This paper presents a frequency offset estimation algorithm based on TRS (tracking reference signal) for 5G NR system in the UE tracking mode. The algorithm makes use of the 4 TRS resources within 2 consecutive slots in 5G NR FR1 to provide a larger frequency tracking range while maintaining a non-decreasing accuracy with a little more calculation complexity.

Neural Radiance Fields (NeRF) is a revolutionary approach for rendering scenes by sampling a single ray per pixel and it has demonstrated impressive capabilities in novel-view synthesis from static scene images. However, in practice, we usually need to recover NeRF from unconstrained image collections, which poses two challenges: 1) the images often have dynamic changes in appearance because of different capturing time and camera settings; 2) the images may contain transient objects such as humans and cars, leading to occlusion and ghosting artifacts. Conventional approaches seek to address these challenges by locally utilizing a single ray to synthesize a color of a pixel. In contrast, humans typically perceive appearance and objects by globally utilizing information across multiple pixels. To mimic the perception process of humans, in this paper, we propose Cross-Ray NeRF (CR-NeRF) that leverages interactive information across multiple rays to synthesize occlusion-free novel views with the same appearances as the images. Specifically, to model varying appearances, we first propose to represent multiple rays with a novel cross-ray feature and then recover the appearance by fusing global statistics, i.e., feature covariance of the rays and the image appearance. Moreover, to avoid occlusion introduced by transient objects, we propose a transient objects handler and introduce a grid sampling strategy for masking out the transient objects. We theoretically find that leveraging correlation across multiple rays promotes capturing more global information. Moreover, extensive experimental results on large real-world datasets verify the effectiveness of CR-NeRF. The code and data can be found at https://github.com/YifYang993/CR-NeRF-PyTorch.git.

We address the problem of facial motion retargeting that aims to transfer facial motion from a 2D face image to 3D characters. Existing methods often formulate this problem as a 3D face reconstruction problem, which estimates the face attributes such as face identity and expression from face images. However, due to the lack of ground-truth labels for both identity and expression, most 3D-face reconstruction-based methods fail to capture the facial identity and expression accurately. As a result, these methods may not achieve promising performance. To address this, we propose an identity-consistent constraint to learn accurate identities by encouraging consistent identity prediction across multiple frames. Based on a more accurate identity, we are able to obtain a more accurate facial expression. Moreover, we further propose an expression-exclusive constraint to improve performance by avoiding the co-occurrence of contradictory expression units (e.g., ``brow lower'' vs. ``brow raise''). Extensive experiments on facial motion retargeting and 3D face reconstruction tasks demonstrate the superiority of the proposed method over existing methods. Our code and supplementary materials are available at https://github.com/deepmo24/CPEM.

With the rapid development of IoT technology in recent years, higher requirements have been put forward for wireless communication technology. Low Power Wide Area Network (LPWAN) technology is emerging rapidly, the technology is characterized by low power consumption, low bandwidth, long-distance, and a large number of connections, and is specifically designed for Internet of Things applications. LoRa (Low Power Long Range Transceiver), as a typical representative of LPWAN technology, has been widely concerned and studied. This paper analyzes the performance of LoRa modulation in the tree topology network and analyzes the performance of LoRa modulation in the imperfect environment for point-to-point communication and multipoint-to-point communication. From theoretical analysis and performance simulation, it can be seen that the influence of frequency offset or multipath fading on LoRa signal is very obvious. However, when LoRa modulation is used for networking, multi-user interference will be introduced. Under the influence of many imperfect factors, the signal receiver performance of LoRa modulation will be difficult to guarantee. Because of these effects, Coordinated Multiple Points based on Timing Delay (DCoMP) is presented. Multiple nodes close to each other send the same data to the target node. Due to the inaccurate synchronization between nodes, there will be a certain relative delay when sending signals to the same target node. After the receiving node combines the signals of multiple nodes according to different relative delays, the reception performance of the signals can be improved. At the same time, the cooperative node can also actively adjust the signal sending time to improve the reception performance of the receiving node signal merging algorithm. LoRa modulation, by using DCoMP transmission, improves the reception of signals and thus the overall capacity of the system. Through the analysis of multipoint communication and single point communication, this paper is of great help to LoRa network deployment.

Compliant actuator is widely accepted for physical human-robot interaction due to its safety aspect, dynamic performance improvements and energy saving abilities. In this paper, based on the variable ratio lever mechanism, a new kind of Serial Variable Stiffness Actuator (SVSA) is proposed by using an Archimedean Spiral Relocation Mechanism (ASRM) to change the position of the pivot, implementing large range of adjustable stiffness. The ASRM introduced here makes the SVSA design has continuous stiffness adjustment ability and simply mechanical structure. Within the Variable Stiffness Mechanism (VSM), two linear springs are assembled antagonistically on a spring shaft. Their displacements are perpendicular to the output link to transmit the spring force more efficiently. Stiffness modeling and analysis of the SVSA are carried out to cover large deflection angle. The physical implementation of the SVSA shows that the output stiffness of the VSM is changed from 1.72 to 150.56 Nm/rad using a linear spring with stiffness 1882 N/m, working range covered from 0 to 360°. Control experiments also proved the wide range of stiffness adjustment ability of the SVSA.