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检索条件"任意字段=International Conference on Measurement and Modeling of Computer Systems"

共 91621 条记录，以下是1451-1460 订阅

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Vertical Launch Angle measurement of a Golf Ball Using Audio and Monocular Video Data 11

Vertical Launch Angle Measurement of a Golf Ball Using Audio...

引用

11th international conference on Sport Sciences Research and Technology Support, icSPORTS 2023

作者： Menke-Zumbragel, Jan-Bernd Brinkmann, Anna Hein, Andreas Assistance Systems and Medical Device Technology Carl von Ossietzky University of Oldenburg Germany

ISBN: (纸本)9789897586736

In golf, several parameters can be measured that describe how the golf ball was hit and how the ball lifts after impact with the golf club, the so-called launch parameters. In addition to the spin rate or the velocity of the ball and club head, the launch angle is an important value that describes the vertical component of the ball's launch direction. For professional use, there are systems called launch monitors that use either radar-technology, multiple high-speed cameras or a combination of both to measure the above parameters. Despite their high accuracy, these systems can suffer from disadvantages such as some inconvenience regarding size, weight or setup, and an inaccessibility regarding high cost. Therefore we present a method for vertical launch angle measurement based on monocular low frame rate video and audio data, by detecting the motion blur structure created by the launching golf ball. This approach allows the vertical launch angle to be measured with a simple and inexpensive setup that achieves an accuracy of } 0.74°, which is comparable to a commercial launch monitor. © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： computer vision

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Enhanced Non-Contact Vital Sign Monitoring via Depth Camera-Assisted Millimeter-Wave Radar

Enhanced Non-Contact Vital Sign Monitoring via Depth Camera-...

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2024 international conference on Artificial Intelligence of Things and systems, AIoTSys 2024

作者： Zhang, Hanwen Dou, Chenglong Liu, Yucheng Wu, Yuan Qian, Liping Shi, Zhiguo Lu, Rongxing University of Macau State Key Laboratory of Internet of Things for Smart City China Zhuhai UM Science and Technology Research Institute Zhuhai China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China University of New Brunswick Faculty of Computer Science FrederictonNBE3B 5A3 Canada

ISBN: (纸本)9798331507886

Non-contact vital sign monitoring with mmWave radar has been regarded as an effective method for identifying lung and heart diseases due to its strong penetration and non-invasive capabilities. Nevertheless, relying solely on millimeter-wave radar for vital sign monitoring is susceptible to interference from multipath reflections, which degrades the measurement accuracy. To address this issue and enhance measurement accuracy, we propose a system design that leverages the depth camera for precise target location and subsequently employs dynamic beamforming techniques to effectively monitor the specified region. The experimental results demonstrate that our proposed system achieves an average error of 2.44 beats per minute (BPM) for heart rate monitoring and 0.22 breaths per minute (BPM) for respiratory rate monitoring. Compared to systems without beamforming, our system reduces the mean error for heart rate monitoring by 4.21 BPM and for respiratory rate monitoring by 0.51 BPM. © 2024 IEEE.

关键词： Heart

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17th international Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2024

17th International Joint Conference on Biomedical Engineerin...

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17th international Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2024

The proceedings contain 204 papers. The special focus in this conference is on Biomedical Engineering systems and Technologies. The topics include: A POCT to Rapid Detect GBS with Highly Sensitivity;Characterization of sEMG Spectral Properties During Lower Limb Muscle Activation;an Event-Driven Closed-Loop Ultrasound Stimulator Composed of a Micro-Transducer and Multi-Site Electrodes in Vitro;3D Nuclei Segmentation by Combining GAN Based Image Synthesis and Existing 3D Manual Annotations;centrality of the Fingerprint Core Location;breast Cancer Detection Using Smart Wearable Devices with Thermal Sensors;preliminary Results on the Evaluation of Different Feedback Methods for the Operation of a Muscle-Controlled Serious Game;integrated Driver Pose Estimation for Autonomous Driving;A Comparison of Recurrent and Convolutional Deep Learning Architectures for EEG Seizure Forecasting;biodegradable Biodevices: A Design Approach Based on Cellular Automaton;prototyping a Low-Cost Flexible Sensor Glove For Diagnostics and Rehabilitation;Performance Review of Retraining and Transfer Learning of DeLTA2 for Image Segmentation for Pseudomonas Fluorescens SBW25;assembling Close Strains in Metagenome Assemblies Using Discrete Optimization;Agent Simulation Using Path Telemetry for modeling COVID-19 Workplace Hazard and Risk;compositional Techniques for Asynchronous Boolean Networks;unsupervised Domain Adaptation for Medical Images with an Improved Combination of Losses;Classification of Fine-ADL Using sEMG Signals Under Different measurement Conditions;feature Selection Improves Speech Based Parkinson’s Disease Detection Performance;Contactless Camera-Based Detection of Oxygen Desaturation Events and ODI Estimation During Sleep in SAS Patients;hand Movement Recognition Based on Fusion of Myography Signals;Prediction of Oxygen Saturation from Graphene Respiratory Signals with PPG Trained DNN.

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Efficiency Electrical Power Edge-end Computing with Patch-based Low-rank Transformer

Efficiency Electrical Power Edge-end Computing with Patch-ba...

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2024 international conference on Energy and Electrical Engineering, EEE 2024

作者： Zhou, Mi Qian, Bin Lin, Xiaoming Zhang, Fan Luo, Yi Wu, Binquan Electric Power Research Institute Csg Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid Guangzhou China

ISBN: (纸本)9798350341768

The development of communication technology has made the need to build more reliable and efficient smart power grid systems imminent. The emergence of edge computing has significantly alleviated the pressure of data transmission in systems, enhancing the reliability and stability of cloud-edge based smart grid systems. However, the deep algorithms on which current Edge Computing platforms rely increase the overall computation time consumption and memory consumption of the whole system, which is inconsistent with the responsiveness and reliability requirements of smart power grid system services. Therefore, in this paper, we propose Patch-based Low-rank Transformer (PLT) that balances accuracy and efficiency, which can greatly alleviate the computation time consumption and memory consumption of Edge Computing platforms. Specifically, we cut the original electric power time series into subsequence-level patches to ensure the accuracy of the model for long and short-term sequential dependencies learning. Then, we introduce a low-rank attention mechanism to reduce the computational consumption of the model, and design a simple but efficient knowledge distillation module that transferring knowledge from the Transformer-based model to a lightweight MLP to speed up the computation of the model at inference phrase. Extensive experiments on 11 public datasets (10 UCR and 1 cluster-trace-v2018 released by Alibaba Cloud) and 13 collected electric power time series datasets show that the proposed PLT outperforms baseline models and reaches the design purpose. © 2024 IEEE.

关键词： Power systems computer aided design

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17th international Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2024

17th International Joint Conference on Biomedical Engineerin...

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17th international Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2024

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M-FUSE: Multi-frame Fusion for Scene Flow Estimation 23

M-FUSE: Multi-frame Fusion for Scene Flow Estimation

引用

23rd IEEE/CVF Winter conference on Applications of computer Vision (WACV)

作者： Mehl, Lukas Jahedi, Azin Schmalfuss, Jenny Bruhn, Andres Univ Stuttgart Inst Visualizat & Interact Syst Stuttgart Germany

ISBN: (纸本)9781665493468

Recently, neural network for scene flow estimation show impressive results on automotive data such as the KITTI benchmark. However, despite of using sophisticated rigidity assumptions and parametrizations, such networks are typically limited to only two frame pairs which does not allow them to exploit temporal information. In our paper we address this shortcoming by proposing a novel multi-frame approach that considers an additional preceding stereo pair. To this end, we proceed in two steps: Firstly, building upon the recent RAFT-3D approach, we develop an improved two-frame baseline by incorporating an advanced stereo method. Secondly, and even more importantly, exploiting the specific modeling concepts of RAFT-3D, we propose a U-Net architecture that performs a fusion of forward and backward flow estimates and hence allows to integrate temporal information on demand. Experiments on the KITTI benchmark do not only show that the advantages of the improved baseline and the temporal fusion approach complement each other, they also demonstrate that the computed scene flow is highly accurate. More precisely, our approach ranks second overall and first for the even more challenging foreground objects, in total outperforming the original RAFT-3D method by more than 16%. Code is available at https://***/cv-stuttgart/M-FUSE

关键词： Extrapolation computer vision Codes Neural networks Buildings Estimation computer architecture

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Focus! Rating XAI Methods and Finding Biases

Focus! Rating XAI Methods and Finding Biases

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IEEE international conference on Fuzzy systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / international Joint conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Arias-Duart, Anna Pares, Ferran Garcia-Gasulla, Dario Gimenez-Abalos, Victor Barcelona Supercomp Ctr BSC Barcelona Spain

ISBN: (数字)9781665467100

ISBN: (纸本)9781665467100

AI explainability improves the transparency and trustworthiness of models. However, in the domain of images, where deep learning has succeeded the most, explainability is still poorly assessed. In the field of image recognition many feature attribution methods have been proposed with the purpose of explaining a model's behavior using visual cues. However, no metrics have been established so far to assess and select these methods objectively. In this paper we propose a consistent evaluation score for feature attribution methods the Focus designed to quantify their coherency to the task. While most previous work adds out-of-distribution noise to samples, we introduce a methodology to add noise from within the distribution. This is done through mosaics of instances from different classes, and the explanations these generate. On those, we compute a visual pseudo-precision metric, Focus. First, we show the robustness of the approach through a set of randomization experiments. Then we use Focus to compare six popular explainability techniques across several CNN architectures and classification datasets. Our results find some methods to be consistently reliable (LRP, GradCAM), while others produce class-agnostic explanations (SmoothGrad, IG). Finally we introduce another application of Focus, using it for the identification and characterization of biases found in models. This empowers bias-management tools, in another small step towards trustworthy AI.

关键词： measurement Visualization Computational modeling computer architecture Robustness Behavioral sciences Labeling

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computer Vision-Based Structural Deformation Monitoring System on Android Smartphones: Design and Implementation 26

Computer Vision-Based Structural Deformation Monitoring Syst...

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26th international conference on Advanced Communications Technology, ICACT 2024

作者： Dong, Xiang Lai, Maokai Liang, Hui Wu, Peng Wang, Chaoxia Peng, Ting Chang'an University Xi'an710064 China Shaanxi Huashan Road and Bridge Group Co. Ltd Xi'an710016 China Shaanxi Zhengcheng Road and Bridge Engineering Research Institute Co. Ltd Xi'an712000 China Xi'an University of Posts and Telecommunications Xi'an710061 China

ISBN: (纸本)9791188428120

computer vision displacement monitoring techniques offer a promising alternative to traditional displacement sensors, but most current approaches or systems use high-cost cameras and require limited measurement sites. To facilitate widespread implementation in real-world engineering, simpler systems or approaches are needed. This paper introduces a computer vision-based structural deformation monitoring system, written and developed using OpenCV and Kotlin. The system uses an Android smartphone camera and telescope as the acquisition device and adopts template matching technology based on digital image correlation to realize target displacement monitoring. A real-time display of the offset curve is realized with MPAndroidChart, and socket TCP communication is used to transmit the monitoring data. The detailed operation process of the system is shown as an example of dynamic detection. The test results show that the system runs smoothly, is easy to operate, and has the following advantages: 1) Low cost of hardware composition and simple construction process;2) High monitoring accuracy (accurate to 0.01 mm);3) High monitoring frequency (up to 10 frames per second in dynamic detection mode);4) Visualization of monitoring data, with the monitoring structural offset presented in real time as a curve for easy user comprehension. Overall, this system can provide technical and auxiliary decision support for structural deformation monitoring. © 2024 Global IT Research Institute - GIRI.

关键词： computer vision

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Integrating Machine Learning and Social Sensing in Smart City Digital Twin for Citizen Feedback 25

Integrating Machine Learning and Social Sensing in Smart Cit...

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25th IEEE international conferences on High Performance Computing and Communications, 9th international conference on Data Science and systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Kumi, Sandra Lomotey, Richard K. Deters, Ralph University of Saskatchewan Department of Computer Science SK Canada The Pennsylvania State University Information Sciences and Technology PA United States

ISBN: (纸本)9798350330014

Smart City Digital Twin (SCDT), a virtual representation of a physical city, is an emerging technology for optimizing urban services and enhancing urban planning and decision-making. The integration of Machine Learning (ML) and social sensing provides valuable insights into public feedback to policymakers and for informed decision-making and responsive urban governance. This study aims to explore the use of social media data and topic modeling algorithms in the context of SCDT to highlight the concerns of citizens in Saskatoon, Canada. In this work, we use the Uniform Manifold Approximation and Projection (UMAP) and K-means clustering algorithm in the BERTopic architecture to extract 30 topics from comments collected through the Saskatoon subreddit posts. The topics were then merged into 15 themes to discover the concerns. A pretrained transformer model, SiEBERT was used to determine the sentiments of the Reddit comments. The research findings highlighted concerns such as - unreliable public transit, high cost of living, long wait times in emergency rooms, shortage of family doctors, drug addiction, and lack of mental awareness. © 2023 IEEE.

关键词： Sentiment analysis

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14th international conference on Optimization and Applications, OPTIMA 2023

14th International Conference on Optimization and Applicatio...

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14th international conference on Optimization and Applications, OPTIMA 2023

ISBN: (纸本)9783031487507

The proceedings contain 21 papers. The special focus in this conference is on Optimization and Applications. The topics include: Agent-Based Model of Cultural Landscape Evolution in Euclidean Space;development of an Agent-Based Optimization Model for the Human Capital Market;exact Algorithm for Generating H-Cores in Simplified Lattice-Based Protein Model;graph Density and Uncertainty of Graphical Model Selection Algorithms;swarm Intelligence Technique for Capacity Optimization of a Transportation Network;improving Background Subtraction Algorithms with Shadow Detection;optimal Route for Drone for Monitoring of Crop Yields;algorithm for Multi-criteria Optimization of Robot Parameters for Fruit Harvesting Based on Evolutionary Methods, Taking into Account the Importance of Criteria;application of Recursive Algorithms for Optimization and Approximation of Workspace of Parallel Robots;optimizing Parallelization Strategies for the Big-Means Clustering Algorithm;discrete Tomography Problems with Paired Projections and Complexity Characteristics;several Edge-Disjoint Spanning Trees with Given Diameter in a Graph with Random Discrete Edge Weights;development of Optimal Feedback for Zooplankton Seasonal Diel Vertical Migration;numerical Solution of an Inverse Problem for a General Hyperbolic Heat Equation;linear Optimization by Conical Projection;LIP Model in Solving RCPSP at the Flow Type Production;optimal Control of Sources with Feedback with Optimization of the Placement of measurement Points Along Given Trajectories;unimodality of Equilibrium Welfare in international Trade Under Monopolistic Competition;mathematical modeling in Forecasting the Development of the Construction Industry in the Russian Federation.

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