检索结果-内蒙古大学图书馆

Inverse Depth Line Model of LiDAR Data for Traversable Region Segmentation 4

学校读者我要写书评

暂无评论

Inverse Depth Line Model of LiDAR Data for Traversable Regio...

4th International Conference on Electronic information technology and computer Engineering, EITCE 2020

作者： Liu, Zhen Ren, Mingwu Jin, Zhong School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing Jiangsu China Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing Jiangsu China

ISBN: (纸本)9781450387811

In this paper, we propose an Inverse Depth Line (IDL) model of LiDAR data for traversable region segmentation. This model first organizes the disordered point cloud with a 2D structure array to store and index the point cloud. Then, IDL is deduced based on the scanning model of LiDAR. With the help of IDL, the road plane is represented as a straight line in 2D space. Based on IDL, an inverse depth segmentation (IDS) algorithm is proposed to classify obstacles. The inverse depth of positive obstacles and negative obstacles are projected above and below IDL respectively. IDS greatly simplifies and accelerates the obstacle segmentation. We conduct experiments on both structured and unstructured environments. Experiments show that the proposed method is robust and gets promising results in road and obstacle detection. The method implements real-Time segmentation of traversable regions and obstacles. The average processing time for each frame is just 14 ms. © 2020 ACM.

关键词： Inverse problems

On the accurate characterization of quantum-dot light-emitting diodes for display applications

学校读者我要写书评

暂无评论

npj Flexible Electronics 2022年第1期6卷 351-362页

作者： Wangxiao Jin Yunzhou Deng Bingbing Guo Yaxiao Lian Baodan Zhao Dawei Di Xiaowei Sun Kai Wang Shuming Chen Yixing Yang Weiran Cao Song Chen Wenyu Ji Xuyong Yang Yuan Gao Shuangpeng Wang Huaibin Shen Jialong Zhao Lei Qian Fushan Li Yizheng Jin Key Laboratory of Excited-State Materials of Zhejiang Province State Key Laboratory of Silicon MaterialsDepartment of ChemistryZhejiang University310027 HangzhouChina State Key Laboratory of Modern Optical Instrumentation College of Optical Science and EngineeringInternational Research Center for Advanced PhotonicsZhejiang University310027 HangzhouChina Department of Electronic and Electrical Engineering Southern University of Science and Technology518055 ShenzhenChina TCL Research ShenzhenGuangdongChina Shenzhen China Star Optoelectronics Semiconductor Display Technology Co. LtdShenzhenGuangdongChina College of Chemistry Chemical Engineering and Materials ScienceSoochow University199 Ren’ai Rd215123 SuzhouJiangsuChina Key Lab of Physics and Technology for Advanced Batteries(Ministry of Education) College of PhysicsJilin University130023 ChangchunChina Key Laboratory of Advanced Display and System Applications of Ministry of Education Shanghai University149 Yanchang RoadShanghai 200072China Najing Technology Corporation Ltd Hangzhou 310052China Institute of Applied Physics and Materials Engineering University of MacaoTaipaMacaoSAR 999078China Key Laboratory for Special Functional Materials of Ministry of Education National&Local Joint Engineering Research Center for High-Efficiency Display and Lighting Technologyand Collaborative Innovation Center of Nano Functional Materials and ApplicationsHenan UniversityKaifeng 475004China School of Physical Science and Technology MOE Key Laboratory of New Processing Technology for Non-ferrous Metals and MaterialsGuangxi Key Laboratory of Processing for Non-ferrous Metals and Featured MaterialsGuangxi UniversityNanning 530004China Division of Functional Materials and Nanodevices Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201China College of Physics and Information Engineering Fuzhou UniversityFuzhou 350108China

Quantum dot light-emitting diodes(QLEDs)are a class of high-performance solution-processed electroluminescent(EL)devices highly attractive for next-generation display *** the encouraging advances in the mechanism investigation,material chemistry,and device engineering of QLEDs,the lack of standard protocols for the characterization of QLEDs may cause inaccurate measurements of device parameters and invalid comparison of different ***,we report a comprehensive study on the characterizations of QLEDs using various *** show that the emission non-uniformity across the active area,nonLambertian angular distributions of EL intensity,and discrepancies in the adopted spectral luminous efficiency functions could introduce significant errors in the device *** errors in the operational-lifetime measurements may arise from the inaccurate determination of the initial luminance and inconsistent methods for analyzing the luminance-decay ***,we suggest a set of recommended practices and a checklist for device characterizations,aiming to help the researchers in the QLED field to achieve accurate and reliable measurements.

关键词： diodes characterization luminous

Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

学校读者我要写书评

暂无评论

Understanding the Robustness of 3D Object Detection with Bir...

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zijian Zhu Yichi Zhang Hai Chen Yinpeng Dong Shu Zhao Wenbo Ding Jiachen Zhong Shibao Zheng Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Dept. of Comp. Sci. and Tech. THBI Lab Institute for AI Tsinghua University BNRist Center Zhongguancun Laboratory Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Computer Science and Technology Anhui University SAIC Motor AI Lab

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations; 2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features; 3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models.

关键词：

Disentangled Generation Network for Enlarged License Plate Recognition and A Unified Dataset

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Li, Chenglong Yang, Xiaobin Wang, Guohao Zheng, Aihua Tan, Chang Jia, Ruoran Tang, Jin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Lab of Intelligent Computing and Signal Processing Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China iFLYTEK Co. Ltd. Hefei230088 China

License plate recognition plays a critical role in many practical applications, but license plates of large vehicles are difficult to be recognized due to the factors of low resolution, contamination, low illumination, and occlusion, to name a few. To overcome the above factors, the transportation management department generally introduces the enlarged license plate behind the rear of a vehicle. However, enlarged license plates have high diversity as they are non-standard in position, size, and style. Furthermore, the background regions contain a variety of noisy information which greatly disturbs the recognition of license plate characters. Existing works have not studied this challenging problem. In this work, we first address the enlarged license plate recognition problem and contribute a dataset containing 9342 images, which cover most of the challenges of real scenes. However, the created data are still insufficient to train deep methods of enlarged license plate recognition, and building large-scale training data is very time-consuming and high lab.r cost. To handle this problem, we propose a novel task-level disentanglement generation framework based on the Disentangled Generation Network (DGNet), which disentangles the generation into the text generation and background generation in an end-to-end manner to effectively ensure diversity and integrity, for robust enlarged license plate recognition. Extensive experiments on the created dataset are conducted, and we demonstrate the effectiveness of the proposed approach in three representative text recognition frameworks. © 2022, CC BY.

关键词： Optical character recognition

Understanding the Robustness of 3D Object Detection with Bird’s-Eye-View Representations in Autonomous Driving

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Dept. of Comp. Sci. and Tech. Institute for AI THBI Lab BNRist Center Tsinghua University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province China SAIC Motor AI Lab China Zhongguancun Laboratory China

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird’s-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDAR fusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. Code availab.e at https: //***/zzj403/BEV_Robust. Copyright © 2023, The Authors. All rights reserved.

关键词： Birds

Hand Hygiene Assessment via Joint Step Segmentation and key Action Scorer

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Li, Chenglong Zhu, Qiwen Liu, Tubiao Tang, Jin Su, Yu Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Hefei Normal University Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230061 China

Hand hygiene is a standard six-step hand-washing action proposed by the World Health Organization (WHO). However, there is no good way to supervise medical staff to do hand hygiene, which brings the potential risk of disease spread. Existing action assessment works usually make an overall quality prediction on an entire video. However, the internal structures of hand hygiene action are important in hand hygiene assessment. Therefore, we propose a novel fine-grained learning framework to perform step segmentation and key action scorer in a joint manner for accurate hand hygiene assessment. Existing temporal segmentation methods usually employ multi-stage convolutional network to improve the segmentation robustness, but easily lead to over-segmentation due to the lack of the long-range dependence. To address this issue, we design a multi-stage convolution-transformer network for step segmentation. Based on the observation that each hand-washing step involves several key actions which determine the hand-washing quality, we design a set of key action scorers to evaluate the quality of key actions in each step. In addition, there lacks a unified dataset in hand hygiene assessment. Therefore, under the supervision of medical staff, we contribute a video dataset that contains 300 video sequences with fine-grained annotations. Extensive experiments on the dataset suggest that our method well assesses hand hygiene videos and achieves outstanding performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

A Novel Economical Design of Multistage Decimators for Low Latency Audio Applications 22

学校读者我要写书评

暂无评论

A Novel Economical Design of Multistage Decimators for Low L...

22nd IEEE International Conference on High Performance Computing and Communications, 18th IEEE International Conference on Smart City and 6th IEEE International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Hu, Wei Zheng, Chongwei Wang, Yonghao Liu, Jing Chen, Cen Zeng, Zeng Wuhan University of Science and Technology College of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Birmingham City University Digital Media Technology Lab Birmingham United Kingdom Institute for Infocomm Research Singapore

ISBN: (纸本)9781728176499

The oversampling sigma-delta analog-To-digital converter (ADC) is widely used in digital audio systems for its low hardware cost and high precision. The digital filter part of the ADC is the primary source of the silicon and power cost. In this work, we proposed an economical multistage decimation filter architecture to meet the requirements of commercial ADC specifications with a further novel multi-objective optimization strategy. We took group delay and computational cost as objective functions and used annealing algorithm to readjust the stopband attenuation, stopband edge and passband ripple of each filter. Our multistage architecture combines cascaded integrator-comb (CIC) filters, compensating filters and equiripple finite impulse response (FIR) filters at the different stages to balance the overall performance. The experimental results showed that our design approach worked with multi-objective optimization strategy could achieve low group delay with lower cost compared to traditional designs. © 2020 IEEE.

关键词： Group delay

Cell-Free Massive MIMO with Normalized Conjugate Beamforming and Channel Non-Reciprocities 13

学校读者我要写书评

暂无评论

Cell-Free Massive MIMO with Normalized Conjugate Beamforming...

13th International Conference on Wireless Communications and Signal processing, WCSP 2021

作者： Zhang, Yao Xia, Wenchao Zhu, Yongxu Zheng, Gan Gao, Feifei Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua321004 China Nanjing University of Posts and Telecommunications Department of Wireless Communication Key Lab of Jiangsu Province Nanjing210003 China London South Bank University Division of Computer Science and Informatics London United Kingdom Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough University LeicestershireLE11 3TU United Kingdom Institute for Artificial Intelligence Tsinghua University State Key Lab of Intelligent Technologies and Systems Beijing National Research Center for Information Science and Technology Department of Automation Tsinghua University Beijing100084 China

ISBN: (纸本)9781665407854

This paper focuses on a generalized downlink cell-free massive multiple-input multiple-output (CF-mMIMO) system with normalized conjugate beamforming (NCB) and channel non-reciprocities, assuming the access points (APs) are equipped with low-resolution digital-to-analog converters (DACs). Specifically, the channel non-reciprocities consist of the amplitude and phase non-reciprocities at the APs and user equipments. Closed-form expressions of the achievable sum-rates are derived for evaluating the impacts of channel non-reciprocities and DAC resolutions and comparing against the conjugate beamforming (CB) counterpart. Simulation results reveal that the downlink sum-rates are immune to the amplitude non-reciprocity of transceivers but strongly sensitive to the AP phase non-reciprocity. In particular, a small degree of AP phase non-reciprocity can result in substantial rate degradations. Besides, 5-bit DACs are enough in CF-mMIMO systems to approach the same sum-rates as CF-mMIMO systems with high-resolution DACs. Moreover, the NCB scheme outperforms the CB scheme in terms of the downlink sum-rates for both reciprocal and non-reciprocal channels. © 2021 IEEE.

关键词： Beamforming

LasHeR: A large-scale high-diversity benchmark for RGBT tracking

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Li, Chenglong Xue, Wanlin Jia, Yaqing Qu, Zhichen Luo, Bin Tang, Jin Sun, Dengdi Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

RGBT tracking receives a surge of interest in the computer vision community, but this research field lacks a large-scale and high-diversity benchmark dataset, which is essential for both the training of deep RGBT trackers and the comprehensive evaluation of RGBT tracking methods. To this end, we present a Large-scale High-diversity benchmark for short-term RGBT tracking (LasHeR) in this work. LasHeR consists of 1224 visible and thermal infrared video pairs with more than 730K frame pairs in total. Each frame pair is spatially aligned and manually annotated with a bounding box, making the dataset well and densely annotated. LasHeR is highly diverse capturing from a broad range of object categories, camera viewpoints, scene complexities and environmental factors across seasons, weathers, day and night. We conduct a comprehensive performance evaluation of 12 RGBT tracking algorithms on the LasHeR dataset and present detailed analysis. In addition, we release the unaligned version of LasHeR to attract the research interest for alignment-free RGBT tracking, which is a more practical task in real-world applications. The datasets and evaluation protocols are availab.e at: https://***/mmic-lcl/Datasets-and-benchmark-code. Copyright © 2021, The Authors. All rights reserved.

关键词： Large dataset