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检索条件"任意字段=IEEE International Conference on Distributed Computing in Sensor Systems"

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A Counterfactual Ultrasound Anti-Interference Self-Supervised Network for B-mode Ultrasound Tongue Extraction

A Counterfactual Ultrasound Anti-Interference Self-Supervise...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yan Jia Yuqing Cheng Kele Xu Yong Dou Peng Qiao Zhouyu He National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue’s surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Traditional supervised learning models often require large labeled datasets, which are labor-intensive to produce and susceptible to noise interference. To address these limitations, we present a novel Counterfactual Ultrasound Anti-Interference Self-Supervised Network (CUAI-SSN), which integrates self-supervised learning (SSL) with counterfactual data augmentation, progressively disentangles confounding factors, ensuring that the model generalizes well across varied ultrasound conditions. Our approach leverages causal reasoning to decouple noise from relevant features, enabling the model to learn robust representations that focus on essential tongue structures. By generating counterfactual image-label pairs, our method introduces alternative, noise-independent scenarios that enhance model training. Furthermore, we introduce attention mechanisms to enhance the network’s ability to capture fine-grained details even in noisy conditions. Extensive experiments on real ultrasound tongue images demonstrate that CUAI-SSN outperforms existing methods, setting a new benchmark for automated contour extraction in ultrasound tongue imaging. Our code is publicly available at https://***/inexhaustible419/CounterfactualultrasoundAI.

关键词： Training Ultrasonic imaging Tongue Self-supervised learning Data augmentation Data models Cognition Data mining Noise measurement Signal to noise ratio

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Taxonomy And Survey Of Performance Prediction systems For The distributed systems Including The Clouds

Taxonomy And Survey Of Performance Prediction Systems For Th...

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ieee/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Sena Seneviratne Liyanage C De Silva Sanjeeva Witharana School of Electrical & Information Engineering Sydney University Sydney Australia Faculty of Integrated Technologies Universiti Brunei Darussalam Brunei University of Leeds United Kingdom

ISBN: (纸本)9781665417631

As the distributed System (DS) including Cloud computing is a congregation of geographically distributed and heterogeneous resources, the efficient scheduling of their resources requires a sound Resource Management System (RMS). The RMS requires the support of a sound Performance Prediction System (PPS). The performance prediction of resources is helpful for both Resource Management systems and users to make optimized resource usage decisions. There have been many PPS projects that span over several resources in several dimensions. Some of these PPS projects have been initiated for prediction of resources on the distributed systems such as Grids, Clusters or Peer-Peer systems and therefore in a software engineering point of view, they are adaptable for the prediction of resources on the Clouds. In this paper the taxonomy for describing the PPS architecture is discussed. The taxonomy is used to categorize and identify approaches which are followed in the implementation of the existing PPSs for the distributed systems such as Grids/ Clusters/ Peer-Peer and their adaptation in the Cloud computing. The taxonomy and the survey results can be used to identify approaches and issues that have not been fully explored in research.

关键词： Cloud computing Social computing Processor scheduling Taxonomy Computer architecture Resource management Engines

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1st international Scientific conference on Telecommunications, computing and Control, TELECCON 2019

1st International Scientific Conference on Telecommunication...

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1st international Scientific conference on Telecommunications, computing and Control, TELECCON 2019

ISBN: (纸本)9789813366312

The proceedings contain 46 papers. The special focus in this conference is on Telecommunications, computing and Control. The topics include: Solving Multicriteria Optimization Problem for an Oil Refinery Plant;methods and Techniques for Increasing the Accuracy of Continuous Non-invasive Blood Pressure Measurement Under Dynamic Loads;computer Modeling of Robust Control of Vibrationless Movement of Multi-mode Flexible Structures;feature-Based Plant Seedlings Classification;medical Training Simulation in Virtual Reality;application of the Hybrid Model to Numerical Modeling of the Urban Transport Network Topology;Synchronization Scheme for UWB Wireless sensor Network System;the Deep Survival Forest and Elastic-Net-Cox Cascade Models as Extensions of the Deep Forest;an Explanation Method for Siamese Neural Networks;detection and Prediction of Safety Faults in Inter-Device Networks Applying a Set of Data-Driven Methods;Hierarchical Multi-agent System for Production Control Using KPI Reconciliation;semi-supervised Learning for Medical Image Segmentation;developing a New Generation of Reconfigurable Heterogeneous distributed High Performance computing System;Usage of a BART Algorithm and Cognitive Services to Research Collaboration Platforms;a Computer-Aided Diagnosis System in the Diagnosis of Multiple Sclerosis;Predicting Students’ Performance on MOOC Using Data Mining Algorithms;on the Implementation of the Planar3D Model Using the Explicit Time Integration Scheme and the Statistical Front Tracking Method;fast Fourier Transform in Planar3D Model Using an Explicit Numerical Integration Scheme;employee Performance Analytics Approach Based on Anomaly Detection in User Activity;deep Predictive Control;development of an Algorithm for Determining the Railway Tracks on Video Image;on the Computational Complexity of Deep Learning Algorithms.

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Marine Intelligent distributed Temperature and Humidity Collection System Based on Narrow-Band IoT Architecture 10th

Marine Intelligent Distributed Temperature and Humidity Coll...

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10th international conference on Computer Engineering and Networks, CENet 2020

作者： Hu, Congliang Wan, Huaqing Ding, Wei 719 Research Institute Wuhan China

ISBN: (纸本)9789811584619

In recent years, the rapid development of the shipping industry at sea has become an essential part of Chinas economic development. Large ships carry more items, and changes in temperature and humidity in the closed cabin are especially crucial for preserving items. To grasp the change of temperature and humidity in the cabin in time, an intelligent distributed temperature and humidity collection system based on narrowband Internet of things is designed. The system combines microprocessor module (STM32), sensor module, and narrowband Internet of things to realize a real-time collection of temperature and humidity in the cabin and alarm function when the change exceeds the threshold. The mobile APP is used to realize remote monitoring and data collection monitoring. narrowband internet of things;temperature and humidity collection;STM32;remote control. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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AutoSec: Multidimensional Timing-Based Anomaly Detection for Automotive Cybersecurity 26

AutoSec: Multidimensional Timing-Based Anomaly Detection for...

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ieee 26th international conference on Embedded and Real-Time computing systems and Applications (RTCSA)

作者： Tepic, Milan Abdelaal, Mohamed Weber, Marc Rothermel, Kurt Univ Stuttgart Inst Parallel & Distributed Syst Stuttgart Germany Vector Informat GmbH Stuttgart Germany

ISBN: (纸本)9781728144030

Nowadays, autonomous driving and driver assistance applications are being developed at an accelerated pace. This rapid growth is primarily driven by the potential of such smart applications to significantly improve safety on public roads and offer new possibilities for modern transportation concepts. Such indispensable applications typically require wireless connectivity between the vehicles and their surroundings, i.e. roadside infrastructure and cloud services. Nevertheless, such connectivity to external networks exposes the internal systems of individual vehicles to threats from remotely-launched attacks. In this realm, it is highly crucial to identify any misbehavior of the software components which might occur owing to either these threats or even software/hardware malfunctioning. In this paper, we introduce AutoSec, a host-based anomaly detection algorithm which relies on observing four timing parameters of the executed software components to accurately detect malicious behavior on the operating system level. To this end, AutoSec formulates the task of detecting anomalistic executions as a clustering problem. Specifically, AutoSec devises a hybrid clustering algorithm for grouping a set of collected timing traces resulted from executing the legitimate code. During the runtime, AutoSec simply classifies a certain execution as an anomaly, if its timing parameters are distant enough from the boundaries of the predefined clusters. To show the effectiveness of AutoSec, we collected timing traces from a testbed composed of a set of real and virtual control units communicating over a CAN bus. We show that using our proposed AutoSec, compared to baseline methods, we can identify up to 21% less false positives and 18% less false negatives.

关键词： Anomaly Detection Real-time Operating systems Timing Constraints DBSCAN Classifiers

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1st international conference of Advanced computing and Informatics, ICACIN 2020

1st International Conference of Advanced Computing and Infor...

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1st international conference of Advanced computing and Informatics, ICACIN 2020

ISBN: (纸本)9789811560477

The proceedings contain 56 papers. The special focus in this conference is on Advanced computing and Informatics. The topics include: Evaluation of Several Artificial Intelligence and Machine Learning Algorithms for Image Classification on Small Datasets;A Comprehensive Study of Dissemination and Data Retrieval in Secure VANET-Cloud Environment;toward to Autonomous Broker for Virtual Network Provisioning and Monitoring;comparative Study on Random Traveling Wave Pulse-Coupled Oscillator Algorithm of Energy-Efficient Wireless sensor Networks;impact of Inspirational Motivation on Organizational Innovation (Administrative Innovation, Process Innovation, and Product Innovation);managing the Smart Grid in Free Market;an Haptic Display for Visually Impaired Pedestrian Navigation;GPU Parallelization for Accelerating 3D Primitive Equations of Ocean Modeling;yemeni Paper Currency Recognition System Using Deep Learning Approach;tuning Hyper-Parameters of Machine Learning Methods for Improving the Detection of Hate Speech;performance Comparison of Machine Learning Techniques in Identifying Dementia from Open Access Clinical Datasets;Clustering Analysis to Improve Web Search Ranking Using PCA and RMSE;preface;optimization of a Similarity Performance on Bounded Content of Motion Histogram by Using distributed Model;A Parallel Global TFIDF Feature Selection Using Hadoop for Big Data Text Classification;robust and Accurate Method for Textual Information Extraction Over Video Frames;a Deep Learning Architecture for Profile Enrichment and Content Recommendation;Predictive Data Analysis Model for Employee Satisfaction Using ML Algorithms;a Novel Approach for Big Data Monetization as a Service;ensemble Feature Selection Method Based on Bio-inspired Algorithms for Multi-objective Classification Problem;automated Binary Classification of Diabetic Retinopathy by Convolutional Neural Networks.

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Efficient Electronic Transaction Query Model for Light Nodes Based On Optimized Blockchain Structure

Efficient Electronic Transaction Query Model for Light Nodes...

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ieee international conference on High Performance computing and Communications (HPCC)

作者： Mingjie Zhao Cheng Dai Yuting Zhao Bing Guo College of Computer Science Sichuan University Chengdu China

Various lightweight devices, such as mobile banks and IoT devices used for wireless payments and others, are increasingly incorporating blockchain-based electronic transaction systems. However, querying transactions requires synchronizing all current blocks, which wastes a great deal of time and storage space and is unsuitable for the timeliness requirements of electronic transaction scenarios and light node's low storage capacity environment. Therefore, studying the electronic transaction query methods appropriate for light nodes is urgent. In this paper, we optimize the block structure by introducing a block storage structure that can dynamically increase the number of transactions; and we also designed an electronic transaction model for an automotive manufacturer that can query data from both blockchain and non-blockchain systems. Simulation results reveal that the electronic transaction query model presented in this work reduces the quantity of data that must be downloaded for querying and boosts query speed by about fourfold compared to the SPV query model of blockchain, it also greatly improves the performance of the original system in the car manufacturer. Our architecture allows light nodes to adapt to the query needs of electronic transaction situations and may be adapted to other blockchain application scenarios.

关键词： Wireless communication Adaptation models Wireless sensor networks Smart cities Simulation Data models Blockchains

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Modeling Datasets in distributed Monitoring systems

Modeling Datasets in Distributed Monitoring Systems

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ieee international Workshop on Intelligent Data Acquisition and Advanced computing systems: Technology and Applications

作者： Yana A. Bekeneva Saint Petersburg Electrotechnical University “LETI” St. Petersburg Russian Federation

ISBN: (纸本)9781665426060

Automated detection of abnormal events in processes in real time is an urgent and important task. To identify various kinds of anomalies, various Data Mining methods are often used. Often, to obtain high accuracy of the analysis, preliminary training is required using a large amount of data and representative samples. However, it is not always possible to obtain a representative sample of various kinds of anomalies from real sources. This paper proposes an approach to modeling datasets generated by distributed heterogeneous monitoring systems.

关键词： Training conferences Data acquisition distributed databases Training data Data models Real-time systems

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distributed Nonlinear Fusion UKF 16

Distributed Nonlinear Fusion UKF

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16th ieee international conference on Control and Automation (ICCA)

作者： Hu, Zhongyao Chen, Bo Wang, Rusheng Hong, Zhen Chen, Qiuxia Zhejiang Univ Technol Dept Automat Hangzhou 310023 Peoples R China Zhejiang Univ Technol Inst Cyberspace Secur Hangzhou 310023 Peoples R China Zhejiang Shuren Univ Coll Informat Sci Hangzhou 310015 Peoples R China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190938

This paper is concerned with the distributed fusion estimation problem for nonlinear multi-sensor systems. As is well known, the weighted fusion methods minimize the trace of fusion estimate error covariance, and it can be divided into three categories: scalar weighting, diagonal matrix weighting and matrix weighting, in which the matrix weighting has the highest accuracy. However, the matrix weighting needs to know the cross covariances between the local estimates, which cannot be easily and precisely calculated in the nonlinear systems. To overcome this shortage, this paper proposes a novel method to calculate the cross covariances between the local unscented Kalman filters based on statistical linear regression, and the corresponding distributed nonlinear matrix weighting fusion criterion is developed. Finally, a target tracking system is employed to show the advantages and the effectiveness of the proposed method.

关键词： Covariance matrices Kalman filters Linear regression Random variables Estimation Taylor series Target tracking

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Efficient and precise sensor fusion for non-linear systems with out-of-sequence measurements by example of mobile robotics

Efficient and precise sensor fusion for non-linear systems w...

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ieee international conference on Robotics and Automation (ICRA)

作者： Boehmler, Pascal Dziedzitz, Jonathan Hopfgarten, Patric Specker, Thomas Lange, Ralph Karlsruhe Inst Technol KIT Inst Mat Handling & Logist Karlsruhe Germany Robert Bosch GmbH Corp Res Renningen Germany

ISBN: (纸本)9781728173955

For most applications in mobile robotics, precise state estimation is essential. Typically, the state estimation is based on the fusion of data from different sensors. In practice, these sensors differ in their characteristics and measurements are available to the sensor fusion algorithm only with delay. Based on a brief survey of sensor fusion approaches that consider delayed and out-of-sequence availability of measurements, suitable approaches for applications in mobile robotics are identified. In a consumer robot use-case, experiments show that the estimation is biased if delayed availability of measurements is not considered appropriately. However, if delays are considered in the fusion process, the estimation bias is reduced to almost zero and in consequence, the estimation performance is distinctly improved. Two computational favorable approximative methods are described and provide almost the same accuracy as - theoretically optimal - brute-force filter recalculation at much lower and well-distributed computational costs.

关键词： State estimation

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