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arXiv 2021年

作者： Fang, Yuchen Qin, Yanjun Luo, Haiyong Zhao, Fang Xu, Bingbing Wang, Chenxing Zeng, Liang School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications China Research Center for Ubiquitous Computing Systems Institute of Computing Technology Chinese Academy of Sciences China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China Tsinghua University China

Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to three aspects: i) current existing works mostly exploit intricate temporal patterns (e.g., the short-term thunderstorm and long-term daily trends) within a single method, which fail to accurately capture spatio-temporal dependencies under different schemas;ii) the under-exploration of the graph positional encoding limit the extraction of spatial information in the commonly used full graph attention network;iii) the quadratic complexity of the full graph attention introduces heavy computational needs. To achieve the effective traffic flow forecasting, we propose an efficient spectral graph attention network with disentangled traffic sequences. Specifically, the discrete wavelet transform is leveraged to obtain the low- and high-frequency components of traffic sequences, and a dual-channel encoder is elaborately designed to accurately capture the spatio-temporal dependencies under long- and short-term schemas of the low- and high-frequency components. Moreover, a novel wavelet-based graph positional encoding and a query sampling strategy are introduced in our spectral graph attention to effectively guide message passing and efficiently calculate the attention. Extensive experiments on four real-world datasets show the superiority of our model, i.e., the higher traffic forecasting precision with lower computational cost. Copyright © 2021, The Authors. All rights reserved.

关键词： Message passing

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MFPN-6D : Real-time One-stage Pose Estimation of Objects on RGB Images

MFPN-6D : Real-time One-stage Pose Estimation of Objects on ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Penglei Liu Qieshi Zhang Jin Zhang Fei Wang Jun Cheng CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing China The Chinese University of Hong Kong Hong Kong China The College of Computer Science and Software Engineering Shenzhen University China

6D pose estimation of objects is an important part of robot grasping. The latest research trend on 6D pose estimation is to train a deep neural network to directly predict the 2D projection position of the 3D key points from the image, establish the corresponding relationship, and finally use Pespective-n-Point (PnP) algorithm performs pose estimation. The current challenge of pose estimation is that when the object texture-less, occluded and scene clutter, the detection accuracy will be reduced, and most of the existing algorithm models are large and cannot take the real-time requirements. In this paper, we introduce a Multi-directional Feature Pyramid Network, MFPN, which can efficiently integrate and utilize features. We combined the Cross Stage Partial Network (CSPNet) with MFPN to design a new network for 6D pose estimation, MFPN-6D. At the same time, we propose a new confidence calculation method for object pose estimation, which can fully consider spatial information and plane information. At last, we tested our method on the LINEMOD and Occluded-LINEMOD datasets. The experimental results demonstrate that our algorithm is robust to textureless materials and occlusion, while running more efficiently compared to other methods.

关键词： Deep learning Three-dimensional displays Conferences Pose estimation Grasping Prediction algorithms Market research

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Formation control of mobile robots with input constraints: An elliptic approximation approach

Formation control of mobile robots with input constraints: A...

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International Conference on Control, Automation and systems ( ICCAS)

作者： Xiaohan Chen Yingmin Jia Junping Du Fashan Yu The Seventh Research Division and Department of Systems and Control Beihang University BUAA Beijing China Beihang University Beijing CN Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo Henan China

This paper considers the problem of formation control for mobile robots which are subject to input constraints. The control strategy based on virtual robot tracking is proposed, which leads to a compact controller form in terms of two feedback parameters by Lyapunov method. The introduced elliptic approximation of input constraints brings much convenience to obtain suitable feedback parameters. It is show that the presented sufficient condition guarantees the existence of feasible feedback parameters. Moreover, guidelines for the selection of feedback parameters are provided with geometric analysis. Simulation results verify the effectiveness of the proposed control strategy.

关键词： Robot kinematics Lead Mobile robots Approximation methods Trajectory Educational institutions

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Uvl: Feature Modelling with the Universal Variability Language

SSRN

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SSRN 2024年

作者： Benavides, David Sundermann, Chico Feichtinger, Kevin Galindo, José A. Rabiser, Rick Thüm, Thomas Department of Computer Languages and Systems Universidad de Sevilla Av. Reina Mercedes Seville41012 Spain Institute of Software Engineering and Programming Languages University of Ulm Albert-Einstein-Allee 11 Ulm89069 Germany CRC 1608 KASTEL – Dependability of Software-intensive Systems Karlsruhe Institute of Technology Am Fasanengarten 5 Karlsruhe76131 Germany Christian Doppler Laboratory VaSiCS LIT CPS Lab Johannes Kepler University Linz Altenberger Straße 69 Linz4040 Austria

Feature modelling is a cornerstone of software product line engineering, providing a means to represent software variability through features and their relationships. Since its inception in 1990, feature modelling has evolved through various extensions, and after three decades of development, there is a growing consensus on the need for a standardised feature modelling language. Despite multiple endeavours to standardise variability modelling and the creation of various textual languages, researchers and practitioners continue to use their own approaches, impeding effective model sharing. In 2018, a collaborative initiative was launched by a group of researchers to develop a novel textual language for representing feature models. This paper introduces the outcome of this effort: the Universal Variability Language (\UVL), which is designed to be human-readable and serves as a pivot language for diverse software engineering tools. The development of \UVL drew upon community feedback and leveraged established literature in the field of variability modelling. The language is structured into three levels -Boolean, Arithmetic, and Type- and allows for language extensions to introduce additional constructs enhancing its expressiveness. \UVL is integrated into various existing software tools, such as FeatureIDE and FLAMA, and is maintained by a consortium of institutions. © 2024, The Authors. All rights reserved.

关键词： Modeling languages

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Fractional discrete neural networks with variable order: solvability, finite time stability and synchronization

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The European Physical Journal Special Topics 2024年 1-14页

作者： Hioual, Amel Alomari, Saleh Al-Tarawneh, Hassan Ouannas, Adel Grassi, Giuseppe Laboratory of Dynamical Systems and Control University of Oum El-Bouaghi Oum El Bouaghi Algeria Faculty of Science and Information Technology Software Engineering Jadara University Irbid Jordan Department of Data Sciences and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Department of Mathematics and Computer Science University of Oum EL-Bouaghi Oum El Bouaghi Algeria Dipartimento Ingegneria Innovazione Universita del Salento Lecce Italy

Research in the field of dynamic behaviors in neural networks with variable-order differences is currently a thriving area, marked by various significant discoveries. However, when it comes to discrete-time neural networks featuring fractional variable-order nonlocal and nonsingular kernels, there has been limited exploration. This paper stands as one of the initial contributions to this subject, focusing primarily on the topics of stability and synchronization in finite-time within discrete neural networks. The research employs the nabla ABC variable-order difference operator, with a primary approach involving the investigation of a novel Gronwall inequality using the Atangana-Baleanu difference variable-order sum operator. This analysis leads to the development of a uniqueness theorem and a criterion for the stability in finite-time of variable-order discrete neural networks. Furthermore, the requirements stemming from this type of stability and the novel Gronwall inequality serve as the foundation for establishing the conditions necessary for achieving finite-time synchronization in these networks, employing a specific control using state feedback method. Finally, the study utilizes numerical solutions to validate the obtained results.

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

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Offsite Evaluation of Localization systems: Criteria, systems, and Results From IPIN 2021 and 2022 Competitions

IEEE Journal on Indoor and Seamless Positioning and Navigati...

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IEEE Journal on Indoor and Seamless Positioning and Navigation 2024年 2卷 92-129页

作者： Potorti, Francesco Crivello, Antonino Lee, Soyeon Vladimirov, Blagovest Park, Sangjoon Chen, Yushi Wang, Long Chen, Runze Zhao, Fang Zhuge, Yue Luo, Haiyong Perez-Navarro, Antoni Jimenez, Antonio R. Wang, Han Liang, Hengyi De Cock, Cedric Plets, David Cui, Yan Xiong, Zhi Li, Xiaodong Ding, Yiming Franco, Fernando Javier Alvarez Polo, Fernando Jesus Aranda Rodriguez, Felipe Parralejo Moreira, Adriano Pendao, Cristiano Silva, Ivo Ortiz, Miguel Zhu, Ni Li, Ziyou Renaudin, Valerie Wei, Dongyan Ji, Xinchun Zhang, Wenchao Wang, Yan Ding, Longyang Kuang, Jian Zhang, Xiaobing Dou, Zhi Yang, Chaoqun Kram, Sebastian Stahlke, Maximilian Mutschler, Christopher Coene, Sander Li, Chenglong Venus, Alexander Leitinger, Erik Tertinek, Stefan Witrisal, Klaus Wang, Yi Wang, Shaobo Jin, Beihong Zhang, Fusang Su, Chang Wang, Zhi Li, Siheng Li, Xiaodong Li, Shitao Pan, Mengguan Zheng, Wang Luo, Kai Ma, Ziyao Gao, Yanbiao Chang, Jiaxing Ren, Hailong Guo, Wenfang Torres-Sospedra, Joaquin Pisa56124 Italy Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School Beijing100876 China Chinese Academy of Sciences Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Beijing100190 China Universitat Oberta de Catalunya Faculty of Computer Sciences Multimedia and Telecommunication Barcelona08035 Spain Arganda del Rey28500 Spain Huawei Technologies Company Ltd. Shenzhen100015 China IMEC-WAVES/Ghent University Department of Information Technology Gent9052 Belgium Nanjing University of Aeronautics and Astronautics College of Automation Nanjing210016 China University of Extremadura Badajoz06006 Spain University of Trás-os-Montes and Alto Douro Department of Engineering School of Sciences and Technology Vila Real5000-801 Portugal Universidade Do Minho Centro Algoritmi Guimarães4800-058 Portugal Ame Department of the University Gustave Eiffel Geoloc Laboratory Bouguenais44344 France Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China Wuhan University Gnss Research Center Wuhan430072 China AutoNavi Software Company Ltd. China Fraunhofer Institute for Integrated Circuits Iis Fraunhofer Iis Nuremberg90411 Germany Erlangen91058 Germany Graz University of Technology Christian Doppler Laboratory for Location-Aware Electronic Systems Graz8010 Austria Institute of Software Chinese Academy of Sciences Beijing100190 China Purple Mountain Laboratories Pervasive Communication Research Center Nanjing210000 China Beijing University of Posts and Telecommunications Beijing100876 China

Indoor positioning is a thriving research area, which is slowly gaining market momentum. Its applications are mostly customized, ad hoc installations;ubiquitous applications analogous to Global Navigation Satellite System for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the indoor positioning and indoor navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site tracks. In contrast with on-site tracks where competitors bring their systems on-site - which were impossible to organize during 2021 and 2022 - in off-site tracks competitors download prerecorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem. © 2023 CCBY.

关键词： Smartphones

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A reinforcement-learning-based energy-efficient framework for multi-task video analytics pipeline

arXiv

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arXiv 2021年

作者： Zhao, Yingying Dong, Mingzhi Wang, Yujiang Feng, Da Lv, Qin Dick, Robert P. Li, Dongsheng Lu, Tun Gu, Ning Shang, Li School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Fudan University Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China Department of Computing Imperial College London London United Kingdom Software Service Company Limited Beijing China University of Colorado Boulder BoulderCO United States Department of Electrical Engineering and Computer Science College of Engineering University of Michigan Ann ArborMI United States Microsoft Research Asia Shanghai China School of Computer Science Fudan University Shanghai China

Deep-learning-based video processing has yielded transformative results in recent years. However, the video analytics pipeline is energy-intensive due to high data rates and reliance on complex inference algorithms, which limits its adoption in energy-constrained applications. Motivated by the observation of high and variable spatial redundancy and temporal dynamics in video data streams, we design and evaluate an adaptive-resolution optimization framework to minimize the energy use of multitask video analytics pipelines. Instead of heuristically tuning the input data resolution of individual tasks, our framework utilizes deep reinforcement learning to dynamically govern the input resolution and computation of the entire video analytics pipeline. By monitoring the impact of varying resolution on the quality of high-dimensional video analytics features, hence the accuracy of video analytics results, the proposed end-to-end optimization framework learns the best non-myopic policy for dynamically controlling the resolution of input video streams to globally optimize energy efficiency. Governed by reinforcement learning, optical flow is incorporated into the framework to minimize unnecessary spatio-temporal redundancy that leads to re-computation, while preserving accuracy. The proposed framework is applied to video instance segmentation which is one of the most challenging computer vision tasks, and achieves better energy efficiency than all baseline methods of similar accuracy on the YouTube-VIS dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy efficiency

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ADVANCES IN LONG-RANGE, HIGH-SPEED OPTICAL COMMUNICATION

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NAVAL ENGINEERS JOURNAL 1981年第4期93卷 39-48页

作者： BARTON, GG FELDMAN, S Mr. Sidney Feldman formerly with the Naval Applied Science Laboratory is presently with the Radiation Division of the Naval Surface Weapons Center White Oak Silver Spring Maryland. He planned and supervised laboratory experiments of many optical systems for fleet evaluation several of his designs now being standard in the fleet. He received his BA degree in Physics from Brooklyn College in 1941 and majored in Physics at the Brooklyn Polytechnic Institute. His professional memberships include the Optical Society of America American Association for the Advancement of Science and The Scientific Research Society of North America. He was the recipient of the Naval Ordnance Development Award in 1945 the Superior Achievement Award in 1957 the Quality Salary Increase Award in 1965 the Superior Accomplishment Award in 1967 1970 and 1979. Additionally he has been granted patents for a relamping tool for searchlights (1962) a lamp positioning mechanism for searchlights (1963) a daylnight digital sextant (1973) a portable sextant-computer system (1974) and with Mr. George Barton the remote-controlled LLLTV camera-sextant (1976) the automatic passive LLLTV-rangefinder (1977) and a patent disclosure for the omnidirectional transceiver (1980). Mr. George G. Barton has been employed by the Naval Research Laboratory Washington D. C. for the past ten years in the scientific and military application design engineering and fabrication of low light level TV camera systems. He was previously associated with the Smithsonian Institution and Northwestern University in the application of TV technology in astronomical investigations. He attended New York University Newark College of Engineering and New Mexico State University where he majored in Physical Optics. Presently he is with his own company BISMARC Inc. Harker's Island N.C. where he is involved principally in the design manufacture and marketing of sonar systems such as the unique VIDISEA fishscope research and development in electrooptic systems an

During periods of radio silence under Emission Control, communication depends on the slow ship-to-ship, manual-visual, 8-10 wpm Morse code signaling shutter searchlight employing the ac 1000-watt incandescent or the ac 1000-watt compact arc mercury-xenon lamp. These sources can be replaced by the dc compact arc xenon lamp to provide longer daylight ranges and highspeed (100 wpm teletype, 150 wpm Morse code, voice, etc.) communication since this source has superior brightness and the facility of considerably more rapid modulation. The excessive weight and size of the ac-dc power convertor and modulating circuitry which had prevented use of the dc xenon lamp have been overcome by developments in solid state electronics such as the rapid, high current, simmer-flash, 100 to one light output xenon lamp pulsing using the reliable, uncomplicated, low-cost silicon controlled rectifier shunt switch which could eliminate the mechanical signaling shutter. laboratory and sea evaluations have proved the feasibility of and provided the long daylight high-speed optical communication ranges that could be attained from operation of the dc 1000-watt and 2200-watt xenon lamps at the focus of 12-inch, 18-inch searchlights and in the Fresnel lens omni-directional beacon which a command ship uses to signal all ships in a task force at once. With these advances, optical communication provides the highspeed capability of radio communication, particularly, also, with the new omnidirectional transceiver.

关键词： SHIPS

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The RTES project - BTeV, and beyond

The RTES project - BTeV, and beyond

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IEEE NPSS Conference on Real Time

作者： M.J. Haney S. Ahuja G. Bapty H. Cheung Z. Kalbarczyk A. Khanna J. Kowalkowski D. Messie D. Mosse S. Neema S. Nordstrom Jae Oh P. Sheldon S. Shetty D. Volper Long Wang Di Yao High Energy Physics University of Illinois Urbana IL USA Institute for Software Integrated Systems Vanderbilt University Nashville TN USA Fenni National Accelerator Laboratory Batavia IL USA Electrical and Computer Science University of Illinois Urbana IL USA Electrical Engineering and Computer Science Syracuse University Syracuse NY USA Computer Science University of Pittsburgh Pittsburgh PA USA Physics and Astronomy Department Vanderbilt University Nashville TN USA

The real time embedded systems (RTES) project was created to study the design and implementation of high-performance, heterogeneous, and fault-adaptive real time embedded systems. The driving application for this research was the proposed BTeV high energy physics experiment, which called for large farms of embedded computational elements (DSPs), as well as a large farm of conventional high-performance processors to implement its Level 1 and Level 2/3 triggers. At the time of BTeV's termination early in 2005, the RTES project was within days of completing a prototype implementation for providing a reliable and fault-adaptive infrastructure to the L2/3 farm; a prototype for the L1 farm had been completed in 2003. This paper documents the conclusion of the RTES focus on BTeV, and provides an evaluation of the applicability of the RTES concepts to other systems

关键词： USA Councils Real time systems Physics computing Runtime computer science Embedded system Domain specific languages Embedded computing Prototypes Fault detection

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