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检索条件"主题词=Signal Processing Algorithms"

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Closed-Loop Control of High-Pressure Common Rail Fuel Injection Volume Based on Fuel Injection Observer

Closed-Loop Control of High-Pressure Common Rail Fuel Inject...

引用

Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Zeyu Jing Xiongqin Li Bingxin Liu Sheng Han Hongzi Fei Liuping Wang Harbin Engineering University College of Power and Energy Engineering Harbin China School of Engineering RMIT University VIC Australia

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

In order to improve the traditional open-loop control of the high-pressure common rail system based on the calibration MAP diagram, a PI closed-loop control system of the injection volume based on the fuel injection observer is proposed and designed. According to the fuel flow law and the rail pressure fluctuation characteristics, a fuel injection volume observer based on rail pressure fluctuation was established. And then a closed-loop PI controller for the fuel injection volume was designed based on the observation of fuel injection volume as feedback. In view of the constrain of the control signal limiting, the structure of PI controller was improved. In addition, in order to ensure the control performance under a wide range of working conditions, the neural network algorithm was applied to predict the controller parameters under multiple working conditions. The performance of the control system was verified by co-simulation. The results show that the error of the observed fuel injection volume is less than 3% compared with the actual value. The designed system can suppress disturbances and successfully achieve closed-loop feedback control of fuel injection volume.

关键词： Rails Employee welfare PI control Fluctuations Neural networks signal processing algorithms Observers Mathematical models Feedback control Fuels

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Research Progress on Sub-band Fusion Technology for Ultra-wideband Radar signals

Research Progress on Sub-band Fusion Technology for Ultra-wi...

引用

Progress in Electromagnetic Research Symposium (PIERS)

作者： Denghui Huang Ying Zhang Huapeng Zhao Muchen He Yipeng Wang Xinxin Qin School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

Ultra-wideband signal which improves radar range resolution and facilitates target recognition is attractive in radar applications. However, it is expensive and unrealistic to construct ultra-wideband radar hardware. Therefore, sub-band fusion technology becomes an alternative because of its low construction cost and high computation accuracy. Sub-band fusion technology involves several key procedures, including sub-band fitting, multi-band coherent processing, and ultra-wideband extrapolation. For sub-band fitting, several existing electromagnetic scattering models can be used and their parameters are extracted using parameter estimation algorithms. It is noted that system errors and asynchronization between sub-bands cause incoherence between multi-bands. To compensate incoherence between multi-bands, sub-bands are extrapolated and a mutual coherence model should be constructed and estimated so that coherence between multi-bands can be restored. Finally, ultra-wideband extrapolation is realized by joint processing of coherent multi-bands. In this paper, research progress on the aforementioned procedures is summarized. Performance of existing algorithms are analyzed and compared. Finally, multi-band fusion results based on simulated data as well as static-range data and future discussion are presented.

关键词： Extrapolation Costs Target recognition Fitting signal processing algorithms Coherence Radar signal processing

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An Efficient and Flexible Deep Learning Method for signal Delineation via Keypoints Estimation

An Efficient and Flexible Deep Learning Method for Signal De...

引用

IEEE International Conference on signal and Image processing (ICSIP)

作者： Adrian Atienza Jakob Eyvind Bardram Peter Karl Jacobsen Sadasivan Puthusserypady Department of Health Technology Technical University of Denmark (DTU) Copenhagen Denmark Department of Cardiac Diseases Rigshospitalet Copenhagen Denmark

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

Deep Learning (DL) methods have been used for electrocardiogram (ECG) processing in a wide variety of tasks, demonstrating good performance compared with traditional signal processing algorithms. These methods offer an efficient framework with a limited need for apriori data pre-processing and feature engineering. While several studies use this approach for ECG signal delineation, a significant gap persists between the expected and the actual outcome. Existing methods rely on a sample-to-sample classifier. However, the clinical expected outcome consists of a set of onset, offset, and peak for the different waves that compose each R-R interval. To align the actual with the expected output, it is necessary to incorporate post-processing algorithms. This counteracts two of the main advantages of DL models, since these algorithms are based on assumptions and slow down the method's performance. In this paper, we present Keypoint Estimation for Electrocardiogram Delineation (KEED), a novel DL model designed for keypoint estimation, which organically offers an output aligned with clinical expectations. By standing apart from the conventional sample-to-sample classifier, we achieve two benefits: (i) Eliminate the need for additional post-processing, and (ii) Establish a flexible framework that allows the adjustment of the threshold value considering the sensitivity-specificity tradeoff regarding the particular clinical requirements. The proposed method's performance is compared with state-of-the-art (SOTA) signal processing methods. Remarkably, KEED significantly outperforms despite being optimized with an extremely limited annotated data. In addition, KEED decreases the inference time by a factor ranging from 52x to 703x.

关键词： Deep learning Image processing Estimation signal processing algorithms Electrocardiography signal processing Distance measurement

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Machine Learning-Based Breast Cancer Probability Prediction

Machine Learning-Based Breast Cancer Probability Prediction

引用

signal processing, Communications and Engineering Management Conference (ISPCEM), International

作者： Weihua Huang Edmonds College Washington America

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

Breast cancer, as the most common cancer among women globally, the key to improving its efficacy lies in early detection and diagnosis. Currently, breast cancer screening mainly relies on mammography, but this technology alone cannot accurately screen for breast cancer, leading to potential misdiagnoses. For breast cancer, the key to improving prognosis and survival rate is early detection. Additionally, breast cancer patients can significantly extend their survival time and improve their quality of life through good prognosis analysis. Therefore, this study focuses on the early diagnosis and prognosis analysis of breast cancer, applying machine learning and data analysis techniques combined with statistical analysis methods to analyze existing clinical data and achieve a high-accuracy disease analysis judgment for breast cancer. This study explores the effectiveness of applying various machine learning models in breast cancer prediction. This study used classic algorithms such as K-nearest neighbors, support vector machine, and Bayesian classifier, and conducted detailed data preprocessing and analysis. In this study, the breast cancer dataset was trained in different models respectively. The obtained evaluation results such as the prediction accuracy were compared to select the machine learning model with the best performance. It can be known from the final experiment that KNN model performs best on the dataset, followed by the SVM and KNN models. This study not only demonstrates the potential of machine learning in medical diagnosis but also provides important references for further optimizing breast cancer prediction models in the future.

关键词： Support vector machines signal processing algorithms Machine learning Nearest neighbor methods Predictive models Prediction algorithms Breast cancer Classification algorithms Bayes methods Prognostics and health management

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A modified LMS-Newton algorithm for multichannel active noise control

A modified LMS-Newton algorithm for multichannel active nois...

引用

Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Lei Wang Fenghua Tian Ruilei Ma Wei Liu The 705 Research Institute of China State Shipbuilding Corporation Limited Xi’an China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

This research project is concerned with enhancing the efficacy of active noise control (ANC) systems, with a particular emphasis on the optimisation of the LMS-Newton algorithm. We introduce a novel method based on the unbiased estimation of the autocorrelation matrix and the dynamic adjustment of step parameters, which collectively enable the innovative development of a new form of the LMS-Newton algorithm. The performance of the algorithm was evaluated through simulation tests conducted under different noise conditions. The results demonstrate that, in comparison to the traditional LMS-Newton algorithm, the new algorithm has exhibited a notable enhancement in convergence speed and noise reduction capability. This research outcome paves the way for a new phase of advancement in ANC technology and has the potential to significantly improve the noise control level in related application fields.

关键词： Heuristic algorithms Simulation Noise Noise reduction signal processing algorithms Estimation Control systems Autocorrelation Optimization Convergence

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Enhancing Bangla Language Text Recognition in OCR Using Levenshtein Distance and Character Grouping Strategy

Enhancing Bangla Language Text Recognition in OCR Using Leve...

引用

IEEE International Conference on signal processing, Information, Communication & Systems (SPICSCON)

作者： Md. Sajid Hasan Avi Pal Sk. Md. Masudul Ahsan Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh

ISBN: (数字)9798331510213

ISBN: (纸本)9798331510220

One crucial technology for text digitization from image is optical character recognition (OCR). Character recognition in languages with complex scripts, like Bangla, is still difficult despite recent advancements. This research proposes an innovative approach to enhance OCR's Bangla text recognition capabilities. In order to improve recognition performance, OCR errors are efficiently corrected and performance is improved by first applying the Levenshtein distance measure. Experiments show a notable improvement, increasing the initial weighted F1-score of Tesseract OCR from 84.96% to 87.26%. In addition, a method of character grouping is demonstrated to address nuances specific to the Bangla script. Characters that share visual similarities are categorized into different groupings. Subsequently, priority is given to dictionary candidate words with characters that belong to the same class as those identified by OCR. Integrating the character grouping technique yields an astounding 90.92% F1-score.

关键词： Visualization Dictionaries Accuracy Text recognition Optical character recognition Measurement uncertainty signal processing algorithms Machine learning signal processing Linguistics

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V2I Interference Management for Collaborative Perception Based on Hierarchical Reinforcement Learning

V2I Interference Management for Collaborative Perception Bas...

引用

IEEE Workshop on Machine Learning for signal processing

作者： Guowei Liu Hao Ye Le Liang Shi Jin National Mobile Communications Research Laboratory Southeast University Nanjing China Department of Electrical and Computer Engineering University of California Santa Cruz USA Purple Mountain Laboratories Nanjing China

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

Collaborative perception technology contributes to the exchange of perception information between connected automated vehicles (CAVs) and roadside units (RSUs) to overcome the limitations of standalone perception and enhance the environmental perception accuracy of collaborators. This paper delves into the interference management issues in vehicular communications for collaborative perception and designs a hierarchical reinforcement learning model-driven resource allocation strategy. The uniqueness of this strategy lies in its comprehensive consideration of spatial confidence and channel state information (CSI) to optimize the efficiency of collaborative perception. Simulation experiments demonstrate that our proposed algorithm significantly improves perception accuracy compared to various baseline methods, especially under bandwidth constrained conditions.

关键词： Machine learning algorithms Accuracy Upper bound Simulation Collaboration signal processing algorithms Interference Bandwidth Reinforcement learning signal processing

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Online Federated Composite Optimization with Multiple Kernels

Online Federated Composite Optimization with Multiple Kernel...

引用

Asian Control Conference

作者： Haibao Tian Xiuxian Li Min Meng Zhen DONG Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China College of Electronics and Information Engineering Tongji University Shanghai China Suzhou SEEExTECH Co. Ltd SEEExTECH Suzhou China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This paper focuses on Online Federated Composite Optimization (OFCO) problem, where the loss function con-tains a non-smooth regularizer and the network environment is time-varying. This problem is prevalent in various real-world applications, spanning from wireless sensor networks to signal processing. To address the challenge posed by OFCO, we propose a novel federated learning algorithm, named FedOE, which draws inspiration from approximate composite mirror descent. Furthermore, FedOE incorporates a multi-kernel strategy to enhance accuracy and flexibility, emphasizing a comprehensive and effective solution to the OFCO problem. Through theoretical analysis, FedOE achieves a regularized regret on the order of O(VT) with the total number of rounds T. Finally, the numerical experiments validate the efficacy of the proposed algorithm.

关键词： Wireless sensor networks Accuracy Federated learning signal processing algorithms signal processing Approximation algorithms Mirrors

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The Influence of Antenna Frequency and Roadway Cross-sectional Area on Radio Wave Propagation in Mine Roadway

The Influence of Antenna Frequency and Roadway Cross-section...

引用

IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： YiWen Wang Xiaojun Huang College of Communication and Information Engineering Xi'an University of Science and Technology Xi'an China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

With the increasing demand for mineral resources, the frequency of coal mining operations has intensified. Consequently, the development of a highly efficient and reliable underground communication system has become a critical concern. This study aims to investigate the radio wave propagation characteristics in mine tunnels by introducing the Finite-Difference TimeDomain (FDTD) algorithm to construct a 3D tunnel electromagnetic field update equation. This equation facilitates the simulation and analysis of radio wave propagation within the tunnel environment. The study specifically examines the impact of antenna emission frequency and tunnel cross-sectional area on the electromagnetic field strength. The findings provide a foundation for further research in channel modeling, modulation techniques, and signal processing algorithms.

关键词： Solid modeling Frequency modulation Three-dimensional displays Fluctuations Transmitting antennas signal processing algorithms Mathematical models

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Energy-Efficient Cell-Free Massive MIMO in Ultra-Dense LEO Networks: A Semi-Centralized Clustering Approach

Energy-Efficient Cell-Free Massive MIMO in Ultra-Dense LEO N...

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IEEE Workshop on signal processing Advances in Wireless Communications (SPAWC)

作者： Reza Mahin Zaeem Juan Carlos Merlano Duncan Vu Nguyen Ha Liz Martinez Marrero Symeon Chatzinotas Björn Ottersten Interdisciplinary Centre for Security Reliability and Trust (SnT) Université du Luxembourg Luxembourg

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

We propose a novel semi-centralized architecture tailored for Ultra-Dense Low Earth Orbit (UD-LEO) satellite communication (SatCom) networks to enhance scalability and energy efficiency across various operational domains. By employing a clustering methodology that assigns each user to a single cluster, we localize critical tasks for UD-LEO SatCom systems such as synchronization, power allocation, and resource management. This approach simplifies the computational demands and reduces backhaul rates while presenting an effective strategy for user scheduling. It tackles synchronization challenges in high-dynamics SatCom environments and reduces reliance on extensive Inter-Satellite Link (ISL) communications. Utilizing a consensus synchronization algorithm within clusters, the proposed scheme can maintain network coherence under fluctuating conditions. Our analysis accounts for imperfect Channel State Information (CSI) at the satellites and statistical channel knowledge at the ground users, as well as unresolved synchronization discrepancies. Analyzed and tested with a Time Division Duplexing (TDD) framework under Rician fading, our approach demonstrates significant potential for improving energy efficiency and adaptability for future SatCom systems.

关键词： Wireless communication Satellites Scalability signal processing algorithms signal processing Energy efficiency Synchronization Resource management Satellite communications Backhaul networks

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