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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Guang-Yu Nie Ming-Ming Cheng Yun Liu Zhengfa Liang Deng-Ping Fan Yue Liu Yongtian Wang Beijing Institute of Technology TKLNDST CS Nankai University National Key Laboratory of Science and Technology on Blind Signal Processing

ISBN: (纸本)9781728132945

Exploiting multi-level context information to cost volume can improve the performance of learning-based stereo matching methods. In recent years, 3-D Convolution Neural Networks (3-D CNNs) show the advantages in regularizing cost volume but are limited by unary features learning in matching cost computation. However, existing methods only use features from plain convolution layers or a simple aggregation of multi-level features to calculate cost volume, which is insufficient because stereo matching requires discriminative features to identify corresponding pixels in rectified stereo image pairs. In this paper, we propose a unary features descriptor using multi-level context ultra-aggregation (MCUA), which encapsulates all convolutional features into a more discriminative representation by intra-and inter-level features combination. Specifically, a child module that takes low-resolution images as input captures larger context information;the larger context information from each layer is densely connected to the main branch of the network. MCUA makes good usage of multi-level features with richer context and performs the image-to-image prediction holistically. We introduce our MCUA scheme for cost volume calculation and test it on PSM-Net. We also evaluate our method on Scene Flow and KITTI 2012/2015 stereo datasets. Experimental results show that our method outperforms state-of-the-art methods by a notable margin and effectively improves the accuracy of stereo matching.

关键词： branch main Volume Costs Unary multilayers stereo imaging Convolution

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Editorial: Introduction to the special issue on advances in radar technology

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Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University 2021年第2期48卷 1-6页

作者： Liu, Hongwei Shui, Penglang Kong, Lingjiang Wei, Yinsheng Liu, Yongxiang Wang, Yu Li, Gang Chen, Bo National Laboratory of Radar Signal Processing Xidian University Xi'an710071 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China College of Intelligence Science National University of Defense Technology Changsha410073 China Institute of Aerospace Information Innovation Chinese Academy of Sciences Beijing100864 China Department of Electronic Engineering Tsinghua University Beijing100084 China

雷达历经了80多年的发展,如今已成为重要的信息传感器之一,在国防领域具有战略性地位,在民用领域也得到越来越广泛的应用。随着软硬件相关技术的快速发展,现代雷达已经不仅仅局限于高功率、高分辨、多波段等传统的性能提升和功能扩展,... 详细信息

雷达历经了80多年的发展,如今已成为重要的信息传感器之一,在国防领域具有战略性地位,在民用领域也得到越来越广泛的应用。随着软硬件相关技术的快速发展,现代雷达已经不仅仅局限于高功率、高分辨、多波段等传统的性能提升和功能扩展,且更多聚焦于信号处理的精细化、信息处理的智能化、单一平台功能的多样化和集成化、多平台的网络化与协同化、器件发展的小型化和片上化、服务界面的便利化和人性化等,使其应用日益广泛。为此,《西安电子科技大学学报》组织了"雷达技术进展"专题,共录用23篇论文,包括目标识别、目标检测与跟踪、雷达成像、电子对抗、协同组网等方向,分别介绍如下。

关键词：雷达信息传感器国防领域

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Precise irrigation of dryland cotton under canal irrigation system constraints based on the CERES-CROPGRO-Cotton model

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Agricultural Water Management 2025年 317卷

作者： Lei Wang Liang He Weihong Sun Chen Gao Zhenxiang Han Meiwei Lin School of Computer Science and Technology Xinjiang University Urumqi 830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi 830017 China Department of Electronic Engineering and Beijing National Research Center for Information Science and Technology Tsinghua University Beijing 100084 China School of Agricultural Engineering Jiangsu University Zhenjiang 212013 China

Xinjiang agriculture faces significant challenges due to water resource scarcity and uneven distribution, making accurate predictions of irrigation's impact on cotton yield crucial for decision-making. Existing studies primarily examine the relationship between irrigation and soil but often overlook the combined effects of irrigation networks, time constraints, and the interactions between crop growth patterns, weather, soil, and irrigation strategies. This study integrates the Decision Support System for Agrotechnology Transfer (DSSAT) model with machine learning models, accounting for weather, soil, crop conditions, and irrigation time constraints, to propose an intelligent irrigation decision-making framework. Meteorological data from 1980 to 2024, 13 sets of soil data, and field experiments conducted in 2023 and 2024 were used to calibrate the DSSAT model (with a calibration rate of 0.856). Additionally, water channel quota time constraints were established to determine the optimal irrigation timing. The framework analyzes the interactions between weather, soil, irrigation strategies, and other factors, enhancing cotton yield prediction. The results indicate that the intelligent decision-making algorithm outperforms traditional methods under data limitations, reducing the irrigation water consumption-yield ratio ( U i ) by 3.99 %, while increasing yield by 8.5 % to 9724 kg/ha, thus achieving both water-saving and yield-enhancing objectives. This research offers a refined solution for intelligent irrigation decision-making in cotton cultivation in arid regions and paves the way for the application of intelligent agricultural decision systems.

关键词： Precise agricultural water management Intelligent irrigation decision-making Production forecasts DSSAT

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Performance Evaluation of an Anomaly-Detection Algorithm for Keystroke-Typing Based Insider Detection

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Tsinghua science and technology 2018年第5期23卷 513-525页

作者： Liang He Zhixiang Li Chao Shen National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu 610041 China Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an 710004 China

Keystroke dynamics is the process to identify or authenticate individuals based on their typing rhythm behaviors. Several classifications have been proposed to verify a user＇s legitimacy, and the performances of these classifications should be confirmed to identify the most promising research direction. However, classification research contains several experiments with different conditions such as datasets and methodologies. This study aims to benchmark the algorithms to the same dataset and features to equally measure all performances. Using a dataset that contains the typing rhythm of 51 subjects, we implement and evaluate 15 classifiers measured by Fl-measure, which is the harmonic mean of a false-negative identification rate and false-positive identification rate. We also develop a methodology to process the typing data. By considering a case in which the model will reject the outsider, we tested the algorithms on an open set. Additionally, we tested different parameters in random forest and k nearest neighbors classifications to achieve better results and explore the cause of their high performance. We also tested the dataset on one-class classification and explained the results of the experiment. The top-performing classifier achieves an Fl-measure rate of 92% while using the normalized typing data of 50 subjects to train and the remaining data to test. The results, along with the normalization methodology, constitute a benchmark for comparing the classifiers and measuring the performance of keystroke dynamics for insider detection.

关键词： keystroke dynamics insider identification Fl-measure normalization one-class classification

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In-Silico Trained AI for Enhanced T2 Spectrum Imaging and Myelin Water Fraction Mapping in Preclinical 7T MRI

In-Silico Trained AI for Enhanced T2 Spectrum Imaging and My...

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IEEE International Symposium on Biomedical Imaging

作者： Daniel Vallejo-Aldana Alonso Ramírez-Manzanares Erick J. Canales-Rodríguez Thomas Yu Abraham Cisneros-Mejorado Luis Concha Jonathan Rafael-Patiño Jean-Philippe Thiran Computer Science Department Centro de Investigación en Matemáticas A.C Guanajuato México Signal Processing Laboratory (LTS5) École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Advanced Clinical Imaging Technology Siemens Healthineers International AG Lausanne Switzerland Instituto de Neurobiología Universidad Nacional Autónoma de México Juriquilla México Radiology Department Centre Hospitalier Universitaire Vaudois and University of Lausanne Lausanne Switzerland CIBM Center for Biomedical Imaging Switzerland

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

This study introduces a Machine Learning (ML) approach for estimating the T 2 spectrum and myelin water fraction (MWF) using multi-echo T 2 (MET 2 ) data from preclinical 7T Magnetic Resonance Imaging (MRI) scanners. ML methods have shown promise in MWF estimation, outperforming Regularized Non-Negative Least Squares (RNNLS). However, existing ML methods were optimized for high signal-to-noise ratios (SNR) typical of 3T clinical MET 2 data with larger voxel sizes. We adapted the Model-Informed Machine Learning (MIML) method to handle challenges in preclinical 7T MRI, including reduced voxel sizes, elevated noise levels (SNR=30-60), and shifts in T 2 lobes. Results from in-silico simulated data demonstrate the superior performance of the proposed multi-layer-perceptron-based solution over RNNLS. Validation with ME T 2 data from two mice—a healthy control and a cuprizone-exposed pathological mouse—confirms the ML method’s success in identifying cuprizone-induced demyelination. Our study showcases the adaptability and enhanced performance of the MIML approach under challenging preclinical 7T MRI conditions, contributing to the advancement of MWF estimation methods in high-field MRI settings.

关键词： Maximum likelihood estimation Pathology Adaptation models Magnetic resonance imaging Machine learning Brain modeling signal to noise ratio

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Learning parallax attention for stereo image super-resolution

arXiv

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arXiv 2019年

作者： Wang, Longguang Wang, Yingqian Liang, Zhengfa Lin, Zaiping Yang, Jungang An, Wei Guo, Yulan College of Electronic Science and Technology National University of Defense Technology China National Key Laboratory of Science and Technology on Blind Signal Processing China

Stereo image pairs can be used to improve the performance of super-resolution (SR) since additional information is provided from a second viewpoint. However, it is challenging to incorporate this information for SR since disparities between stereo images vary significantly. In this paper, we propose a parallax-attention stereo superresolution network (PASSRnet) to integrate the information from a stereo image pair for SR. Specifically, we introduce a parallax-attention mechanism with a global receptive field along the epipolar line to handle different stereo images with large disparity variations. We also propose a new and the largest dataset for stereo image SR (namely, Flickr1024). Extensive experiments demonstrate that the parallax-attention mechanism can capture correspondence between stereo images to improve SR performance with a small computational and memory cost. Comparative results show that our PASSRnet achieves the state-of-the-art performance on the Middlebury, KITTI 2012 and KITTI 2015 datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Optical resolving power

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signal classification against repeat radar jammers with optimized MIMO radar waveforms

Signal classification against repeat radar jammers with opti...

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IET International Radar Conference (IET IRC 2020)

作者： G. Dou S. Zhou H. Liu J. Liu L. Xu H. Ma P. Wang National Laboratory of Radar Signal Processing Xi’an China University of Science and Technology of China Department of Electronic Engineering and Information Science Hefei 230027 China

The digital radio frequency memory (DRFM) repeat jammer, which can transmit signals of close signature to real target returns, is a typical low-cost electronic attacker and poses threats to radar systems. One can optimize multiple-input multiple-output (MIMO) radar waveforms to magnify the difference between deceptive signals and real targets, and in this paper, we examine the performance improvement achieved by designing classification algorithms. The training samples are theoretical target returns and theoretical constant-modulus deceptive signals, both modulated with different Doppler frequencies. Three classifiers, namely the support vector machines (SVM), Naive Bayes and k-Nearest Neighbor (kNN), are considered and the SVM performs the best. With 128-chips phase-coded transmit signals, the waveform optimization algorithm achieves a 6dB signal-to-noise ratio (SNR) improvement at the 90% classification rate.

关键词： jamming radar signal processing support vector machines signal classification MIMO radar signal sampling Doppler radar naive Bayes methods nearest neighbour methods phase coding optimisation radar computing

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Kalmannet: Data-Driven Kalman Filtering

Kalmannet: Data-Driven Kalman Filtering

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IEEE International Conference on Acoustics, Speech and signal processing

作者： Guy Revach Nir Shlezinger Ruud J. G. van Sloun Yonina C. Eldar Signal Processing Laboratory (ISI) ETH Zurich Switzerland School of ECE Ben-Gurion University of the Negev Beer Sheva Israel Eindhoven University of Technology and with Phillips Research Eindhoven The Netherlands Faculty of Math and CS Weizmann Institute of Science Rehovot Israel

ISBN: (纸本)9781728176055;9781728176062

The Kalman filter (KF) is a celebrated signal processing algorithm, implementing optimal state estimation of dynamical systems that are well represented by a linear Gaussian state-space model. The KF is model-based, and therefore relies on full and accurate knowledge of the underlying model. We present KalmanNet, a hybrid data-driven/model-based filter that does not require full knowledge of the underlying model parameters. KalmanNet is inspired by the classical KF flow and implemented by integrating a dedicated and compact neural network for the Kalman gain computation. We present an offline training method, and numerically illustrate that KalmanNet can achieve optimal performance without full knowledge of the model parameters. We demonstrate that when facing inaccurate parameters KalmanNet learns to achieve notably improved performance compared to KF.

关键词： Training Computational modeling signal processing algorithms signal processing Filtering algorithms Numerical models State-space methods

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signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks

arXiv

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arXiv 2022年

作者： Gao, Dawei Guo, Qinghua Liao, Guisheng Eldar, Yonina C. Li, Yonghui Yu, Yanguang Vucetic, Branka The Hangzhou Institute of Technology Xidian University Hangzhou311200 China The National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China The School of Electrical Computer and Telecommunications Engineering University of Wollongong NSW2522 Australia The Faculty of Math and CS Weizmann Institute of Science Rehovot7610001 Israel The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia

In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments, such as power amplifier nonlinearity and in-phase/quadrature imbalance. To deal with the complex combined effects of hardware imperfections, neural network (NN) techniques, in particular deep neural networks (DNNs), have been studied to directly compensate for the impact of hardware impairments. However, it is difficult to train a DNN with limited pilot signals, hindering its practical applications. In this work, we investigate how to achieve efficient Bayesian signal detection in MIMO systems with hardware imperfections. Characterizing combined hardware imperfections often leads to complicated signal models, making Bayesian signal detection challenging. To address this issue, we first train an NN to ‘model’ the MIMO system with hardware imperfections and then perform Bayesian inference based on the trained NN. Modelling the MIMO system with NN enables the design of NN architectures based on the signal flow of the MIMO system, minimizing the number of NN layers and parameters, which is crucial to achieving efficient training with limited pilot signals. We then represent the trained NN with a factor graph, and design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm. The implementation of a turbo receiver with the proposed Bayesian detector is also investigated. Extensive simulation results demonstrate that the proposed technique delivers remarkably better performance than state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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JCMF: A Novel Wideband DOA Estimator

JCMF: A Novel Wideband DOA Estimator

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IEEE International Conference on Radar

作者： Xiaoyu Zhang Haihong Tao Junqi Xue Jian Xie Jianpu Li Huihui Ma National Laboratory of Radar Signal Processing Xidian University Xi'an China State Administration of Science Technology and Industry for National Defence Beijing China School of Electronics and Information Northwestern Polytechnical University Xi'an China Shanghai Aerospace Electronic Technology Institute Shanghai China Taiyuan University of Technology Taiyuan China

ISBN: (纸本)9781665468893

Covariance matrix fitting is a classic problem in direction-of-arrival (DOA) estimation, but there has been little discussion about wideband signals. In this paper, a novel wideband DOA estimation method based on covariance matrix fitting is developed. First of all, a novel wideband signal model is proposed and the wideband joint covariance matrix is formulated. The fitting criterion is deduced by the statistical characteristics of this model. Then the optimization problem of the criterion is transformed into a block sparsity problem using Holder's inequality. The problem can be divided into two subproblems and the closed-form solution of each part is obtained by dual method. The result of our wideband estimator can be solved by alternating iteration. Finally, the Cramer-Rao bound (CRB) is derived for wideband signals. Compared with other algorithms, the presented estimator has higher accuracy. Simulation results show the effectiveness and feasibility of the proposed algorithm.

关键词： Direction-of-arrival estimation Cramer-Rao bounds Closed-form solutions Simulation Fitting Estimation Radar

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