We propose an efficient calculation method to display three-dimensional information with purephase computer-generated holograms (CGHs). The object composed of points is conventionally divided into a set of planar segments vertically along with the axis. In this way, the number of planar segments is less than the number of points, which means that the total number of diffractions needed to calculate decreases. However, it is low efficient, for sometimes one planar segment only contains one point. We propose to quantize the locations of planar segments to solve this problem. The simplest way to quantize locations is to divide the object into the equal interval planar segments parallel with each other in the axis. The points out of these planar segments are projected to their nearest segments. Compared to traditional CGHs, the proposed method dramatically reduces the computing cost. Both simulations and experiments demonstrate the feasibility of the location quantization algorithm.
Abstract Developing angular trapping methods, which enable optical tweezers to rotate a micronsized bead, is of great importance for studies of biomacromolecules in a wide range of torque‐generation processes. Here a novel controlled angular trapping method based on model composite Janus particles is reported, which consist of two hemispheres made of polystyrene and poly(methyl methacrylate). Through computational and experimental studies, the feasibility to control the rotation of a Janus particle in a linearly polarized laser trap is demonstrated. The results show that the Janus particle aligned its two hemispheres interface parallel to the laser propagation direction and polarization direction. The rotational state of the particle can be directly visualized by using a camera. The rotation of the Janus particle in the laser trap can be fully controlled in real time by controlling the laser polarization direction. The newly developed angular trapping technique has the great advantage of easy implementation and real‐time controllability. Considering the easy chemical preparation of Janus particles and implementation of the angular trapping, this novel method has the potential of becoming a general angular trapping method. It is anticipated that this new method will significantly broaden the availability of angular trapping in the biophysics community.
With ability to eliminate the cliff effect and achieve graceful degradation, analog-like transmission such as SoftCast has become a hot research issue for robust image/video delivery over wireless channels. However, it has not been well studied in the case of bandwidth compression, which usually occurs in bandwidth-constrained wireless environments such as satellite communication scenarios. In this paper, we propose an analoglike robust transmission scheme based on Compressive Sensing (CS) for satellite image delivery over bandwidth-constrained wireless channels. The motivation for integration of CS in the proposed scheme lies in the fact that the simple dropping strategy is not optimal for satellite image transmission when bandwidth is insufficient. Considering high information entropy and rich structure information properties of satellite images, we perform an amplitude offset operation and the block-based CS (BCS) procedure to improve the energy efficiency and meet the bandwidth budget respectively. We analyze the system distortion of the proposed scheme and formulate the distortion minimization problem as a resource allocation problem. Then, we propose an efficient two-step strategy to find the optimal solution for bandwidth and power allocation. The simulation results show that the proposed scheme achieves up to 5.5dB gain over the state-of-the-art transmission schemes for satellite image delivery.
As real-scanned point clouds are mostly partial due to occlusions and viewpoints, reconstructing complete 3D shapes based on incomplete observations becomes a fundamental problem for computer vision. With a single incomplete point cloud, it becomes the partial point cloud completion problem. Given multiple different observations, 3D reconstruction can be addressed by performing partial-to-partial point cloud registration. Recently, a large-scale Multi-View Partial (MVP) point cloud dataset has been released, which consists of over 100,000 high-quality virtual-scanned partial point clouds. Based on the MVP dataset, this paper reports methods and results in the Multi-View Partial Point Cloud Challenge 2021 on Completion and Registration. In total, 128 participants registered for the competition, and 31 teams made valid submissions. The top-ranked solutions will be analyzed, and then we will discuss future research directions.
The end-to-end Human Mesh Recovery (HMR) approach has been successfully used for 3D body reconstruction. However, most HMR-based frameworks reconstruct human body by directly learning mesh parameters from images or videos, while lacking explicit guidance of 3D human pose in visual data. As a result, the generated mesh often exhibits incorrect pose for complex activities. To tackle this problem, we propose to exploit 3D pose to calibrate human mesh. Specifically, we develop two novel Pose Calibration frameworks, i.e., Serial PC-HMR and Parallel PC-HMR. By coupling advanced 3D pose estimators and HMR in a serial or parallel manner, these two frameworks can effectively correct human mesh with guidance of a concise pose calibration module. Furthermore, since the calibration module is designed via non-rigid pose transformation, our PC-HMR frameworks can flexibly tackle bone length variations to alleviate misplacement in the calibrated mesh. Finally, our frameworks are based on generic and complementary integration of data-driven learning and geometrical modeling. Via plug-and-play modules, they can be efficiently adapted for both image/video-based human mesh recovery. Additionally, they have no requirement of extra 3D pose annotations in the testing phase, which releases inference difficulties in practice. We perform extensive experiments on the popular benchmarks, i.e., Human3.6M, 3DPW and SURREAL, where our PC-HMR frameworks achieve the SOTA results.
Abstract The monitoring and prediction of the health status and the end of life of batteries during the actual operation plays a key role in the battery safety management. However, although many related studies have achieved exciting results, there are few systematic and comprehensive reviews on these prediction methods. In this paper, the current prediction models of remaining useful life of lithium‐ion batteries are divided into mechanism‐based models, semi‐empirical models and data‐driven models. Their advantages, technical obstacles, improvement methods and prediction performance are summarized, and the latest research results are shown by comparison. We highlight that the fusion models of convolution neural network, long short term memory network and so on, which have great practical application prospects because of their outstanding computing efficiency and strong modeling ability. Finally, we look forward to the future work in simplifying the model and improving its interpretability.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
A preference based multi-objective evolutionary algorithm is proposed for generating solutions in an automatically detected knee point region. It is named Automatic Preference based DI-MOEA (AP-DI-MOEA) where DI-MOEA stands for Diversity-Indicator based Multi-Objective Evolutionary Algorithm). AP-DI-MOEA has two main characteristics: firstly, it generates the preference region automatically during the optimization; secondly, it concentrates the solution set in this preference region. Moreover, the real-world vehicle fleet maintenance scheduling optimization (VFMSO) problem is formulated, and a customized multi-objective evolutionary algorithm (MOEA) is proposed to optimize maintenance schedules of vehicle fleets based on the predicted failure distribution of the components of cars. Furthermore, the customized MOEA for VFMSO is combined with AP-DI-MOEA to find maintenance schedules in the automatically generated preference region. Experimental results on multi-objective benchmark problems and our three-objective real-world application problems show that the newly proposed algorithm can generate the preference region accurately and that it can obtain better solutions in the preference region. Especially, in many cases, under the same budget, the Pareto optimal solutions obtained by AP-DI-MOEA dominate solutions obtained by MOEAs that pursue the entire Pareto front.
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