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A unified leader-follower scheme for mobile robots with uncalibrated on-board camera

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A unified leader-follower scheme for mobile robots with unca...

IEEE International Conference on robotics and Automation (ICRA)

作者： Dejun Guo Hesheng Wang Weidong Chen Ming Liu Zeyang Xia Kam K. Leang DARC (Design Automation Robotics & Control) Lab University of Utah Robotics Center Salt Lake City Utah USA State Key Laboratory of Robotics and System (HIT) Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai Jiao Tong University Shanghai China Department of Electronics and Computer Engineering Hong Kong University of Science and Technology Hong Kong China Institute of Biomedical and Health Engineering Chinese Academy of Sciences Shenzhen China

This paper studies the problem of image-based leader-follower formation control for mobile robots, where the controller is designed independently of the leader's motion. An adaptive control scheme, which is suitable for both omnidirectional and perspective cameras, is proposed. The proposed approach avoids the need for accurate calibration of the extrinsic parameters of the omnidirectional camera as well as the intrinsic and extrinsic parameters of perspective camera. Additionally, the coefficients of the plane where the feature point moves relative to the camera frame can be uncertain. These uncertain constant parameters are estimated using an adaptive estimator. Uniform Semi-global Practical Asymptotic Stability (USPAS) of the system is shown using the Lyapunov approach. Experimental results are presented to demonstrate the effectiveness of the proposed control scheme.

关键词： Cameras Robot vision systems Kinematics Mobile robots Calibration Automation

Ten simple rules for reproducible research in jupyter notebooks

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arXiv 2018年

作者： Rule, Adam Birmingham, Amanda Zuniga, Cristal Altintas, Ilkay Huang, Shih-Cheng Knight, Rob Moshiri, Niema Nguyen, Mai H. Rosenthal, Sara Brin Pérez, Fernando Rose, Peter W. Design Lab UC San Diego San DiegoCA United States Center for Computational Biology and Bioinformatics UC San Diego San DiegoCA United States Department of Pediatrics UC San Diego San DiegoCA United States Data Science Hub San Diego Supercomputer Center UC San Diego San DiegoCA United States Departments of Bioengineering and Computer Science and Engineering Center for Microbiome Innovation UC San Diego San DiegoCA United States Bioinformatics and Systems Biology Graduate Program UC San Diego San DiegoCA United States Department of Statistics Berkeley Institute for Data Science UC Berkeley Lawrence Berkeley National Laboratory BerkeleyCA United States Biomedical Informatics Graduate Program Stanford University StanfordCA United States

Reproducibility of computational studies is a hallmark of scientific methodology. It enables researchers to build with confidence on the methods and findings of others, reuse and extend computational pipelines, and thereby drive scientific progress. Since many experimental studies rely on computational analyses, biologists need guidance on how to set up and document reproducible data analyses or simulations. In this paper, we address several questions about reproducibility. For example, what are the technical and non-technical barriers to reproducible computational studies? What opportunities and challenges do computational notebooks offer to overcome some of these barriers? What tools are available and how can they be used effectively? We have developed a set of rules to serve as a guide to scientists with a specific focus on computational notebook systems, such as Jupyter Notebooks, which have become a tool of choice for many applications. Notebooks combine detailed workflows with narrative text and visualization of results. Combined with software repositories and open source licensing, notebooks are powerful tools for transparent, collaborative, reproducible, and reusable data analyses. Copyright © 2018, The Authors. All rights reserved.

关键词： Digital storage

A New Data-Driven Sparse-Learning Approach to Study Chemical Reaction Networks

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arXiv 2017年

作者： Harirchi, Farshad Kim, Doohyun Khalil, Omar Liu, Sijia Elvati, Paolo Hero, Alfred Violi, Angela Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109-2125 United States Department of Mechanical Engineering University of Michigan Ann ArborMI48109-2125 United States Biophysics Program Departments of Chemical Engineering Biomedical Engineering Macromolecular Science and Engineering University of Michigan Ann ArborMI48109-2125 United States

Chemical kinetic mechanisms can be represented by sets of elementary reactions that are easily translated into mathematical terms using physicochemical relationships. The schematic representation of reactions captures the interactions between reacting species and products. Determining the minimal chemical interactions underlying the dynamic behavior of systems is a major task. In this paper, we introduce a novel approach for the identification of the inuential reactions in chemical reaction networks for combustion applications, using a data-driven sparse-learning technique. The proposed approach identifies a set of inuential reactions using species concentrations and reaction rates, with minimal computational cost without requiring additional data or simulations. The new approach is applied to analyze the combustion chemistry of H2andC3H8in a constant-volume homogeneous reactor. The inuential reactions identified by the sparse-learning method are consistent with the current kinetics knowledge of chemical mechanisms. Additionally, we show that a reduced version of the parent mechanism can be generated as a combination of the inuential reactions identified at different times and conditions and that for both H2andC3H8this reduced mechanism performs closely to the parent mechanism as a function of ignition delay over a wide range of conditions. Our results demonstrate the potential of the sparse-learning approach as an effective and efficient tool for mechanism analysis and mechanism reduction. Copyright © 2017, The Authors. All rights reserved.

关键词： Learning systems

Total variation superiorized conjugate gradient method for image reconstruction

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arXiv 2017年

作者： Zibetti, Marcelo V. W. Lin, Chuan Herman, Gabor T. Graduate Program in Electrical Engineering and Computer Science Federal University of Technology - Paraná Curitiba80230-901 Brazil School of Electrical Engineering Southwest Jiaotong University Chengdu Sichuan610031 China Department of Computer Science Graduate Center City University of New York New YorkNY10016 United States

The conjugate gradient (CG) method is commonly used for the relatively-rapid solution of least squares problems. In image reconstruction, the problem can be ill-posed and also contaminated by noise;due to this, approaches such as regularization should be utilized. Total variation (TV) is a useful regularization penalty, frequently utilized in image reconstruction for generating images with sharp edges. When a non-quadratic norm is selected for regularization, as is the case for TV, then it is no longer possible to use CG. Nonlinear CG is an alternative, but it does not share the efficiency that CG shows with least squares and methods such as fast iterative shrinkage-thresholding algorithms (FISTA) are preferred for problems with TV norm. A different approach to including prior information is superiorization. In this paper it is shown that the conjugate gradient method can be superiorized. Five different CG variants are proposed, including preconditioned CG. The CG methods superiorized by the total variation norm are presented and their performance in image reconstruction is demonstrated. It is illustrated that some of the proposed variants of the superiorized CG method can produce reconstructions of superior quality to those produced by FISTA and in less computational time, due to the speed of the original CG for least squares problems. In the Appendix we examine the behavior of one of the superiorized CG methods (we call it S-CG);one of its input parameters is a positive number ". It is proved that, for any given " that is greater than the half-squared-residual for the least squares solution, S-CG terminates in a finite number of steps with an output for which the half-squared-residual is less than or equal to ". Importantly, it is also the case that the output will have a lower value of TV than what would be provided by unsuperiorized CG for the same value " of the half-squared residual. Copyright © 2017, The Authors. All rights reserved.

关键词： Conjugate gradient method

Selective strong structural minimum cost resilient co-design for regular descriptor linear systems

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arXiv 2017年

作者： Popli, Nipun Pequito, Sérgio Kar, Soummya Pedro Aguiar, A. Ilić, Marija Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA15213 United States Department of Electrical and Systems Engineering School of Engineering and Applied Science University of Pennsylvania PhiladelphiaPA19104 United States Institute for System and Robotics Instituto Superior Técnico University of Lisbon Lisbon Portugal Porto Portugal

This paper addresses the problem of minimum cost resilient actuation-sensing-communication co-design for regular descriptor systems while ensuring selective strong structural system's properties. More specifically, the problem consists of determining the minimum cost deployment of actuation and sensing technology, as well as communication between the these, such that decentralized control approaches are viable for an arbitrary realization of regular descriptor systems satisfying a pre-specified selective structure, i.e., some entries can be zero, nonzero, or either zero/nonzero. Towards this goal, we rely on strong structural systems theory and extend it to cope with the selective structure that casts resiliency/robustness properties and uncertainty properties of system's model. Upon such framework, we introduce the notion of selective strong structural fixed modes as a characterization of the feasibility of decentralized control laws. Also, we provide necessary and sufficient conditions for this property to hold, and show how these conditions can be leveraged to determine the minimum cost resilient placement of actuation-sensing-communication technology ensuring feasible solutions. In particular, we study the minimum cost resilient actuation and sensing placement, upon which we construct the solution to our problem. Finally, we illustrate the applicability the main results of this paper on an electric power grid example. Copyright © 2017, The Authors. All rights reserved.

关键词： Describing functions

Quantum annealing versus classical machine learning applied to a simplified computational biology problem

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arXiv 2018年

作者： Li, Richard Di Felice, Rosa Rohs, Remo Lidar, Daniel Department of Chemistry University of Southern California Los AngelesCA90089 United States Computational Biology and Bioinformatics Program Department of Biological Sciences University of Southern California Los AngelesCA90089 United States Center for Quantum Information Science & Technology University of Southern California Los AngelesCA90089 United States Department of Physics and Astronomy University of Southern California Los AngelesCA90089 United States Center S3 CNR Institute of Nanoscience Via Campi 213/A Modena41125 Italy Department of Computer Science University of Southern California Los AngelesCA90089 United States Department of Electrical Engineering University of Southern California Los AngelesCA90089 United States

Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to predict binding specificity. Using simplified datasets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified datasets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems. Copyright © 2018, The Authors. All rights reserved.

关键词： Simulated annealing

Self-Catalyzed Core-Shell GaAs/GaNAs Nanowires Grown on Patterned Si(111)by Gas-Source Molecular Beam Epitaxy

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Self-Catalyzed Core-Shell GaAs/GaNAs Nanowires Grown on Patt...

第十二届全国分子束外延学术会议

作者： Rui La Ren Liu Weichuan Yao Mattias Janssond Shula Chend Janet L.Pan Irina A.Buyanovad S.A.Dayeh Charles W.Tu Graduate Program of Material Science and Engineering University of CaliforniaSan DiegoLa JollaCal Department of Electrical and Computer Engineering University of CaliforniaSan DiegoLa JollaCalifo Department of Physics Chemistry and BiologyLink(o)ping UniversityLink(o)pingSweden 58183 Graduate Program of Material Science and Engineering University of CaliforniaSan DiegoLa JollaCal

Group Ⅲ-Ⅴ semiconductor nanowires(NWs) on Si platform have attracted an increasing interest in recent *** integrating the direct band gap Ⅲ-Ⅴ materials that have high absorption coefficient onto the cost-effective Si platform,it would create novel optoelectronic devices for Si *** advantage of Ⅲ-Ⅴ nanowires grown on Si substrate is that the lattice-match constraint can be relaxed.

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Acoustic supercoupling in a zero-compressibility waveguide

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arXiv 2018年

作者： Esfahlani, H. Byrne, M.S. McDermott, M. Alù, A. Photonics Initiative Advanced Science Research Center City University of New York New York CityNY10031 United States Department of Electrical and Computer Engineering University of Texas at Austin AustinTX78712 United States LausanneCH-1015 Switzerland Naval Surface Warfare Center Carderock Division West BethesdaMD20817 United States Physics Program Graduate Center City University of New York New York CityNY10026 United States Department of Electrical Engineering City College of New York New York CityNY10031 United States

Funneling acoustic waves through largely mismatched channels is of fundamental importance to tailor and transmit sound for a variety of applications. In electromagnetics, zero-permittivity metamaterials have been used to enhance the coupling of energy in and out of ultranarrow channels, based on a phenomenon known as supercoupling. These metamaterial channels can support total transmission and complete phase uniformity, independent of the channel length, despite being geometrically mismatched to their input and output ports. In the field of acoustics, this phenomenon is challenging to achieve, since it requires zero-density metamaterials, typically realized with waveguides periodically loaded with membranes or resonators. Compared to electromagnetics, the additional challenge is due to the fact that conventional acoustic waveguides do not support a cut-off for the dominant mode of propagation, and therefore zero-index can be achieved only based on a collective resonance of the loading elements. Here we propose and experimentally realize acoustic supercoupling in a dual regime, using a compressibility-near-zero acoustic channel. Rather than engineering the channel with subwavelength inclusions, we operate at the cut-off of a higher-order acoustic mode, demonstrating the realization and efficient excitation of a zero-compressibility waveguide with effective soft boundaries. We experimentally verify strong transmission through a largely mismatched channel and uniform phase distribution, independent of the channel length. Our results open interesting pathways towards the realization of extreme acoustic parameters, and their implementation in relevant applications, such as ultrasound imaging, sonar technology, and sound transmission. Copyright © 2018, The Authors. All rights reserved.

关键词： Compressibility

A Bayesian mixture model for clustering on the stiefel manifold

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arXiv 2017年

作者： Sengupta, Subhajit Pal, Subhadip Mitra, Riten Guo, Ying Banerjee, Arunava Ji, Yuan Program for Computational Genomics and Medicine NorthShore University HealthSystem Department of Bioinformatics and Biostatistics University of Louisville Department of Biostatistics and Bioinformatics Emory University Department of Computer and Information Science and Engineering University of Florida Department of Public Health Sciences University of Chicago

Analysis of a Bayesian mixture model for the Matrix Langevin distribution on the Stiefel manifold is presented. The model exploits a particular parametrization of the Matrix Langevin distribution, various aspects of which are elaborated on. A general, and novel, family of conjugate priors, and an efficient Markov chain Monte Carlo (MCMC) sampling scheme for the corresponding posteriors is then developed for the mixture model. Theoretical properties of the prior and posterior distributions, including posterior consistency, are explored in detail. Extensive simulation experiments are presented to validate the efficacy of the framework. Real-world examples, including a large scale neuroimaging dataset, are analyzed to demonstrate the computational tractability of the approach. Copyright © 2017, The Authors. All rights reserved.

关键词： Mixtures