In human-centric applications, a common requirement is to extract information about the people present in an environment. In this paper, we introduce a static human sensing method using MIMO FMCW radar, i.e., to detect human presence and location when a person is stationary. The primary challenge for radar-based static human sensing is to separate human radar signals from environmental background. Conventional methods use signal processing techniques and handcrafted features to identify radar signals reflected from human body, which has high limitation on signal quality. We propose a deep learning model named SHSNet that automatically learn human features in radar signals from data. Our experimental results show that our method has superior accuracy in detecting human presence, identifying empty room, and localizing individuals.

Non-contact cardiac monitoring using radar technology is crucial for advancing health monitoring capabilities in smart transportation environments. This paper introduces a robust method for accurately extracting cardiac signals from radar data, specifically addressing the challenge of respiratory interference. Our approach utilizes a bank of Gabor filters, tuned to the seismocardiogram (SCG) frequency band, to effectively minimize respiratory artifacts and isolate clear heartbeat waveforms. Signal quality is further improved by selectively integrating high-fidelity cardiac data extracted from multiple locations using different filters. We validated this method with data collected from 34 participants seated up to 1 meter from the radar. Experimental results demonstrate a average median inter-beat interval (IBI) error of 20ms, with over 80% of IBI errors below 50ms.

FMCW radar-based human keypoints detection has been increasingly used in health monitoring, human-computer interaction, safety detection and other fields due to its characteristics of contact-free, privacy-preserving and less environmentdependence. Recently, most of studies have adopted deep learning method to detect human keypoints for its powerful feature extraction ability. However, its excellent performance relies on large-scale labeled datasets participating in model training, while the data labeling process is difficult and time-consuming, making it unsuitable for promotion and application. To address this issue, we propose a two-branch semi-supervised method to improve the accuracy and generalization of keypoints detection through using both labeled and unlabeled radar data. Models of two branches deal with different radar point clouds generated by same human activity, and promote the consistency and complementarity in the learning features of two branches through introducing a consistency loss function. Optimal performance in human keypoints detection can be achieved through adjusting weights between consistency loss and supervised loss. In addition, we develop a grouping processing technique of radar point clouds to obtain two different point cloud clusters with minimal feature overlap. We evaluate our model on a real-world radar dataset and compare its performance with a supervised method that only uses 20 labeled subjects. The experimental results demonstrate that the proposed method achieves a 10.7% improvement in accuracy compared to traditional supervised learning method, highlighting the significant advantages of our method in human keypoints detection and its substantial potential for broad application and promotion.

Human key-points play a vital role in smart home, elderly-care, gaming, etc. The detection methods based on millimeter wave (mmWave) radars have attracted substantial attentions and been applied in various fields because of the contactless and no privacyinvasion characteristics. In this paper, we present a machining learning-based framework, Mikey, that utilizes the commercial frequency-modulated continuous-wave (FMCW) radar point cloud to estimate the key-points. The framework consists of three steps, including point cloud adaptive merging pre-processing, key-point detection and key-point post-processing. The point cloud adaptive merging pre-processing method not only addressed the challenges stemming from the point sparsity and specular reflection but also the individual and action differences. A point cloud set is constructed adaptively from consecutive multiple frames limited by both frame and point numbers. The detection model combines the temporal and spatial features by adopting a local feature encoding block and a global self-attention block. A key-point post-processing step is added to smooth the key-point predictions and remove the jagged edge by exploring the prediction coherence in consecutive frames. We also make up for the scarcity of mmWave radar dataset for a variety of targets and actions. A dataset with 88 participants for 8 different actions is collected. We evaluate the proposed framework using additional 5 participants and obtain an average mean absolute error smaller than 6cm, confirming the effectiveness of the proposed framework.

Radar based human sensing especially vital signs(respiration/heartbeat) detection has attracted much attention. The basic principle of vital signs detection is to detect the tiny displacement caused by physiological movements, which are difficult to detect at long distance and are easily disturbed by human random body movements. To address this issue, we propose two strategies in this paper. In order not to lose the target signal, we select radar signals from multiple range bins within the neighborhood of target position as candidate signals, where the target position is obtained through examining radar signal variance. But the cost of candidate signals is to bring in noise signals that cannot be distinguished from physiological signals by using traditional signal processing methods. So a spatio-temporal dense network (ST-DenseNet) is proposed to extract physiological signals from candidate signals, which learns the most discriminative features to distinguish between physiological signals and noise signals through convolving spatial features at different temporal scales for strengthening feature fusion and using dense connections in the network for enhancing generalization capability. Based on these two strategies, our method realizes accurate vital signs detection over a large spatial range and achieves good robustness to disturbances such as body movements. Extensive experiments on wide space and multiple subjects confirms the superiority of our method. The error of respiration and heartbeat detection are reduced to 0.93bpm (beats per minute) and 3.83bpm when the sensing scope is improved to 2 meters.

Although a FMCW MIMO radar can effectively track moving people, it is a challenge for radar to detect static people. When a person is at rest, radar signals reflected from the human body are mixed up with that from stationary objects. In this paper, we present a static human localization method using FMCW MIMO radar. The method uses radar signal variations and spectral characteristics caused by vital activities to identify human locations. We test the proposed method with an off-the-shelf radar. The results show that our method can detect human presence and get human locations in a large area. Furthermore, the detected human location can be used to judge whether the target is lying on the floor, which is helpful for fall detection.

The accuracy of O/X mode separation in vertical ionograms directly determines the quality of pattern discrimination results and metrics, which is of great significance to ionospheric research. It is extremely complicated to separate the O/X mode of the vertical ionograms because of the environmental noise, interference, and the time-varying dispersion characteristics of the ionosphere itself. In this article, we propose a method for separating the O/X mode signal in vertical ionograms based on an improved U-shaped encoder–decoder network, named vertical ionogram separation U-shaped network (VIS-UNet). Our model is based on the encoder–decoder architecture. It introduces the residual convolution to avoid network performance degradation and utilizes the attention module to improve the attention of the signal characteristic. In addition, we design an adaptive loss function to expedite the convergent speed of the model. Experimental results show that our model performs better than the baselines for the task of the O/X mode signal separation: 1) the method in this article has low requirements on the vertical ionospheric sounding system and the ionograms obtained by the single-channel vertical ionospheric sounding system can realize the separation of O/X mode signal at the pixel level; 2) it has strong universality and is insensitive to the signal integrity and the ionospheric pattern of the vertical ionograms; and 3) it performs better for the separation task. The mean intersection over union (MIOU) of the O/X mode separation task reaches 91.97% and the performance is significantly improved compared with the existing methods.

SUMMARY: ProDy, an integrated application programming interface developed for modelling and analysing protein dynamics, has significantly evolved in recent years in response to the growing data and needs of the computational biology community. We present major developments that led to ProDy 2.0: (i) improved interfacing with databases and parsing new file formats, (ii) SignDy for signature dynamics of protein families, (iii) CryoDy for collective dynamics of supramolecular systems using cryo-EM density maps and (iv) essential site scanning analysis for identifying sites essential to modulating global dynamics. AVAILABILITY AND IMPLEMENTATION: ProDy is open-source and freely available under MIT License from https://github.com/prody/ProDy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Non-contact human sensing based on radar has attracted numerous attentions and been applied in various applications, such as localization, vital sign monitoring and activity identification. This paper presents a wireless healthcare system to achieve life detection and vital sign monitoring based on frequency-modulated continuous-wave (FMCW) radar. Most of previous methods have studied to detect moving targets. The proposed life detection method has achieved stationary human target detection by utilizing the inherent characteristics of human breathing motion: spatial correlation and periodicity. Based on the phase sensitivity of FMCW radar, breathing motion is monitored within the range provided by the life detection step. Besides, different algorithms are adopted for different detection range to remove the distance and environment impacts. Experiments are carried out and have demonstrated that the proposed method can detect the stationary human targets accurately and 95% of the breathing rate estimation error is less than 3 BPM(Beat Per Minute).

SUMMARY: QuartataWeb is a user-friendly server developed for polypharmacological and chemogenomics analyses. Users can easily obtain information on experimentally verified (known) and computationally predicted (new) interactions between 5494 drugs and 2807 human proteins in DrugBank, and between 315 514 chemicals and 9457 human proteins in the STITCH database. In addition, QuartataWeb links targets to KEGG pathways and GO annotations, completing the bridge from drugs/chemicals to function via protein targets and cellular pathways. It allows users to query a series of chemicals, drug combinations or multiple targets, to enable multi-drug, multi-target, multi-pathway analyses, toward facilitating the design of polypharmacological treatments for complex diseases. AVAILABILITY AND IMPLEMENTATION: QuartataWeb is freely accessible at http://quartata.csb.pitt.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Environment has an essential effect on the selection of relay node positions. Real application environment of Wireless Sensor Networks usually is complex and heterogeneous where wireless channel has different characteristics at different locations. In network deployment, the environment also constrains relay node locations. There are areas unfeasible for node deployment. This paper studies the relay node placement problem in such complex environment. Our target of relay node position optimization is to minimize the relay node number and improve network performance by optimizing route paths of sensor nodes. Heuristic operations for genetic algorithms are proposed to optimize relay node positions. Simulation results show that the relay nodes number of the proposed algorithm is less than that of 2-approximation Steiner tree algorithm and the route paths of selected relay nodes have almost the best cost among route paths of all feasible relay nodes.

One serious challenge of wireless multi-hop ad hoc networks is frequent link and node failures due to lossy wireless channels and low-cost hardware. Failure detection plays an important role in network maintenance. In order to reduce communication overhead, local-based failure detection methods conduct diagnosis processes in a local area. However, the overhead of them is still very large because several redundant detection processes are triggered for one failure by several nodes. In this paper, we present a new failure detection approach named Root Node Reduction (RNR), which can reduce the number of detection processes by a backoff scheme. Furthermore, we propose two additional strategies to improve diagnosis accuracy, including multi fusion which guarantees complete evidence collection, and multi report which improves the reception ratio of diagnosis conclusion report. The comparison results show that the performance of communication overhead, diagnosis accuracy and data packet reception is improved by these schemes.