5G-enabled unmanned aerial vehicles (UAVs) based emergency communications have attracted considerable attention for their rapid deployment and high-capacity links enabled by UAV mobility and mmWave capabilities. However, most existing studies prioritize service coverage optimization, often neglecting accurate 6D pose estimation of users and UAV-mounted base stations (BSs) calibration, which are crucial for robust beam alignment, positioning, and navigation. Traditional approaches generally assume known BS poses, an assumption invalidated by UAV mobility and deployment uncertainties, resulting in degraded positioning performance. To tackle the challenge of inaccurate BS poses, we first leverage multi-view geometry to estimate clock bias, thereby correcting synchronization errors, and subsequently derive an initial estimate of the user’s 6D pose using delay and angle measurements. Building on this, we propose a multi-view joint estimation framework that simultaneously estimates the user’s 6D pose and calibrates BS poses. This framework integrates a manifold-based BS pose uncertainty model with a hybrid maximum a posterior estimator, which combines prior pose information with the geometry of the Special Euclidean GroupSE(3). To benchmark performance, we derive a hybrid Cramér-Rao lower bound under uncertain BS pose priors. Simulation results verify that the proposed method significantly enhances user pose accuracy under BS pose uncertainty, achieving about 75% error reduction compared with the conventional method at a representative transmit power.

As cybersecurity risks increase, reliable user authentication has become crucial. Traditional static methods, such as facial recognition, are vulnerable to data hijacking threats. This paper presents a novel new paradigm for CAPTCHA (Completely automated public turing test to tell computers and humans apart) verification, dynamic gesture, aimed at enhancing security and robustness. By integrating visual and inaudible sound signals across two complementary dimensions, this approach reduces blind spots and increases the cost of spoofing for CAPTCHA verification. Additionally, a trustworthiness fusion network is introduced, which incorporates a modality trustworthiness calculation method based on Dirichlet distribution, and factors of information entropy and distance depth, enabling dynamic decision-making, significantly improving accuracy and adaptability. Experimental results demonstrate the method’s practical feasibility and achieve an accuracy of 97% in distinguishing between humans and bots.

Achieving high-precision, universal localization services remains a significant challenge, as existing solutions typically rely on specialized hardware or complex algorithms. This paper aims to develop a lightweight and ubiquitous localization scheme that utilizes commercial audio devices (two speakers and a microphone). We control the two speakers to transmit the Orthogonal Frequency Division Multiplexing (OFDM) signals within the same frequency band, creating a composite interference fields formed by multiple subcarriers. Our main observation is that the initial phase difference between coherent signals leads to a spatial shift of the interference sound field. Therefore, we design a phase modulation mechanism that applies unique initial phase differences to each pair of subcarriers, producing an asymmetric interference sound field that provides an interference intensity distribution with significant spatial diversity. Finally, based on the intensity information recorded by the microphone, we construct the Multi-subcarrier Interference Intensity (MII) curve and propose effective curve matching method for location estimation. Extensive simulations and experiments have verified the effectiveness of the proposed method, and the median localization accuracy in real environments can reach 3.21 cm.

Seismograms, the fundamental seismic records, have revolutionized earthquake research and monitoring. Recent advancements in deep learning have further enhanced seismic signal processing, leading to even more precise and effective earthquake monitoring capabilities. This paper introduces a foundational deep learning model, the Seismogram Transformer (SeisT), designed for a variety of earthquake monitoring tasks. SeisT combines multiple modules tailored to different tasks and exhibits impressive out-of-distribution generalization performance, outperforming or matching state-of-the-art models in tasks like earthquake detection, seismic phase picking, first-motion polarity classification, magnitude estimation, back-azimuth estimation, and epicentral distance estimation. The performance scores on the tasks are 0.96, 0.96, 0.68, 0.95, 0.86, 0.55, and 0.81, respectively. The most significant improvements, in comparison to existing models, are observed in phase-P picking, phase-S picking, and magnitude estimation, with gains of 1.7%, 9.5%, and 8.0%, respectively. Our study, through rigorous experiments and evaluations, suggests that SeisT has the potential to contribute to the advancement of seismic signal processing and earthquake research.

Early symptom monitoring is an essential measure for pneumoconiosis prevention. However, one severe limitation is the high requirement for a dedicated device. This paper proposes$p^{3}Warning$to realize low-cost warnings for potential pneumoconiosis patients via contactless sensing. For the first time, the designed framework utilizes the inaudible acoustic signal with a pair of commercial speaker and microphone to monitor early symptoms of pneumoconiosis including abnormal respiration and cough. We introduce and address unique technical challenges, such as designing a delay elimination method to synchronize transceiver signals and providing a search-based signal variation amplification strategy to support highly accurate and long-distance vital sign sensing. Comprehensive experiments are conducted to evaluate$p^{3}Warning$. The results show that it can achieve a median error of 0.52 bpm for abnormal respiration pattern monitoring and an accuracy of 95 % for cough detection in total, and support the furthest range of up to 4 m.

Fall detection is an essential measure for the safety of elders. While traditional contact-based methods support acceptable detection performance, the recent advance in wireless sensing could enable contact-free fall detection. However, two severe limitations are short sensing range and weak through-wall capability, which hampers wide applications in smart homes. This paper proposes a novel system LoFall, which is the first time to utilize the LoRa signal to realize contact-free long-range through-wall fall detection. We address unique technical challenges, such as proposing a novel strategy of candidate signal search to reduce the calculation time of fall detection and designing a weighted feature fusion algorithm based on fuzzy entropy to improve the accuracy of through-wall fall detection. Comprehensive experiments are conducted to evaluate LoFall. Results show that it can achieve a total accuracy of 93.3% for through-wall fall detection, and support the furthest detection range of up to 10 m.

Hand gesture recognition is an essential Human Computer Interaction (HCI) mechanism for users to control smart devices. While traditional device-based methods support acceptable recognition performance, the recent advance in wireless sensing could enable device-free hand gesture recognition. However, two severe limitations are serious environmental interference and high-cost hardware, which hamper the wide deployment. This paper proposes a novel system TaGesture, which employ the inaudible acoustic signal to realize device-free and training-free hand gesture recognition with a pair of commercial speaker and microphone array. We address unique technical challenges, such as proposing a novel acoustic hand tracking smoothing algorithm with Interaction Multiple Model (IMM) Kalman Filter to address the issue of localization angle ambiguity, and designing a classification algorithm to realize acoustic-based hand gesture recognition without training. Comprehensive experiments are conducted to evaluate TaGesture. Results show that it can achieve a total accuracy of 97.5% for acoustic-based hand gesture recognition, and support the furthest sensing range of up to 3 m.

Respiration rate is an essential vital indicator for health monitoring. While traditional sensor-based methods support acceptable sensing performance, the recent advance in wireless sensing could enable sensor-free and contact-free respiration sensing, which is particularly important during the practice of social distancing against a pandemic like COVID-19. Among a variety of wireless technologies employed for respiration sensing, Wi-Fi-based solutions are most popular due to the pervasive development of infrastructure. However, the existing Wi-Fi-based approaches need to retrieve Wi-Fi readings from access points, which are not often accessible for the end users. In this article, we propose a novel system, MoBreath, in which we utilize the Wi-Fi channel state information (CSI) readings extracted from the end-user device, a smartphone, to monitor the respiration rate for the first time. We introduce and address unique technical challenges, such as selecting the optimum CSI subcarriers from many noisy candidates and providing smartphone placement strategies for both single and multiple human target scenarios based on the Fresnel zone model to support highly accurate respiration sensing. Our evaluation of MoBreath using commodity smartphones in different environments shows that it can accurately estimate the respiration rate at a low error rate of 0.34 breaths per minute and support the sensing range of up to 3–4 m. Even for challenging scenarios such as the target is covered by a quilt and multiple targets are in the sensing area, MoBreath can still support highly accurate results.

Aiming at problems of under-fitting and poor model robustness in learning-based wireless sensing methods caused by the lack of large-scale wireless sensing datasets, this paper proposes a privacy-friendly collaborative wireless sensing framework, called Co-Sense. It builds a community with multiple clients and a server, which aggregates the clients' local models into a federated model with cross-domain capability. To protect the privacy of users' local data, we innovatively introduce the idea of federated learning into the field of wireless sensing, by uploading users' local model parameters instead of their local data. Then, in response to the uneven computing power of different users' edge devices, we propose a local model update algorithm based on adaptive computing power. Furthermore, a client selection algorithm based on test nodes is designed to reduce the negative influence of malicious clients on Co-Sense. Finally, we evaluate Co-Sense on three well-known public wireless datasets, including the gesture dataset, the activity dataset, and the gait dataset. Experimental results show that the sensing accuracy of Co-Sense is more than 10% higher than that of the most advanced wireless sensing models.

COVID-19 outbreaks rapidly around the world, which is the enemy faced by all humankind. Since COVID-19 is mainly spread through close personal contact, searching close-contacts is key to controlling this virus's spread. This paper designs COVID-19 Tracer, a novel low-cost passive system for searching COVID-19 patients' close-contacts. Utilizing ubiquitous Wi-Fi probe requests, COVID-19 Tracer can quickly determine whether a person stays in one small space with a COVID-19 patient in the same period. Furthermore, it seeks to find out a close-contact with a novel rang-free judgment algorithm for location similarity. Finally, extensive experiments conducted in a school office building show our system's good performance, and the accuracy in finding out close-contacts is more than 98%.