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检索条件"主题词=filtering algorithms"

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Mango Sorting Mechanism Design and Experimental Research Based on Image Processing

Mango Sorting Mechanism Design and Experimental Research Bas...

引用

IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Yunjin Yang Bin Zheng School of Intelligent Manufacturing Panzhihua University Panzhihua

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

Aiming at the problems of manual sorting and packaging of mango, such as sorting errors, time-consuming and labor-consuming, a mango sorting device was designed based on image processing, and a robust mango surface detection algorithm based on frequency domain filtering was proposed. Firstly, a machine vision image acquisition system is designed to obtain mango images with surface defects. The image is divided into R, G and B color channels, and the R channel is selected for mean filtering, noise reduction and Emphasize enhancement. Then, the preprocessed image is converted into spectrum image by FFT, and then transformed into spatial image by IFFT after removing the components representing texture details, which is different from B color channel to obtain a strong contrast image. Image segmentation based on Otsu method, morphological processing of segmented region, to obtain ideal segmentation results. Analysis results show that the detection accuracy is 84.9%, the accuracy rate is 92.3%, the recall rate is 91.4%, and the F1 score is 0.918. After processing mango images, mango images are classified according to the size of surface defect area, so as to finally realize mango sorting.

关键词： Image segmentation filtering Image color analysis Frequency-domain analysis Surface morphology Object segmentation Manuals filtering algorithms Surface treatment Sorting

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Research on Online Identification Method of Foreign Object Invasion in Transmission Lines Based on Image Monitoring

Research on Online Identification Method of Foreign Object I...

引用

Electrical Automation and Artificial Intelligence (ICEAAI), International Conference on

作者： Cheng Chen Han Zhang Xianman Chen Peng Lin Shipeng Yuan Jianpeng Wen Chaozhou Power Supply Bureau of Guangdong Power Grid Co. Ltd. Chaozhou China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

This paper proposes a wire foreign object alarm method for online monitoring of transmission lines to address the issues of low accuracy in monitoring floating objects invading transmission lines and insufficient effectiveness of alarms. This method first constructs an intelligent detection model for floating objects based on a multi-scale RPN network, which realizes the recognition of small-sized hidden danger targets in monitoring scenarios. Subsequently, the article used the improved canny algorithm to extract wires from the images and constructed an effective area for wire foreign object alarm, which was used to remove invalid floating object recognition results such as the ground. Finally, this article conducted tests on the dataset, and the results showed that the proposed method achieved a 97.7% recall rate for wire foreign object alarms, and could greatly reduce false alarms, meeting the needs of intelligent monitoring of transmission lines.

关键词： Power transmission lines Image recognition Automation Accuracy Target recognition Object detection filtering algorithms Wire Artificial intelligence Monitoring

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Deep Learning for Modulo Sampling of FRI Signals

Deep Learning for Modulo Sampling of FRI Signals

引用

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

作者： Sem Koenen Vincent Van De Schaft Ruud J. G. van Sloun Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven The Netherlands

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The finite rate of innovation (FRI) of certain signal classes allows for sub-Nyquist sampling, while modulo folding enables sampling signals with dynamic ranges beyond that of an analog-to-digital converter (ADC). Combining these techniques enables sampling at sub-Nyquist rates using an ADC with a lower dynamic range than the signal. Current signal recovery techniques use algorithmic approaches with high oversampling factors (OFs) and constraints on the signal parameters. In this paper, we introduce a deep learning method to modulo unfolding and combine it with an annihilating filter approach for signal parameter recovery. Our method significantly improves unfolding accuracy and reduces error compared to the state-of-the-art, and performs well at OFs as low as 2 times the rate of innovation, with no constraints on the signal parameters. This approach offers a practical tool for signal recovery from modulo sampled FRI signals, potentially reducing the hardware demands of measurement devices.

关键词： Deep learning Performance evaluation Technological innovation Accuracy Heuristic algorithms Signal processing algorithms filtering algorithms Dynamic range Signal processing Speech processing

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Automatic Recognition System for Gear Defects Based on Machine Vision

Automatic Recognition System for Gear Defects Based on Machi...

引用

IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Shan Luo Bo Yang Jinmin Wei Jihong Liu School of Electrical and Information Engineering Panzhihua University Panzhihua China School of Health and Wellness Panzhihua University Panzhihua China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

It is particularly necessary to conduct defect detection on the gears during the production process to ensure that the quality of the gear products meets the requirements when they leave the factory. Adopting adaptive median filtering algorithm to denoise gear images, using genetic neural network algorithm for gear defect segmentation, extracting geometric and shape features of gear defect regions based on their characteristics. Given the significant differences in the characteristics of cracks, dents, holes, and tooth breakage defects, different gear defect recognition algorithms are adopted. Based on the research on gear defect recognition algorithms, a machine vision based automatic gear defect recognition system was designed and developed, and gear sample images were collected for simulation experiments of the system. The experimental results show that the system can accurately identify defects in gears and classify them with high recognition accuracy. The system also runs very fast, with an average time of 2 seconds required to detect an image. It has high real-time performance and greatly reduces the time required by manual detection methods.

关键词： Image segmentation Image recognition Gears Neural networks Object segmentation Manuals filtering algorithms Feature extraction Genetics Real-time systems

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Optimal Fractional Order Internal Model Control Approach For Altitude Control of UAV

Optimal Fractional Order Internal Model Control Approach For...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Akriti Jain Ashish Kumar Singhal Manish Yadav Manish Awasti Anubhav Pandey Navneet Kumar Electrical Engineering Department SISTec-E Bhopal MP India Jawaharlal Nehru College of Technology Rewa India Department of Electrical Engineering Sagar Institute of Science Technology & Engineering Bhopal

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

This research validates a novel control scheme for controlling the altitude (z-direction) of a quadrotor Mini drone. The simultaneous task of setpoint tracking, dead-time compensation, and handling large disturbances is particularly challenging for integrating plants. Consequently, this innovative control method embeds a disturbance filter and dead-time compensator within a fractional-filter-based PID controller. The controller is specifically designed using the Internal Model Control (IMC) approach, with the fractional filter tuned using the delayed Bode’s ideal transfer function method. Stability is evaluated through the Riemann sheet principle, and the controller settings are fine-tuned with the Teaching Learning Based Algorithm (TLBO). Measurement noise is introduced to test the control scheme’s performance. This method can manage ramp setpoints with plant uncertainty, addressing real-world demands. Numerical results show enhanced step and ramp responses compared to recent techniques utilizing fractional orders for quadrotor altitude control. The Integral Absolute Error (IAE) and Integral Square Error (ISE) are employed for quantitative assessment. The simplicity and intuitive nature of the proposed scheme render it appealing for industrial applications.

关键词： Uncertainty Stability criteria Noise Education Transfer functions filtering algorithms filtering theory Noise measurement Numerical stability Quadrotors

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Influence of Primary and Secondary Path Estimation Accuracy on Active Noise Control Performance

Influence of Primary and Secondary Path Estimation Accuracy ...

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Systems of Signals Generating and Processing in the Field of on Board Communications

作者： Victor I. Djigan Institute of Microdevices and Control Systems National Research University of Electronic Technology Moscow Russia

ISBN: (数字)9798331532635

ISBN: (纸本)9798331532642

An Active Noise Control (ANC) system based on a modified architecture is considered. This architecture allows to accelerate the convergence of the adaptive filtering algorithms in use. Besides, it allows to use the most efficient Recursive Least Squares adaptive filtering algorithms which provide the fastest convergence comparing to the gradient search-based adaptive algorithms. The architecture of the ANC system and its mathematic description are presented. The simulation results demonstrate the ANC system performance. It is shown, that the number of the adaptive filter weighs can be decreased about 30% of the theoretically required value without any observable loss of the ANC performance. This is possible because of fading shape of the primary and secondary path acoustic impulse responses. As the result, the contribution of the impulse response “tails” to the signals at the error microphone of the ANC system is comparable with that of the ambient noise. This also allows to decrease the required number of the adaptive filter weights and to decrease the computational resources required to the implementation of the ANC system.

关键词： Shape System performance Simulation Noise Adaptive filters Computer architecture Tail filtering algorithms Control systems Convergence

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Parallel Median Filter with Arbitrary Window Size and Image Depth

Parallel Median Filter with Arbitrary Window Size and Image ...

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Moreno Marzolla Michele Ravaioli Elena Loli Piccolomini Department of Computer Science and Engineering Center for Inter-Department Industrial Research ICT University of Bologna Bologna Italy Department of Computer Science and Engineering University of Bologna Bologna Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Removing noise in digital images is a fundamental operation that arises in many application domains. In this paper we consider the median filter, a filtering technique that replaces the color of each pixel with the median of those in a square neighborhood of fixed radius. For some use cases, the size of the neighborhood or the image depth may be large, making existing algorithms either too slow, or not applicable at all due to excessive memory requirements. In this paper we describe architecture-specific optimizations that enable the computation of the median filter with arbitrary window size and image depth on multicore processors and GPUs. We report preliminary results that indicate that the parallel implementations are suitable for practical use, with the GPU version outperforming the CPU.

关键词： Histograms Single instruction multiple data Multicore processing Image color analysis Memory management Graphics processing units filtering algorithms Programming Parallel algorithms Optimization

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Designing Multi-Channel Filter for Processing Coherent Pulse Train with Staggered Pulse Repetition Frequency On FPGA

Designing Multi-Channel Filter for Processing Coherent Pulse...

引用

Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Koshelev Vitaly Ivanovich Trinh Ngoc Hieu Federal State Budgetary Educational Institution of Higher Education Ryazan State Radio Engineering University named after V.F. Utkin Ryazan Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

In pulse and digital radio engineering systems, the Doppler frequency of a coherent pulse train (CPT) is measured by the response in a multichannel Doppler filter (MDF) based on the fast Fourier transform (FFT) algorithm. When using a coherent pulse train with different pulse repetition frequency (PRF), the efficiency of signal accumulation in the channels of the traditional FFT algorithm decreases. The coherent pulse train with different PRF will be called a coherent pulse train with staggered PRF. To eliminate this drawback when processing the CPT with staggered PRF, the authors use a modified FFT algorithm that increases the signal-to-noise ratio at the MDF outputs against the background of uncorrelated interference. The report presents the design of a modified FFT algorithm for processing the CPT with staggered PRF on field-programmable gate array (FPGA).

关键词： Fast Fourier transforms Signal processing algorithms Computer architecture filtering algorithms Radar signal processing Frequency measurement Doppler effect Velocity measurement Field programmable gate arrays Signal to noise ratio

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Efficient Infrared Image Super-Resolution Reconstruction via Guided Filter Coefficients Estimation with Parallax Attention Mechanism

Efficient Infrared Image Super-Resolution Reconstruction via...

引用

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

作者： Qingyao Wu Bosheng Chen Chen Li Xiaotong Tu Xinghao Ding Yue Huang Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University P.R. China National Key Laboratory of Infrared Detection Technologies Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Due to the spectral range mismatch between the images, building an efficient infrared (IR) image super-resolution algorithm suitable for embedded devices remains a significant challenge. Given that visible images possess more abundant high-frequency information compared to infrared images, we utilize the visible light to guide infrared image super-resolution reconstruction. Specifically, we transfer the reconstruction task to a guided filter learning process, whose coefficients are estimated by joint learning of visible and infrared image to complete the reconstruction through homologous constraints. In order to efficiently predict guided filter coefficients, we design a lightweight network which incorporates reparameterized differential convolution blocks and a feature fusion strategy. Striving to enhance the fusion strategy performance, we utilize parallax attention mechanism to solve the non-pixel registration problem between infrared and visible images. Extensive experiments on two challenging IR image datasets show that our method performs SOTA in terms of PSNR, SSIM and LPIPS as compared to current state-of-the-art approaches while showing its effectiveness and practicality in the edge platform of RK3588.

关键词： Measurement Attention mechanisms Convolution Image edge detection Superresolution Network architecture filtering algorithms Information filters Speech processing Image reconstruction

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A Hybrid Approach for Detection and Reduction of different types of noises in gray scale images

A Hybrid Approach for Detection and Reduction of different t...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Amanpreet Kaur Sandhu University Institute of Computing Chandigarh University Gharuan Mohali Punjab India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Image denoising is a critical task in various fields such as digital photography, medical imaging, satellite or remote sensing, and, also preserving important image details while removing noise is essential. This paper introduces the Hybrid Adaptive Median-Gaussian Filter (HAMGF), a novel method designed to effectively mitigate both salt-and-pepper and Gaussian noise especially in grayscale images. The proposed HAMGF technique combines adaptive median filtering, which dynamically adjusts the window size to remove impulsive noise, with adaptive Gaussian filtering, which varies the smoothing strength based on local image variance to reduce Gaussian noise. However, the proposed hybrid technique is implemented on a variety of noisy images, demonstrating its ability to outperform traditional filtering methods that are typically specialized for single noise types. HAMGF not only excels in reducing noise but also in preserving critical image features or details, such as edges and fine textures, making it particularly suited for applications requiring high-fidelity image restoration. The results highlight the flexibility and robustness of HAMGF, showcasing its potential for broader adoption in noise reduction tasks. The proposed research work demonstrates an important advancement in the area of image processing which is offering a versatile as well as powerful tool for the enhancement of image quality across varied domains.

关键词： Smoothing methods filtering Gaussian noise Image edge detection Noise reduction Adaptive filters filtering algorithms Image restoration Remote sensing Image denoising

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