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检索条件"主题词=Filtering algorithms"

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Trajectory PHD Filter for Extended Traffic Target Tracking with Interaction and Constraint

Trajectory PHD Filter for Extended Traffic Target Tracking w...

引用

International Conference on Information Fusion

作者： Xi Cao Yunlian Tian Jiaye Yang Wujun Li Wei Yi School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

With the increasing demand for traffic situation awareness, extended traffic target (ETT) tracking is a significant yet challenging task especially in tough scenarios with dense and various ETTs. Due to the spatial proximity of ETTs and a noisy sensor, it is challenging for multi-target tracking algorithms to effectively distinguish and track ETTs. To improve the accuracy and robustness of ETT tracking in tough traffic scenarios, we analyze the interaction among ETTs and the lane constraint. Firstly, we develop an interactive motion model for collision avoidance to address trajectory confusion when ETTs are in close proximity. Additionally, we propose a lane constraint method that models lanes as pseudo measurements and constrains the motion of ETTs via pseudo update. Considering the complexity and extendibility, the extended target trajectory probability hypothesis density (ETTPHD) filter is adopted to achieve a more accurate estimation of ETT trajectories. Specifically, we realize the proposed interactive motion model and lane constraint method based on the ETTPHD (IC-ET-TPHD) filter. Performance comparisons between our proposed filter and other algorithms are conducted through both simulations and experiments.

关键词： Target tracking Accuracy Estimation filtering algorithms Information filters Robustness Trajectory Noise measurement Collision avoidance Standards

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Distributed Estimation of Anomalies using a Low Complexity PSO Algorithm

Distributed Estimation of Anomalies using a Low Complexity P...

引用

Emerging Trends in Smart Technologies (ICETST), 2020 International Conference on

作者： Muhammad Omer Bin Saeed Salman A. Khan Naveed R. Butt Nazeeruddin Mohammad Azzedine Zerguine Faculty of Engineering Sciences GIK Institute of Engineering Sciences & Technology Topi Pakistan College of Computing & Information Sciences Karachi Institute of Economics & Technology Karachi Pakistan College of Computer Engineering and Science Prince Mohammad Bin Fahd University AlKhobar Saudi Arabia Department of Electrical Engineering King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

ISBN: (数字)9798331515850

ISBN: (纸本)9798331515867

Any wireless sensor network can experience anomalies that can hinder performance. This work presents a distributed estimation algorithm that estimates any such anomalies using a PSO algorithm. PSO algorithms are computationally expensive. This work presents a low complexity version of the algorithm that removes redundant computations and achieves excellent performance as well. Low complexity algorithms are essential for a longer battery life of wireless sensor so this algorithm provides an excellent alternative to the basic linear adaptive filtering algorithms that have low complexity but require a lot more time to achieve similar results.

关键词： Wireless communication Wireless sensor networks Estimation filtering algorithms Market research Computational efficiency Particle swarm optimization Computational complexity Standards Convergence

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Optimal Design of Tunable Bandpass Audio Filter using Variable Learning Rate Backpropagation Algorithm

Optimal Design of Tunable Bandpass Audio Filter using Variab...

引用

IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Adedayo Olukayode Ojo Oladapo Tolulope Ibitoye Moses Oluwafemi Onibonoje Saheed Lekan Gbadamosi Oluwasemilore E. Aina Ezeobidi Clementina Oyinyechukwu Department of Electrical/Electronics and Computer Engineering College of Engineering Afe Babalola University Ado-Ekiti Ado-Ekiti Ekiti State Nigeria Department of Electrical Electronics Engineering Bowen University Iwo Osun State Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Artificial intelligence techniques are increasingly being employed for solving optimization problems due to their simplicity and speed. And artificial neural networks and their many variants are unsurprisingly at the forefront of this growing trend. In this paper, a variable learning rate backpropagation algorithm (VLRBA) was deployed to train a designed tunable bandpass audio filter for optimal performance. Different configurations of the neural network were evaluated and the best performing network was selected for the task. The performance of the neural network was analyzed using standard metrics such as regression and mean square error. The high training regression value of 0.99328 and testing regression value of 0.98381, achieved after 129 iterations, demonstrate the effectiveness of the variable learning rate backpropagation model in optimizing the tunable bandpass filter circuit. This, therefore, represents an important addition in the quest for faster and efficient circuit designs, especially in the sound engineering domain.

关键词： Band-pass filters Training Measurement Backpropagation Backpropagation algorithms Artificial neural networks Learning (artificial intelligence) filtering algorithms Integrated circuit modeling Optimization

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Deployment of an Advanced Wi-Fi Data Firewall System to Enhance Security in the IoT Ecosystem

Deployment of an Advanced Wi-Fi Data Firewall System to Enha...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Blessy Thankachan Hilda Jerlin C M Rakshit Kothari KK Dewan Gotte Ranjith Kumar Shashikant JECRC University Jaipur India Panimalar Engineering College Chennai India College of Technology and Engineering MPUAT Udaipur India NITRA Technical Campus Ghaziabad India SR University Warangal India Babu Banarasi Das University Lucknow India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

The need for effective security measures has expanded enormously in the fast-changing Internet of Things (IoT) environment of today. Protecting the confidentiality and integrity of data flows has become increasingly important as more devices connect to networks, especially in corporate settings where sensitive information is shared. The proposed set out on a quest to envision and put into practice a thorough Wi-Fi Data Firewall using the adaptable ESP32 development board in order to address these important issues, strengthen IoT security, and Employ optimization techniques like algorithmic efficiency and resource management to reduce computation load. This research primarily focuses on developing an intelligent security gateway to safeguard data for the Internet of Things while monitoring, auditing, and identifying intrusions. The system's brain is the ESP32 microcontroller, which is used to connect to Wi-Fi networks and gather all the data in the suggested innovation. To increase the high security for IoT data, a solution has been proposed: a filter algorithm based on the ESP32 board used here to filter all the data that organization used to send or receive through internet or intranet. All the algorithm has been stored in the esp32 so that real-time monitoring and filtering can be done based on the stored algorithm. IP filtering is accomplished by comparing source and destination IP addresses with predefined rules deciding whether to allow or deny packets based on these addresses. The main goal of the proposed hardware to provide a flexible & adaptive Wi-Fi Data Firewall based on hardware filter for use in office and commercial networks so that it can improve those networks' overall security posture. Additionally, this hardware-based firewall helps to monitor and quick detection of security threats and provide alert of any breaches to the organization's authority. The purpose of this study is to provide IoT data security filter and monitoring for the organization

关键词： Technological innovation Firewalls (computing) Organizations filtering algorithms Hardware Real-time systems Security

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Automatic Filter Design Algorithm based on Machine Learning and Synthesis

Automatic Filter Design Algorithm based on Machine Learning ...

引用

IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Lihang Liao Xiangyu Zou Zhiyuan Wang Chen Ma Feixuan Huang Xi He Qinghua Ren Fengyuan Yang Yiming Ma Jianlin Chen Nan Wang School of Microelectronics Shanghai University Shanghai China Proto Univista Shanghai China

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

The traditional Film Bulk Acoustic Resonator (FBAR) filter design process is usually assisted by optimization algorithms. The design process requires multiple iterations of parameters in a large range to reach convergence, resulting in high time cost and low design efficiency.A algorithm combining machine learning and circuit synthesis is proposed in order to improve the design efficiency of FBAR filters. Through this algorithm, the structure of each FBAR can be obtained by only inputting the sampled value of S parameter, instead of iterating the structure of FBAR in the conventional *** filter constructed by this algorithm has better performance. At the same time,the design time is much shorter than the traditional algorithm, thus the design efficiency can be significantly improved.

关键词： Machine learning algorithms Resonator filters Process control Machine learning filtering algorithms Scattering parameters Film bulk acoustic resonators Passband Frequency control Optimization

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Analysis of the Kalman Filter with the MPU6050 Accelerometer and Gyroscope

Analysis of the Kalman Filter with the MPU6050 Accelerometer...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Pranav Swarup Kumar U. Vignesh School of Computer Science and Engineering Vellore Institute of Technology (VIT) Chennai Tamil Nadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Abstmet -Reliable sensor measurements are essental for autonomous navigation, control systems and various other applications. We studied a model of a gesture controlled wheelchair. A key finding is that the processing unit must be able to filter out noise so that the efficacy of the wearable device is improved. In this paper, we demonstrate the application of the Kalman filter to combine measurements in the presence of noise or drift, to calculate the orientation from the inertial measurement unit, MPU6050. We report the behoviour of the gyroscope and accelerometer by measuring their output values individually first, and then passing them through the Kalman Filter. Alternatives such as the recursive average and moving average filters are also discussed. The characteristics of the filter for different parameters such as the standard deviation are plotted. Finally, the Kalman gain convergence for a steady state is verified.

关键词： Accelerometers Wheelchairs Noise Process control filtering algorithms Gyroscopes Kalman filters Noise measurement Standards Convergence

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Using a Stochastic Differential Equations to Form of Non-Gaussian Random Processes. Part 1

Using a Stochastic Differential Equations to Form of Non-Gau...

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Dynamics of Systems, Mechanisms and Machines (Dynamics)

作者： V. M. Artyushenko V. I. Volovach Information technology and management systems department Technological University Korolev city Russia Informational and electronic service department Volga Region State University of Service Togliatty city Russian Federation Voronezh State University Voronezh city Russia

ISBN: (数字)9798331509019

ISBN: (纸本)9798331509026

The formation of non-Gaussian random processes by using stochastic differential equations is described. algorithms for the formation of scalar continuousvalued, vector and discrete processes, as well as multi-linked sequences are presented. The analysis of the forming filter with parametric noise is carried out. The block diagram of the named filter is given. It is shown that the choice of parameters of input influences of the forming filter allows receiving an output signal with density of distribution of probabilities of Pearson family. It is also shown that in systems with parametric noise the probability density of the output variables is not Gaussian.

关键词： Maximum likelihood detection Noise Differential equations Nonlinear filters filtering algorithms Probability density function Markov processes Vectors Mathematical models Random processes

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Talent Recommendation Algorithm Integrating Knowledge Grap

Talent Recommendation Algorithm Integrating Knowledge Grap

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Information and Communication Technology (ICTech), International Conference of

作者： Zhenhao Gu Zhibo Wang Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350376258

ISBN: (纸本)9798350376265

Due to the limitations of traditional recommendation algorithms in considering semantic information and achieving high precision in talent recommendation for cadre team construction, this paper proposes a talent recommendation algorithm called TransR-CF. This algorithm integrates a knowledge graph and a collaborative filtering algorithm. By constructing a knowledge graph of employee information and incorporating semantic information among employees as a key recommendation factor, the TransR translation model is used to learn knowledge representation and map the employee knowledge graph to a lower-dimensional continuous vector space, capturing entities and their relationships. Semantic similarity between employees is then calculated and linearly combined with behavior similarity derived from the collaborative filtering recommendation algorithm. This algorithm takes into account semantic information while building upon the traditional collaborative filtering approach, mitigating the impact of data sparsity and cold start issues on recommendation results, and improving the reliability and precision of recommendations. Experimental results on a personnel information dataset from a power enterprise's cadre team construction system demonstrate that this algorithm achieves a significant improvement in precision, recall, and F-value compared to the benchmark algorithm.

关键词： Translation Collaborative filtering Semantics Knowledge graphs filtering algorithms Information filters Vectors Information and communication technology Reliability Personnel

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A Filter Capsule Detection and Correction Method Based on Visual Detection and PID Control Linkage

A Filter Capsule Detection and Correction Method Based on Vi...

引用

International Conference on Computer and Communications (ICCC)

作者： Jianhua Liao Jun Cao Wenjie Long Guozhong Wu Yang Li Shihao Tang Yang Cao Jing Yang Guiyang Cigarette Factory China Tobacco Guizhou Industrial Co. Ltd. Guiyang China School of Mechanical Engineering Guiyang University Guiyang China School of Mechanical Engineering Guizhou University Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

With the rapid development of the domestic filter capsule cigarette market, the quality control of filter capsule filter rods has become critical. Current production is mainly faced with problems such as filter capsule deflection, and traditional detection and adjustment methods have the challenges of low accuracy and untimely response. For this reason, this study proposes a method for correcting filter capsule deflection detection based on the interaction of visual detection and PID regulation linkage. First, an improved SIFT image stitching algorithm is used to improve the accuracy of visual localization. Then, the PID control algorithm is introduced to adjust the flywheel speed in real time to realize high-speed deflection correction. Finally, experiments have verified that this method can effectively improve the qualification rate of filter capsule filter rods, reduce production costs, and be of great significance to the quality control of filter capsule cigarettes in the tobacco industry.

关键词： Industries Visualization Accuracy PI control Production Quality control filtering algorithms Regulation PD control Qualifications

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Truncation Mitigation Using the Holographic PNF Filter

Truncation Mitigation Using the Holographic PNF Filter

引用

Antenna Measurement Techniques Association Symposium (AMTA)

作者： Scott T. McBride NSI-MI Technologies a business of AMETEK Inc. Suwanee GA

ISBN: (数字)9781736235164

ISBN: (纸本)9798331517854

Planar near-field (PNF) acquisition always samples a limited or truncated subset of the infinite plane in front of an antenna under test (AUT). That truncation of the sampled field has two primary impacts: Power radiating to or from angles beyond the probing boundary is not fully captured and thus not included and erroneous ripple is injected throughout the pattern when the transformation algorithm sees by default a sudden drop to zero power beyond that boundary. The topic of truncation mitigation in the PNF geometry has been addressed with a variety of algorithms. This paper introduces a new algorithm that is similar to, yet distinct from, some that have come before. The new algorithm makes use of the recently introduced holographic PNF filter [1]–[3], treating the truncation effects like stray signals. Where the most common technique [2] [4] uses the known planar-AUT bounds (2D) and a computed “valid region” of the plane-wave spectrum as truth through iterative transformations, this algorithm treats as truth the known AUT volume (3D) and the measured PNF data. The new algorithm is evaluated herein by retruncating a large set of measured PNF data.

关键词： Three-dimensional displays Prevention and mitigation Current measurement Volume measurement Scattering filtering algorithms Time measurement Distortion measurement Reliability Probes

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