A Novel Combined Modelling and Optimization Technique for Microwave Components

This paper presents a novel combined parametric modelling and design optimization technique for microwave components utilizing the neural networks. The proposed technique provides an iterative mechanism between ANN model training and design optimization update. This iterative mechanism is fully automated and requires no manual intervention. Furthermore, the proposed technique overcomes the limitations of the common ANN optimization strategy where the fixed training region of the ANN model limits the freedom of design optimization. The proposed technique automatically enlarges the ANN training region until an optimization solution satisfying the user’s design specification is met. Once the whole iterative process is finished, an accurate parametric model and an optimal solution satisfying the design specification are simultaneously generated. A parametric modelling and design optimization example of a wideband QuasiElliptic filter design is presented to demonstrate the validity of this technique

A Cooperative Perception System Robust to Localization Errors

Cooperative perception is challenging for safety-critical autonomous driving applications.The errors in the shared position and pose cause an inaccurate relative transform estimation and disrupt the robust mapping of the Ego vehicle. We propose a distributed object-level cooperative perception system called OptiMatch, in which the detected 3D bounding boxes and local state information are shared between the connected vehicles. To correct the noisy relative transform, the local measurements of both connected vehicles (bounding boxes) are utilized, and an optimal transport theory-based algorithm is developed to filter out those objects jointly detected by the vehicles along with their correspondence, constructing an associated co-visible set. A correction transform is estimated from the matched object pairs and further applied to the noisy relative transform, followed by global fusion and dynamic mapping. Experiment results show that robust performance is achieved for different levels of location and heading errors, and the proposed framework outperforms the state-of-the-art benchmark fusion schemes, including early, late, and intermediate fusion, on average precision by a large margin when location and/or heading errors occur.Comment: Accepted by IEEE IV 202

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)

Production of Glycopeptide Derivatives for Exploring Substrate Specificity of Human OGA Toward Sugar Moiety.

O-GlcNAcase (OGA) is the only enzyme responsible for removing N-acetyl glucosamine (GlcNAc) attached to serine and threonine residues on proteins. This enzyme plays a key role in O-GlcNAc metabolism. However, the structural features of the sugar moiety recognized by human OGA (hOGA) remain unclear. In this study, a set of glycopeptides with modifications on the GlcNAc residue, were prepared in a recombinant full-length human OGT-catalyzed reaction, using chemoenzymatically synthesized UDP-GlcNAc derivatives. The resulting glycopeptides were used to evaluate the substrate specificity of hOGA toward the sugar moiety. This study will provide insights into the exploration of probes for O-GlcNAc modification, as well as a better understanding of the roles of O-GlcNAc in cellular physiology

Using Different Single-Step Strategies to Improve the Efficiency of Genomic Prediction on Body Measurement Traits in Pig

In genomic prediction, single-step method has been demonstrated to outperform multi-step methods. This study investigated the efficiency of genomic prediction for seven body measurement traits in Yorkshire population and simulated data using single-step method. For Yorkshire population, in total, 592 individuals were genotyped with Illumina PorcineSNP80 marker panel. We compared the prediction accuracy obtained from a traditional pedigree-based method (BLUP), a genomic BLUP (GBLUP) and a single-step genomic BLUP (ssGBLUP) through 20 replicates of 5-fold cross-validation (CV). In addition, we also compared the performance of two-trait ssGBLUP and single-trait ssGBLUP for the traits with different gradients of genetic correlation. Our results indicated the GBLUP method generally provided lower accuracies of prediction than BLUP and ssGBLUP methods, and the average standard deviation of unbiasedness was as large as 0.278. For single-step methods, the accuracies of ssGBLUP for seven body measurement traits ranged from 0.543 to 0.785, and the unbiasedness of ssGBLUP ranged from 0.834 to 1.026, respectively. ssGBLUP generally generated 1% on average higher prediction accuracy than traditional BLUP, the improvement of ssGBLUP and the performance of GBLUP was lower than expected mainly due to the small number of genotyped animals, it was further demonstrated by our simulation study. We simulated two traits with heritabilities 0.1, 0.3, and with high genetic correlation 0.7, our results also showed that the prediction accuracies were low for GBLUP compared with other three methods with different genotyped reference population sizes and the accuracies were improved with increasing the genotyped reference population size. However, the increase was small for ssGBLUP compared with BLUP when the genotyped reference population size was <500. Our results also demonstrated that the accuracies of genomic prediction can be further improved by implementing two-trait ssGBLUP model, the maximum gain on accuracy was 2 and 2.6% for trait of chest width compared to single-trait ssGBLUP and traditional BLUP, while the gain was decreased with the weakness of genetic correlation. Two-trait ssGBLUP even performed worse than single trait analysis in the scenario of low genetic correlation