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检索条件"主题词=Filtering algorithms"

共 10367 条记录，以下是971-980 订阅

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Efficient Trust Management for Blockchain-Enabled Smart City and Medical IoT with Eigenvector-Based ETMS

Efficient Trust Management for Blockchain-Enabled Smart City...

引用

International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Ashu Katyal Nalini Sunil M S. A. Tiwaskar C. Karthikeyan Mithhil Arora B. Geetha Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement-PG Management JAIN (Deemed to be University) Bangalore Karnataka India Computer Engg. Vishwakarma Institute of Technology Pune India Department of Computer Science Engineering Karpagam Institute of Technology Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of S&H Prince Shri Venkateshwara Padmavathy Engineering College Chennai India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Safe environment is aimed at creating within the context of Internet of Things (IoT) networks through the use of the Enhanced Trust Management System (ETMS) with the capability of detecting and eliminating the presence of malicious nodes and their contributions from the trust data. This is achieved using an eigenvector technique, which is integrated into a smart contract during the entire process. This allows edge nodes to invoke the contract at any time when it is necessary to invoke contract. To further enhance the security of the compiled trust information, the trust information is stored within a blockchain in blocks. As for routing the packets to the desired destinations, the result of the simulation demonstrates that ETMS is more effective than the systems currently in existence. In contrast to the systems that are currently in use, the suggested mechanism has a tool that will enable detection of contributions that have been faked in dispersed contexts and will therefore overcome a major deficiency. Moreover, the integration of ETMS into a number of circumstances that involve the Internet of Things, for example, Smart Cities, Medical IoT, and Industrial IoT, is entirely possible without any sort of complications. In the same regard, there is need to undertake further assessment on the capability of ETMS operating in real time across all these scenarios.

关键词： Smart cities Image edge detection Simulation Smart contracts filtering algorithms Real-time systems Blockchains Internet of Things Security Trust management

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Multi-UAV Cooperative Formation Control Considering Finite Time and Collision Avoidance

Multi-UAV Cooperative Formation Control Considering Finite T...

引用

Chinese Automation Congress (CAC)

作者： Rui Tang Yongfu Li Longwang Huang Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of Automation Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In this paper, the formation control problem for UAVs subject to model uncertainty and external disturbance is studied. To realize the control goals, the formation control problem of UAVs is decomposed into position consensus control and attitude consensus control. Firstly, a finite-time position consensus controller is developed for UAVs such that the desired formation can be formed in a finite time. In the position controller, a collision avoidance mechanism is designed on the basis of a novel artificial potential field function. Secondly, to handle dynamic model uncertainties and realize attitude tracking control, a robust filter based finite-time attitude tracking controller is developed. Finally, stability of the position control subsystem and the attitude control subsystem is analyzed using Lyapunov method. And simulation results on MATLAB and RflySim show that the proposed algorithm enables UAVs to form stable formation while avoiding collisions within a finite time.

关键词： Uncertainty Attitude control filtering algorithms Consensus control Formation control Stability analysis Mathematical models Delays Numerical stability Quadrotors

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Achieving Robustness in Blind Modulo Analog-to-Digital Conversion

Achieving Robustness in Blind Modulo Analog-to-Digital Conve...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Amir Weiss Faculty of Engineering BarIlan University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The need to digitize signals with intricate spectral characteristics often challenges traditional analog-to-digital converters (ADCs). The recently proposed modulo-ADC architecture offers a promising alternative by leveraging inherent features of the input signals. This approach can dramatically reduce the number of bits required for the conversion while maintaining the desired fidelity. However, the core algorithm of this architecture, which utilizes a prediction filter, functions properly only when the respective prediction error is bounded. In practice, this assumption may not always hold, leading to considerable instability and performance degradation. To address this limitation, we propose an enhanced modulo-unfolding solution without this assumption. We develop a reliable detector to successfully unfold the signals, yielding a robust solution. Consequently, the reinforced system maintains proper operation in scenarios where the original approach fails, while also reducing the quantization noise. We present simulation results that demonstrate the superior performance of our approach in a representative setting.

关键词： Quantization (signal) Simulation Noise Stability criteria Signal processing algorithms filtering algorithms Robustness Speech processing Analog-digital conversion Sensor arrays

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Compression Algorithm of Convolutional Neural Network Model

Compression Algorithm of Convolutional Neural Network Model

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Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe Conference on

作者： Yanchun Liu Lishuang Zhao College of Information Science and Technology Bohai University Jinzhou China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

The exponential growth of data requires powerful analytical methods and processing tools, and deep learning demonstrates powerful analytical and predictive capabilities. CNN is excellent model in the field of deep learning, enabling powerful feature abstraction and image understanding that goes beyond the accuracy of recognition using the human eye. However, while the CNN model is constantly approaching the precision limit, the network depth and model parameters have multiplied, and the huge memory consumption and excessive storage occupation cannot be applied on many embedded and mobile devices. This paper constructs a compression algorithm model, including convolution layer, filter and pruning algorithm, and focuses on structured pruning and filter importance calculation. Using MNIST data set, the LeNet-5 model was trained by parameter pruning according to different pruning ratios, and the model compression was realized. The application of the research results in this paper can significantly reduce the redundant parameters in the network model, thus achieving the purpose of reducing the computing cost and storage space, and promoting the development of deep learning.

关键词： Deep learning Training Costs Accuracy Computational modeling Memory management filtering algorithms Mobile handsets Convolutional neural networks Compression algorithms

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Optimal Design of Output Filter for Low Switching Frequency Multi Level Inverter Based Medium Voltage Induction Motor Drive

Optimal Design of Output Filter for Low Switching Frequency ...

引用

Power Electronic and Drive Systems and Technologies Conference (PEDSTC)

作者： Yosef Hafezi Parsa Ziaei MohammadReza Zolghadri Department of Electrical Engineering Sharif University of Technology Tehran Iran

ISBN: (数字)9798331533946

ISBN: (纸本)9798331533953

The use of variable-speed electric drives has gained increasing importance due to their enhanced technical performance. To address issues like current ripple, additional motor and cables losses, electromagnetic interference, overvoltage on the stator windings, and acoustic noise of PWM controlled variable-speed electric drives and achieve suitable voltage quality at the motor terminals, the use of a sine wave output filter has become a practical industrial solution. This paper presents the optimal design of a sine wave filter for a medium voltage (MV) induction motor powered by a low switching frequency multilevel inverter. The design process considers resonance and stability issues, and determines the optimal filter structure and components by minimizing the weighted sum of the objective function components. The proposed method utilizes genetic algorithms and component modeling, incorporating objective functions such as voltage total harmonic distortion (THD), current THD, and filter components cost. Constraints, including the voltage drop across the inductor and losses due to damping resistance, are also considered in the optimal filter configuration. The sine wave filter is designed with a reduced ratio of switching frequency to filter resonance frequency $\left(r_{f}\right)$, resulting in a higher resonant frequency of filter and therefore smaller inductance and capacitance, and ultimately better voltage quality for the motor across all operating frequencies. Analysis and simulations have been conducted to validate the proposed design.

关键词： Resistance Switching frequency Resonant frequency Voltage filtering algorithms Linear programming Multilevel inverters Resonance Voltage control Power harmonic filters

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Stochastic Integration Based Estimator: Robust Design and Stone Soup Implementation

Stochastic Integration Based Estimator: Robust Design and St...

引用

International Conference on Information Fusion

作者： Jindřich Duník Jakub Matoušek Ondřej Straka Erik Blasch John Hiles Ruixin Niu Dept. of Cyber. Univ. of West Bohemia Univerzitní 8 Pilsen Czech Rep. MOVEJ Analytics Fairborn OH USA Dept. of ECE Virginia Commonwealth University

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

This paper deals with state estimation of nonlinear stochastic dynamic models. In particular, the stochastic integration rule, which provides asymptotically unbiased estimates of the moments of nonlinearly transformed Gaussian random variables, is reviewed together with the recently introduced stochastic integration filter (SIF). Using SIF, the respective multi-step prediction and smoothing algorithms are developed in full and efficient square-root form. The stochastic-integration-rule-based algorithms are implemented in Python (within the Stone Soup framework) and in MATLAB® and are numerically evaluated and compared with the well-known unscented and extended Kalman filters using the Stone Soup defined tracking scenario.

关键词： Smoothing methods Heuristic algorithms Stochastic processes filtering algorithms Prediction algorithms Random variables Kalman filters State estimation MATLAB

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Multi-Leaf Collimator Status Monitoring Based on Geometric Constraint of Strip Type Detector

Multi-Leaf Collimator Status Monitoring Based on Geometric C...

引用

IEEE International Conference on Orange Technologies (ICOT)

作者： Hsin-Hao Lee Miao-Ci Wang Tung-Lin Yu Tzu-Fang Chang Yu-Jen Wang Chi-Wen Hsieh Augustine Ei-Fong Chen Dept. of Electrical Engineering National Chung Cheng University Chiayi Taiwan Dept. of Radiation Oncology Fu Jen Catholic University Hospital New Taipei City Taiwan School of Medicine College of Medicine Fu Jen Catholic University New Taipei City Taiwan Dept. of Physics National Central University Taoyuan Taiwan

ISBN: (数字)9798331523602

ISBN: (纸本)9798331523619

This study presents an algorithm for multi-Leaf collimator status using photon signals acquired from a strip type detector. The multi-leaf collimator is rotated from 0 to 180 degrees in 10-degree increments, and at each angle, photon signals are captured and digitized using an analog-to-digital converter. The data is processed with a filtered back projection technique combined with two categories masks to improve and verify the reconstruction accuracy. A preliminary result shows the algorithm offering a reliable solution for verifying multi-leaf collimator configurations in radiation therapy. Meanwhile, the mask type will affect the reconstructed images and we found that the strip type detector could effectively detect and reconstruct the photon signals as well.

关键词： Collimators Strips Detectors filtering algorithms Reliability engineering Radiation therapy Social implications of technology Image reconstruction Monitoring Photonics

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Diagnosis of Pulmonary Embolism from CT Pulmonary Angiogram by Using Deep Transfer Learning and Sobel Filter

Diagnosis of Pulmonary Embolism from CT Pulmonary Angiogram ...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Md. Azizul Haque Atanu Shome Anupam Kumar Bairagi Computer Science and Engineering Discipline Khulna University Khulna Bangladesh

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Pulmonary embolism (PE) indicates a blood thrombus that travels toward the lungs and can lead to significant morbidity and mortality. Thus, early diagnosis and treatments are crucial. An efficient PE detection mechanism can improve radiologists’ productivity by helping to prioritize critical cases and speed up at-risk patient diagnoses. The standard mechanism for diagnosing PE is the chest computed tomography pulmonary angiogram (CTPA) images. A subset of 15000 samples, including normal and PE-positive lung CTPA images were taken from the largest multinational publicly available RSNA-PE-CT dataset for our purpose. Noise and spatial aliasing in CTPA images can make it challenging to discern boundaries between different anatomical structures or abnormalities; therefore, an edge detection filter (Sobel) was deployed to remove noise and highlight the edges in the original image. In this study, PE in lung CTPA image is detected using deep transfer learning models, which utilize Convolutional Neural Network (CNN), a widely used deep learning (DL) technique. All selected models were trained and validated by adjusting the hyperparameters, which included the layers, loss function, activation function, rate of learning, and epoch number. Nonetheless, compared to other CNN-based frameworks such as MobileNetV2, Xception, InceptionV3, and ResNet50, the customized Sobel Filter-based DenseNet169 (SF-DenseNet169) model performs better. The SF-DenseNet169 model obtained the maximum training accuracy (97.32%) and validation accuracy (96.67%) with minimal loss. As a result, we propose the improved SF-DenseNet169 model to be the effective and reliable means to diagnose PE-positive patients automatically.

关键词： Accuracy Lungs Pulmonary diseases Computed tomography Image edge detection Transfer learning Noise filtering algorithms Convolutional neural networks Residual neural networks

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Robust Kalman filtering for Systems Subject to Polytopic Uncertainties*

Robust Kalman Filtering for Systems Subject to Polytopic Unc...

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Mediterranean Conference on Control and Automation (MED)

作者： Kaio D. T. Rocha José Nuno A. D. Bueno Lucas B. Marcos Marco H. Terra Dept. of Electrical and Computer Engineering S&#x00E3 o Carlos School of Engineering University of S&#x00E3 o Paulo S&#x00E3 o Carlos Brazil

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

Measuring the entire state vector of a dynamic system in practical applications is often infeasible due to various factors. State estimation is usually adopted to overcome this issue. However, the system model inevitably undergoes the degrading effects of parametric uncertainties. Hence, it is crucial to estimate the state irrespective of such unknown parameter variations. In this paper, we address the robust filtering problem regarding linear discrete-time systems subject to polytopic uncertainties. We formulate a min-max optimization problem whose cost function weights the polytope vertices altogether. The solution yields a robust recursive filter as a Kalman-like correction-prediction algorithm, suitable for realtime applications. We further provide stability conditions for the steady-state filter. Furthermore, we validate our approach with a numerical example, comparing the results with other methods from the literature on robust filtering.

关键词： Discrete-time systems Uncertainty filtering Simulation filtering algorithms Stability analysis Real-time systems

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Kernel Weibull M-transform Least Mean Square Algorithm

Kernel Weibull M-transform Least Mean Square Algorithm

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International Conference on Computer Science and Network Technology (ICCSNT)

作者： Chenchong Bi Yongchun Miao Liping Li Yingsong Li Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

ISBN: (数字)9798350350791

ISBN: (纸本)9798350350807

The paper introduces a Kernel Weibull M-transform Least-Mean Square (LMS) (KWMLMS) algorithm aimed to enhance filtering performance for a nonlinear system. By incorporating the Weibull M-transformation into the cost function of the Kernel LMS (KLMS) algorithm, a weighting updated rule is gotten. Simulation experiments conducted for nonlinear-system identification under impulse noise conditions demonstrate that the created KWMLMS algorithm achieves significantly faster convergence compared to existing methods, indicating its superiority in dealing with impulsive-noises.

关键词： Computer science filtering Simulation Noise filtering algorithms Cost function Robustness Kernel Nonlinear systems Convergence

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