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The spatial-temporal evolution of coronal plasma parameters of the solar outer atmosphere at global scales, derived from solar full-disk imaging spectroscopic observation in the extreme-ultraviolet band, is critical for understanding and forecasting solar eruptions. We propose a multi-slits extreme ultraviolet imaging spectrograph for global coronal diagnostics with high cadence and present the preliminary instrument designs for the wavelength range from 18.3 to 19.8 nm. The instrument takes a comprehensive approach to obtain global coronal spatial and spectral information, improve the detected cadence and avoid overlapping. We first describe the relationship between optical properties and structural parameters, especially the relationship between the overlapping and the number of slits, and give a general multi-slits extreme-ultraviolet imaging spectrograph design process. Themultilayer structure is optimized to enhance the effective areas in the observation band. Five distantly-separated slits are set to divide the entire solar field of view, which increase the cadence for raster scanning the solar disk by 5 times relative to a single slit. The spectral resolving power of the optical system with an aperture diameter of 150 mm are optimized to be greater than 1461. The spatial resolution along the slits direction and the scanning direction are about 4.4''and 6.86'', respectively. The Al/Mo/B4C multilayer structure is optimized and the peak effective area is about 1.60 cm2 at 19.3 nm with a full width at half maximum of about 1.3 nm. The cadence to finish full-disk raster scan is about 5 minutes. Finally, the instrument performance is evaluated by an end-to-end calculation of the system photon budget and a simulation of the observational image and spectra. Our investigation shows that this approach is promising for global coronal plasma diagnostics., Comment: This version of the article has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10686-024-09961-9

Real-time high-accuracy optical flow estimation is crucial for various real-world applications. While recent learning-based optical flow methods have achieved high accuracy, they often come with significant computational costs. In this paper, we propose a highly efficient optical flow method that balances high accuracy with reduced computational demands. Building upon NeuFlow v1, we introduce new components including a much more light-weight backbone and a fast refinement module. Both these modules help in keeping the computational demands light while providing close to state of the art accuracy. Compares to other state of the art methods, our model achieves a 10x-70x speedup while maintaining comparable performance on both synthetic and real-world data. It is capable of running at over 20 FPS on 512x384 resolution images on a Jetson Orin Nano. The full training and evaluation code is available at https://github.com/neufieldrobotics/NeuFlow_v2.

The current methods of video-based 3D human pose estimation have achieved significant progress; however, they continue to confront the significant challenge of depth ambiguity. To address this limitation, this paper presents the spatio-temporal GraphFormer framework for 3D human pose estimation in video, which integrates body structure graph-based representations with spatio-temporal information. Specifically, we develop a spatio-temporal criss-cross graph (STG) attention mechanism. This approach is designed to learn the long-range dependencies in data across both time and space, integrating graph information directly into the respective attention layers. Furthermore, we introduce the dual-path modulated hop-wise regular GCN (MHR-GCN) module, which utilizes modulation to optimize parameter usage and employs spatio-temporal hop-wise skip connections to acquire higher-order information. Additionally, this module processes temporal and spatial dimensions independently to learn their respective features while avoiding mutual influence. Finally, we demonstrate that our method achieves state-of-the-art performance in 3D human pose estimation on the Human3.6M and MPI-INF-3DHP datasets.

Micro-pattern gas detectors (MPGD) find wide-ranging applications in particle physics experiments, industry, and medical services, owing to their large area, fine spatial resolution, and relatively low material content within the sensitive region. However, the demand for a large number of readout channels poses a bottleneck, limiting the application of MPGD to achieve higher accuracy and more extensive area. This requirement also presents significant challenges regarding system integration, power consumption, cooling, and cost. Previous studies have shown that, under sparse effective hits, a channel multiplexing method based on position encoded can address this issue. Nonetheless, improving the compression ratio and addressing the high event rate problem remain key challenges requiring further investigation. In this research, we have developed two types of high compression ratio multiplexing methods and their mathematical models. It is shown that a maximum of $n \times (n-1)/2 - (n - 2)/2 + 1$ detector channels can be read out with n electronics channels if n is even. Using these methods, several multiplexing boards were designed and implemented. Considering the real condition of the detectors, we achieved an multiplexing board with 64 readout electronics reading out 1024 detector channels, marking the highest compression ratio in current research. Moreover, these multiplexing circuits were utilized and verified in our cosmic-ray muon imaging facilities, demonstrating their advantage of reducing the required number of front-end electronics cards., Comment: This is the first submitted version to the IEEE-TNS

Muon imaging technology is an innovative imaging technique that can be applied in volcano imaging, heavy nuclear material detection, and archaeological research. The Micromegas detector is a promising choice for muon imaging due to its high spatial resolution and large area. However, the large number of readout channels poses a challenge for electronics. In this paper, a compact front-end electronics (FEE) for reading Micromegas detectors is presented. The electronics use the commercial current-to-digital readout chip, ADAS1128, which integrates 128 current amplifiers for multi-channel charge measurement. After verifying the basic performance of the electronics, the energy resolution was obtained with a radioactive source. Furthermore, a muon imaging system prototype was set up and its spatial resolution was evaluated in a test with cosmic ray muons. The system prototype can reconstruct the boundaries of sufficiently massive objects with a size of 2 cm in a scattering imaging test.

Real-time high-accuracy optical flow estimation is a crucial component in various applications, including localization and mapping in robotics, object tracking, and activity recognition in computer vision. While recent learning-based optical flow methods have achieved high accuracy, they often come with heavy computation costs. In this paper, we propose a highly efficient optical flow architecture, called NeuFlow, that addresses both high accuracy and computational cost concerns. The architecture follows a global-to-local scheme. Given the features of the input images extracted at different spatial resolutions, global matching is employed to estimate an initial optical flow on the 1/16 resolution, capturing large displacement, which is then refined on the 1/8 resolution with lightweight CNN layers for better accuracy. We evaluate our approach on Jetson Orin Nano and RTX 2080 to demonstrate efficiency improvements across different computing platforms. We achieve a notable 10x-80x speedup compared to several state-of-the-art methods, while maintaining comparable accuracy. Our approach achieves around 30 FPS on edge computing platforms, which represents a significant breakthrough in deploying complex computer vision tasks such as SLAM on small robots like drones. The full training and evaluation code is available at https://github.com/neufieldrobotics/NeuFlow.

3D human pose estimation and mesh recovery have attracted widespread research interest in many areas, such as computer vision, autonomous driving, and robotics. Deep learning on 3D human pose estimation and mesh recovery has recently thrived, with numerous methods proposed to address different problems in this area. In this paper, to stimulate future research, we present a comprehensive review of recent progress over the past five years in deep learning methods for this area by delving into over 200 references. To the best of our knowledge, this survey is arguably the first to comprehensively cover deep learning methods for 3D human pose estimation, including both single-person and multi-person approaches, as well as human mesh recovery, encompassing methods based on explicit models and implicit representations. We also present comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions. A regularly updated project page can be found at https://github.com/liuyangme/SOTA-3DHPE-HMR.