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Joint learning of discriminative low-dimensional image representations based on dictionary learning and two-layer orthogonal projections

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arXiv 2019年

作者： Wei, Xian Shen, Hao Li, Yuanxiang Tang, Xuan Jin, Bo Zhao, Lijun Murphey, Yi Lu Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences China Technical University of Munich Germany and fortiss GmbH Munich Germany Shanghai Key Lab for Trustworthy Computing School of Computer Science and Software Engineering East China Normal University China School of Aeronautics & Astronautics Shanghai Jiao Tong University Shanghai200240 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150006 China Department of Electrical and Computer Engineering University of Michigan-Dearborn DearbornMI48128 United States

This work investigates the problem of efficiently learning discriminative low-dimensional representations of multi-class large-scale image objects. We propose a generic deep learning approach by taking advantages of Convolutional Neural Networks (CNN), sparse dictionary learning, and orthogonal projections. CNN is not only powerful on feature extraction, but also robust to spatial variance and changes. Sparse dictionary learning is well known for disentangling nonlinear underlying discriminative factors in data. The orthogonal projection is a notable efficient tool to project multi-class data onto low dimensional discriminative subspace. The proposed procedure can be summarized as follows. At first, a CNN is employed to extract high-dimensional (HD) preliminary convolutional features. Secondly, to avoid the high computational cost of direct sparse coding on HD CNN features, we present to learn sparse representation (SR) in an orthogonal projected space over a task-driven sparsifying dictionary. We then exploit the discriminative projection on SR. The whole learning process is treated as a joint optimization problem of trace quotient maximization, which involves the CNN parameters, the orthogonal projection on CNN features, the dictionary and the discriminative projection on sparse codes. The related cost function is well defined on product manifold of the Stiefel manifold, the Oblique manifold, and the Grassmann manifold. It is optimized via a geometrical stochastic gradient descent algorithm. Finally, the quality of dictionary and projections learned by the proposed approach is further investigated in terms of image classification. The experimental results show that the approach can achieve a strongly competitive performance with state-of-the-art image classification methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

Stability Analysis for Kalman Filters with Random Measurement Matrices 13

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Stability Analysis for Kalman Filters with Random Measuremen...

13th IEEE International Conference on Control & Automation (ICCA)

作者： Marelli, Damian Sui, Tianju Fu, Minyue Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan Cloud System Software Institute Institute for Information Industry Taipei Taiwan

ISBN: (纸本)9781538626795

This paper addresses the stability of a Kalman filter when measurements are intermittently available due to an unreliable communication channel between sensors and the estimator. This intermittent behaviour can be modelled as a random and time-varying measurement system. We consider a general discrete-time system with a random measurement matrix and study the stability condition for the associate Kalman filter. By deep dissecting the system structure, the necessary and sufficient stability condition for the Kalman filter is given. We also give methods to checking the stability condition for specific linear systems. Our results generalize previously known stability conditions where the system matrix structure is restricted.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for computer Vision

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

Efficient rate adaptation algorithm in high-dense vehicular ad hoc network 13

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Efficient rate adaptation algorithm in high-dense vehicular ...

13th IEEE Malaysia International Conference on Communications, MICC 2017

作者： Chaab, Wafaa Al Ismail, Mahamod Altahrawi, Mohammed A. Mahdi, Hussain Ramli, Nordin Dept. of Electrical Electronic and System Engineering Universiti Kebangsaan Malaysia Bangi Selangor43600 Malaysia Computer and Software Engineering Dept. University of Diyala Iraq MIMOS Berhad Technology Park Malaysia Kuala Lumpur57000 Malaysia

ISBN: (纸本)9781538631324

In the Vehicular Ad hoc NETwork (VANET) communications, the presence of intersections, vehicles, and obstacles such as buildings may obstruct signal propagation especially in urban areas. Moreover, the vehicle's mobility and density also cause intermittent in inter-vehicle connection due to unpredictable and diverse channel conditions. Rate adaptation is the key method in VANET to predict the prevailing channel conditions and decide appropriate transmission rate quickly based on channel status. Although various rate adaptation algorithms using closed and open loop exist, it is difficult to select the best rate adaptation method due to unpredictable nature of the vehicular environment, while inappropriate data selection leads poor network performance as well. This paper aims to evaluate the performance of different data rate adaptation algorithms in VANET, using closed-loop and open-loop based approaches. The two-targeted open loops are Atsushi Onoe Algorithm and Minstrel, while Collision-Aware Rate Adaptation (CARA) and Robust Rate Adaptation Algorithm (RRAA) are closed-loop based approaches. The mentioned algorithms are simulated under high-density VANET scenario with a number of nodes starts from 40 up to a hundred using the discrete event network simulator NS-3. Moreover, the distance between the nodes is investigated also over the selected approaches. Simulation results, in general, show that the close-looped outperform the open-looped approach, with RRAA better than others by at least 9.4% and nearby reach to 62% in the average throughput over the different node density as the distance varied. The packet delivery ratio is slightly diverse over the node density. However, an end-end delay is 233.39 ms which is the worst among others, but still adequate for the high critical demand applications. © 2017 IEEE.

关键词： Vehicular ad hoc networks

Self-Awareness for Autonomous systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

An approach to enhance selective catalytic reduction system

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An approach to enhance selective catalytic reduction system

IEEE Southeastcon

作者： Anuj Sharma Amay Desai Ilteris Demirkiran Electrical Computer Software and System Engineering Embry-Riddle Aeronautical University 600 South Clyde Morris Blvd. Daytona Beach FL USA

An approach to to implement the selective catalytic reduction system is presented. We utilize a raspberry pi controller to calculate the injection rate of diesel exhaust fluid. The system uses an arduino to collect engine exhaust data, and communicate the data to the controller using a control network. The control network is established using a user datagram protocol. The control network enables communication between the sensing and actuation of the system.

关键词： Diesel engines Nitrogen Carbon dioxide Heat pumps Sensors Water

Topic-based integrator matching for pull request

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arXiv 2017年

作者： Liao, Zhifang Li, Yanbing He, Dayu Wu, Jinsong Zhang, Yan Fan, Xiaoping Department of Software Engineering Central South University China Department of Electrical Engineering Universidad de Chile Santiago Chile Department of Computer Communication and Interactive System School of Engineering and Built Environment Glasgow Caledonian University United Kingdom Hunan University of Finance and Economics China

Pull Request (PR) is the main method for code contributions from the external contributors in GitHub. PR review is an essential part of open source software developments to maintain the quality of software. Matching a new PR for an appropriate integrator will make the PR reviewing more effective. However, PR and integrator matching are now organized manually in GitHub. To make this process more efficient, we propose a Topic-based Integrator Matching Algorithm (TIMA) to predict highly relevant collaborators(the core developers) as the integrator to incoming PRs . TIMA takes full advantage of the textual semantics of PRs. To define the relationships between topics and collaborators, TIMA builds a relation matrix about topic and collaborators. According to the relevance between topics and collaborators, TIMA matches the suitable collaborators as the PR integrator. Copyright © 2017, The Authors. All rights reserved.

关键词： Semantics

A static analysis tool with optimizations for reachability determination 17

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A static analysis tool with optimizations for reachability d...

IEEE International Conference on Automated software engineering (ASE)

作者： Yuexing Wang Min Zhou Yu Jiang Xiaoyu Song Ming Gu Jiaguang Sun Ministry of Education Key Laboratory for Information System Security China School of Software Tsinghua University China Tsinghua National Laboratory for Information Science and Technology (TNList) China Electrical and Computer Engineering Portland State University USA

ISBN: (纸本)9781538626849

To reduce the false positives of static analysis, many tools collect path constraints and integrate SMT solvers to filter unreachable execution paths. However, the accumulated calling and computing of SMT solvers are time and resource consuming. This paper presents TsmartLW, an alternate static analysis tool in which we implement a path constraint solving engine to speed up reachability determination. Within the engine, typical types of constraint-patterns are firstly defined based on an empirical study of a large number of code repositories. For each pattern, a constraint solving algorithm is designed and implemented. For each program, the engine predicts the most suitable strategy and then applies the strategy to solve path constraints. The experimental results on some well-known benchmarks and real-world applications show that TsmartLW is faster than some state-of-the-art static analysis tools. For example, it is 1.32× faster than CPAchecker and our engine is 369× faster than SMT solvers in solving path constraints. The demo video is available at https://***/watch?v=5c3ARhFclHA&t=2s.

关键词： Tools Algorithm design and analysis Engines Benchmark testing Visualization Optimization

A prediction model of the project life-span in open source software ecosystem

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arXiv 2017年

作者： Liao, Zhifang Zhao, Benhong Liu, Shengzong Jin, Haozhi He, Dayu Yang, Liu Wu, Jinsong Zhang, Yan Department of Software Engineering Central South University China Information Management Department Hunan University of Finance and Economics Changsha Hunan China Department of Electrical Engineering Universidad de Chile Santiago Chile Department of Computer Communication and Interactive System School of Engineering and Built Environment Glasgow Caledonian University

In nature ecosystems, animal life-spans are determined by genes and some other biological characteristics. Similarly, the software project life-spans are related to some internal or external characteristics. Analyzing the relations between these characteristics and the project life-span, may help developers, investors, and contributors to control the development cycle of the software project. The paper provides an insight on the project life-span for a free open source software ecosystem. The statistical analysis of some project characteristics in GitHub is presented, and we find that the choices of programming languages, the number of files, the label format of the project, and the relevant membership expressions can impact the life-span of a project. Based on these discovered characteristics, we also propose a prediction model to estimate the project life-span in open source software ecosystems. These results may help developers reschedule the project in open source software ecosystem. Copyright © 2017, The Authors. All rights reserved.

关键词： Open source software