow-moving cellular aging markers to guide users toward age-aware eating habits. ChronoBite is a mobile-based prototype that combines food recognition, DII analysis, and cellular aging insights to deliver age-aware dietary feedback. Powered by large language models (LLMs), it offers real-time recommendations while supporting long-term tracking of inflammation patterns and telomere dynamics.

Federated Learning (FL) collaboratively trains a shared global model without exposing clients' private data. In practical FL systems, clients (e.g., smartphones and wearables) typically have disparate system resources. Traditional FL, however, adopts a one-size-fits-all solution, where a homogeneous large model is sent to and trained on each client. This method results in an overwhelming workload for less capable clients and starvation for others. To tackle this, we proposeFedConv, a client-friendly FL framework, minimizing the system overhead on resource-constrained clients by providing heterogeneous customized sub-models.FedConvfeatures a novellearning-on-modelparadigm that learns the parameters of heterogeneous sub-models viaconvolutional compression. To aggregate heterogeneous sub-models, we proposetransposed convolutional dilationto convert them back to large models with a unified size while retaining personalized information. The compression and dilation processes, transparent to clients, are tuned on the server using a small public dataset. We further propose ahierarchical and clustering-based local trainingstrategy for enhanced performance. Extensive experiments on six datasets show thatFedConvoutperforms state-of-the-art FL systems in terms of model accuracy (by more than 35% on average), computation and communication overhead (with 33% and 25% reduction, respectively).

Integrated Sensing and Communication (ISAC) is gaining a tremendous amount of attention from both academia and industry. Recent work has brought communication capability to sensing-oriented mmWave radars, enabling more innovative applications. These solutions, however, either require hardware modifications or suffer from limited data rates. This paper presents Talk2Radar, which builds a faster communication channel between smartphone speakers and mmWave radars, without any hardware modification to either commodity smartphones or off-the-shelf radars. In Talk2Radar, a smartphone speaker sends messages by playing carefully designed sounds. A mmWave radar acting as a data receiver captures the emitted sounds by detecting the sound-induced smartphone vibrations, and then decodes the messages. Talk2Radar characterizes smartphone speakers for speaker-to-mmWave radar communication and addresses a series of technical challenges, including modulation and demodulation of extremely weak sound-induced vibrations, multi-speaker concurrent communication and human motion suppression. We implement and evaluate Talk2Radar in various practical settings. Experimental results show that Talk2Radar can achieve a data rate of up to 400bps with an average BER of less than 5%, outperforming the state-of-the-art by approximately 33×.

Federated Learning (FL) facilitates collaborative training of a shared global model without exposing clients' private data. In practical FL systems, clients (e.g., edge servers, smartphones, and wearables) typically have disparate system resources. Conventional FL, however, adopts a one-size-fits-all solution, where a homogeneous large global model is transmitted to and trained on each client, resulting in an overwhelming workload for less capable clients and starvation for other clients. To address this issue, we propose FedConv, a client-friendly FL framework, which minimizes the computation and memory burden on resource-constrained clients by providing heterogeneous customized sub-models. FedConv features a novel learning-on-model paradigm that learns the parameters of the heterogeneous sub-models via convolutional compression. Unlike traditional compression methods, the compressed models in FedConv can be directly trained on clients without decompression. To aggregate the heterogeneous sub-models, we propose transposed convolutional dilation to convert them back to large models with a unified size while retaining personalized information from clients. The compression and dilation processes, transparent to clients, are optimized on the server leveraging a small public dataset. Extensive experiments on six datasets demonstrate that FedConv outperforms state-of-the-art FL systems in terms of model accuracy (by more than 35% on average), computation and communication overhead (with 33% and 25% reduction, respectively).

Voice assistants are widely integrated into a variety of mobile devices, enabling users to easily complete daily tasks and even critical operations like online transactions with voice commands. Thus, once attackers replay a secretly-recorded voice command by loudspeakers to compromise users' voice assistants, this operation will cause serious consequences, such as information leakage and property loss. Unfortunately, most voice liveness detection approaches against replay attacks mainly rely on detecting lip motions or subtle physiological features in speech, which are limited within a very short range. In this paper, we propose VoShield to check whether a voice command is from a genuine user or a loudspeaker imposter. VoShield measures sound field dynamics, a feature that changes fast as the human mouths dynamically open and close. In contrast, it would remain rather stable for loudspeakers due to the fixed size. This feature enables VoShield to largely extend the working distance and remain resilient to user locations. Besides, sound field dynamics are extracted from the difference between multiple microphone channels, making this feature robust to voice volume. To evaluate VoShield, we conducted comprehensive experiments with various settings in different working scenarios. The results show that VoShield can achieve a detection accuracy of 98.2% and an Equal Error Rate of 2.0%, which serves as a promising complement to current voice authentication systems for smart mobile devices.

Integrated Sensing and Communication (ISAC) has emerged as a promising technology for next-generation mobile networks. Towards ISAC, we developmmRipplethat empowers commodity mmWave radars with communication capabilities through smartphone vibrations. InmmRipple, a smartphone (transmitter) sends messages by modulating smartphone vibrations, while a mmWave radar (receiver) receives the messages by detecting and decoding the smartphone vibrations. By doing so, a smartphone user can not only be passively sensed by a mmWave radar, but also actively send messages to the radar without any hardware modifications. Although promising, the data rate ofmmRippleis limited by Morse-style communication. To address this, we presentmmRipple+, which leverages the Pulse Width and Amplitude Modulation (PWAM) technique and suppresses inter-symbol interference to enable faster communication. We prototypemmRippleandmmRipple+on commodity mmWave radars and different types of smartphones. Experimental results show thatmmRippleachieves an average vibration pattern recognition accuracy of 98.60% within a$ 2$m communication range, and 97.74% within$ 3$m. The maximum communication range extends to$ 5$m. Meanwhile,mmRipple+achieves a bit rate of 100 bps with a BER of less than 3%, improving the data rate by 4× overmmRippewith the same symbol duration. This work pioneers smartphone-to-COTS mmWave radar communication via vibrations, unlocking diverse applications.

Traditional indoor location information management methods based on centralized servers have problems such as safe and reliable transmission, personal privacy leaks, location information tampering, and computing and storage loads. These problems have seriously affected the development of personalized services based on indoor location information. In this paper, a novel mobile blockchain-enabled federated learning (MBFL) information management framework for indoor positioning is presented, comprising the mobile blockchain model, the federated learning (FL) model, and the InterPlanetary file storage model. Then, we design the MBFL algorithm, establishing a robust foundation for collaborative model training, efficient block mining, and secure data storage. Moreover, we derive training and mining latency as well as the individual user rewards, and formulate latency-limited resource allocation strategies as a non-cooperative game. We propose an efficient alternating iterative algorithm to achieve the Nash equilibrium of this game. Numerical results demonstrate that the proposed alternating iterative algorithm achieves rapid convergence and strikes an effective balance between economic and time efficiency. Furthermore, when confronted with model poisoning attacks, the MBFL algorithm exhibits superior security performance compared to the traditional FL algorithm. Future work will focus on adapting the MBFL framework for various indoor environments and enhancing consumption and computational efficiency with hybrid consensus mechanisms.

This paper presents the design and implementation of mmRipple, which empowers commodity mmWave radars with the communication capability through smartphone vibrations. In mmRipple, a smartphone (transmitter) sends messages by modulating smartphone vibrations, while a mmWave radar (receiver) receives the messages by detecting and decoding the smartphone vibrations with mmWave signals. By doing so, a smartphone user can not only be passively sensed by a mmWave radar, but also actively send messages to the radar using her smartphone without any hardware modifications to either the smartphone or the mmWave radar. mmRipple addresses a series of unique technical challenges, including vibration signal generation, tiny vibration sensing, multiple object separation, and movement interference mitigation. We implement and evaluate mmRipple using commodity mmWave radars and smartphones in different practical conditions. Experimental results show that mmRipple achieves an average vibration pattern recognition accuracy of 98.60% within a 2m communication range, and 97.74% within 3m on 11 different types of smartphones. The communication range can be further extended up to 5m with an accuracy of 91.67% with line-of-sight path. To our best knowledge, mmRipple is the first work that allows smartphones to send data to COTS mmWave radars via smartphone vibrations and will enable many new applications such as vibration-based near field communication and pedestrian-to-sensing-infrastructure communication.

Recent years have witnessed the booming development of RF sensing, which supports both identity authentication and behavior recognition by analysing the signal distortion caused by human body. In particular, RF-based identity authentication is more attractive to researchers, because it can capture the unique biological characteristics of users. However, the openness of wireless transmission raises privacy concerns since human behaviors could expose massive private information of users, which impedes the real-world implementation of RF-based user authentication applications. It is difficult to filter out the behavior information from the collected RF signals. In this article, we propose a privacy-preserving deep neural network namedBPCloakto erase the behavior information in RF signals while retaining the ability of user authentication. We conduct extensive experiments over mainstream RF signals collected from three real wireless systems, including the WiFi, radio frequency identification (RFID), and millimeter-wave (mmWave) systems. The experimental results show thatBPCloaksignificantly reduces the behavior recognition accuracy, i.e., 85%+, 75%+, and 65%+ reduction for WiFi, RFID, and mmWave systems respectively, merely with a slight penalty of accuracy decrease when using these three systems for user authentication, i.e., 1%-, 3%-, and 5%-, respectively.

UHF RFID technology becomes increasingly popular in stores, since it can quickly read a large number of RFID tags from afar. The deployed RFID infrastructure, however, does not directly benefit smartphone users in stores, mainly because smartphones cannot read UHF RFID tags or fetch relevant information. This paper aims to bridge the gap and allow users to 'read' UHF RFID tags using their smartphones, without any hardware modification to either deployed RFID systems or smartphone hardware. To ‘read’ an interested tag, a user makes a pre-defined smartphone gesture in front of an interested tag. The smartphone gesture causes changes in 1) RFID measurement data captured by RFID infrastructure, and 2) motion sensor data captured by the user's smartphone. By matching the two data, our system (named ShakeReader) can pair the interested tag with the corresponding smartphone, thereby enabling the smartphone to indirectly 'read' the interested tag. We build a novel reflector polarization model to analyze the impact of smartphone gesture to RFID backscattered signals. We enhance the basic version of ShakeReader [6] by improving its performance in densely deployed scenarios. Experimental results show that ShakeReader can accurately pair interested tags with their corresponding smartphones with an accuracy of >96.3%.

Multi-factor user authentication becomes increasingly popular due to its superior security comparing with single-factor user authentication. However, existing multi-factor user authentication methods usually require multiple interactions between users and different authentication components when inputting the multiple factors, leading to extra overhead and bad user experience. In this paper, we propose a secure and user-friendly multi-factor user authentication system named BioDraw. It utilizes four categories of biometrics (impedance, geometry, behavior, and composition) of human hand plus the pattern-based password to identify and authenticate users. User only needs to draw a pattern on a radio frequency identification tag array, while four biometrics can be collected simultaneously. Specifically, we first design a gradient-based pattern recognition algorithm to precisely extract user’s secret pattern. Then, a convolutional neural network- and long short-term memory-based classifier is utilized for user recognition. Furthermore, to guarantee the systemic security, an anti-replay method called Binary ALOHA is proposed to detect replayed signals. We conduct extensive experiments with 30 volunteers. The experiment results show that BioDraw can achieve high authentication accuracy (with a 2%– false reject rate) and is effective in defending against various attacks.

Millimeter wave (mmWave) radar has demonstrated excellent performance in object tracking and micro-displacement detection. Besides the powerful sensing function, this work brings the communication function, allowing daily devices to communicate with mmWave radars through vibrations. In this work, we present VibBeat, in which a daily device (e.g., smartphone and smartwatch) sends messages by modulating vibrations, while a mmWave radar receives the messages by detecting and decoding the vibrations with reflected mmWave signals. By doing so, the device (user) can not only be passively sensed by a mmWave radar, but also actively send messages to the radar for a personalized response. We implement our system using a COTS mmWave radar and smartphones without any hardware modification. Experimental results show that VibBeat supports multiple object communication and achieves a communication range of up to 5m.

UHF RFID technology becomes increasingly popular in RFID-enabled stores (e.g., UNIQLO), since UHF RFID readers can quickly read a large number of RFID tags from afar. The deployed RFID infrastructure, however, does not directly benefit smartphone users in the stores, mainly because smartphones cannot read UHF RFID tags or fetch relevant information (e.g., updated price, real-time promotion). This paper aims to bridge the gap and allow users to `read' UHF RFID tags using their smartphones, without any hardware modification to either deployed RFID systems or smartphone hardware. To `read' an interested tag, a user makes a pre-defined smartphone gesture in front of an interested tag. The smartphone gesture causes changes in 1) RFID measurement data (e.g., phase) captured by RFID infrastructure, and 2) motion sensor data (e.g., accelerometer) captured by the user's smartphone. By matching the two data, our system (named ShakeReader) can pair the interested tag with the corresponding smartphone, thereby enabling the smartphone to indirectly `read' the interested UHF tag. We build a novel reflector polarization model to analyze the impact of smartphone gesture to RFID backscattered signals. Experimental results show that ShakeReader can accurately pair interested tags with their corresponding smartphones with an accuracy of >94.6%.

Recent years have witnessed the booming development of RF sensing, which supports both identity authentication and behavior recognition by analysing the signal distortion caused by human body. In particular, RF-based identity authentication is more attractive to researchers, because it can capture the unique biological characteristics of users. However, the openness of wireless transmission raises privacy concerns since human behaviors can expose the massive private information of users, which impedes the real-world implementation of RF-based user authentication applications. Unfortunately, it is difficult to filter out the behavior information from the collected RF signals.In this paper, we propose a privacy-preserving deep neural network named BPCloak to erase the behavior information in RF signals while retaining the ability of user authentication. We conduct extensive experiments over mainstream RF signals collected from three real wireless systems, including the WiFi, Radio Frequency IDentification (RFID), and millimeter-wave (mmWave) systems. The experimental results show that BPCloak significantly reduces the behavior recognition accuracy, i.e., 85%+, 75%+, and 65%+ reduction for WiFi, RFID, and mmWave systems respectively, merely with a slight penalty of accuracy decrease when using these three systems for user authentication, i.e., 1%-, 3%-, and 5%-, respectively.

In this paper, we propose a novel multi-dimensional reconstruction method based on the low-rank plus sparse tensor (L+S) decomposition model to reconstruct dynamic magnetic resonance imaging (dMRI). The multi-dimensional reconstruction method is formulated using a non-convex alternating direction method of multipliers (ADMM), where the weighted tensor nuclear norm (WTNN) andl1-norm are used to enforce the low-rank inLand the sparsity inS, respectively. In particular, the weights used in the WTNN are sorted in a non-descending order, and we obtain a closed-form optimal solution of the WTNN minimization problem. The theoretical properties provided guarantee the weak convergence of our reconstruction method. In addition, a fast inexact reconstruction method is proposed to increase imaging speed and efficiency. Experimental results demonstrate that both of our reconstruction methods can achieve higher reconstruction quality than the state-of-the-art reconstruction methods.

Presents corrections to the acknowledgement section for the above named article.

There have been increasing interests in exploring the sensing capabilities of RFID to enable numerous IoT applications, including object localization, trajectory tracking, and human behavior sensing. However, most existing methods rely on the signal measurement either in a low multipath environment, which is unlikely to exist in many practical situations, or with special devices, which increase the operating cost. This paper investigates the possibility of measuring `multi-path-free' signal information in multipath-prevalent environments simply using a commodity RFID reader. The proposed solution, Clean Physical Information Extraction (CPIX), is universal, accurate, and compatible to standard protocols and devices. CPIX improves RFID sensing quality with near zero cost - it requires no extra device. We implement CPIX and study two major RFID sensing applications: tag localization and human behavior sensing. CPIX reduces the localization error by 30% to 50% and achieves the MOST accurate localization by commodity readers compared to existing work. It also significantly improves the quality of human behaviour sensing.

Passive Radio Frequency Identification (RFID) tags have been widely applied in many applications, such as logistics, retailing, and warehousing. In many situations, the order of objects is more important than their absolute locations. However, state-of-art ordering methods need a continuing movement of tags and readers, which limit the application domain and scalability. In this paper, we propose a 2-dimension ordering approach for passive tags that requires no device movement. Instead, our method utilizes signal changes caused by arbitrary movement of human beings around tags, who carry no device for horizontal dimension ordering. Hence, our method is called Human Movement based Ordering (HMO). The basic idea of HMO is that when people pass between the reader antenna and tags, the received signal strength will change. By observing the time-series RSS changes of tags, HMO can obtain the order of tags along with a specific horizontal direction. For vertical dimension, we employ a linear programming method that is tolerant of tiny errors in practice. We implement HMO with commodity off-the-shelf RFID devices. The experimental results show that HMO can achieve up to 88.71 and 90.86 percent average accuracies in the signal-and multi-person cases, respectively.

There have been increasing interests in exploring the sensing capabilities of RFID to enable numerous IoT applications, including object localization, trajectory tracking, and human behavior sensing. However, most existing methods rely on the signal measurement either in a low multipath environment, which is unlikely to exist in many practical situations, or with special devices, which increase the operating cost. This paper investigates the possibility of measuring `multipath-free' signal information in multipath-prevalent environments simply using a commodity RFID reader. The proposed solution, Clean Physical Information Extraction (CPIX), is universal, accurate, and compatible to standard protocols and devices. CPIX improves RFID sensing quality with near zero cost - it requires no extra device. We implement CPIX and evaluate its effectiveness on improving the performance on tag localization. The results show that CPIX reduces the localization error by 30% to 50% and achieves the MOST accurate localization by commodity readers compared to existing work.