Most researches on indoor target trajectory tracking using millimeter-wave radar have faced challenges such as interference from multipath effects, which led to corrupted point clouds, and large tracking errors in multi-target scenarios. This study aims to improve the accuracy of human multiple-object tracking, addressing the practical challenges of target trajectory tracking in indoor environments. We proposed an extended Distributed Radar Multi-Target Tracking (DRMTrack) framework that enabled the fusion of point clouds from multiple radar nodes. Additionally, by exploiting the spatial distribution characteristics of the target point clouds for clustering, tracking filtering and integrating historical data for tracking association, the framework enhances multiple-object trajectory tracking performance. Experimental results demonstrate that for various trajectory paths, the minimum position tracking error for multi-target is 5.2 cm. In the five-target tracking scenario, the 90th percentile tracking error is 18.8 cm, representing a 27.7% improvement in accuracy compared to single-radar tracking. The DRMTrack system effectively reduces interference from clutter point clouds, enhances multiple-object tracking precision, and supports real-time computation. This system is suitable for motion monitoring in indoor environments such as homes and hospital rooms, effectively fulfilling the needs of real-time, non-contact health management.

Human immunodeficiency virus type 1 (HIV-1) is one of the main causative agents of acquired immunodeficiency syndrome (AIDS), and effectively identifying HIV-1 protease cleavage sites (PCSs) is of great importance for the design of new anti-AIDS inhibitors. Computational prediction of HIV-1 PCSs can be used to discover new cleavable substrates, and further facilitates the understanding of substrate specificity. A novel deep learning model, namely DeepHIV, is designed to predict HIV-1 PCSs from substrate sequence information alone. In particular, DeepHIV first applies a convolutional neural network combined with an attention mechanism to capture the rich contextual information of position-specific amino acids in the substrate sequences, thus improving the quality of features learned for substrates. Considering the imbalance observed between cleavable and uncleavable substrates, a biased support vector machine is adopted as the classifier of DeepHIV to complete the prediction task. Experimental results demonstrate that DeepHIV outperforms several state-of-the-art prediction methods across all benchmark datasets and evaluation metrics. Hence, DeepHIV is an accurate and robust tool to predict HIV-1 PCSs. Moreover, the promising predictive performance of DeepHIV also reveals that our deep learning model is capable of fully leveraging the sequence information to effectively learn the latent features of substrates.

Cardio-pulmonary resuscitation (CPR) is an effective first aid measure to deal with cardiac arrest and is the cornerstone of saving patients ’ lives. Chest compression is the most important part of CPR. This work proposed a non-contact CPR compression action quality detection system based on the depth camera in the first time. The RGB camera probe in the system identifies and tracks the position of the rescuer ’s hand in the RGB image and maps it to the depth image to obtain the CPR compression curve. Then the CPR parameters such as compression depth and compression frequency are obtained by solving the compression curve. Experiments with different camera deflection angles, different camera measurement distances and different compression frequencies were carried out to evaluate the measurement error of the system. The results showed that under appropriate conditions, the system performance is ideal, and the compression depth and frequency of CPR can be tracked stably.

This work presents a technique for measuring blood pressure waveform using a lightweight, stretchable, and wearable ultrasound patch. A system containing ultrasound transceiver hardware and signal processing software was developed for blood pressure waveform evaluation. By acquiring echo frames from 3 to 4 adjacent arterial locations and employing peak tracking techniques, stable arterial distension waveforms were evaluated. The time delay between channels was utilized to determine local pulse wave velocity (PWV) and arterial compliance, followed by the calculation of arterial blood pressure waveform based on arterial diameter. This technology has been validated on the carotid arteries of 10 human subjects, and the blood pressure waveform measured by the ultrasound patch demonstrated good consistency when compared with the arterial tonometer. The current results indicate that the wearable ultrasound patch can measure blood pressure waveform at central artery sites.

BACKGROUND: The site information of substrates that can be cleaved by human immunodeficiency virus 1 proteases (HIV-1 PRs) is of great significance for designing effective inhibitors against HIV-1 viruses. A variety of machine learning-based algorithms have been developed to predict HIV-1 PR cleavage sites by extracting relevant features from substrate sequences. However, only relying on the sequence information is not sufficient to ensure a promising performance due to the uncertainty in the way of separating the datasets used for training and testing. Moreover, the existence of noisy data, i.e., false positive and false negative cleavage sites, could negatively influence the accuracy performance. RESULTS: In this work, an ensemble learning algorithm for predicting HIV-1 PR cleavage sites, namely EM-HIV, is proposed by training a set of weak learners, i.e., biased support vector machine classifiers, with the asymmetric bagging strategy. By doing so, the impact of data imbalance and noisy data can thus be alleviated. Besides, in order to make full use of substrate sequences, the features used by EM-HIV are collected from three different coding schemes, including amino acid identities, chemical properties and variable-length coevolutionary patterns, for the purpose of constructing more relevant feature vectors of octamers. Experiment results on three independent benchmark datasets demonstrate that EM-HIV outperforms state-of-the-art prediction algorithm in terms of several evaluation metrics. Hence, EM-HIV can be regarded as a useful tool to accurately predict HIV-1 PR cleavage sites.

A two‐stream remote sensing image fusion network (RCAMTFNet) based on the residual channel attention mechanism is proposed by introducing the residual channel attention mechanism (RCAM) in this paper. In the RCAMTFNet, the spatial features of PAN and the spectral features of MS are extracted, respectively, by a two‐channel feature extraction layer. Multiresidual connections allow the network to adapt to a deeper network structure without the degradation. The residual channel attention mechanism is introduced to learn the interdependence between channels, and then the correlation features among channels are adapted on the basis of the dependency. In this way, image spatial information and spectral information are extracted exclusively. What is more, pansharpening images are reconstructed across the board. Experiments are conducted on two satellite datasets, GaoFen‐2 and WorldView‐2. The experimental results show that the proposed algorithm is superior to the algorithms to some existing literature in the comparison of the values of reference evaluation indicators and nonreference evaluation indicators.

The substrate specificity of human immunodeficiency virus 1 (HIV-1) plays an essential role in designing HIV-1 inhibitors for therapy purpose. Hence, to predict the existence of cleavage sites in HIV-1 protease, a variety of computational algorithms have been developed by following the homogeneous information in substrate sequences. However, few of them can fully exploit such information, as they are not capable of identifying variable-length coevolutionary patterns. To overcome this limitation, we propose a novel algorithm with which variable-length coevolutionary patterns can be identified. Based on these patterns, we compose the feature vector for each of substrates and train the SVM classifier to the purpose of predicting HIV1 protease cleavage sites. Experimental results show that the use of variable-length coevolutionary patterns can improve the prediction performance in terms of AUC and PR-AUC analysis.