Noncontact detection of human vital signs based on miniaturized Doppler radar systems (DRSs) can be widely used in healthcare and biomedical applications. Although significant progresses have been achieved, a reliable wireless vital signs detection in the presence of large-scale random human body movements remains a technical challenge. In this paper, based on a comprehensive use of the high-dynamic-range radar architecture and linearized Doppler phase demodulation algorithms, we further introduce matched filters to retrieve the respiration and heartbeat spectra completely concealed under the wideband noise floor caused by large-scale body movements. Along with an existing 5.8-GHz system, a single-board integrated DRS operating at 24 GHz was designed to verify the effectiveness of the used architecture and algorithms. Experimental results comply with the theoretical expectation. The obtained results imply the potential to implement practical bioradar systems for human noncontact vital signs detections.

Miniaturized Doppler radar sensor (DRS) for noncontact motion detection is a hot topic in the microwave community. Previously, small-scale physiological signals such as human respiration and heartbeat rates are the primary interest of study. In this paper, we propose a comprehensive approach that can be used to improve the demodulation linearity of microwave DRSs, such that detailed time-domain motion information ranging from micro-scale to large scale can be accurately reconstructed. Experiments show that based on a digital-IF receiver architecture, dynamic dc offset tracking, and the extended differentiate and cross-multiply arctangent algorithm, the displacement and velocity of both micrometer-scale vibration of a tuning fork and meter-scale human walking can be accurately recovered. Our work confirms that substantial time-domain motion information is carried by the signals backscattered from moving objects. Retrieval of such information using DRSs can be potentially used in a wide range of healthcare and biomedical applications, such as motion pattern recognition and bio-signal measurements.

Object detection for remote sensing is a fundamental task in image processing of remote sensing; as one of the core components, small or tiny object detection plays an important role. Despite the considerable advancements achieved in small object detection with the integration of CNN and transformer networks, there remains untapped potential for enhancing the extraction and utilization of information associated with small objects. Particularly within transformer structures, this potential arises from the disregard of the complex and the intertwined interplay between spatial context information and channel information during the global modeling of pixel-level information within small objects. As a result, valuable information is prone to being obfuscated and annihilated. To mitigate this limitation, we propose an innovative framework, YOLO-DCTI, that capitalizes on the Contextual Transformer (CoT) framework for the detection of small or tiny objects. Specifically, within CoT, we seamlessly incorporate global residuals and local fusion mechanisms throughout the entire input-to-output pipeline. This integration facilitates a profound investigation into the network’s intrinsic representations at deeper levels and fosters the fusion of spatial contextual attributes with channel characteristics. Moreover, we propose an improved decoupled contextual transformer detection head structure, denoted as DCTI, to effectively resolve the feature conflicts that ensue from the concurrent classification and regression tasks. The experimental results on the Dota, VISDrone, and NWPU VHR-10 datasets show that, on the powerful real-time detection network YOLOv7, the speed and accuracy of tiny targets are better balanced.

Autonomous aerial vehicle (AAV)-enabled Internet of Things (IoT) exhibits great application potential with its wide coverage, flexible network topology, and diversified services. However, ensuring communication security and efficient spectrum resource utilization in multiuser access scenarios is challenging, given the open nature of AAV channels and the proliferation of communication devices in IoT. To address the above challenges, this article proposes a novel reconfigurable intelligent surface (RIS)-aided AAV collaborative communication framework, where RIS-equipped AAV flexibly serves multiple users. In this work, a rate splitting multiple access (RSMA)-based secure transmission scheme is proposed, where the split public information serves both as useful signals and noise to disrupt eavesdropping. For the proposed scheme, a sum secrecy rate maximization problem is formulated and solved by optimally deploying the AAV’s location, designing the RIS’s phase shift, and power allocation. For this nonconvex problem with a couple of variables, we decompose it and form three separate subissues. Specifically, leveraging the successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques, we first exploit an iterative algorithm for optimizing beamforming vectors and phase-shift matrix of RIS, and the optimal position of the AAV is obtained according to the deep deterministic policy gradient (DDPG). Then, we design an alternating optimization (AO) framework for joint solving. Finally, simulation results validate the efficacy of the proposed scheme in enhancing security, e.g., relative to the nonorthogonal multiple access (NOMA) scheme and benchmark scheme, the secrecy rate of the proposed scheme increased by 29.7% and 71.9%, respectively.

Capable of remote presentations of human heart activities, the Doppler cardiogram (DCG) can be detected by an agile Doppler radar sensor. However, as a recently developed technology, its performance needs sustained improvements for the future practical applications. In this work, a continuous-wave, ${V}$ -band Doppler radar sensor was implemented to improve the discrimination of the receiver, for detecting micrometer-level details of heartbeat motions conducted to the chest skin. Besides, new algorithms were derived to achieve high linearity, to demodulate the Doppler phases and retrieve the fine details of DCG waveforms. Experimental measurements to different subjects showed significantly enhanced robustness and accuracy. The obtained results imply a promising potential of DCGs in wide application scenarios such as medical wisdom and personal healthcare.