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Conference on Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO)

作者： Zhodzishsky, M., I Kurynin, R., V Serkin, F. B. Veitsel, A. V. Natl Res Univ Moscow Aviat Inst Topcon Positioning Syst Moscow Russia Natl Res Univ Higher Sch Econ Topcon Positioning Syst Moscow Russia

ISBN: (纸本)9781665415491

The main algorithms for measuring the carrier-to-noise density ratio (C/N-0) of signals from global satellite navigation systems (GNSS) are considered, including well-known algorithms that require minimal a priori information about signals (robust algorithms), and less well-known, but more promising noise-immune algorithms that require to obtain some preliminary information. Ways of obtaining this a priori information are considered. Two types of robust and noise-immune algorithms have been analyzed in the paper. One type (for low dynamic applications) uses only the in-phase I component of the input signal, and the second (for high dynamic applications) uses a combination of the in-phase I component and quadrature Q component (Z(2)=I-2+Q(2)). Simulation modeling and full-scale experiments are used to analyze the algorithms.

关键词： Carrier-to-Noise density ratio estimate robust algorithm noise-immune algorithm PLL DLL global navigation satellite systems receiver

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Dominating Induced Matchings for P₇-free Graphs in Linear Time

Dominating Induced Matchings for <i>P</i><sub>7</sub>-free G...

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22nd International Symposium on algorithms and Computation (ISAAC)

作者： Brandstaedt, Andreas Mosca, Raffaele Univ Rostock Inst Informat D-18051 Rostock Germany Univ G D Annunzio Dipartimento Sci I-65121 Pescara Italy

ISBN: (纸本)9783642255908

Let G be a finite undirected graph with edge set E. An edge set E' subset of E is an induced matching in G if the pairwise distance of the edges of E' in G is at least two;E' is dominating in G if every edge e is an element of E\E' intersects some edge in E'. The Dominating Induced Matching Problem (DIM, for short) asks for the existence of an induced matching E' which is also dominating in G;this problem is also known as the Efficient Edge Domination Problem. The DIM problem is related to parallel resource allocation problems, encoding theory and network routing. It is NP-complete even for very restricted graph classes such as planar bipartite graphs with maximum degree three. However, its complexity was open for P-k-free graphs for any k >= 5;P-k denotes a chordless path with k vertices and k - 1 edges. We show in this paper that the weighted DIM problem is solvable in linear time for P-7-free graphs in a robust way.

关键词： dominating induced matching efficient edge domination P-7-free graphs linear time algorithm robust algorithm

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Voluminator-Approximating the Volume of 3D Buildings to Overcome Topological Errors 18th

Voluminator-Approximating the Volume of 3D Buildings to Over...

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18th Association-of-Geographic-Information-Laboratories-for-Europe Conference on Geographic Information Science (AGILE)

作者： Steuer, Horst Machl, Thomas Sindram, Maximilian Liebel, Lukas Kolbe, Thomas H. Tech Univ Munich Munich Germany

ISBN: (纸本)9783319167879;9783319167862

Many research fields analysing urban space depend on 3D city models. However, 3D city models still are often of a low geometric quality. Due to topological errors it is not possible to compute volumetric information for many buildings in real world datasets using analytical approaches. Since this volumetric information is important for many applications we present a method to approximate the volume of building models overcoming topological errors. The method is based on a voxelisation and a generalisation of the point-in-polygon test to three dimensions. We show in extensive tests that the method produces accurate results and is able to cope with different types of errors. Beyond the computation of volumes, the proposed approach potentially has a high impact for numerous applications like healing of building models, indoor routing or model transformation.

关键词： Building models Topological errors Approximation Voxelisation robust algorithm

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Uncertainty Aware Geo-localization of Multi-Targets with Multi-UAV using Neural Network and Extended Kalman Filter

Uncertainty Aware Geo-localization of Multi-Targets with Mul...

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AIAA Science and Technology (SciTech) Forum and Exposition

作者： Khanapuri, Eshaan Sharma, Rajnikant Univ Cincinnati Dept Aerosp Engn & Engn Mech Cincinnati OH 45221 USA

ISBN: (数字)9781624105784

ISBN: (纸本)9781624105784

In this paper we propose a Neural Network based approach to improve the accuracy in the estimated positions of multiple targets and minimize their uncertainty using multiple small fixed wing Unmanned Aerial Vehicle (UAV). The UAVs are equipped with a gimbal camera to track the targets. To Geo-localize the targets they should remain inside the Field of View (FOV) of the camera. Extended Kalman Filter (EKF) is used to estimate target's positions. As long as the targets are in the FOV the error in the estimated positions of the targets will be low. The challenge here is to predict the azimuth and elevation angles of gimbal cameras such that multiple targets remain inside the FOV. This is achieved by training a Neural Network with backpropogation to predict the required azimuth and elevation angles for each UAV. In certain cases when multiple targets cannot be kept in the FOV of the camera, neural network predicts gimbal angles for each UAV such that they can observe atleast one target each. Using simulation results we can see the effectiveness of the proposed approach and also show that the estimated position and uncertainty of targets are better with Neural Network compared to Model Predictive Control method.

关键词： Neural Network Methods Extended Kalman Filter Unmanned Aerial Vehicle Predictive Control Method Computer Vision Geolocation robust algorithm Pressure Sensors Yaw

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Finding Triangles in Restricted Classes of Graphs

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Electronic Notes in Discrete Mathematics 2006年 27卷 25-26页

作者： Eschen, Elaine Spinrad, Jeremy Computer Science and Electrical Engineering West Virginia University Morgantown United States Electrical Engineering and Computer Science Vanderbilt University Nashville United States

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Deep multi-view robust representation learning

Deep multi-view robust representation learning

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Zhenyu Jiao Chao Xu School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

ISBN: (纸本)9781509041183

Multi-view representations are widely existed in practical applications, the quality of latent representation learned from multi-view observations often suffer from noise and outliers in original data. In this work, we propose an auto encoder based deep multi-view robust representation learning (DMRRL) algorithm, which can learn a shared representation from multi-view observations and the algorithm is robust to noise and outliers by using Cauchy estimator as loss function. When the label of the original data is available, the algorithm can gain better representation by adding an auxiliary loss. We validate our methods on the CMU PIE dataset (noise applied) with face recognition task and UCF101 dataset with human motion recognition task, demonstrating that DMRRL is an effective algorithm for practical applications.

关键词： Multi-view learning Cauchy Estimators auto encoder robust algorithm

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Online allocation with traffic spikes: Mixing adversarial and stochastic models 15

Online allocation with traffic spikes: Mixing adversarial an...

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16th ACM Conference on Economics and Computation, EC 2015

作者： Esfandiari, Hossein Korula, Nitish Mirrokni, Vahab University of Maryland United States Google Research New YorkNY United States

ISBN: (纸本)9781450334105

Motivated by Internet advertising applications, online allocation problems have been studied extensively in various adversarial and stochastic models. While the adversarial arrival models are too pessimistic, many of the stochastic (such as i.i.d or random-order) arrival models do not realistically capture uncertainty in predictions. A significant cause for such uncertainty is the presence of unpredictable traffic spikes, often due to breaking news or similar events. To address this issue, a simultaneous approximation framework has been proposed to develop algorithms that work well both in the adversarial and stochastic models;however, this framework does not enable algorithms that make good use of partially accurate forecasts when making online decisions. In this paper, we propose a robust online stochastic model that captures the nature of traffic spikes in online advertising. In our model, in addition to the stochastic input for which we have good forecasting, an unknown number of impressions arrive that are adversarially chosen. We design algorithms that combine an stochastic algorithm with an online algorithm that adaptively reacts to inaccurate predictions. We provide provable bounds for our new algorithms in this framework. We accompany our positive results with a set of hardness results showing that that our algorithms are not far from optimal in this framework. As a byproduct of our results, we also present improved online algorithms for a slight variant of the simultaneous approximation framework.

关键词： stochastic analyses robust algorithm factor revealing lp traffic spikes online advertisments budgeted allocation online allocation

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Probing of two-dimensional grid patterns by means of camera based image processing

Probing of two-dimensional grid patterns by means of camera ...

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Conference on Machine Vision Applications in Industrial Inspection VIII

作者： Schroeck, M Doiron, T Inst Phys High Technol Jena Germany

ISBN: (纸本)0819435848

Camera based probes and machine vision have found increased use in coordinate measuring machines over the past years and the calibration of artifacts for these probes has become an important task for NIST. Until recently these artifacts have been calibrated using one or two dimensional measuring machines with electro-optic microscopes or scanning devices as probes. These sensors evaluate only a small section of the edge of a grid mark, and irregularities in this particular spot from local deformations or contamination influence the measurement result. Since these measurements result in a single number based on the entire field of view, the influence of small irregularities are not easily detected. Since different probes scan different parts of the grid mark edge they may give systematically different positions of the mark. The conversion to video based sensors has allowed more flexibility it edge detection, although most instruments still use least squares fits as the substitute geometry of straight edges. This method is very susceptible to noise and edge irregularities. We present some experiments for finding the sub-pixel edge point locations and fitting the set of edge points to a line using a fairly simple least sum of absolute deviations fit. Data from a high accuracy 2D measuring machine is used to show the strengths of the algorithms.

关键词： robust algorithm computer vision edge finding least sum of absolute deviations

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KNOWLEDGE-BASED SYSTEMS 2020年 199卷 105835-105835页

作者： Meng, Xiangzhu Wang, Huibing Feng, Lin Dalian Univ Technol Sch Comp Sci & Technol Dalian 116024 Peoples R China Dalian Maritime Univ Informat Sci & Technol Coll Dalian 116024 Peoples R China

During the last decades, learning a low-dimensional space with discriminative information for dimension reduction (DR) has gained a surge of interest. However, it is not accessible for these DR methods to achieve satisfactory performance when dealing with the features from multiple views. For multi-view learning problems, one instance can be represented by multiple heterogeneous features, which are highly relevant but sometimes look different from each other. In addition, correlations between features from multiple views always vary greatly, which challenges the capability of multi-view learning methods. Consequently, constructing a multi-view learning framework with generalization and scalability, which could take advantage of multi-view information as much as possible, is extremely necessary but challenging. To implement the above target, this paper proposes a novel multi-view learning framework based on similarity consensus, which makes full use of correlations among multi-view features while considering the scalability and robustness of the framework. It aims to straightforwardly extend those existing DR methods into multi-view learning domain by preserving the similarity consensus between different views to capture the low-dimensional embedding. Two schemes based on pairwise-consensus and centroid-consensus are separately proposed to force multiple views to learn from each other, then an iterative alternating strategy is developed to obtain the optimal solution. The proposed method is evaluated on 5 benchmark datasets and comprehensive experiments show that our proposed multi-view framework can yield comparable and promising performance with some famous methods. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Multi-view learning Similarity consensus robust algorithm Dimension reduction

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Topology-oriented construction of line arrangements

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2002年第5期E85A卷 930-937页

作者： Fogaras, D Sugihara, K Univ Tokyo Grad Sch Engn Dept Math Engn & Informat Phys Tokyo 1138656 Japan

The paper presents a topology-oriented robust algorithm for the incremental construction of line arrangements. In order to achieve a robust implementation, the topological and geometrical computations are strictly separated. The topological part is proved to be: reliable without any assumption on the accuracy of the geometrical part. A self-correcting property is introduced to minimize the effect of numerical errors. Computational experiments show how the self-correcting property works, and we also discuss some applications of the algorithm.

关键词： robust algorithm topology-oriented line arrangement

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