Spacecraft pose estimation from monocular images presents significant challenges due to complex environmental conditions such as occlusions, illumination variations, and background interference, as well as estimation inaccuracies caused by multi-scale variations in object distance and viewpoint. To address these issues, this paper proposes a novel monocular pose estimation algorithm. In concrete terms, a high-resolution feature extraction framework is constructed using Higher-HRNet, a variant of the High-Resolution Network (HRNet), to generate multi-scale feature maps and enhance spatial feature representation. To balance estimation accuracy with real-time performance under limited computational resources, lightweight Ghost-BasicBlock and Ghost-Bottleneck modules are designed to reduce model complexity. Moreover, to mitigate the loss of feature representation capacity induced by model compression, a Biformer-Receptive Attention (BRA) mechanism is incorporated in the decoding stage to strengthen spatial-context modeling and improve keypoint localization accuracy. As the culminating process, the six-degree-of-freedom pose of the spacecraft is estimated by integrating the Efficient Perspective-n-Point (EPnP) algorithm with Random Sample Consensus (RANSAC). Experimental results on the SPEED dataset demonstrate that the proposed method achieves a mean translation error (meanET) of 0.0077m and a mean rotation error (meanER) of 0.0232°, with respective medians (medianET and medianER) corresponding to 0.0038m and 0.0112°, respectively—outperforming current state-of-the-art methods.

Bradykinesia is a hallmark symptom of Parkinson’s disease (PD) that significantly affects patients’ functional abilities and quality of life. This study proposed a fine-grained classification method for evaluating the level of bradykinesia in PD patients. Based on inertial signals, surface electromyographic (sEMG) signals, and videos obtained from 40 PD patients and 13 healthy subjects, the proposed data preprocessing method extracts 69-D features from inertial and sEMG (IE) signals, and 7-D skeleton features from videos. A two-stream network, including IE stream, skeleton stream, and decision fusion module, was developed using long short-term memory, full convolutional neural networks, and fully connected neural networks. In addition, the IE stream incorporated a feature shrinking module to process high-dimensional features to reduce redundant features. Furthermore, an LSTM-variational autoencoders method was proposed for data augmentation of categories with fewer samples. The proposed method achieved higher recognition rates (pro/supination movements of hands: 85.51%, finger tapping: 88.06%, hand movements: 90.00%) compared to other methods. With low-cost, compact and lightweight methods, bradykinesia in PD patients can be intelligently assessed, which will enhance patient management and treatment efficiency.

As space activities expand, the quantity of space debris also increases, posing significant risks to spacecraft and infrastructure. Space situational awareness (SSA) is essential for avoiding collisions and limiting the generation of extra debris. Accurate and efficient detection of space objects plays a critical role in achieving this goal. Our research focuses on the development of detection algorithms that are both precise and quick, taking into account the real-time and safety of spacecraft operations in orbit. For the first time, we take a fully Convolutional Neural Networks (ConvNets) to run the query-based end-to-end object detection for SSA. We further compare its performance with the newest YOLOv9 algorithm. This is an innovative attempt at SSA. First of all, it does not require predefined a priori anchor boxes or complex post-processing strategies such as Non-Maximum Suppression (NMS), and can directly achieve end-to-end target detection. Secondly, the fully ConvNets are selected as the basic framework, which not only retains the advantages of self-attention mechanism, but also greatly improves the computing efficiency. These methods show outstanding performance on the challenging SPARK data set. The fully ConvNets approach achieves end-to-end detection by utilizing the query attention mechanism, excluding the need for complicated post-processing in traditional object detection methods and with higher efficiency. YOLOv9 involves an enhanced feature pyramid fusion and a more powerful detection head, potentially resulting in higher precision. Following that, we will thoroughly assess the speed, accuracy, and trade-offs of the two algorithms using actual data sets in order to deliver an efficient and dependable solution for detecting targets in aerospace sensing missions.