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检索条件"主题词=Filtering algorithms"

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Experimental Investigation of the Coordinate Estimation Algorithm for the Local Navigation System With Signals Time Division

Experimental Investigation of the Coordinate Estimation Algo...

引用

International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Aleksander P. Malyshev Alice O. Kozhevnikova Sergey V. Chernyh Igor B. Galuev Pavel A. Bondarev Vladislav A. Yushin Radio Systems Department National Research University “MPEI” Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

Alternative sources of absolute positioning to global navigation satellite systems are currently being investigated, including local navigation systems. The most promising type of signals used in local navigation systems are ultra-wideband signals. Their peculiarity is low influence of multipath propagation in case of re-reflection. The disadvantage is significant attenuation during propagation. The maximum range is 300 meters. The purpose of the work is to develop and experimentally evaluate the effectiveness of the algorithm for estimating the coordinates of the consumer taking into account the instability of the reference oscillators of all devices. As a result of the work was synthesized algorithm for estimating the coordinates of the consumer taking into account the known height of the consumer. RMS deviation of the consumer coordinates in steady state does not exceed 3 cm, The transient time is 55 clock cycles of the filter operation.

关键词： Time division multiple access Radio navigation filtering algorithms Robustness Steady-state Velocity measurement Synchronization Transient analysis Clocks Ultra wideband technology

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Novel Civil Engineering Construction Site Safety Monitoring and Warning System based on Machine Vision

Novel Civil Engineering Construction Site Safety Monitoring ...

引用

Electronics and Renewable Systems (ICEARS), International Conference on

作者： Qiankun Zhang Qibin Cheng Zhejiang Construction Investment Innovation Technology Co. Ltd. Hangzhou China Zhejiang Construction Engineering Group Co. Ltd. Hangzhou China

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Machine vision is widely used in the engineering field, especially in the intelligent monitoring, safety warning and information recognition scenarios. In this study, the novel civil engineering construction site safety monitoring and warning system based on machine vision is proposed. As a pioneering research, this system realizes all-weather real-time security monitoring by combining the daytime and nighttime image processing technology. In daytime image processing scenarios, this study proposes a new image de-hazing algorithm. This algorithm models the imaging process by improving the atmospheric scattering model. At the same time, the guided filtering algorithm is used for optimization, which significantly improves the image clarity in low-visibility environments. In the night image processing scenario, this study uses infrared imaging technology and filtering algorithms for image enhancement. This operation can effectively solve the challenges of image noise and detail loss in low-light environments. As the core of the model, this study proposes a safety warning framework based on image anomaly detection to meet the special needs of construction sites. This innovative architecture includes human posture rotation detection and image color deviation anomaly recognition. Through such operations, automatic recognition and warning of dangerous behaviors are achieved. Experimental results prove the effectiveness of the recognition algorithm.

关键词： Renewable energy sources Machine vision Atmospheric modeling Scattering Alarm systems filtering algorithms Real-time systems Monitoring Civil engineering Optimization

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Deep Feedback Cancellation for Hearing Aids with Improved System Stability and Sound Quality

Deep Feedback Cancellation for Hearing Aids with Improved Sy...

引用

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

作者： Eleftheria Lydaki Zheng-Hua Tan Jesper Jensen Meng Guo Oticon A/S Denmark Aalborg University Denmark

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Acoustic feedback cancellation is an important task in audio processing systems, aiming to mitigate the effects of feedback loops on system stability and sound quality. State-of-the-art methods rely on adaptive filtering algorithms and face challenges in balancing between rapid convergence and low steady-state error. In this work, we introduce a novel approach inspired by traditional adaptive filtering and deep learning techniques to achieve a significantly faster convergence and lower steady-state errors at the same time. Our proposed system, termed Deep Feedback Cancellation (DFC), leverages deep neural networks to predict the impulse response of the feedback path directly. Hence, it replaces traditional gradient based adaptive estimation of the impulse responses. Experimental evaluation, in a hearing aid setting, conducted on real-world data demonstrates the superiority of DFC over traditional methods. Specifically, in a practically very important situation, where the feedback path undergoes rapid changes, the proposed DFC achieves increased convergence rate by a factor of 30, while decreasing the steady-state error by 2 dB. Generally, DFC leads to very significant improvements over traditional methods. These improvements are confirmed by objective evaluations and subjective listening tests. Our findings suggest that DFC presents a promising alternative for acoustic feedback cancellation in hearing aid applications.

关键词： Deep learning filtering Signal processing filtering algorithms Hearing aids Acoustics Steady-state Transient analysis Speech processing Convergence

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Short-time quantum Fourier transform processing

Short-time quantum Fourier transform processing

引用

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

作者： Sreeraj Rajindran Nair Benjamin Southwell Christopher Ferrie Dolby Laboratories University of Technology Sydney Sydney Australia Dolby Laboratories Sydney Australia University of Technology Sydney Sydney Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

algorithms for processing data in short-time batches are critical for both online and offline processing of streamed and large data respectively due to the quadratic relation between signal length and computational cost of convolution-based processing schemes. Whilst quantum analogs to some digital signal processing algorithms have been discovered, including the quantum Fourier transform (QFT), there has been no development of short-time processing techniques in the quantum domain. In this manuscript, we introduce the short-time QFT (STQFT) processing technique to bridge this gap in research. We develop a novel overlap-add reconstruction technique in the quantum domain using a permutation gate to combine subsequent windows. With this in mind, we discuss convolution under our novel STQFT processing scheme. We demonstrate filtering in the quantum Fourier domain with a filter stored in a quantum register as well as in a block encoded unitary gate. Throughout the paper, we elaborate upon implementation details such as applying DC offsets to input signals, skipping input data frames whenever necessary, the use of overlap-save as a reconstruction technique and mitigating time-varying scaling due to normalization of the windowed input data and filters.

关键词： Fourier transforms Convolution filtering Signal processing algorithms Digital signal processing Logic gates filtering algorithms Real-time systems Registers Speech processing

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A Hybrid Feature Selection Model for Early Prediction of Metabolic Syndrome

A Hybrid Feature Selection Model for Early Prediction of Met...

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

作者： A S Sreepadh P Sheshu R G Reshiha T Vinay Kumar Gurusamy Jeyakumar Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Metabolic Syndrome (MetS) is a medical condition that increases the risk of cardiovascular diseases, stroke, and type 2 diseases. Some of the symptoms of MetS are high blood pressure, elevated blood sugar, excess body fat, and abnormal cholesterol levels. All these symptoms are interconnected, which has made early diagnosis of MetS critical. In current medical strategies, there is no personalized medication which leads to unfavorable health outcomes and further increased risks. This paper aims to address these challenges by identifying optimal feature subsets required for the early prediction of MetS. To get the optimal features a hybrid model is proposed in this paper. The proposed model utilizes both the filter method and the wrapper method. Firstly, features are selected using the correlation coefficient (CC) method which is a filter method, and then the selected features are further refined using genetic algorithm (GA) which is a wrapper method. This hybrid model increases the prediction accuracy and reduces computational cost. The methodology of the proposed model, its experimental setup, the results obtained, and the inferences gained are presented in this paper.

关键词： Correlation coefficient Sentiment analysis Accuracy Computational modeling Predictive models filtering algorithms Stroke (medical condition) Feature extraction Medical diagnostic imaging Genetic algorithms

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Intelligent Intrusion Detection in IoT: Integrating Machine Learning and Feature Automation

Intelligent Intrusion Detection in IoT: Integrating Machine ...

引用

International Conference on Computer and Automation Engineering, ICCAE

作者： Nazratun Naim Neha A. A. M. Muzahid Md Shamsuzzoha Imrul Jahid Hua Han Yujin Zhang Md Musabbir Hossain Ferdous Sohel School of Electronic and Electric Engineering Shanghai University of Engineering Science Shanghai China Hawaii Natural Energy Institute University of Hawaii at Manoa Honolulu HI USA School of Information Technology Murdoch University Murdoch WA Australia

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

The rapid growth of the internet of things (IoT) raises serious security concerns, demanding effective protection from cyber-attacks. Intrusion detection depends on identifying key features, but despite advances in automation, manual feature selection remains necessary, limiting scalability. To address this limitation, we introduced a hybrid feature selection method that combines filter and wrapper techniques to automatically select important features and enhance the efficiency of machine learning (ML) models for intrusion detection tasks. We utilized the mutual information (MI) algorithm as the filter method and recursive feature elimination (RFE) as the wrapper method. We evaluated the performance of the proposed model on publicly available datasets, HIKARI2021 and UNSW-NB15. We compared the results with several existing methods, and our approach outperformed the state-of-the-art (SOTA) methods in terms of accuracy and training time. We presented comprehensive results, including both quantitative and qualitative analysis, to demonstrate the effectiveness and efficiency of our proposed methods.

关键词： Training Automation Machine learning algorithms Limiting Intrusion detection filtering algorithms Feature extraction Information filters Internet of Things Security

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PDMX: A Large-Scale Public Domain MusicXML Dataset for Symbolic Music Processing

PDMX: A Large-Scale Public Domain MusicXML Dataset for Symbo...

引用

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

作者： Phillip Long Zachary Novack Taylor Berg-Kirkpatrick Julian McAuley Computer Science & Engineering UC San Diego La Jolla USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The recent explosion of generative AI-Music systems has raised numerous concerns over data copyright, licensing music from musicians, and the conflict between open-source AI and large prestige companies. Such issues highlight the need for publicly available, copyright-free musical data, in which there is a large shortage, particularly for symbolic music data. To alleviate this issue, we present PDMX: a large-scale open-source dataset of over 250K public domain MusicXML scores collected from the score-sharing forum MuseScore, making it the largest available copyright-free symbolic music dataset to our knowledge. PDMX additionally includes a wealth of both tag and user interaction metadata, allowing us to efficiently analyze the dataset and filter for high quality user-generated scores. Given the additional metadata afforded by our data collection process, we conduct multitrack music generation experiments evaluating how different representative subsets of PDMX lead to different behaviors in downstream models, and how user-rating statistics can be used as an effective measure of data quality.

关键词： filtering Data integrity Companies Signal processing filtering algorithms Data collection Explosions Multiple signal classification Speech processing Capacity planning

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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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Experimental Insights into the Modeling and Control of Heater Systems Using Discrete PID Controllers

Experimental Insights into the Modeling and Control of Heate...

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Sustainable Energy Technologies and Computational Intelligence (SETCOM), International Conference on

作者： K. Shah Ankit Markana Anilkumar Hardik R. Patel Instrumenation & Control Engineering Department L. D. College of Engineering Ahmedabad Gujarat India Department of Electrical Engineering Pandit Deendayal Energy University Gandhinagar Gujarat India Instrumenation & Control Engineering Department Government Engineering College Rajkot Gujarat India

ISBN: (数字)9798331520540

ISBN: (纸本)9798331520557

This research presents the real-time implementation of discrete PI and PID control algorithms on a Heater System, with the system model identified through open-loop experiments using the Scilab platform. A First-Order Plus Dead Time model is fitted to the experimental data, and controller parameters are determined using the tuning method. The study investigates the impact of disturbances, modeled by a DC fan, on temperature control and compares the performance of different control strategies. The PID and PID with filter controllers show improved disturbance rejection, faster response, and reduced steady-state error, offering valuable insights for selecting the most effective control algorithm for temperature control in such systems.

关键词： Heating systems Solid modeling PI control filtering algorithms Solids Real-time systems Regulation Steady-state PD control Tuning

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Achieving Robustness in Blind Modulo Analog-to-Digital Conversion

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

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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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