Quality Adaptive Least Squares Trained Filters for Video Compression Artifacts Removal Using a No-reference Block Visibility Metric

Compression artifacts removal is a challenging problem because videos can be compressed at different qualities. In this paper, a least squares approach that is self-adaptive to the visual quality of the input sequence is proposed. For compression artifacts, the visual quality of an image is measured by a no-reference block visibility metric. According to the blockiness visibility of an input image, an appropriate set of filter coefficients that are trained beforehand is selected for optimally removing coding artifacts and reconstructing object details. The performance of the proposed algorithm is evaluated on a variety of sequences compressed at different qualities in comparison to several other deblocking techniques. The proposed method outperforms the others significantly both objectively and subjectively

Motor Noise and Vibration Test Research

Some factors, such as friction, vibration, and so on, can result in the fault and abnormal noise in the motor. Based on the detection and analysis of noise and vibration, we can identify and eliminate the faults of the motor. This is helpful not only to ensure the completion of production tasks, but also to prevent accidents. In this paper, we briefly introduce the motor noise generation principle. A laptop computer and LabVIEW software are used to design the experiment system to detect and analysis the noise and vibration of motor. External microphone and computer with sound card constitute noise detection system hardware. Vibration sensor and the data acquisition card constitute vibration detection system hardware. LabVIEW software combined with FFT analysis is used to realize the noise signal acquisition, recording and spectral analysis. Detecting and analyzing the noise of the permanent magnet DC motor and three-phase asynchronous motor proves that the motor noise and vibration detecting experimental platform is fully meet the requirements of motor test and research. This detection and analysis system has a good man-machine interface and strong operability

Towards a Unified Analysis of Kernel-based Methods Under Covariate Shift

Covariate shift occurs prevalently in practice, where the input distributions of the source and target data are substantially different. Despite its practical importance in various learning problems, most of the existing methods only focus on some specific learning tasks and are not well validated theoretically and numerically. To tackle this problem, we propose a unified analysis of general nonparametric methods in a reproducing kernel Hilbert space (RKHS) under covariate shift. Our theoretical results are established for a general loss belonging to a rich loss function family, which includes many commonly used methods as special cases, such as mean regression, quantile regression, likelihood-based classification, and margin-based classification. Two types of covariate shift problems are the focus of this paper and the sharp convergence rates are established for a general loss function to provide a unified theoretical analysis, which concurs with the optimal results in literature where the squared loss is used. Extensive numerical studies on synthetic and real examples confirm our theoretical findings and further illustrate the effectiveness of our proposed method.Comment: Poster to appear in Thirty-seventh Conference on Neural Information Processing System

Three-dimensional Reconstruction of Coronal Mass Ejections by CORAR Technique through Different Stereoscopic Angle of STEREO Twin Spacecraft

Recently, we developed the Correlation-Aided Reconstruction (CORAR) method to reconstruct solar wind inhomogeneous structures, or transients, using dual-view white-light images (Li et al. 2020; Li et al. 2018). This method is proved to be useful for studying the morphological and dynamical properties of transients like blobs and coronal mass ejection (CME), but the accuracy of reconstruction may be affected by the separation angle between the two spacecraft (Lyu et al. 2020). Based on the dual-view CME events from the Heliospheric Imager CME Join Catalogue (HIJoinCAT) in the HELCATS (Heliospheric Cataloguing, Analysis and Techniques Service) project, we study the quality of the CME reconstruction by the CORAR method under different STEREO stereoscopic angles. We find that when the separation angle of spacecraft is around 150{\deg}, most CME events can be well reconstructed. If the collinear effect is considered, the optimal separation angle should locate between 120{\deg} and 150{\deg}. Compared with the CME direction given in the Heliospheric Imager Geometrical Catalogue (HIGeoCAT) from HELCATS, the CME parameters obtained by the CORAR method are reasonable. However, the CORAR-obtained directions have deviations towards the meridian plane in longitude, and towards the equatorial plane in latitude. An empirical formula is proposed to correct these deviations. This study provides the basis for the spacecraft configuration of our recently proposed Solar Ring mission concept (Wang et al. 2020b).Comment: 18 pages, 9 figure

FF-LINS: A Consistent Frame-to-Frame Solid-State-LiDAR-Inertial State Estimator

Most of the existing LiDAR-inertial navigation systems are based on frame-to-map registrations, leading to inconsistency in state estimation. The newest solid-state LiDAR with a non-repetitive scanning pattern makes it possible to achieve a consistent LiDAR-inertial estimator by employing a frame-to-frame data association. In this letter, we propose a robust and consistent frame-to-frame LiDAR-inertial navigation system (FF-LINS) for solid-state LiDARs. With the INS-centric LiDAR frame processing, the keyframe point-cloud map is built using the accumulated point clouds to construct the frame-to-frame data association. The LiDAR frame-to-frame and the inertial measurement unit (IMU) preintegration measurements are tightly integrated using the factor graph optimization, with online calibration of the LiDAR-IMU extrinsic and time-delay parameters. The experiments on the public and private datasets demonstrate that the proposed FF-LINS achieves superior accuracy and robustness than the state-of-the-art systems. Besides, the LiDAR-IMU extrinsic and time-delay parameters are estimated effectively, and the online calibration notably improves the pose accuracy. The proposed FF-LINS and the employed datasets are open-sourced on GitHub (https://github.com/i2Nav-WHU/FF-LINS)

Transport of topologically protected photonic waveguide on chip

We propose a new design on integrated optical devices on-chip with an extra width degree of freedom by using a photonic crystal waveguide with Dirac points between two photonic crystals with opposite valley Chern numbers. With such an extra waveguide, we demonstrate numerically that the topologically protected photonic waveguide keeps properties of valley-locking and immunity to defects. Due to the design flexibility of the width-tunable topologically protected photonic waveguide, many unique on-chip integrated devices have been proposed, such as energy concentrators with a concentration efficiency improvement by more than one order of magnitude, topological photonic power splitter with arbitrary power splitting ratio. The topologically protected photonic waveguide with the width degree of freedom could be beneficial for scaling up photonic devices, which provides a new flexible platform to implement integrated photonic networks on chip.Comment: 19 pages, 5 figure

EvaSurf: Efficient View-Aware Implicit Textured Surface Reconstruction on Mobile Devices

Reconstructing real-world 3D objects has numerous applications in computer vision, such as virtual reality, video games, and animations. Ideally, 3D reconstruction methods should generate high-fidelity results with 3D consistency in real-time. Traditional methods match pixels between images using photo-consistency constraints or learned features, while differentiable rendering methods like Neural Radiance Fields (NeRF) use differentiable volume rendering or surface-based representation to generate high-fidelity scenes. However, these methods require excessive runtime for rendering, making them impractical for daily applications. To address these challenges, we present $\textbf{EvaSurf}$, an $\textbf{E}$fficient $\textbf{V}$iew-$\textbf{A}$ware implicit textured $\textbf{Surf}$ace reconstruction method on mobile devices. In our method, we first employ an efficient surface-based model with a multi-view supervision module to ensure accurate mesh reconstruction. To enable high-fidelity rendering, we learn an implicit texture embedded with a set of Gaussian lobes to capture view-dependent information. Furthermore, with the explicit geometry and the implicit texture, we can employ a lightweight neural shader to reduce the expense of computation and further support real-time rendering on common mobile devices. Extensive experiments demonstrate that our method can reconstruct high-quality appearance and accurate mesh on both synthetic and real-world datasets. Moreover, our method can be trained in just 1-2 hours using a single GPU and run on mobile devices at over 40 FPS (Frames Per Second), with a final package required for rendering taking up only 40-50 MB.Comment: Project Page: http://g-1nonly.github.io/EvaSurf-Website