Dynamic Filtering Method for GPS Based on Multi-Scale

Aiming at GPS dynamic filtering method, this paper uses the method of multi-scale analysis, combines the “current” statistical model of automotive carrier with multi-scale signal transformation which is based on statistical characteristic, establishes the new algorithm of multi-scale data fusion for GPS dynamic filter combining with normal Kalman filter algorithm, at last achieves the optimal fusion estimated value of the target states based on global information at the finest scale. When the above algorithm is used to GPS dynamic filter, the simulated results show that the proposed algorithm can effectively increase estimated precision of target states compared with the conventional KF. DOI: http://dx.doi.org/10.11591/telkomnika.v11i1.191

Application of Fast Deviation Correction Algorithm Based on Shape Matching Algorithm in Component Placement

For contradiction PC template matching between accuracy and speed, combined with the advantages of FPGA high speed parallel computing. This paper presents a FPGA-based rapid correction shape matching algorithm. Mainly in the FPGA, using shape matching and least squares method to calculate the angular deviation chip components. Use single instruction stream algorithm acceleration. Experimental results show that compared with traditional PC template matching algorithms, this algorithm to further improve the correction accuracy and greatly reducing correction time. And SMT machine vision correction can be obtained in a stable and efficient use

Diagnosis of Autism Spectrum Disorders Using Multi-level High-order Functional Networks Derived from Resting-State Functional MRI

Functional brain networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used for Autism Spectrum Disorder (ASD) diagnosis. Typically, these networks are constructed by calculating functional connectivity (FC) between any pair of brain regions of interest (ROIs), i.e., using Pearson's correlation between rs-fMRI time series. However, this can only be called as a low-order representation of the functional interaction, because the relationship is investigated just between two ROIs. Brain disorders might not only affect low-order FC, but also high-order FC, i.e., the higher-level relationship among multiple brain regions, which might be more crucial for diagnosis. To comprehensively characterize such relationship for better diagnosis of ASD, we propose a multi-level, high-order FC network representation that can nicely capture complex interactions among brain regions. Then, we design a feature selection method to identify those discriminative multi-level, high-order FC features for ASD diagnosis. Finally, we design an ensemble classifier with multiple linear SVMs, each trained on a specific level of FC networks, for boosting the final classification accuracy. Experimental results show that the integration of both low-order and first-level high-order FC networks achieves the best ASD diagnostic accuracy (81%). We further investigated those selected discriminative low-order and high-order FC features and found that the high-order FC features can provide complementary information to the low-order FC features in the ASD diagnosis

COLO: A Contrastive Learning based Re-ranking Framework for One-Stage Summarization

Traditional training paradigms for extractive and abstractive summarization systems always only use token-level or sentence-level training objectives. However, the output summary is always evaluated from summary-level which leads to the inconsistency in training and evaluation. In this paper, we propose a Contrastive Learning based re-ranking framework for one-stage summarization called COLO. By modeling a contrastive objective, we show that the summarization model is able to directly generate summaries according to the summary-level score without additional modules and parameters. Extensive experiments demonstrate that COLO boosts the extractive and abstractive results of one-stage systems on CNN/DailyMail benchmark to 44.58 and 46.33 ROUGE-1 score while preserving the parameter efficiency and inference efficiency. Compared with state-of-the-art multi-stage systems, we save more than 100 GPU training hours and obtaining 3~8 speed-up ratio during inference while maintaining comparable results.Comment: Accepted by COLING 202

Robust Visual Tracking Using the Bidirectional Scale Estimation

Object tracking with robust scale estimation is a challenging task in computer vision. This paper presents a novel tracking algorithm that learns the translation and scale filters with a complementary scheme. The translation filter is constructed using the ridge regression and multidimensional features. A robust scale filter is constructed by the bidirectional scale estimation, including the forward scale and backward scale. Firstly, we learn the scale filter using the forward tracking information. Then the forward scale and backward scale can be estimated using the respective scale filter. Secondly, a conservative strategy is adopted to compromise the forward and backward scales. Finally, the scale filter is updated based on the final scale estimation. It is effective to update scale filter since the stable scale estimation can improve the performance of scale filter. To reveal the effectiveness of our tracker, experiments are performed on 32 sequences with significant scale variation and on the benchmark dataset with 50 challenging videos. Our results show that the proposed tracker outperforms several state-of-the-art trackers in terms of robustness and accuracy

Widespread occurrence of an emerging fungal pathogen in heavily traded Chinese urodelan species

Understanding introduction routes for wildlife pathogens is vital for the development of threat abatement plans. The chytrid fungus Batrachochytrium salamandrivorans (Bsal) has recently emerged in Europe, where it is considered to be a serious threat for urodelan conservation. If the highly diverse Chinese urodelans were to constitute a Bsal reservoir, then the significant international trade in these species may vector Bsal into naive urodelan communities. Here, we analyzed a total of 1,143 samples, representing 36 Chinese salamander species from 51 localities across southern China for the presence of Bsal. We found Bsal was present across a wide taxonomic, geographical, and environmental range. In particular, Bsal DNA was detected in 33 samples from the genera Cynops, Pachytriton, Paramesotriton, Tylototriton, and Andrias, including the heavily traded species Paramesotriton hongkongensis and Cynops orientalis. The true Bsal prevalence across our data set was estimated between 2% and 4%, with a maximum of 50% in a population of P. hongkongensis. Even at this overall relatively low Bsal prevalence, the exportation of millions of animals renders Bsal introduction in naive, importing countries a near certainty, which calls for the urgent implementation of proper biosecurity in the international wildlife trade