While earables present a promising avenue for cardiac sensing, whether they may replace the stethoscope to perform heart sound (a.k.a. PCG) monitoring remains questionable. The latest effort attempts to generate PCG-like waveform out of in-ear audio collected via earphones, yet its data-driven approach does not seem to be grounded in the underlying physics. To this end, this paper introduces EarPCG, a system for continuous PCG monitoring leveraging physics-informed neural models. As opposed to the debatable belief that bone-conducted PCG appears within ear canal, EarPCG generates PCG waveforms from the (actually existing) photoplethysmography (PPG) waveforms conveyed via blood vessels. Arising from pressure variations induced by heartbeats, PPG can be mathematically described by a Partial Differential Equation (PDE). Therefore, solving this PDE inversely may reconstruct cardiac dynamics and in turn enable the generation of PCG waveforms with another PDE characterizing the pressure oscillations propagating through soft tissues. Pipelining the two PDE-solving neural models, EarPCG achieves accurate PCG monitoring from in-ear audio, while requiring minimal training. Our extensive experiments leveraging a custom-built prototype demonstrate the efficacy of our proposed system. Furthermore, we have conducted clinical trials, with clinicians reporting no perceptible difference between authentic PCG and the sounds reconstructed by EarPCG.

Integrated sensing and communication (ISAC) has emerged as a pivotal technology for next-generation wireless communication and radar systems, enabling high-resolution sensing and high-throughput communication with shared spectrum and hardware. However, achieving a fine radar resolution often requires high-rate analog-to-digital converters (ADCs) and substantial storage, making it both expensive and impractical for many commercial applications. To address these challenges, this paper proposes an orthogonal time frequency space (OTFS)-based ISAC architecture that operates at reduced ADC sampling rates, yet preserves accurate radar estimation and supports simultaneous communication. The proposed architecture introduces pilot symbols directly in the delay-Doppler (DD) domain to leverage the transformation mapping between the DD and time-frequency (TF) domains to keep selected subcarriers active while others are inactive, allowing the radar receiver to exploit under-sampling aliasing and recover the original DD signal at much lower sampling rates. To further enhance the radar accuracy, we develop an iterative interference estimation and cancellation algorithm that mitigates data symbol interference. We propose a code-based spreading technique that distributes data across the DD domain to preserve the maximum unambiguous radar sensing range. For communication, we implement a complete transceiver pipeline optimized for reduced sampling rate system, including synchronization, channel estimation, and iterative data detection. Experimental results from a software-defined radio (SDR)-based testbed confirm that our method substantially lowers the required sampling rate without sacrificing radar sensing performance and ensures reliable communication.

A key challenge in integrated sensing and communications (ISAC) is the synthesis of waveforms that can modulate communication messages and achieve good sensing performance simultaneously. In ISAC systems, standard communication waveforms can be adapted for sensing, as the sensing receiver (co-located with the transmitter) has knowledge of the communication message and consequently the waveform. However, the randomness of communications may result in waveforms that have high sidelobes masking weak targets. Thus, it is desirable to refine communication waveforms to improve the sensing performance by reducing the integrated sidelobe levels (ISL). This is similar to the peak-to-average power ratio (PAPR) mitigation in orthogonal frequency division multiplexing (OFDM), in which the OFDM-modulated waveform needs to be refined to reduce the PAPR. In this paper, inspired by PAPR reduction algorithms in OFDM, we employ trellis shaping in OFDM-based ISAC systems to refine waveforms for specific sensing metrics using convolutional codes and Viterbi decoding. In such a scheme, the communication data is encoded and then mapped to the signaling constellation in different subcarriers, such that the time-domain sidelobes are reduced. An interesting observation is that sidelobe reduction in OFDM-based ISAC is dual to PAPR reduction in OFDM, thereby sharing a similar signaling structure. Numerical simulations and hardware software defined radio USRP experiments are carried out to demonstrate the effectiveness of the proposed trellis shaping approach.

Integrated sensing and communication (ISAC) promises high spectral and power efficiencies by sharing waveforms, spectrum, and hardware across sensing and data links. Yet commercial cellular networks struggle to deliver fine angular, range, and Doppler resolution due to limited aperture, bandwidth, and coherent observation time. In this paper, we propose a space-time-frequency synthetic ISAC architecture that fuses observations from distributed transmitters and receivers across time intervals and frequency bands. We develop a unified signal model for multistatic and monostatic configurations, derive Cramer-Rao lower bounds (CRLBs) for the estimations of position and velocity. The analysis shows how spatial diversity, multiband operation, and observation scheduling impact the Fisher information. We also compare the estimation performance between a concentrated maximum likelihood estimator (MLE) and a two stage information fusion (TSIF) method that first estimates per-path delay and radial speed and then fuses them by solving a weighted nonlinear least-squares problem via the Gauss-Newton algorithm. Numerical results show that MLE approaches the CRLB in the high signal-to-noise ratio (SNR) regime, while the two stage method remains competitive at moderate to high SNR but degrades at low SNR. A central finding is that fully synthesized network processing is essential, as estimations by individual base stations (BSs) followed by fusion are consistently inferior and unstable at low SNR. This framework offers a practical guidance for upgrading existing communication infrastructure into dense sensing networks.

Millimeter-wave (mmWave) technology represents a promising avenue in integrated sensing and communication (ISAC), leveraging wide bandwidth to accommodate growing demands for high data-rate communication and high-resolution radar sensing. However, in non-line-of-sight (NLOS) scenarios, mmWave signals suffer from severe attenuation, and integrating precise radar sensing into bandwidth-limited communication systems remains an open problem. To address this, we propose an NLOS-ISAC system that jointly provides accurate target positioning and robust communication. Our approach synthesizes many narrowband signals into a virtual wideband radar via stepped-frequency techniques, eliminating the need for additional hardware. By fusing time-of-flight (ToF) measurements of multipath reflections with environmental maps, the system achieves submeter positioning accuracy for NLOS targets. Building on these position estimates, we then employ position-aware beamforming to significantly enhance the NLOS communication throughput. Extensive experimental results demonstrate that the proposed NLOS-ISAC system reliably achieves high-accuracy localization while improving data rates in challenging NLOS environments.

Integrated Sensing and Communication (ISAC) is emerging as a promising technique for future communication and radar systems. A high-resolution estimation is essential for radar applications, necessitating large bandwidth. However, a large bandwidth requires dedicated Analog-to-Digital Converters (ADCs) with high sampling rates and substantial storage capacities to fully capture waveform signals, making it prohibitively expensive and impractical for many commercial systems. Therefore, it is crucial to develop methods for radar estimation with reduced sampling rates. In this paper, we propose a method to achieve accurate radar estimations for Orthogonal Time Frequency Space (OTFS)-based ISAC systems with reduced sampling rates. Specifically, we design pilot symbols in the delay-Doppler domain to ensure that the pilots in the time-frequency (TF) domain occupy specific subcarriers, leaving the remaining ones inactive. By leveraging the aliasing effect caused by undersampling, the receiver captures all aliased active subcarrier information with a reduced ADC sampling rate, enabling the restoration of the original delay-Doppler signal. Finally, we introduce an iterative estimation and interference cancellation algorithm to mitigate the interference from data symbols and provide accurate estimation results. Extensive experiments demonstrate that our method effectively reduces the sampling rate without compromising the ISAC performance.

Orthogonal Time Frequency Space (OTFS) modulation has emerged as a promising waveform for next-generation wireless communications. OTFS demonstrates robust non-fading properties even under doubly-dispersive conditions, making it a preferred modulation scheme in complex scenarios involving high mobility. Despite its advantages, developing a low-complexity multiaccess OTFS protocol for environments with a large number of user terminals (UTs) and high mobility remains challenging due to severe multiuser interference (MUI) and rapidly changing channel states. In this paper, we propose a low-complexity algorithm to find a suboptimal solution for the joint resource block and power allocation problem in multiple access OTFS systems. Specifically, we decompose the resource block allocation and power allocation into two sub-problems. First, we perform the resource block allocation algorithm by considering the channel conditions. Then, we employ a two-stage water-filling algorithm to achieve a suboptimal power allocation. Numerical results indicate that our proposed resource allocation scheme achieves higher sum-rate than existing schemes in both 8-user and 16-user systems while reducing the complexity from exponential to linear in terms of the number of users. This makes our approach practical even in high mobility and larger user scenarios.

Using 3D hand poses as the input of user interfaces can enable many novel human-computer interaction applications. However, conventional solutions for precisely reconstructing the hand poses are either vision-based, which are compute-intensive and may cause privacy issues, or wearable devices-based, which are intrusive to users. In this paper, we propose RAM-Hand, a Robust Acoustic 3D Multi-Hand pose reconstruction system built on a microphone array. Our RAM-Hand system can support multiple hands and is designed to be highly adaptable to new scenarios even when training data is limited. Specifically, it should robustly accommodate variations in environment, subject, and hand positions. To achieve this, on one hand, we propose a customized signal processing pipeline to segment multiple hands' reflections and extract the features corresponding to each hand, then feed those features into a transformer-based neural network for precise pose reconstruction. On the other hand, to tackle the challenge that the training data is limited, we propose a series of data augmentation methods to generate virtual training data, and utilize contrastive learning to ensure our model behaves well on new subjects. We conduct extensive experiments on a real-world microphone array testbed to evaluate the performance of the proposed system. The results show that our RAM-Hand system can localize each hand joint with an average error of 10.71 mm, handle multiple hands, and generalize well to the above mentioned new scenarios.

Thick metal barriers pose a fundamental challenge to internal wireless monitoring due to their electromagnetic shielding properties, rendering traditional radio frequency (RF) communications ineffective. Acoustic communication offers a promising alternative that is also inherently secure against remote eavesdropping. However, the solid medium itself, when acting as a communication channel, exhibits complex characteristics of high loss and severe dispersion, which severely constrains reliability. This paper aims to solve this core problem by designing and implementing a novel intra-solid acoustic communication system. The core of our approach lies in proposing and validating a robust physical layer communication paradigm for the reliable propagation of acoustic signals in such an extreme channel environment. This paradigm, by encoding and spreading signal energy across a wide frequency band, is designed to fundamentally combat the severe attenuation and frequency-selective fading introduced by the channel. We build a complete system prototype and validate the effectiveness of this paradigm through a series of experiments in a real-world solid metal environment. The experimental results demonstrate that our system can successfully establish reliable communication links through thick metal barriers, providing a validated and feasible solution for the deployment of wireless sensor networks in extremely shielded environments.

Orthogonal Time Frequency Space (OTFS) modulation emerges as a promising waveform for next generation wireless communications. The efficacy of OTFS, in both communication and sensing realms, is critically dependent on the design of its frame structure. This study delves into the design and optimization of a pilot-symbol-aided OTFS frame, with an emphasis on enhancing spectrum efficiency, minimizing the Peak-to-Average Power Ratio (PAPR), and reducing bit error rate (BER). Specifically, we first analyze the impact of specific channel characteristics on BER. Subsequently, we engage in a detailed exploration of the interplay between frame parameters and performance metrics, namely the spectrum efficiency, PAPR, and BER. We eventually propose an optimization framework for embedded-pilot OTFS frames, aimed at attaining optimal performance across these metrics. Through both simulations and experiments, we demonstrate that the optimized OTFS frame architecture offers significant improvements in BER and our optimization framework provides profound insights into determining the optimal frame parameters tailored to specific use cases, with an emphasis on varying priorities.

Aerial Acoustic Communication (AAC) has attracted much attention recently due to its ubiquitous device support. However, the transmission rate has largely throttled the boom of AAC-enabled applications. In this paper, we propose a high-speed AAC system that modulates messages over the entire bandwidth but is unobtrusive to end users. We achieve high bandwidth efficiency through a dynamic modulation mechanism. Specifically, we propose a high-order phase-based modulation mechanism on existing audio channels. Additionally, we leverage the auditory masking effect to embed data streams over the unused frequency band. Through a sophisticated power analysis and frequency allocation scheme, the changes made by these two modulation techniques on existing audio can be imperceptible to human ears. Furthermore, we utilize the loose orthogonal modulation on the inaudible channel to further boost the transmission rate. Our system prototype reveals that we can achieve up to 2 kbps link rate.

Acoustic-based respiration sensing is promising due to its ubiquitous device support and great freedom in signal design. However, existing proposals often either fail to function properly when a target is non-static or is under multipath interference, or address it in an algorithmic manner. To this end, in this paper, we propose CORA, a COntinuous RespirAtion monitoring system using purely analytical signal processing methods. CORA is the first approach that achieves physical separation between motion artifacts and respiration, other than existing algorithmic solutions, and hence can obtain results that are closer to ground truth. CORA leverages the edges of Orthogonal Time Frequency Space signals in monitoring motion states and addressing multipath interference. The ability to tackle these challenges can help to compensate motion-induced artifacts for FMCW-based sensing techniques, enabling continuous respiration monitoring even in non-static scenarios. To achieve high-quality compensation, a pipeline of signal processing techniques is proposed, including robust moving target tracking, accurate frequency bin selection, and effective phase denoising. Unlike existing deep learning-based approaches, CORA is explainable and is readily deployable, without sophisticated adaptation or exhausted training processes. We have implemented a system prototype and evaluated its performance. Experiment results demonstrate a median error of 0.86 respiration per minute.

Acoustic sensing has attracted significant attention recently, thanks to the pervasive availability of device support. However, adopting consumer-grade devices (e.g., smartphones) to deploy acoustic sensing applications faces the challenge of device/OS heterogeneity. Researchers have to pay tremendous efforts in tackling platform-dependent details even in simply accessing raw audio samples, thus losing focus on innovating sensing algorithms. To this end, this paper presents the first Acoustic Software Defined Platform (ASDP): a versatile sensing and general benchmarking platform. ASDP encompasses several customized acoustic modules running on a ubiquitous computing board, backed by a dedicated software framework. It is superior to commodity devices in controlling and reconfiguring physical layer settings, thus offering much better usability. The tailored software framework abstracts platform details and provides user-friendly interface for fast prototyping, while maintaining adequate programmability. To demonstrate the usefulness of ASDP, we showcase several relevant applications based on it. The promising outcomes make us believe that the release of our ASDP could greatly advance acoustic sensing research.

Though measuring ambient temperature is often deemed as an easy job, collecting large-scale temperature readings in real-time is still a formidable task. The recent boom of network-ready (mobile) devices and the subsequent mobile crowdsourcing applications do offer an opportunity to accomplish this task, yet equipping commodity devices with ambient temperature sensing capability is highly non-trivial and hence has never been achieved. In this paper, we proposeAcousticThermometer (AcuTe+) as an interference-resilient ambient temperature sensor empowered by a single commodity smartphone. AcuTe+ utilizes on-board dual microphones to estimate air-borne sound propagation speed, thereby deriving ambient temperature. To accurately estimate sound propagation speed, we leverage the phase of chirp signals to circumvent the low sample rate on commodity hardware. In addition, we propose to use both structure-borne and air-borne propagations to address the multipath problem. Most importantly, we equip AcuTe+ with a mask-based desnoising algorithm to handle intensive acoustic interference. As a mobile, economical, highly accurate sensor, AcuTe+ may potentially enable many relevant applications, in particular large-scale indoor/outdoor temperature monitoring in real-time. We have conducted extensive experiments on AcuTe+; the results demonstrate a median error of 0.6$^\circ$C even under severe acoustic interference (overall median 0.3$^\circ$C), and they also showcase the practical ability of AcuTe+ in real-time distributed temperature sensing.

Aerial acoustic communication attracts substantial attention for its simplicity and cost-effectiveness. Unfortunately, the preferred inaudible transmission has to strike a balance between the transmission rate and communication range, when the Bit-Error-Rate (BER) is under a certain threshold. Additionally, the performance of previous proposals can be deteriorated by dynamic channel conditions including near-far problem, device heterogeneity, and multipath fading. To this end, we propose a High-speed, long-range, and Robust Chirp Spread Spectrum (HRCSS) scheme for inaudible aerial acoustic communication under dynamic channels. HRCSS innovates in the definition of a loose orthogonality condition, and it leverages this orthogonality to overlap multiple chirp carriers in a single time duration to form a data symbol representing multiple bits, thereby substantially promoting the data rate. To further enhance system robustness in long communication ranges and dynamic channel conditions, we construct a lightweight rate adaptation algorithm and design a simple yet efficient normalization method. Experiment results reveal that HRCSS achieves a significant improvement in data rate over existing methods: it delivers 500 bps data rate with a BER of 0.24 percent at 10 m, and achieves 125 bps with zero BER at 20 m. Meanwhile, HRCSS can work adaptively under dynamic channel conditions while still retaining a BER below 3 percent.

Temperature is an important data source for weather forecasting, agriculture irrigation, anomaly detection, etc. While temperature measurement can be achieved via low-cost yet standalone hardware with reasonable accuracy, integrating thermal sensing into ubiquitous computing devices is highly non-trivial due to the design requirement for specific heat isolation and proper device layout. In this paper, we present the first integrated thermometer using commercial-off-the-shelf acoustic-enabled devices. Our software sonic thermometer (SST) utilizes on-board dual microphones on commodity mobile devices to estimate sound speed, which has a known relation with temperature. To precisely measure temperature via sound speed, we propose a chirp mixing approach to circumvent low sampling rates on commodity hardware and design a pipeline of signal processing blocks to handle channel distortions. SST, for the first time, empowers ubiquitous computing devices with thermal sensing capability. It is portable and cost-effective, making it competitive with current thermometers using dedicated hardware. SST is potential to facilitate many interesting applications such as large-scale distributed thermal sensing, yielding high temporal/spatial resolutions with unimaginable low costs. We implement SST on a commodity platform and results show that SST achieves a median accuracy of${0.5^\circ \mathrm{C}}$even at varying humidity levels.

Though measuring ambient temperature is often deemed as an easy job, collecting large-scale temperature readings in real-time is still a formidable task. The recent boom of network-ready (mobile) devices and the subsequent mobile crowdsourcing applications do offer an opportunity to accomplish this task, yet equipping commodity devices with ambient temperature sensing capability is highly non-trivial and hence has never been achieved. In this paper, we propose Acoustic Thermometer (AcuTe) as the first ambient temperature sensor empowered by a single commodity smartphone. AcuTe utilizes on-board dual microphones to estimate air-borne sound propagation speed, thereby deriving ambient temperature. To accurately estimate sound propagation speed, we leverage the phase of chirp signals to circumvent the low sample rate on commodity hardware. In addition, we propose to use both structure-borne and air-borne propagations to address the multipath problem. Furthermore, to prevent disruptive audible transmissions, we convert chirp signals into white noises and propose a pipeline of signal processing algorithms to denoise received samples. As a mobile, economical, highly accurate sensor, AcuTe may potentially enable many relevant applications, in particular large-scale indoor/outdoor temperature monitoring in real-time. We conduct extensive experiments on AcuTe; the results demonstrate a robust performance, a median accuracy of 0.3° C even at a varying humidity level, and the ability to conduct distributed temperature sensing in real-time.

Recently, acoustic-based indoor localization has attracted much attention due to its affordable infrastructure costs and high localization accuracy. However, previous work is infeasible in mobile target tracking for its long latency in obtaining sufficient beacon messages. In addition, the performance can further deteriorate due to device diversity, varying channel gains, and background noises. To this end, we propose an asynchronous acoustic localization and tracking system (AALTS), which utilizes distributed acoustic anchor nodes to locate passive off-the-shelf mobile devices. In AALTS, we propose an orthogonal chirp spread spectrum (OCSS) modulation technique, which doubles the data rate and thus mitigates the latency. We design a more robust method to capture acoustic signals which embody timestamps for localization, accounting for device diversity, varying channel gains, and the multipath effect. Finally, we incorporate an acoustic Doppler speed estimation module with a path-based particle filter framework to accurately track the moving targets. We have evaluated AALTS in an indoor testbed of size 8×12 m2with commodity mobile phones and customized acoustic anchors. Our evaluation results demonstrate remarkable performance: AALTS achieves 90-percentile tracking errors of 0.49 m for mobile targets and a median of 0.12 m for stationary ones with only four anchor nodes.