Many sensing tasks, such as plant stress sensing and blood pressure estimation, require co-located multi-modal measurements from two to five sensors at one site. RF backscatter enables low-power sensing, but existing tags usually support only one sensor; using multiple tags increases footprint and antenna coupling. We present Matrix, a fully-analog single-chain backscatter tag that supports multiple onboard sensors by multiplexing them into a composite voltage for transmission through one analog modulation chain. Unlike time-division polling, which introduces inter-sensor sampling offsets, or frequency-division, which requires separate chains, Matrix uses voltage-division multiplexing. Each sensor is encoded as a PWM waveform whose duty cycle represents the measurement, while amplitude enables multiplexing. Binary-weighted voltage-division weights make each active-sensor set uniquely invertible for reliable demultiplexing. The composite voltage is then converted into backscatter frequency shifts through the same chain. At the receiver, Matrix uses a Hidden Markov Model to recover per-sensor readings. Its ASIC consumes 25.56μW. A five-sensor prototype achieves 20 dB average reconstruction SNR at 30 kHz sampling, and we validate Matrix in plant sensing, health monitoring, and microphone-based direction finding.

Smart devices are proliferating in every aspect of our lives, providing convenience but also exposing us to the risk of information leakage at any moment. Attackers can monitor the user and infer private information such as personality and preferences by stealing the behavioral information. In this paper, we investigated the potential threat of information stealing via the leakage current of laptops and electrodes in wearable devices (e.g., smart watches and bracelets). Specifically, the leakage current in the laptop adapter can flow from the metal casing into the human body and be collected by electrodes in wearable devices when the user is using a laptop with a metal casing (e.g., MacBook). We verified the correlation between leakage current and the working states of the laptop, where different operations corresponding to different CPU instructions can generate different leakage currents. Based on this, we proposeLeakThief, a system that consists of three components: leakage current detection, application operation detection, and application recognition. The experiments in a real-world environment demonstrated that the proposed system can recognize 25 common applications with high accuracy, including launching-based (96.4%) and in-application operation-based recognition (81.2%).

Real-time audio streaming transmission and processing play a crucial role in time-sensitive applications such as food delivery services and ride-hailing platforms, where rapid response is essential. However, existing server-based audio streaming architectures struggle to handle the high concurrency of massive mobile devices efficiently. Traditional compression methods like MP3 and AAC offer limited compression ratios, while deep learning-based approaches often fail to meet the real-time transmission demands of edge computing environments. In this paper, we propose a novel edge-to-server audio streaming architecture that leverages Mel filter bank spectral features to achieve ultra-high compression efficiency. Our system integrates audio denoising, Mel feature extraction, and quantization-based compression at the edge, effectively suppressing environmental and device-induced noise while achieving an extreme compression ratio of 0.39% relative to the original uncompressed audio. Compared to conventional methods like MP3, our approach further reduces the file size by 96.1%. The decompressed Mel features remain task-independent, enabling seamless support for various general-purpose audio processing tasks in the server. We evaluate our system across three key audio tasks: speech recognition, speech emotion recognition, and audio classification. Extensive experiments on five different mobile devices demonstrate a 93.10% reduction in transmission latency at 1 Mbps bandwidth compared to 64 kbps MP3 audio, while maintaining task performance within a 5% deviation from state-of-the-art (SOTA) models across six mainstream audio datasets. These results highlight the efficiency, robustness, and scalability of our approach for real-time edge-to-server audio processing.

We introduce M 2 Silent, which enables multi-user silent speech interactions in shared spaces using multi-directional speakers.Ensuring privacy during interactions with voice-controlled systems presents significant challenges, particularly in environments with multiple individuals, such as libraries, offices, or vehicles.M 2 Silent addresses this by allowing users to communicate silently, without producing audible speech, using acoustic sensing integrated into directional speakers.We leverage FMCW signals as audio carriers, simultaneously playing audio and sensing the user's silent speech.

Bluetooth-based location services have experienced significant growth over the past decades. RSSI-based techniques using beacons only provide meters-level accuracy. Angular-based approaches rely on customized antenna arrays, introducing high costs and limited usability. In 2020, Bluetooth Special Interest Group (Bluetooth SIG) released version 5.1, integrating Angle of Arrival (AoA) estimation to enable direction finding capabilities, which has the potential to improve localization across various fields, including logistics and industry. However, more than 4.1 billion devices (68% of the total) still do not support the direction finding feature. To address this issue and ensure backward compatibility, we proposed Bridge, a solution that leverages an additional trigger node (referred to as Trigger) to make the direction finding feature compatible with all Bluetooth devices without requiring modifications to existing hardware or firmware. The Trigger mimics communication behaviors with both locators and targets simultaneously by sending a nesting packet. Subsequently, processes and algorithms are delicately designed to estimate AoA. Bridge also supports large-scale deployment through dynamic packet flow switching, enabling it to handle concurrent targets and manage handover with a consistent operation pattern. We implemented and evaluated Bridge in real-world scenarios. The system achieved an average localization error of 33.4cm while extending the direction-finding feature to 10 target devices of different Bluetooth versions, indicating the effectiveness of Bridge.

BLE direction finding provides high-accuracy localization based on Angle-of-Arrival (AoA), but this feature is only available on BLE 5.1+ devices. Billions of existing Bluetooth devices are excluded from direction finding indoor localization systems. We present Bridge that enables direction finding for all Bluetooth versions without any hardware or firmware modifications. Bridge introduces a novel Trigger that mimics communication behaviors of both locators and targets, allowing the locator to extract AoA information from originally unsupported devices. We implement Bridge on COTS direction finding system and evaluate it on 10+ BLE devices, achieving a median localization error of 33.4cm.

Bluetooth Low Energy (BLE) direction finding, a feature introduced in BLE version 5.1, enables precise localization through Angle of Arrival (AoA) estimation. However, this advancement introduces new risk to BLE direction finding based localization system. Specifically, the AoA estimation based on phase sampling of constant-tone-extension (CTE) is susceptible to the signal injection attack. This paper presents SaDiF, a feasible spoofing attack mechanism to mislead the locators into mistaking the positioning result as a continuous path. By eavesdropping on BLE packets and injecting attack signals containing pre-designed disturbing phase shift, SaDiF subtly alters the AoA estimation without detection, thus interfere the localization results. Moreover, SaDiF address the challenges posed by hardware imperfections by proposing an injection timing optimization to improve attack robustness. Extensive experiments demonstrates the effectiveness of SaDiF in successfully attacking multiple BLE targets in real-time scenarios. In conclusion, our findings reveal critical security risks in BLE direction finding feature and provide insights into strengthening its defenses.

Speech-based interaction systems are widely used in mobile devices like smartphones. With advances in deep neural networks, tasks such as speech emotion recognition (SER) enhance these systems user-friendliness. However, deploying SER models on mobile devices is challenging due to their complexity and computational demands. While pruning can reduce complexity, it often compromises accuracy, and hardware accelerators like FPGAs are difficult to integrate into mobile devices. This paper proposes Amser+, a real-time speech emotion recognition framework using signal compression and task offloading. Amser+utilizes logarithmic Mel-filter bank coefficients (Fbank) and singular value decomposition (SVD) for feature extraction and compression. The compressed signal is only 6.25% of the original size, achieving 2.24× faster transfer rates and 55.35% energy savings compared to raw audio transmission. Despite the compression, the features preserve key audio information for text and emotion recognition, performed server-side. Experiments show a WER of 4.68% (Librispeech), 10.69% (CommonVoice), and 72.85% emotion recognition accuracy (IEMOCAP).

Wearable devices, including smartwatches, are increasingly popular among consumers due to their user-friendly services. However, transmitting sensitive data like social media messages and payment QR codes via commonly used low-power Bluetooth exposes users to privacy breaches and financial losses. This study introducesTouchHBC, a secure and reliable communication scheme leveraging a smartwatch's built-in electrodes. This system establishes a touch-based human communication system utilizing a laptop's leakage current. As the transmitting device, the laptop modulates this current via the CPU. Simultaneously, the smartwatch, equipped with built-in electrodes, captures the current traversing the human body and decodes it. The modulation and decoding processes involve techniques such as amplitude modulation, variational mode decomposition, channel estimation, and retransmission mechanisms.TouchHBCfacilitates communication between laptops and smartwatches. Real-world tests demonstrate that our prototype achieves a throughput of$19.83bps$. Moreover,TouchHBCoffers the potential for enhanced interaction, including improved gaming experiences through vibration feedback and secure touch login for smartwatch applications by synchronizing with a laptop. Furthermore, the system can be integrated with high-throughput communication protocols such as Bluetooth, enhancing its scalability while maintaining a strong foundation of security.

With the increase in motor vehicles, more convenient and accurate interactions are expected while retaining a high standard of safe driving. However, complex and dynamic vehicle environments challenge sensing tasks such as breathing monitor and hand gesture recognition. In this paper, we propose DASIV, which utilizes the highly directional nature of ultrasonic signals to achieve fine-grained directional acoustic sensing in vehicle environments. Due to air nonlinearity, the system enables synchronized directional acoustic communication to transmit information (e.g., navigation) to the driver without affecting other passengers. By optimizing the frequency of the Frequency Modulated Continuous Wave (FMCW) signals, DASIV avoids mutual interference between the sensing and communication signals and achieves breathing detection and hand gesture recognition for the driver. Specifically, the system extracts breathing-induced weak thoracic bullying through the signal phase, captures and analyses breathing patterns using bandpass and Gaussian filters, and develops a breathing model. Then, the system defines 10 interaction hand gestures to meet daily interaction needs, uses spectral features to mine complex and fast hand movement features, and proposes a hand gesture recognition model. Extensive experiments in real environments show that DASIV achieves high-precision breathing monitor (Pearson correlation coefficient of 0.89) and hand gesture recognition (Precision of 91.7%).

This paper introduces a novel multi-directional speaker, named MuDiS, which utilizes a parametric array to generate highly focused sound beams in multiple directions. The system capitalizes on air nonlinearity to reproduce sound from ultrasounds, successfully overcoming challenges inherent in traditional parametric arrays, such as transducer size and wavefront shape. It supports three important features simultaneously: independent beams, wide-angle digital steering, and unintended leakage suppression. To address these challenges, we designed a specialized cell structure that connects ultrasonic transducers, redirecting an approximately omnidirectional wavefront with optimal interspacing. An optimization-based algorithm is developed to minimize unintended leakages, and a nonlinear distortion reduction scheme is proposed to enhance sound quality. The paper showcases a prototype demonstrating the system's capabilities as a multidirectional speaker with a wide sound projection angle. Experimental results validate the effectiveness of our approach. The proposed multi-beam projection system rivals the performance of commercially available single-beam projection directional speakers, and improved steering angle and sound fidelity compared to multi-beamforming performance using traditional parametric arrays.

Acoustic imaging is attractive due to its ability to work under occlusion, different lighting conditions, and privacy-sensitive environments. Existing acoustic imaging methods require large transceiver arrays or device movement, which makes it challenging to use in many scenarios. In this paper, we develop a novel acoustic imaging system for low-cost devices with few speakers and microphones without any device movement. To achieve this goal, we leverage a 3D-printed passive acoustic metasurface to significantly enhance the diversity of the measurement data, thereby improving the imaging quality. Specifically, we jointly design the transmission signal, transceivers' beamforming weights, metasurface, and imaging algorithm to minimize the imaging reconstruction error in an end-to-end manner. We further develop a scheme to dynamically adapt the imaging resolution based on the distance to the target. We implement a system prototype. Using extensive experiments, we show that our system yields high-quality images across a wide range of scenarios.

The convenient text input system is a pain point for devices such as AR glasses, and it is difficult for existing solutions to balance portability and efficiency. This paper introduces HandPad, the system that turns the hand into an on-the-go touchscreen, which realizes interaction on the hand via human capacitance. HandPad achieves keystroke and handwriting inputs for letters, numbers, and Chinese characters, reducing the dependency on capacitive or pressure sensor arrays. Specifically, the system verifies the feasibility of touch point localization on the hand using the human capacitance model and proposes a handwriting recognition system based on Bi-LSTM and ResNet. The transfer learning-based system only needs a small amount of training data to build a handwriting recognition model for the target user. Experiments in real environments verify the feasibility of HandPad for keystroke (accuracy of 100%) and handwriting recognition for letters (accuracy of 99.1%), numbers (accuracy of 97.6%) and Chinese characters (accuracy of 97.9%).

Over the past years, smart ecology has attracted much attention, especially for smart home applications. As a key component, monitoring appliances performs significant impact. However, appliances under monitoring usually contain smart modules such as WiFi or Bluetooth, which are limited to traditional appliances. Existing approaches such as distributed sensing, energy disaggregation, and infrastructure-mediated sensing, require the installation of external hardware or have a limited sensing range. In this study, we developed AUDIOSENSE to leverage the acoustic signal generated by the power supply to monitor electrical appliances throughout the house remotely from a single point. In realizing AUDIOSENSE, we proposed an optimized Variation Mode Decomposition scheme to extract the frequency components, as well as a data augmentation scheme to improve generalizability and enable multi-label classification. In experiments, AUDIOSENSE achieved mAP values of 99.3% in multi-label classification.

Quick response (QR) codes have been widely used in mobile applications, especially mobile payments, such as Alipay, WeChat, PayPal, etc due to their convenience and the pervasive built-in cameras on smartphones. Recently, however, attacks against QR codes have been reported and attackers can capture a QR code of the victim and replay it to achieve a fraudulent transaction or intercept private information, just before the original QR code is scanned. In this study, we enhance the security of a QR code by identifying its authenticity. We propose ScreenID, which embeds a QR code with information of the screen which displays it, thereby the QR code can reveal whether it is reproduced by an adversary or not. In ScreenID, PWM frequency of screens is exploited as the unique screen fingerprint. To improve the estimation accuracy of PWM frequency, ScreenID incorporates a model for the interaction between the camera and screen in the temporal and spatial domains. Extensive experiments demonstrate that ScreenID can differentiate screens of different models, types, and manufacturers and thus improve the security of QR codes.

Quick response (QR) codes have been widely used in mobile applications due to its convenience and the pervasive built-in cameras on smartphones. Recently, however, attacks against QR codes have been reported that attackers can capture a QR code of the victim and replay it to achieve a fraudulent transaction or intercept private information, just before the original QR code is scanned. In this study, we enhance the security of a QR code by identifying its authenticity. We propose SCREENID, which embeds a QR code with information of the screen which displays it, thereby the QR code can reveal whether it is reproduced by an adversary or not. In SCREENID, PWM frequency of screens is exploited as the unique screen fingerprint. To improve the estimation accuracy of PWM frequency, SCREENID incorporates a model for the interaction between the camera and screen in the temporal and spatial domains. Extensive experiments demonstrate that SCREENID can differentiate screens of different models, types, and manufacturers, thus improve the security of QR codes.

Quick response (QR) codes have been widely used in mobile applications, due to its convenience and the pervasive built-in cameras on smartphones. Recently, however, QR codes have been reported suffering attacks for being sniffed just before the QR code is scanned, which lead to financial loss. In this study, we propose ScreenID, for enhancing the QR code security by identifying its authenticity, which embeds a QR code with information of unique screen fingerprint - PWM frequency. PWM frequencies are adjusted to different values by screen manufacturers, therefore can successfully differentiate screens. To improve the estimation accuracy of PWM frequency, ScreenID incorporates a model for the interaction between the camera and screen in the temporal and spatial domains. Extensive experiments demonstrate that ScreenID can differentiate screens of different models, types and manufacturers and thus improve the security of QR codes.