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Localized Target Detection Algorithms in Low Illumination Wide-Field of View High-Resolution Environments 24

Localized Target Detection Algorithms in Low Illumination Wi...

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4th International conference on Artificial Intelligence, Big data and Algorithms, CAIBDA 2024

作者： Xie, Chengyou Huang, Xiaohui Jia, Zhenhong Song, Sensen Wang, Jiajia Zhou, Gang Shi, Fei Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi Xinjiang China

ISBN: (纸本)9798400710247

To solve the problem of difficult detection of small targets in wide-field videos captured by eagle-eye equipment in low-illumination environments. we propose a local moving target detection algorithm based on sample consistency. First, the initial background model is established using the first ten frames of the video. the contrast and texture features between the current and background frames are calculated, and the S component in the HSV space is extracted and fused to obtain the final features. Finally, the target area is located through the global segmentation map and the local tracking detection module is activated. Experimental results demonstrate that Compared to other algorithms, the proposed algorithm reduces the running time by implementing local detection. It effectively minimizes the interference caused by light sources and dark spot noise in low-light environments, thereby enhancing the accuracy of the algorithm. © 2024 Copyright held by the owner/author(s).

关键词： Object detection

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Integrated Long-range Sensing and Communications in Multi Target Scenarios using CP-OFDM

Integrated Long-range Sensing and Communications in Multi Ta...

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International ITG conference on Source and Channel Coding (SCC)

作者： Benedikt Geiger Silvio Mandelli Marcus Henninger Daniel Gil Gaviria Charlotte Muth Laurent Schmalen Communications Engineering Lab (CEL) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Nokia Bell Labs Stuttgart Stuttgart Germany

ISBN: (数字)9798331522896

ISBN: (纸本)9798331522902

6G communication systems promise to deliver sensing capabilities by utilizing the orthogonal frequency division multiplexing (OFDM) communication signal for sensing. How-ever, the cyclic prefix inherent in OFDM systems limits the sensing range, necessitating compensation techniques to detect small, distant targets like drones. In this paper, we show that state-of-the-art coherent compensation methods fail in scenarios involving multiple targets, resulting in an increased noise floor in the radar image. Our contributions include a novel multi target coherent compensation algorithm and a generalized signal-to-interference-and-noise ratio for multiple targets to evaluate the performance. Our algorithm achieves the same detection performance at long distances requiring only 3.6 % of the radio resources compared to classical OFDM radar processing. this enables resource efficient sensing at long distances in multi target scenarios with legacy communications-only networks.

关键词： 6G mobile communication OFDM Radar detection Interference Radar Radar imaging Sensors Reliability signal to noise ratio Drones

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A versatile distributed MCMC algorithm for large scale inverse problems 30

A versatile distributed MCMC algorithm for large scale inver...

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30th European signal processing conference (EUSIPCO)

作者： thouvenin, Pierre-Antoine Repetti, Audrey Chainais, Pierre Univ Lille CNRS Cent Lille UMR CRIStAL 9189 F-59000 Lille France Heriot Watt Univ Dept Actuarial Math & Stat Edinburgh EH14 4AS Midlothian Scotland Heriot Watt Univ Inst Sensors Signals & Syst Edinburgh EH14 4AS Midlothian Scotland

ISBN: (纸本)9789082797091

For large scale inverse problems, inference can be tackled with distributed algorithms, dividing the task over multiple computing nodes or cores referred to as workers. Since random sampling methods yield not only estimates but also credibility intervals, we leverage data augmentations and MCMC algorithms to design a distributed sampler. In contrast with usual approaches relying on a client-server architecture, we propose a flexible distributed sampler relying on a Single Program Multiple data implementation, in which all workers have a similar task. this distributed strategy allows the computing time and volume of communications to be reduced by separately handling blocks of data and parameters on different workers. Experiments on a large synthetic image inpainting problem illustrate the performance of the proposed approach to produce high quality estimates in a small amount of time.

关键词： Markov chain Monte-Carlo methods distributed algorithm inverse problems Single Program Multiple data architecture

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14th International Workshop on Computational Diffusion MRI, CDMRI 2023 held in conjunction with 26th International conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023

14th International Workshop on Computational Diffusion MRI, ...

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14th International Workshop on Computational Diffusion MRI, CDMRI 2023 held in conjunction with 26th International conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023

ISBN: (纸本)9783031472916

the proceedings contain 17 papers. the special focus in this conference is on Computational Diffusion MRI. the topics include: FASSt: Filtering via Symmetric Autoencoder for Spherical Superficial White Matter Tractography;anisotropic Fanning Aware Low-Rank Tensor Approximation Based Tractography;BundleCleaner: Unsupervised Denoising and Subsampling of Diffusion MRI-Derived Tractography data;a Deep Network for Explainable Prediction of Non-imaging Phenotypes Using Anatomical Multi-view data;reTrace: Topological Evaluation of White Matter Tractography Algorithms Using Reeb Graphs;Advanced Diffusion MRI Modeling Sheds Light on FLAIR White Matter Hyperintensities in an Aging Cohort;Neural Spherical Harmonics for Structurally Coherent Continuous Representation of Diffusion MRI signal;A Unified Learning Model for Estimating Fiber Orientation Distribution Functions on Heterogeneous Multi-shell Diffusion-Weighted MRI;Diffusion Phantom Study of Fiber Crossings at Varied Angles Reconstructed with ODF-Fingerprinting;improving Multi-Tensor Fitting with Global Information from Track Orientation Density Imaging;bundleSeg: A Versatile, Reliable and Reproducible Approach to White Matter Bundle Segmentation;Automated Mapping of Residual Distortion Severity in Diffusion MRI;automatic Fast and Reliable Recognition of a small Brain White Matter Bundle;Self Supervised Denoising Diffusion Probabilistic Models for Abdominal DW-MRI;voxlines: Streamline Transparency through Voxelization and View-Dependent Line Orders.

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Using data Analytics to Strengthen Monitoring and Surveillance of Routine Immunization Coverage for Children under One Year in Uganda 14

Using Data Analytics to Strengthen Monitoring and Surveillan...

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14th International Joint conference on Biomedical Engineering Systems and Technologies (BIOSTEC) / 14th Int Conf on Bio-inspired Systems and signal processing (BIOsignalS) / 14th Int Conf on Biomedical Electronics and Devices (BIODEVICES)

作者： Nantongo, Bartha Alexandra Nabukenya, Josephine Nabende, Peter Makerere Univ Coll Comp & Informat Syst Dept Informat Syst POB 7062 Kampala Uganda

ISBN: (纸本)9789897584909

Immunization coverage is a traditional key performance indicator that enables stakeholders to monitor child health, investigate gaps, and take remedial actions. It is continuously challenged by validity due to the neglect of unstructured data and process indicators that track small changes/milestones. While empirical evidence indicates digitalized immunization systems establish coverage from structured data, renowned administrative and household survey estimates are often inaccurate/untimely. Government instituted awareness, accessibility, and results-based performance approaches, but stakeholders are challenged by accurate monitoring of performance against Global Vaccination Action Plan coverage targets. this heightens inappropriate strategy implementation leading to persistent low coverage and declining trends. there is scanty literature substantiating the essence of comprehensive immunization indicators in monitoring evidence-based and timely interventions. For this reason, health workers failed to appreciate immunization process indicators and monitoring role. the study aims at developing a real-time immunization coverage monitoring framework that supports evidence-based strategy implementation using prescriptive analytics. the envisaged artifact analyzes a variety of data and monitors immunization performance against comprehensive indicators. It is a less resource-demanding strategy that prompts accurate and real-time insights to support intervention implementation decisions. this study will follow an explanatory research approach by first collecting quantitative data and later qualitative for in-depth analysis.

关键词： data Analytics Monitoring Routine Immunization Coverage Performance

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Classification of Left/Right Hand Movement from EEG signal by Empirical Mode Decomposition with Ensemble of classifiers

Classification of Left/Right Hand Movement from EEG Signal b...

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International conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Neeraj Choudhary Kodidasu Murali Kumar Jimson Mathew Sravanthi Acchugatla Dept. of Computer Science and Engg. Mahindra University Hyderabad India Dept. of Computer Science and Engg. Indian Institute of Technology Patna Patna India

Electroencephalography (EEG) is a brain wave signal that plays a significant role in the neural control interface. EEG signals have many nonlinear dynamic properties; because of these properties, handling EEG signals for modeling with standard linear approaches will lead to inconsistent results. EEG has a significant importance in the brain-computer interface (BCI). BCI has demonstrated a great deal of potential to benefit the human way of life. Additionally, its application targets people with disabilities. BCI extracts the informative featuresfrom acquired brain signals and generates signals knowing these features, which can control external devices. EEG signals can show improvement in classifying motor imagery tasks. In this paper, we introduce a new method for extracting time-frequency-related properties on different scales using empirical mode decomposition (EMD) and classifying the left and right hand movements of the subject. EMD is a signal processing technique that decomposes a signal into different frequency bands of the same length. We use an ensemble of classifiers with bootstrapped data for classification. the proposed method perform better than the previous method [1] in terms of accuracy and F1-score. the empirical results show that the proposed method has an average accuracy of 97.30% and an average F1 score of 96.35%.

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Feature Space Reduction for Human Activity Recognition based on Multi-channel Biosignals 14

Feature Space Reduction for Human Activity Recognition based...

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作者： Hartmann, Yale Liu, Hui Schultz, Tanja Univ Bremen Cognit Syst Lab Bremen Germany

ISBN: (纸本)9789897584909

In this paper, we study the effect of Feature Space Reduction for the task of Human Activity Recognition (HAR). For this purpose, we investigate a Linear Discriminant Analysis (LDA) trained with Hidden Markov Models (HMMs) force-aligned targets. HAR is a typical application of machine learning, which includes finding a lower-dimensional representation of sequential data to address the curse of dimensionality. this paper uses three datasets (CSL19, UniMiB, and CSL18), which contain data recordings from humans performing more than 16 everyday activities. data were recorded with wearable sensors integrated into two devices, a knee bandage and a smartphone. First, early-fusion baselines are trained, utilizing an HMM-based approach with Gaussian Mixture Models to model the emission probabilities. then, recognizers with feature space reduction based on stacking combined with an LDA are evaluated and compared against the baseline. Experimental results show that feature space reduction improves balanced accuracy by ten percentage points on the UniMiB and seven points on the CSL18 datasets while remaining the same on the CSL19 dataset. the best recognizers achieve 93.7 +/- 1.4% (CSL19), 69.5 +/- 8.1% (UniMiB), and 70.6 +/- 6.0% (CSL18) balanced accuracy in a leave-one-person-out cross-validation.

关键词： Human Activity Recognition Biosignals Multi-channel signal processing Feature Space Reduction Stacking

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Reliability Evaluation of Time Synchronization Device Based on BP Neural Network 14

Reliability Evaluation of Time Synchronization Device Based ...

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14th IEEE Global Reliability and Prognostics and Health Management conference, PHM-Hangzhou 2023

作者： Zhang, Zhenjun Wen, Tan Su, Yan Zhou, Feiyu State Grid Electric Power Research Institute Nanjing China Nanjing University of Aeronautics and Astronautics College of Civil Aviation Nanjing China

ISBN: (纸本)9798350301359

the time synchronization device can provide stable synchronization signal for the whole station equipment to ensure the synchronization performance of the whole station equipment. If there is a fault, the reliability of the whole system cannot be guaranteed. therefore, the reliability of the newly developed time synchronization device needs to be evaluated. As a newly developed device, it cannot provide sufficient reliability analysis data, while BP neural network has good data prediction ability and the advantages of Bootstrap method in small sample expansion. the fusion algorithm based on BP neural network and Bootstrap method is established to realize the data expansion from small sample size to large sample size. the least squares linear fitting method is used to estimate the Weibull distribution parameters, which can effectively evaluate the reliability of the time synchronization device. © 2023 IEEE.

关键词： Bootstrap BP neural network reliability assessment small sample Time synchronization device

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Graph signal processing Based Product Inspection Using Polarization Imaging 1

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14th International conference on Sensing Technology, ICST 2022

作者： Gigie, Andrew Sahu, Saurabh Anil Kumar, A. Kumar, Kriti Girish Chandra, M. Chakravarty, Tapas Embedded Devices and Intelligent Systems TCS Research Bangalore India Embedded Devices and Intelligent Systems TCS Research Kolkata India

ISBN: (数字)9783030988869

ISBN: (纸本)9783030988852

Polarization imaging has the ability to reveal certain material characteristics. With the availability of integrated polarization camera systems, they are increasingly being deployed for inspection tasks. In this paper, we process the data from polarization imaging using the emerging concepts of graph signal processing to effectively detect and localize these defects. Graph total variation is exploited to quantify the spread of minuscule defects such as fractures or cuts and graph filter is employed to localize these defects. Experimental results indicate that the proposed approach is capable of detecting and localizing defects as small as 1 mm with good accuracy compared to conventional approach. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Defects

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Transfer of Industry 4.0 Knowledge to SME Employing the Concept of a Learning Factory as a Roadshow 14th

Transfer of Industry 4.0 Knowledge to SME Employing the Conc...

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14th International conference on Learning Factories (CLF)

作者： Prell, Bastian Gunther, Norman Wilbers, Simon van de Sand, Ron Reiff-Stephan, Jorg Univ Appl Sci Wildau Hsch Ring 1 D-15745 Wildau Germany

ISBN: (纸本)9783031653995;9783031654008

this contribution reports on the roadshow, a collection of Industry 4.0 applications operated by the department of smart production of the University of Applied Sciences Wildau. It targets the transfer between the institutions of higher education and small- and medium-sized enterprises but also integrates the transfer between students and university respective industry. the roadshow compiles several use cases for Industry 4.0 applications in a linked production system, arranged around the task of producing awooden 3D puzzle. thus, the roadshowis a mobile-mounted Learning Factory on a truck that can be transported to various locations, such as company events or fares. Participants can choose from different options of digital support for the respective task at hand. E.g., for the assembly of the 3D puzzle the options consist of a traditional paper-based manual, an interactive assembly guide through Mixed Reality (MR) via Microsoft HoloLens, a voice-controlled laser projection, or a tablet application also using MR. Once all steps are finished, the customised 3D puzzle is handed out to the visitor. Such a setup allows for sensitising participants and building up their acceptance toward the technologies of Industry 4.0. the contribution closes by presenting results achieved so far in terms of lessons learned as well as a first validation of the effectiveness of such a transfer approach considering vital data through a model for technostress.

关键词： Industry 4.0 Knowledge Transfer Mixed Reality Technostress and Learning Vital data Technology Acceptance

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