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An Efficient Algorithm for 3-D Interconnect Capacitance Extraction Considering the Floating Dummy-Fills

An Efficient Algorithm for 3-D Interconnect Capacitance Extr...

引用

2004 7th International conference on Solid-State and Integrated Circuits Technology（ICSICT 2004）

作者： Wenjian Yu~*,Mengsheng Zhang,Zeyi Wang Dept.of computer Science & Technology,Tsinghua University,Beijing 100084,China

the insertion of dummy metals is necessary to reduce the pattern-dependent variations of the dielectric thickness in the CMP *** makes conventional tools of capacitance extraction exhibit prohibitive calculation *** paper presents an efficient method for 3-D capacitance extraction with taking the floating dummies into *** on the QMM-accelerated BEM,our method inherits high computational speed while considering the floating conditions and using a new *** some typical structures including floating dummies,our method shows high accuracy and over 1000x speed-up over Raphael,about 10x speed-up over the method in Ref[5].

关键词： An Efficient Algorithm for 3-D Interconnect Capacitance Extraction Considering the Floating Dummy-Fills

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RunningPoseEval: A Dataset with Evaluation Standards for 3D Human Pose Estimation in Running Training

RunningPoseEval: A Dataset with Evaluation Standards for 3D ...

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Advanced Algorithms and Control Engineering (ICAACE), International conference on

作者： Wenwen Tong Wen Zhang Weimin Wang Guoqing Deng University of Science and Technology of China Hefei China Anhui Institute of Optics and Fine Mechanics Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China The Second Medical Center Chinese PLA General Hospital Beijing China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

3D human pose estimation is pivotal in the domains of computer vision and kinematic analysis. However, the majority of existing 3D datasets focus on indoor environments, resulting in a significant gap in extensive research on outdoor and specific athletic settings. this gap is notably evident in activities like outdoor running, where there is a stark lack of adequate data for reliable pose estimation. Contemporary motion capture systems face challenges in capturing precise ground truth data in outdoor settings, highlighting the necessity for alternative methods to address this shortfall. In response, our study introduces a dataset created using the Unreal Engine (UE) simulation environment, meticulously designed for assessing poses in outdoor multi-person running scenarios. this dataset comprises a diverse range of human models, running speeds, and gait patterns, striving to mirror a wide array of outdoor running conditions. To mimic real-world intricacies, the dataset integrates factors such as occlusion and varying lighting conditions. We provide high-fidelity ground truth data, free from temporal delays, to enhance the evaluation of algorithmic performance. through thorough validation and experimental scrutiny of our dataset, its efficacy and utility have been demonstrated. this dataset stands as a critical resource for studies in outdoor multi-person running pose estimation, adeptly filling the void in data for outdoor athletic scenarios. this research substantially aids in furthering the exploration and development in the field of 3D human pose estimation.

关键词： Training computer vision three-dimensional displays Pose estimation Rendering (computer graphics) Reliability Task analysis

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Pedestrian Detection in Infrared Images Using HOG,LBP,Gradient Magnitude and Intensity Feature Channels

Pedestrian Detection in Infrared Images Using HOG,LBP,Gradie...

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17th International IEEE conference on Intelligent Transportation Systems & the Asia-Pacific Council on Systems Engineering conference 2014

作者： Raluca Brehar Sergiu Nedevschi the Image Processing and Pattern Recognition Group Computer Science DepartmentTechnical University of Cluj-Napoca

Recent work in monocular pedestrian detection is trying to improve the execution time while keeping the accuracy as high as possible.A popular and successful approach for monocular intensity pedestrian detection is based on the approximation（instead of computation） of image features for multiple scales based on the features computed on set of predefined *** port this idea to the infrared *** contributions reside in the combination of four channel features,namely infrared,histogram of gradient orientations,normalized gradient magnitude and local binary patterns with the objective of detecting pedestrians for night vision applications dealing with far infrared *** scale feature computation is done by feature *** contribution is the study of different formulations for Local Binary patterns like uniform patterns and rotation invariant patterns and their effect on detection *** detection speed is also boosted by the aid of a fast morphological based region of interest *** vary the number of approximated scales per octave and study the impact on execution time and accuracy.A reasonable result hits a speed of 18 fps with a log average miss rate of 39%.

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On the results of the first mobile biometry (MOBIO) face and speaker verification evaluation

On the results of the first mobile biometry (MOBIO) face and...

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20th International conference on pattern recognition, ICPR 2010

作者： Marcel, Sébastien McCool, Chris Matějka, Pavel Ahonen, Timo Černocký, Jan Chakraborty, Shayok Balasubramanian, Vineeth Panchanathan, Sethuraman Chan, Chi Ho Kittler, Josef Poh, Norman Fauve, Benoît Glembek, Ondřej Plchot, Oldřich Jančík, Zdeněk Larcher, Anthony Lévy, Christophe Matrouf, Driss Bonastre, Jean-François Lee, Ping-Han Hung, Jui-Yu Wu, Si-Wei Hung, Yi-Ping Machlica, Lukáš Mason, John Mau, Sandra Sanderson, Conrad Monzo, David Albiol, Antonio Nguyen, Hieu V. Bai, Li Wang, Yan Niskanen, Matti Turtinen, Markus Nolazco-Flores, Juan Arturo Garcia-Perera, Leibny Paola Aceves-Lopez, Roberto Villegas, Mauricio Paredes, Roberto Idiap Research Institute Switzerland University of Oulu Finland Brno University of Technology Czech Republic Center for Cognitive Ubiquitous Computing Arizona State University United States Centre for Vision Speech and Signal Processing University of Surrey United Kingdom Validsoft Ltd. United Kingdom University of Avignon LIA France National Taiwan University Taiwan University of West Bohemia Czech Republic Swansea University United Kingdom NICTA Australia iTEAM Universidad Politecnica de Valencia Spain University of Nottingham United Kingdom Visidon Ltd. Finland Tecnologico de Monterrey Mexico Instituto Tecnológico de Informática Universidad Politécnica de Valencia Spain

ISBN: (纸本)3642177107

this paper evaluates the performance of face and speaker verification techniques in the context of a mobile environment. the mobile environment was chosen as it provides a realistic and challenging test-bed for biometric person verification techniques to operate. For instance the audio environment is quite noisy and there is limited control over the illumination conditions and the pose of the subject for the video. To conduct this evaluation, a part of a database captured during the "Mobile Biometry" (MOBIO) European Project was used. In total there were nine participants to the evaluation who submitted a face verification system and five participants who submitted speaker verification systems. the results have shown that the best performing face and speaker verification systems obtained the same level of performance, respectively 10.9% and 10.6% of HTER. © 2010 Springer-Verlag.

关键词： Face recognition

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Information Bottleneck Based Target Tracking Algorithm

Information Bottleneck Based Target Tracking Algorithm

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International conference on Information Systems and computer Aided Education (ICISCAE)

作者： Dan Wang Feng Li Jun Jia Institute of Measurement and Testing Technology Chinese Flight Test Establishment Xi'an China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

Object tracking plays a crucial role in computer vision and finds extensive applications in various areas such as video surveillance, autonomous driving, and intelligent security systems. However, existing target tracking methods is deficient in handling information redundancy, and extracting key information, which affects the accuracy and robustness of tracking. In this paper, we propose a new method of target tracking that combines the information bottleneck theory, which optimizes the information transmission path and extracts key information related to the target tracking task by introducing an information bottleneck module in the tracking model. the information bottleneck approach can be used to extract robust feature representations that help the tracking algorithm remain stable and accurate in the face of target changes, occlusions and noise. the method is combined with Visual Transformer (ViT) to enhance the model's ability to adapt to targets in complex scenes. Experimental findings indicate that the proposed method delivers outstanding performance across multiple public datasets. With significant improvement in accuracy and robustness compared to existing methods, the research in this paper provides new ideas and methods to improve the performance of target tracking algorithms.

关键词： Deep learning Adaptation models Visualization Target tracking Accuracy Information processing Feature extraction Robustness Data mining Object tracking

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Analysis on Urban Collective Call Behavior to Earthquake

Analysis on Urban Collective Call Behavior to Earthquake

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IEEE International conference on High Performance Computing and Communications (HPCC)

作者： Xiaoping Yu Tao Pei Keke Gai Li Guo College of Information Science and Technology Shihezi University Shihezi China State Key Laboratory of Resource and Environmental Information System Chinese Academy of Sciences Beijing China Department of Computer Science Pace University New York NY USA

ISBN: (纸本)9781479989386

Despite recent advances in uncovering the quantitative features of human activities in routine life, human call behavior in earthquake is still less clear. Taking the data of mobile phone records produced by the users in 3 days in the northwest of China, we systematically analyze the characteristics of mobile phone call patterns to earthquake by using ratio analysis and degree distribution method. We find that as a whole, earthquake brings about the significant growth in the indices of data volume, phone volume, duration, distant call volume and local call volume, etc. And the duration of the calls has a significant increase than that of the number of the calls. From the temporal perspective, we discover that at the first 2 hours that earthquake took place, people tend to make much more local calls rather than distant calls. However, unlike the local call, the big volume of distant calls last a whole day except for 3 hours in the afternoon. More interesting, although there is the biggest data volume and phone volume in the day that earthquake happened, only those who contact less than 10 phone number dominate the biggest data volume and phone volume. this demonstrates the pattern difference between the business calls and the private calls. the in-depth understanding of human behavior in emergency help us understand many complex socio-economic phenomena, and find applications in public opinion monitoring, disease control, transportation system design, calling center services, and information recommendation.

关键词： Earthquakes Mobile handsets Indexes Probability distribution Cities and towns Mobile communication Embedded systems

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Gaze Estimation with Multi-Scale Channel and Spatial Attention 2020

Gaze Estimation with Multi-Scale Channel and Spatial Attenti...

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Proceedings of the 2020 9th International conference on Computing and pattern recognition

作者： Song Liu Danping Liu Haiyang Wu Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences School of Microelectronics and Communication Engineering Chongqing University

ISBN: (纸本)9781450387835

Gaze estimation is well established as a significant research topic in computer vision given its importance for different applications. Recent studies demonstrate that other regions of the face beyond the two eyes contain valuable information for gaze estimation. Motivated by these works, we propose a novel and powerful deep convolutional network with multi-scale channel and spatial attention, which only takes the full-face image as input without additional modules to detect the eyes and estimate the head pose, to handle the gaze estimation task. It uses multi-scale channel and spatial information to adaptively select and increase important features and suppress some unnecessary facial regions which may not contribute to estimate gaze. By rigorously evaluating our module, we show that our method significantly outperforms the state-of-the-art for 3D gaze estimation on multiple public datasets.

关键词： Eye tracking attention mechanism appearance-based gaze estimation machine learning

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Modeling of Virtual Traffic Environment For Driving Simulator

Modeling of Virtual Traffic Environment For Driving Simulato...

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the 7th International conference on computer-Aided Industrial Design and Conceptual Design (CAID&CD’2006)

作者： Guangdong University of Technology, Guangzhou, Guangdong, China He Hanwu,Guangdong University of Technology Lou Yan, Guangdong University of Technology D. Lu Yongming

Viewing scene system is an important component for driving simulator. Its authenticity can give user sense of reality and immersion. Aiming at overcoming the shortcomings of the previous virtual traffic environment model, a new method is presented in this paper, i.e. using software 3dsMAX to construct 3D entity, then transform to .3ds format and import to Eon Studio for simulation. the finished virtual traffic environment model not only includes different kinds of road, such as road of city, road of mountain, road of village, but also can reflect time change a day and variation of weather. Far scene and close-by scene in the model can give driver a distinct vision sense. Furthermore, the virtual vehicles have dynamic intelligent behavior. Finally, this virtual traffic environment was applied to the Autonomous Virtual Vehicle vision Perception Behavior System (AVV-VPB System) for simulation test. the result was satisfying and proved that the virtual traffic environment can provide a better viewing scene for driving simulator.

关键词： dynamic simulation, scene modeling, virtual reality, virtual traffic environment

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chinese Native Dog Breed Classification Method based on Improved ResNet

Chinese Native Dog Breed Classification Method based on Impr...

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International conference on Information and computer Technologies (ICICT)

作者： Xinhua Zhang Chenwang Shen Jiaxi Xu Fenghao Yuan Chaofan Li Xu Liu University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Shenyang Institute of Computing Technology Shenyang China School of Information Engineering Nanjing Xiaozhuang University Nanjing China Department of Computer Engineering Jinling Institute of Technology Nanjing China

ISBN: (数字)9798350385625

ISBN: (纸本)9798350385632

Dog classification has a wide range of applications in computer vision and zoology. However, traditional deep learning methods face challenges in classifying chinese native dog breeds due to the limitations and uniqueness of sample data. this paper proposes a classification method for domestic local dog breeds based on an improved ResNet (Residual Network). the model employs ResNet50 as the backbone network. By adjusting the fully connected layer to the Sigmoid output function and the BCELoss loss function, the misclassification probability of unseen categories is effectively reduced. Initially, crawler technology was utilized to obtain image data for nine types of native dogs, which were then preprocessed. Subsequently, these nine dog classification tasks were categorized into two groups. For each category, an improved ResNet was trained independently to obtain two distinct models. Finally, the two models were tested using multithreaded parallel techniques to compare their outputs and determine the most suitable dog breed classification result. Results demonstrate that the proposed algorithm performs well in the chinese native dog breed classification task. Compared with the unimproved model without parallel technology and the model using parallel technology but without improvement, the accuracy improved from 93.8% and 94.5% to 96.4%. Additionally, precision, recall, and F1-score reached 96.7%, 96.4%, and 96.5%, respectively. these findings fully confirm the effectiveness of the algorithm. this study provides a robust solution to promote the protection and research of indigenous dog breeds in China.

关键词： Training Adaptation models Computational modeling Neural networks Zoology Dogs Classification algorithms

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High-level spatial modeling in convolutional neural network with application to pedestrian detection

High-level spatial modeling in convolutional neural network ...

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Canadian conference on Electrical and computer Engineering (CCECE)

作者： Feng Liu Yongzhen Huang Wankou Yang Changyin Sun School of Automation. Southeast University Nanjing China Key Lab of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Key Lab of Child Development and Learning Science of Ministry of Education. Southeast University Nanjing China

ISBN: (纸本)9781479958306

Convolutional neural network (CNN) has achieved great success in many vision tasks. A key to this success is its ability to powerful automatically learns both high-level and low-level features. In general, low-level features have a small size of receptive fields and appear multiple times in different locations of objects, while high-level semantic features have a relatively large size of receptive fields and only appear once in a specific location of objects. However, traditional CNN treats these two kinds of features in the same manner, i.e., learning them by the convolution operation, which can be approximately considered as cumulating the probabilities that a feature appears in different locations. this strategy is reasonable for low-level features but not for high-level semantic ones, especially in the case of pedestrian detection, where a local feature can be shared by different locations but a semantic part, e.g., a head, only appears once for a human. To jointly model the spatial structure and appearance of high-level semantic features, we propose a new module to learn spatially weighted max pooling in CNN. the proposed method is evaluated on several pedestrian detection databases and the experimental results show that it achieves much better performance than traditional CNN.

关键词： Convolution Semantics Kernel Deformable models Computational modeling Feature extraction Training

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