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10th International Conference on e-Health 2018, the 11th International Conference on ICT, Society, and Human Beings 2018 and of the 15th International Conference Web Based Communities and Social Media 2018, part of the Multi Conference on computer Science and Information Systems 2018, MCCSIS 2018

作者： Gialelis, John Pavlou, Panos Panagiotou, Chris Koubias, Stavros Pombo, Nuno Garcia, Nuno Applied Electronics Lab Electrical and Computer Engineering Department University of Patras Patras Greece Instituto de Telecomunicações ALLab Assisted Living Computing and Telecommunications Lab Department of Informatics Universidade da Beira Interior Covilhã Portugal Universidade Lusófona de Humanidades e Tecnologias Lisbon Portugal

ISBN: (纸本)9789898533777

Advances in Wearable Devices and Artificial Intelligence upgrade the use of ICT aiming both at improving humans’ life quality as well as safety. This paper presents a system for the detection of mental focus depletion comprising a robust embedded system used for aggregating EEG data and transferring them in real time to a central server where specific purpose software is used for further processing. The performance of the system is tested using experimental data, as well as its accuracy is depicted against a well-known commercial device. © 2018 MCCSIS 2018 - Multi Conference on computer Science and Information Systems;Proceedings of the International Conferences on e-Health 2018, ICT, Society, and Human Beings 2018 and Web Based Communities and Social Media 2018. All rights reserved.

关键词： Social networking (online)

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Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression Tasks

Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertain...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Ziliang Xiong Arvi Jonnarth Abdelrahman Eldesokey Joakim Johnander Bastian Wandt Per-Erik Forssén Department of Electrical Engineering Computer Vision Laboratory LiU Sweden Husqvarna Group Huskvarna Sweden Visual Computing Center KAUST Saudi Arabia Zenseact Sweden

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

computer vision systems that are deployed in safety-critical applications need to quantify their output uncertainty. We study regression from images to parameter values and here it is common to detect uncertainty by predicting probability distributions. In this context, we investigate the regression-by-classification paradigm which can represent multimodal distributions, without a prior assumption on the number of modes. Through experiments on a specifically designed synthetic dataset, we demonstrate that traditional loss functions lead to poor probability distribution estimates and severe overconfidence, in the absence of full ground truth distributions. In order to alleviate these issues, we propose hinge-Wasserstein – a simple improvement of the Wasserstein loss that reduces the penalty for weak secondary modes during training. This enables prediction of complex distributions with multiple modes, and allows training on datasets where full ground truth distributions are not available. In extensive experiments, we show that the proposed loss leads to substantially better uncertainty estimation on two challenging computer vision tasks: horizon line detection and stereo disparity estimation.

关键词： Training computer vision Uncertainty Conferences Estimation Probability distribution Pattern recognition

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Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

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computers, Materials & Continua 2022年第6期71卷 4989-5003页

作者： El-Sayed M.El-kenawy Abdelhameed Ibrahim Seyedali Mirjalili Yu-Dong Zhang Shaima Elnazer Rokaia M.Zaki Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Nile Higher Institute for Engineering and Technology MansouraEgypt Computer and Information Technology College Taif UniversityTaifSaudi Arabia Higher Institute of Engineering and Technology Kafrelsheikh Department of Electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve *** antennas can overcome the bandwidth constraint associated with tiny *** learning is receiving a lot of interest in optimizing solutions in a variety of *** learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s *** accuracy of the forecast is mostly determined by the model *** purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial *** Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic *** Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the *** suggested model is compared to models based on five variables and to the average ensemble *** findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for *** is superior to other models and can accurately predict antenna bandwidth and gain.

关键词： Metamaterial antenna machine learning ensemble model feature selection guided whale optimization support vector machines

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Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty 13

Non-Uniform Camera Shake Removal Using a Spatially-Adaptive ...

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Annual Conference on Neural Information Processing Systems

作者： Haichao Zhang David Wipf School of Computer Science Northwestern Polytechnical University Xi'an ChinaDepartment of Electrical and Computer Engineering Duke University USA Visual Computing Group Microsoft Research Asia Beijing China

ISBN: (纸本)9781632660244

Typical blur from camera shake often deviates from the standard uniform convo-lutional assumption, in part because of problematic rotations which create greater blurring away from some unknown center point. Consequently, successful blind deconvolution for removing shake artifacts requires the estimation of a spatially-varying or non-uniform blur operator. Using ideas from Bayesian inference and convex analysis, this paper derives a simple non-uniform blind deblurring algorithm with a spatially-adaptive image penalty. Through an implicit normalization process, this penalty automatically adjust its shape based on the estimated degree of local blur and image structure such that regions with large blur or few prominent edges are discounted. Remaining regions with modest blur and revealing edges therefore dominate on average without explicitly incorporating structure-selection heuristics. The algorithm can be implemented using an optimization strategy that is virtually tuning-parameter free and simpler than existing methods, and likely can be applied in other settings such as dictionary learning. Detailed theoretical analysis and empirical comparisons on real images serve as validation.

关键词： camera shake penalty Bayesian inference fuzzy operator IMAGE STRUCTURE images

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Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression Tasks

arXiv

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arXiv 2023年

作者： Xiong, Ziliang Jonnarth, Arvi Eldesokey, Abdelrahman Johnander, Joakim Wandt, Bastian Forssén, Per-Erik Computer Vision Laboratory Department of Electrical Engineering LiU Sweden Visual Computing Center KAUST Saudi Arabia Husqvarna Group Huskvarna Sweden Zenseact Sweden

关键词： Probability distributions

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Path planning for mobile robots using a video camera network

Path planning for mobile robots using a video camera network

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： A. Hoover B.D. Olsen Electrical and Computer Engineering Department Visual Computing Lab University of California San Diego La Jolla CA USA Department of Medical Inform. & Image Analysis Laboratory of Image Analysis University of Aalborg Denmark

We describe a novel method to plan paths for mobile robots in an environment observed by a network of video cameras. The multiple video streams are fused to generate a spatial-temporal map of freespace. Updated at 5 Hz, this map concurrently locates both static and dynamic obstacles. Our novel method computes a polynomial-shaped path from current position to goal position for each map update (at 5 Hz), thus accomplishing both path planning and dynamic obstacle avoidance simultaneously. We demonstrate our methods operating in several dynamic scenarios, and compare our methods against paths planned using a numeric potential field.

关键词： Path planning Mobile robots Robot vision systems Cameras Robotics and automation Robot sensing systems computer vision computer networks Polynomials Mechanical sensors

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A real-time occupancy map from multiple video streams

A real-time occupancy map from multiple video streams

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IEEE International Conference on Robotics and Automation (ICRA)

We describe an algorithm to fuse intensity data from multiple video cameras to create a spatial-temporal occupancy map. The camera layout is assumed to resemble a security video network. The occupancy map is a two-dimensional raster image, uniformly distributed in the floor-plane. Each map pixel contains a binary value, signifying whether the designated floorspace is empty or occupied. Our algorithm requires only one difference and one look-up table operation to determine each pixel's effect upon the map. This brevity of operations allows the spatial occupancy map to be temporally computed at real-time video rates. We demonstrate our algorithm operating in several dynamic scenarios.

关键词： Streaming media Cameras Signal processing algorithms Fuses Data security Table lookup Image analysis Image processing Biomedical imaging laboratories

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Processor allocation for tasks that is robust against errors in computation time estimates

Processor allocation for tasks that is robust against errors...

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19th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2005

作者： Sugavanam, Prasanna V. Siegel, H.J. Maciejewski, Anthony A. Ali, Syed Amjad Al-Otaibi, Mohammad Aydin, Mahir Guru, Kumara Horiuchi, Aaron Krishnamurthy, Yogish G. Lee, Panho Mehta, Ashish Oltikar, Mohana Pichel, Ron Pippin, Alan J. Raskey, Michael Shestak, Vladimir Zhang, Junxing Colorado State University Department of Electrical and Computer Engineering Fort Collins CO 80523-1373 Colorado State University Department of Computer Science Fort Collins CO 80523-1373 Hewlett-Packard Company Systems and VLSI Technology Division Fort Collins CO 80528 Hewlett-Packard Company Linux and Open Source Lab. Fort Collins CO 80528 University of Utah School of Computing Salt Lake City UT 84112 Colorado State University IEEE Department of Computer Science ACM Eta Kappa Nu Electrical Engineering Honor Society Sigma Xi Science Honor Society Upsilon Pi Epsilon Computing Sciences Honor Society Department of Electrical and Computer Engineering IBM Department of Computer Science New Mexico Institute of Mining and Technology Premiere Systems Fairfax VA CSU Hewlett Packard Hewlett-Packard Company Systems-VLSI Lab. Hewlett-Packard Fort Collins CO Hewlett-Packard Company Fort Collins CO School of Computing University of Utah

ISBN: (纸本)0769523129

Heterogeneous computing systems composed of interconnected machines with varied computational capabilities often operate in environments where there may be sudden machine failures, higher than expected load, or inaccuracies in estimation of system parameters, Makespan (defined as the completion time for an entire set of tasks) is often the performance feature that is optimized in such systems. It is important that the makespan of a resource allocation (mapping) be robust against errors in task computation time estimates. The problem of optimally mapping tasks onto machines of a heterogeneous computing environment has been shown, in general, to be NP-complete. Therefore, heuristic techniques to find near optimal solutions to this mapping problem are required. The goal of this research is to find a static mapping of tasks so that the robustness of the desired system feature, makespan, is maximized against the errors in task execution time estimates. Seven heuristics to derive near-optimal solutions and an upper bound to this problem are presented and evaluated.

关键词： Resource allocation

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Automated Vulnerability Analysis of AC State Estimation under Constrained False Data Injection in Electric Power Systems

Automated Vulnerability Analysis of AC State Estimation unde...

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IEEE Annual Conference on Decision and Control

作者： Sicun Gao Le Xie Armando Solar-Lezama Dimitrios Serpanos Howard Shrobe MIT Computer Science and Artificial Intelligence Lab Department of Electrical and Computer Engineering Texas A&M University Qatar Computing Research Institute

ISBN: (纸本)9781479978878

We introduce new methods for the automatic vulnerability analysis of power grids under false data injection attacks against nonlinear (AC) state estimation. We encode the analysis problems as logical decision problems that can be solved automatically by SMT solvers. To do so, we propose an analysis technique named "symbolic propagation," which is inspired by symbolic execution methods for finding bugs and exploits in software programs. We show that the proposed methods can successfully analyze vulnerability of AC state estimation in realistic power grid models. Our approach is generalizable towards many other applications such as power flow analysis and state estimation.

关键词： power systems State estimation Power grids Power flow analysis False Vulnerability AUTOMATIC

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An exploratory study on the relation between user interface complexity and the perceived quality

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8541卷 370-379页

作者： Taba, Seyyed Ehsan Salamati Keivanloo, Iman Zou, Ying Ng, Joanna Ng, Tinny School of Computing Queen’s University Canada Department of Electrical and Computer Engineering Queen’s University Canada IBM Toronto Lab MarkhamON Canada

The number of mobile applications has increased drastically in the past few years. Some applications are superior to the others in terms of user-perceived quality. User-perceived quality can be defined as the user’s opinion of a product. For mobile applications, it can be quantified by the number of downloads and ratings. Earlier studies suggested that user interface (UI) barriers (i.e., input or output challenges) can affect the user-perceived quality of mobile applications. In this paper, we explore the relation between UI complexity and user-perceived quality in Android applications. Furthermore, we strive to provide guidelines for the proper amount of UI complexity that helps an application achieve high user-perceived quality through an empirical study on 1,292 mobile applications in 8 different categories. © Springer International Publishing Switzerland 2014.

关键词： User interfaces

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