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检索条件"主题词=Filtering algorithms"

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Flogista: audio encryption with filtered triple Logistic map

Flogista: audio encryption with filtered triple Logistic map

引用

Automatics and Informatics (ICAI), International Conference

作者： Borislav Stoyanov Tsvetelina Ivanova Dimitar Dobrev Plamen Ribarski Department of Computer Informatics University of Shumen Shumen Bulgaria

ISBN: (数字)9798350353907

ISBN: (纸本)9798350353914

Faster and more secure ways are needed to give assurance for the security of audio transmissions in this rapid revolution of communication. A better alternative is now critical to be more secure in today's world, where everyone walks with their phones in pockets and may send a text or voice message at any moment. To increase data transmission confidentiality, we will provide a secure audio encryption model based on a filtered triple Logistic map. The procedure is put through several security checks. A powerful examination demonstrates that the approach with enhanced features outperforms other comparable audio encryption techniques.

关键词： filtering algorithms Data models Encryption Complexity theory Security Data communication Informatics Logistics

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A Hybrid Method for INS/GPS Integrated Navigation System Based on the Improved Karman Filter and Back Propagation Neural Network

A Hybrid Method for INS/GPS Integrated Navigation System Bas...

引用

Robotics, Control and Automation (ICRCA), International Conference on

作者： Mutian Hu Tao Song Jianchuan Ye School of Aerospace Engineering Beijing Institute of Technology Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350344721

ISBN: (纸本)9798350344738

The navigation system for unmanned aerial vehicle is commonly integrated by the inertial navigation system (INS) and global positioning system (GPS). Nevertheless, errors of INS/GPS accumulate over time when GPS suffers from outages. In this paper, a comprehensive filter called MH∞-5thCKF is proposed for taking the place of Karman filter (KF). It combines superiorities of H-infinity filter and multiple fading filter to enhance filtering precision and robustness. In addition to MH∞-5thCKF, an optimized back propagation neural network (BPNN) is utilized for GPS outages. The ELSHADE-SPACMA algorithm is introduced to select BPNN parameters. When satellite signals are available, the improved BPNN uses angular rates, specific forces and GPS increments to train the model. Once satellite signals are lost, the improved BPNN predicts pseudo-GPS information so that MH∞-5thCKF continues compensating INS errors. Compared with the conventional KF and BPNN, simulation results demonstrate that proposed algorithms not only enhance filtering performance, but also avoid BPNN falling into local optimum to guarantee model stability when GPS fails to work.

关键词： Backpropagation Satellites filtering Neural networks filtering algorithms Prediction algorithms Autonomous aerial vehicles

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Performance Analysis of Online Machine Learning Frameworks for Anomaly Detection in IoT Data Streams

Performance Analysis of Online Machine Learning Frameworks f...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Santosh Kumar Ray Seba Susan Department of Information Technology Delhi Technological University Delhi India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

With the rapid progress of technology, the Internet of Things (IoT) is vital in connecting real-time data sources. These sources generate a substantial volume of streaming data through various applications. When dealing with streaming data, there can be a significant amount of abnormal data, referred to as anomalies, which are completely unknown and can negatively affect the system’s performance. In this context, unsupervised anomaly detection methods are expected to perform better than supervised methods. In this paper, we evaluate the performance scores of nine unsupervised anomaly detection models belonging to two recently introduced online machine learning frameworks: River and Python Streaming Anomaly Detection (PySAD). The methodologies investigated in the River framework include Half Space Trees (HST), Quantile Filter One-Class Support Vector Machine (QF_OCSVM), Quantile Filter Half Space Trees (QF_HST), and Threshold Filter Half Space Trees (TF_HST). The Histogram-Based Outlier Scores (HBOS), Kitsune’s core algorithms (KitNet), Robust Random Cut Forest (RRCF), Isolation Forest Anomaly Streaming Data (IForestASD), and K Nearest Neighbor Conformal Anomaly Detection (KNNCAD) belong to the PySAD framework. The models are compared in terms of Accuracy, Sensitivity, Specificity, and ROCAUC on the IoTID20 streaming dataset. The paper concludes with a comparative study through performance plots on both frameworks. The River models HST, QF_HST, and TF_HST attained the highest Sensitivity scores of 1.00, whereas only the PySAD model KitNet passed this Sensitivity level. In terms of ROCAUC, the PySAD model IForestASD performs best (0.7937) followed by the River model QF_HST (0.7810), implying that QF_HST performs consistently best among all models.

关键词： Sensitivity Accuracy Computational modeling Machine learning filtering algorithms Nearest neighbor methods Real-time systems Rivers Anomaly detection Streams

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Low Illumination Image Enhancement Algorithm Based on Illumination Equalization and Edge Detail Enhancement

Low Illumination Image Enhancement Algorithm Based on Illumi...

引用

International Conference on Communications, Information System and Computer Engineering (CISCE)

作者： Wenchen Zhu Ruifeng Wang School of Automation and Electrical Engineering Lanzhou Jiaotong University Lanzhou Gansu China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

In order to ensure the safe and efficient operation of the railway, the railway foreign object intrusion monitoring system has become the main way to obtain information. Due to the low visibility of the images collected at night in the low illumination environment, the loss of detail information is serious, which seriously reduces the accuracy of video intelligent recognition. To solve these problems, this paper proposes an image enhancement algorithm based on illumination equalization and edge detail enhancement. Firstly, the S component and V component of the image in the color space HSV are stretched, and the adaptive bilateral filter function is used as the center surrounding function to extract the light component of the low illumination image. Then, the two-dimensional gamma function is used to correct the image brightness to obtain the illumination balanced image; Then the four direction Sobel operator is used to extract the edge details of the image, and the image depicting the edge details is obtained; Finally, the enhanced image is obtained by multi weight feature pyramid image fusion. The method proposed in this paper is tested by selecting the video images in the low illumination environment at night in the railway integrated monitoring system. The experimental results show that the method proposed in this paper is superior to other classical algorithms in terms of SSIM, PSNR, NIQE evaluation indicators. The algorithm in this paper can effectively improve the brightness and clarity of low illumination images at night, while making the enhanced image contain more image edge details.

关键词： Image color analysis Image edge detection Brightness Lighting filtering algorithms Rail transportation filtering theory

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Label Management for Multi-Object Estimates via Labelled Multi-Bernoulli Filters

Label Management for Multi-Object Estimates via Labelled Mul...

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International Conference on Control, Automation and Information Sciences (ICCAIS)

作者： Hoa Van Nguyen Tran Thien Dat Nguyen Changbeom Shim Diluka Moratuwage Seonggun Joe School of Electrical Engineering Curtin University WA Australia School of Engineering and Technology Central Queensland University QLD Australia Department of Autonomous Vehicle Engineering Korea Aerospace University Goyang Republic of Korea

ISBN: (数字)9798331542047

ISBN: (纸本)9798331542054

This work addresses the challenging problem of online managing labels from a sequence of unlabelled multi-object estimates, which is a crucial task in multi-object tracking, particularly in noisy environments with unknown and time-varying numbers of mobile objects. The considered problem requires solving a multi-dimensional assignment problem, which is an NP-hard problem. In this paper, we propose reformulating the aforementioned label management problem within the recur-sive Bayesian framework and leveraging the effectiveness of the labelled multi-Bernoulli (LMB) filter to accurately and efficiently assign labels to unlabelled multi-object estimates in real-time. The proposed LMB labelling (LMBL) algorithm is agnostic to the filtering method and is capable of labelling any unlabelled multi-object estimates. Experimental results demonstrate significant labelling performance improvements of our proposed LMBL approach compared to other state-of-the-art methods. This work presents a robust and efficient solution for the critical problem of label management in multi-object tracking applications.

关键词： Automation Accuracy NP-hard problem filtering algorithms Information filters Robustness Real-time systems Bayes methods Labeling Noise measurement

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SLSC Beamforming with Filtered Cross-Correlation-Induced Harmonics to Improve Spatial Resolution

SLSC Beamforming with Filtered Cross-Correlation-Induced Har...

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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Manik Kakkar Muyinatu A. Lediju Bell Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD Department of Biomedical Engineering Johns Hopkins University Baltimore MD Department of Computer Science Johns Hopkins University Baltimore MD

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Improving resolution and reducing clutter in ultrasound imaging remains a significant challenge for conventional beamforming techniques. This paper introduces a novel algorithm that integrates Hermite (H)-scan filtering with short-lag spatial coherence (SLSC) beamforming. The proposed algorithm applies Gaussian-weighted Hermite polynomials (GWHPs) to raw channel data before performing spatial correlations, aiming to enhance lateral resolution and reduce clutter. Simulations indicate that the filtered cross-correlation effectively introduces harmonic components, while phantom experiments confirm that these induced harmonics enhance lateral resolution, as demonstrated by the reduction in FWHM measurements of point targets. Quantitative evaluations further verify that our algorithm consistently achieves superior lateral resolution compared to fundamental and harmonic SLSC techniques. These results highlight the potential of GWHP filters to significantly improve the resolution and quality of coherence-based ultrasound images.

关键词： Ultrasonic imaging Array signal processing Ultrasonic variables measurement Imaging filtering algorithms Harmonic analysis Spatial databases Spatial resolution Power harmonic filters Signal resolution

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False Data Injection Attacks Against Error-State Kalman Filter-Based IMU-GPS Fusion Models

False Data Injection Attacks Against Error-State Kalman Filt...

引用

International Conference on Information, Communication and Networks (ICICN)

作者： Junqi Zhang Linqing Hu Jizhong Zheng Shaoyin Cheng Weiming Zhang Nenghai Yu Anhui Province Key Laboratory of Digital Security School of Cyber Science and Technology University of Science and Technology of China Hefei Anhui China

ISBN: (数字)9798350355802

ISBN: (纸本)9798350355819

This study investigates the vulnerability of error-state Kalman filter (ESKF) based IMU-GPS fusion localization systems to false data injection attacks. Through systematic analysis of the ESKF's internal structure and update mechanism, we derive the recursive relationships governing position and velocity deviations resulting from injected false data. Our findings reveal that position deviation is a function of previous deviation, lateral velocity, and injected data, while lateral velocity is influenced by the differential between injected data and current deviation, as well as the previous lateral velocity. Leveraging these insights, we propose a novel false data injection strategy formulated as a constrained optimization problem, designed to minimize attack energy while achieving desired deviations. Building upon the derived model, we systematically identify optimal solutions that achieve desired position deviations with minimal data injection. Extensive simulations conducted under diverse scenarios demonstrate the efficacy of our proposed method. Furthermore, our results elucidate discrepancies between theoretical predictions and experimental outcomes, highlighting the complexities inherent in real-world applications. This research underscores the need for more secure multi-sensor fusion localization algorithms.

关键词： Location awareness Systematics Buildings filtering algorithms Prediction algorithms Information filters Data models Complexity theory Kalman filters Optimization

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Algorithm for Complex Data Processing of On-board Navigation System of a Small UAV

Algorithm for Complex Data Processing of On-board Navigation...

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Information, Control, and Communication Technologies (ICCT), International Conference on

作者： Petr Trefilov V. A. Trapeznikov Institute of Control Sciences of the RAS Moscow Russia

ISBN: (数字)9798331517564

ISBN: (纸本)9798331517571

This paper proposes algorithmic and technical solutions aimed at improving the accuracy of UAV navigation parameters without altering the onboard navigation system's structure. The main focus is on introducing additional algorithms that enhance navigation parameter estimation accuracy by processing data from external sources and eliminating unreliable information. These solutions are implemented based on a weakly coupled system for integrating measurement information, ensuring modularity and flexibility in UAV configurations with various sensors and systems. An additional module performs qualitative assessments based on environmental parameters, allowing the exclusion of unreliable information sources from the navigation loop, thereby improving overall navigation accuracy.

关键词： Accuracy Parameter estimation Navigation filtering algorithms Autonomous aerial vehicles Information filters Data processing Sensor systems Communications technology Sensors

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Research on battery pack consistency assessment and fault diagnosis method based on anomaly factor

Research on battery pack consistency assessment and fault di...

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Chinese Control and Decision Conference, CCDC

作者： Lu Peng Wanyin Du Yichao Li Xuefeng Liu Lubin Ma Bin Duan School of Control Science and Engineering Shandong University Jinan China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Lithium-ion batteries (LIBs) have become the main power source for electric vehicles and energy storage systems. However, inconsistency and internal short-circuit (ISC) faults of LIBs increase operational risks. In severe cases, thermal runaway accidents are triggered, posing a threat to the safety of users' lives and properties. Therefore, this paper presents research on risk warning strategies for inconsistency assessment and fault diagnosis of LIBs. First, in order to solve the problem of random noise in the measured voltages of the battery module, a data noise reduction method based on Savitzky-Golay filter is proposed. After that, the voltage differences between the cells of the battery pack and the median cell is calculated and recorded as the anomaly factor δ. Thus, the inconsistency assessment of the battery pack is realized. The battery pack fault diagnosis algorithm is constructed by using δ and sample entropy to realize early real-time fault diagnosis of battery packs. Finally, battery pack consistency and ISC faults experiments are performed. Experimental results show that the proposed consistency assessment and fault diagnosis method have good effectiveness, robustness and reliability.

关键词： Fault diagnosis Voltage measurement Noise reduction Noise filtering algorithms Robustness Real-time systems

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An Algorithm for Key Object Point Cloud Extraction Suitable for Subway Tunnels

An Algorithm for Key Object Point Cloud Extraction Suitable ...

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Chinese Automation Congress (CAC)

作者： LiHang Xia JunDing Xiong AoZe Wang Li Xiao Gang Peng ZhiGang Sun School of Artificial Intelligence and Automation Huazhong University of Science and Technology Autonomous Intelligent Unmanned System Engineering Research Center Ministry of Education of China WuHan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In subway tunnel environments where GPS signals cannot be received, autonomous train positioning faces significant challenges. To address this issue, this paper proposes an algorithm that integrates multiple point cloud processing techniques in tunnel scenarios to extract key objects and calculate distances, aiming to utilize critical objects within the tunnel for assisted positioning. Specifically, the algorithm combines techniques such as point cloud filtering, clustering, and plane segmentation. Initially, radar point cloud data is preprocessed using the coordinates and intensity information to remove noise points while retaining essential data. Statistical filtering methods are then applied to further refine the point cloud by setting appropriate parameters to eliminate outliers and enhance data quality. Subsequently, the filtered point cloud data undergoes plane segmentation, where threshold parameters are set to segment the data into multiple planes. Post segmentation, the Minimum Bounding Box (MBB) algorithm is employed to obtain the volume of the smallest bounding box for each plane. These processing steps enable assessment of whether the extracted planes meet requirements, followed by distance calculation using the centroids of compliant planes. Using data recorded from a hardware platform installed on a city subway, the algorithm extracts point clouds of landmark objects within the tunnel and calculates their real distances relative to the train. Experimental results demonstrate that the algorithm effectively extracts landmark point clouds in tunnel environments, thereby enhancing the accuracy of train positioning. This validates the feasibility and effectiveness of the approach in practical applications. The study provides a novel solution for autonomous train positioning in subway tunnels where GPS signals are unavailable.

关键词： Point cloud compression Accuracy Simultaneous localization and mapping Urban areas Clustering algorithms Wheels filtering algorithms Sensors Public transportation Global Positioning System

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