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检索条件"主题词=Filtering algorithms"

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BloomWalk and CuckooWalk: Fast Random Walks Utilizing Probabilistic Data Structure

BloomWalk and CuckooWalk: Fast Random Walks Utilizing Probab...

引用

IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Ren Inayoshi Han Nay Aung Hiroyuki Ohsaki Graduate School of Science and Technology Kwansei Gakuin University

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Random walks are being used for target node search and graph exploration in various fields, including communications and social networking. However, frequent revisits of the random walk agent to the same node degrade the efficiency of node search and graph exploration. To address this issue and improve the efficiency of node search and graph exploration, a variety of random walk-based algorithms with history, such as self-avoiding random walk (SARW) and k-history random walk (k-History), have been developed. Although these approaches accelerate node search and graph exploration with a random walk agent, they require the agent to have a non-negligible amount of memory space to store many nodes on large-scale graphs. To address this issue, it is essential to clarify efficient memory management strategies for random walk agents. In this paper, we propose novel random walk-based algorithms called BloomWalk and CuckooWalk, in which an agent performs history-based random walks on a graph using a probabilistic data structure to record previously visited nodes in memory efficiently. Experimental results demonstrate that Bloom Walk and CuckooWalk enable efficient node search on unknown graphs even with the very limited memory capacity.

关键词： Social networking (online) Accelerated aging Memory management Software algorithms filtering algorithms Probabilistic logic Data structures

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Research and Implementation of Fuzzy Test Method Based on Multi-armed Bandit

Research and Implementation of Fuzzy Test Method Based on Mu...

引用

Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Yiwen Qin Jincheng Jiang Changzhi Li Futao Lu Kaiwen Deng Zengyu Yang Network Control Lab Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

Thereare a lot of vulnerabilities in industrial control protocols due to rothole in protocol implementation and problems with exception handling of function codes. In order to solve low effectiveness of generating test cases and the inability to adjust the test case generation by feedback from the device, fuzzy test method based on multi-armed bandit(MAB) algorithm is proposed. Firstly, the protocol unit is divided into feature sets and a matrix model is established by deeply analyzing the protocol specification, so as to initially preprocess the input data set. Secondly, the protocol constraint rule set is constructed to filter the invalid cases that violate the protocol specification. Finally, the exploration and utilization phase of the model is customized by using multi-armed bandit algorithm and fuzzy test method to increase the selection probability of mutation factors that easily trigger vulnerabilities, and establish a test case queue to save the fuzzy test cases, moreover, the probability distribution is adjusted by using the feedback of test *** results show that this method improves the abnormality rate of the test system, and the vulnerability mining ability is significantly improved.

关键词： Protocols Control engineering Industrial control Diversity reception Data preprocessing Focusing filtering algorithms

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Synchronizing Local Oscillator Frequencies in Beamforming Nodes

Synchronizing Local Oscillator Frequencies in Beamforming No...

引用

Advanced Telecommunication and Networking Technologies (ATNT), IEEE International Conference on

作者： Umar Suleiman Dauda Nik Noordini Nik Abd Malik Nurul Muazzah Abdul Latiff Department of Electrical & Electronic Engineering Federal University of Technology Minna Nigeria Department of Communication Engineering Universiti Teknologi Malaysia Johar Malaysia Faculty of Electrical Engineering Universiti Teknologi Malaysia Jahar Malaysia

ISBN: (数字)9798350353501

ISBN: (纸本)9798350353518

Collaborative Beamforming (CB) which is a cooperative transmission technique where nodes act as a virtual antenna array can achieve an order of magnitude in improved signal strength, directional signal transmission, energy efficiency and node redundancy when practically deployed. However, signals arrive out of phase at the target receiver due to unsynchronized clock frequencies of local oscillators (LOs). In this article, an iterative procedure synchronizes the common message signal from multiple transmitters to the receiver. LO frequency synchronization and random phase adjustments are carried out by CB nodes at each time slot to increase the signal to noise ratio (SNR). Mean square error (MSE) of the frequency offset of the feedback signal are estimated with the developed LO estimation algorithm. This algorithm is seen to have tracked the modified Cramer-Rao lower bound (MCRLB) more closely for SNR between 7 dB and 20 dB. An improvement of 99.57% and 93.74% is recorded for LO frequency offset MSE for CB node 1 and CB node 2, respectively, when compared to another estimator. When beamforming is enabled, the percentage increase in amplitude between single and combine node with convergence achieved is 66.67%.

关键词： Local oscillators Array signal processing Estimation Collaboration Receivers filtering algorithms Frequency estimation Synchronization Frequency synchronization Signal to noise ratio

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A Dual-Approach to Drone Image Enhancement: Software-Based Processing and FPGA Implementation

A Dual-Approach to Drone Image Enhancement: Software-Based P...

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Smart Electronics and Communication Systems (ISENSE), International Conference on

作者： Arun T Nair Nanditha N Varma Nandakumar R National Institute of Electronics and Information Technology Calicut

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528133

This work offers a thorough method for real-time dehazing of drone-captured images by different filtering techniques with post-processing improvements. Enhancing visibility and picture clarity in hazy situations is the main goal since it is essential for applications like environmental monitoring, navigation, and surveillance. In order to estimate the atmospheric light and transmission map, the suggested methodology makes use of the dark channel. A farrow filter and a guided filter are then applied to improve the transmission. Metrics including the PSNR, SSIM, Execution time, and MSE that are derived from Python-based image processing implementations are used to assess the effectiveness of the dehazing algorithms. The outcomes show notable gains in processing efficiency and image clarity. In order to accelerate the performance, this method was implemented on an FPGA based SoC. Power consumption and throughput of the FPGA - based solution were evaluated, demonstrating its effectiveness and appropriateness for real-time applications.

关键词： Surveillance Video sequences Random access memory Gray-scale filtering algorithms Throughput Real-time systems System-on-chip Field programmable gate arrays Tuning

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Exploring Methods of Mitigation against DDoS Attack in an IoT Network

Exploring Methods of Mitigation against DDoS Attack in an Io...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Piyush M. Prajapati Priyesh P. Gandhi Sheshang Degadwala Gujarat Tech. University Ahmedabad Gujarat India Sigma University Vadodara Gujarat India Dept. of Comp. Eng Sigma University Vadodara Gujarat India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

Smart homes and industrial systems alike have benefited greatly from the increased connection and ease made possible by the explosion of Internet of Things (IoT) devices. Nevertheless, Distributed Denial of Service (DDoS) assaults have become more common due to the widespread use of these linked devices, which has put IoT networks at risk. In order to protect IoT networks against Distributed Denial of Service (DDoS) assaults, this study looks into the topic and suggests new ways to do so. The research delves into advanced tactics, such as adaptive traffic filtering, anomaly detection, machine learning algorithms, and hardware-based solutions, by examining the specific vulnerabilities of IoT ecosystems. Also covered is the possibility of using edge computing for mitigation and detection in real-time. Through simulations and experiments, the research evaluates the performance and efficacy of these methods under diverse attack scenarios, considering resource constraints and scalability. The findings not only contribute to academic discussions on IoT security but also provide practical insights for industry practitioners and policymakers. The research aims to enhance the resilience of IoT networks, ensuring the integrity, availability, and confidentiality of data in the face of evolving DDoS threats.

关键词： Industries Machine learning algorithms Scalability Smart homes filtering algorithms Real-time systems Internet of Things

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Machine Learning Techniques Applied to Intrusion Detection Systems

Machine Learning Techniques Applied to Intrusion Detection S...

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IEEE Ecuador Technical Chapters Meeting (ETCM)

作者： Gabriel Lara Ricardo Flores-Moyano Maria Baldeon-Calisto Daniel Riofrío Noel Pérez-Pérez Diego Benítez Colegio de Ciencias e Ingenierías “El Politécnico” Universidad San Francisco de Quito USFQ Quito Ecuador

ISBN: (数字)9798350391589

ISBN: (纸本)9798350391596

As cyber threats continue to evolve, the necessity of Intrusion Detection Systems (IDS) to protect sensitive information has become increasingly apparent. To address the limitations of conventional signature-based IDS, machine learning-based approaches have been proposed. However, the dearth of up-to-date network datasets impedes the advancement of these innovative methods. Furthermore, robust and reproducible results are required to ensure that the performance of ML models is not merely a product of randomness but a reflection of genuine and reliable capabilities. In this context, this paper presents a comprehensive evaluation of standalone and hybrid machine learning algorithms aimed at improving the differentiation between normal and malicious traffic. The hybrid model combines rule-based filtering with machine learning-based evaluation. A set of predefined rules is first applied to filter the dataset, and then the ML algorithms focus on the most relevant data, thus improving the efficiency in detecting previously unseen threats and reducing computational complexity. This approach is designed to improve detection accuracy while maintaining a low false positive rate, addressing both precision and operational efficiency in real-world scenarios.

关键词： Machine learning algorithms Accuracy filtering Computational modeling Intrusion detection Machine learning filtering algorithms Reflection Reliability Computational complexity

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UAV Target Tracking Algorithm for Ground Moving Targets

UAV Target Tracking Algorithm for Ground Moving Targets

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Intelligent Design (ICID), International Conference on

作者： Xin Xu Bin Liu Yun Zhong Xinyu Bai Jia Guo Huitong Fu Xiaoyan Li Information and Navigation School Air Force Engineering University Xi'an China School of Electronic Information Engineering Xi'an Technological University Xi'an China

ISBN: (数字)9798331541491

ISBN: (纸本)9798331541507

With the goal of effectively estimating the target's location, an algorithm for adaptive target tracking that based on target's response is put forth. First, the extracted features are compressed by an encoder and a soft quantizer. Secondly, the target response regular term is added to the target training model, and the optimal target response function is updated in the process of solving, so that the tracking algorithm is able to precisely track the target after being disturbed, so as to improve the representation ability of the target. Finally, reliability coefficient is introduced to evaluate the response graph of each feature channel to express the ability of the model to express the target. According to experimental data, this method can achieve 94.5 % tracking accuracy.

关键词： Training Adaptation models Target tracking Correlation Accuracy filtering algorithms Autonomous aerial vehicles Feature extraction Reliability engineering Robustness

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Dynamic Filter Length Reduction in NLMS for System Identification

Dynamic Filter Length Reduction in NLMS for System Identific...

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Measurement and Simulation (ATOMS), Advanced Topics on

作者： Elena-Valentina Dumitrașcu Robert-Alexandru Dobre Computers Department National University of Science and Technology Politehnica Bucharest Bucharest Romania Electronic Technology and Reliability National University of Science and Technology Politehnica Bucharest Bucharest Romania

ISBN: (数字)9798350358377

ISBN: (纸本)9798350358384

Adaptive filtering techniques are fundamental in system identification, enabling the estimation and modeling of unknown systems from observed input-output data. Among these methods, the Normalized Least Mean Squares (NLMS) algorithm holds paramount importance due to its versatility and widespread application in real-time adaptive signal processing. In system identification, NLMS algorithms serve as robust tools for approximating unknown systems by continuously adjusting the impulse response of adaptive filters based on incoming data. Conventional implementations of the NLMS algorithm assume a fixed filter length, creating challenges in scenarios where the length of the filter to be identified is unknown, thus hindering accurate system modeling, often leading to "overmodeling" and ineffective use of computational resources. This paper introduces an adaptation in the NLMS algorithm to dynamically modify the length of the adaptive filter, enhancing its computational resource efficiency. The proposed methodology addresses the challenge of accurately approximating unknown filters while decreasing the computational complexity. Simulation results demonstrate the efficacy of the proposed adaptive length modification technique, showcasing reduced computational overhead compared to traditional fixed-length NLMS methods, while still obtaining good misalignment values.

关键词： Adaptive systems Accuracy filtering Heuristic algorithms Adaptive filters Signal processing algorithms filtering algorithms Approximation algorithms System identification Yield estimation

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Near-Field Diffraction Tomography for Weak-Scattering, Cylindrical Vector Wave Problems

Near-Field Diffraction Tomography for Weak-Scattering, Cylin...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： J. L. Alvarez Perez F. Borrelli Department of Signal Theory and Communications University of Alcala Madrid Spain Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’ Informazione Università di Napoli Federico II Naples Italy

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

A new method for full vector-wave diffraction tomography in weak-scattering, cylindrical scenarios is proposed. It is based on the standard filtered backpropagation algorithm but it is derived in the framework of electromagnetic vector theory and includes the action of the dyadic Green's function. It is this dyadic that describes mathematically the depolarization processes occurring inside the object of study. The weak scattering phenomena are described within a version of the Distorted-Wave Born Approximation that allows for depolarization effects to take place. The resulting reconstruction algorithm is applied to the well-known Shepp-Logan phantom with excellent results.

关键词： Diffraction Scattering Phantoms Reconstruction algorithms filtering algorithms Vectors filtering theory

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An optimization method for point cloud registration based on the rate of point cloud density variation

An optimization method for point cloud registration based on...

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Electronic Information Engineering and Computer Communication (EIECC), International Conference on

作者： Chengcheng Xin School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

An optimization method for point cloud registration combined with the spatial density change rate of point clouds is put forward. This method is utilized to address the issue where the registration optimization process is deviated due to a large number of mismatched point pairs, resulting in the algorithm converging to the wrong position. Adaptive voxel down-sampling is carried out based on the density difference of voxel grids. Mismatched points are filtered through the density change rate. The ratio of the spatial volume change to the point cloud quantity change under different radius coefficient weights is restricted within an appropriate range. This enhances the probability of inner points being selected on the basis of reducing the sampling set, thereby enhancing the accuracy and robustness of point cloud registration.

关键词： Point cloud compression Accuracy Noise Optimization methods Interference filtering algorithms Rendering (computer graphics) Feature extraction Robustness Noise measurement

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