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2022 IEEE Biennial Congress of Argentina, ARGENCON 2022

作者： Quispe H, Juan Carlos Morales, John Orduna, Eduardo Unsj - Conicet Instituto de Energía Eléctrica San Juan Argentina Tu Dresden Dresden Germany

ISBN: (数字)9781665480147

ISBN: (纸本)9781665480147

Multiresolution analysis (MRA) is a sequence of decompositions of a signal to observe it at multiple resolutions. Therefore, it allows extracting signal features over a wide range of frequency spectrum. In this sense, high-frequency signals generated by electrical faults (short-circuits) in transmission lines can be characterized. The wavelet transform has become the most developed method by researchers for MRA, but this technique suffers from drawbacks that still makes it difficult to implement in real applications. However, a new method based on mathematical morphology has been proving useful in image processing, so this work proposes to develop the MRA of high-frequency signals by kilometer resolution based on fault location. In this regard, several simulations were performed on a power electrical system modeled in software ATP-Draw with a frequency-dependent transmission line model. These events were developed with high sampling frequencies and also consider the scenarios of different fault resistances and inception angles. Finally, the comparison with the wavelet transform is described. © 2022 IEEE.

关键词： Mathematical morphology

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Automated Recognition of Hand Gestures From Multichannel EMG Sensor Data Using Time-Frequency Domain Deep Learning for IoT applications

IEEE SENSORS LETTERS

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IEEE SENSORS LETTERS 2024年第6期8卷 1页

作者： Mohapatra, Amlan Deep Aggarwal, Amol Tripathy, Rajesh Kumar BITS Pilani Dept Elect & Elect Engn Hyderabad Campus Hyderabad 500078 India BITS Pilani Dept Pharm Pilani Campus Pilani 333031 India

The automated recognition of hand gestures from multicha- nnel electromyogram (MEMG) sensor data is essential for human-computer interaction, prosthetic control, rehabilitation, and biometric-related applications. This letter proposes a time-frequency domain deep neural network (TFDDNN)-based approach to recognize hand gestures using MEMG recordings. The MEMG recordings are segmented into frames, and the average of all channel information (mean EMG signal) for each frame is evaluated. The continuous wavelet transform is applied to the mean EMG signal to obtain the joint time-frequency representation (TFR). The TFR-based image of the mean EMG signal is used as the input to a deep representation learning network (DRLN) model to recognize hand gestures. Two publicly available databases are utilized to evaluate the performance of the proposed TFDDNN approach. The results show that the proposed TFDDNN has obtained overall accuracy values of 92.73% and 80.33% using MEMG signals from database 1 and database 2 for multiclass-based recognition of hand gestures. The suggested DRLN model has demonstrated higher accuracy for automated hand gesture recognition than existing deep-learning-based methods. We have deployed the proposed TFDDNN approach in a web application for Internet of Things (IoT)-enabled real-time recognition of hand gestures using MEMG sensor data.

关键词： Databases Electromyography Solid modeling Sensors Recording Continuous wavelet transforms Gesture recognition Sensor signal processing accuracy continuous wavelet transform (CWT) deep representation learning electromyogram (EMG) hand gestures Internet of Things (IoT)

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Momentum Study Based on CRITIC Evaluation Method and Machine Learning

Momentum Study Based on CRITIC Evaluation Method and Machine...

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image processing and Computer applications (ICIPCA), IEEE International Conference on

作者： Jinghua Zhou Jingruo Zhang Beijing Jiaotong University Beijing China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

This paper mainly focuses on the evaluation and prediction of sports competition results, and uses the methods of feature extraction, CRITIC weighted analysis and machine learning algorithm to discuss and analyze. First, after data preprocessing, the dimension of column data is reduced by feature extraction and the evaluation index is obtained. These metrics are then weighted using the CRITIC method to obtain a measure of quantified performance. Secondly, the correlation analysis model is introduced to prove the existence of momentum. Finally, by comparing the performance of XGBoost, Extra Trees and Random Forest algorithm in training and prediction, it is found that XGBoost algorithm has the best performance in AUC of test set, reaching 95.05%. The XGBoost parameters were further optimized by Bayesian optimization, and the signal was reconstructed by combining wavelet analysis to improve the accuracy and robustness of the model.

关键词： Analytical models Machine learning algorithms Correlation Games Feature extraction wavelet analysis Prediction algorithms Robustness Random forests Sports

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Design and Optimization of Homomorphic Medical image Fusion Algorithm 7

Design and Optimization of Homomorphic Medical Image Fusion ...

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7th IEEE International Conference on signal and image processing applications, ICSIPA 2021

作者： Awang, Mat Kamil Marzuki, Muhd Aliff Haiqal Mohd Kamil, Nurul Kamilah Mat Department of Electrical and Electronics Engineering National Defence University of Malaysia Kuala Lumpur Malaysia Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany

ISBN: (纸本)9781665435925

Multimodal image fusion is a method of fusing images of different modalities into one image without losing the overall meaning of the input images. The homomorphic method is one method to enhance digital images by increasing the high-frequency image signals and reducing the low-frequency of unwanted illumination. This paper will demonstrate how the homomorphic fusion method can improve the fused image quality compared to basic fusion methods such as principal component analysis (PCA) and discrete wavelet transform (DWT) methods. The design and simulation are carried out by MATLAB software on selected medical modalities, MR-PET and MRI. The results are compared using Mutual Information (MI) with aforementioned methods. The results showed that the homomorphic method has higher efficiency than DWT and PCA methods. © 2021 IEEE

关键词： Discrete wavelet transforms

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image Compression using Orthogonal Ramanujan wavelet Transform

Image Compression using Orthogonal Ramanujan Wavelet Transfo...

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IEEE Applied signal processing Conference (ASPCON)

作者： Soumya Prabha Maiti Arijit Saha Debaprasad De Mrinal Kanti Naskar Dept. of ETCE ADES Lab Jadavpur University Kolkata India Techno Main Kolkata India

In this paper, an image compression algorithm based on the Orthogonal Ramanujan Sums (ORS) is presented. The proposed algorithm is novel in that it combines ORS-based Discrete wavelet Transform (DWT) with the well-known JPEG image compression technique. The software implementation of the algorithm is done using Python. The results are compared against the standard JPEG compression technique and it is observed that at similar compression ratios, the proposed algorithm achieves upto 69.88% lower MSE, 24.2% higher PSNR and 27.3% higher SSIM compared to JPEG, which makes it a potential candidate for applications where preserving the quality of the images is important.

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Advancements in signal processing: A Comprehensive Review of Discrete wavelet Transform and Fractional wavelet Filter Techniques

Advancements in Signal Processing: A Comprehensive Review of...

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Advances in Computational Intelligence and Communication (ICACIC), International Conference on

作者： Ahsan Waseem Ilma Shah Mohd Azhan Umar Kamil Department of Electrical Engineering Aligarh Muslim University Aligarh India

This review paper explores the fundamental importance of fractional wavelet filter (FrWF) and discrete wavelet transform (DWT) techniques in signal processing. These techniques are thoroughly examined and serve as the foundation for many different applications, including data compression, telecommunications, and medical diagnostics. DWT’s multi-resolution analysis, facilitated by breaking signals into various scales, plays a vital role in image compression, denoising, and feature extraction. FrWF, designed for memory-constrained environments, notably reduces memory overhead through external memory utilization. The adaptive memory allocation technique enhances memory efficiency, pivotal for real-time applications like sensors and cameras. These techniques, distinct yet impactful, bolster science and technology. This review highlights their vital roles, progressions, and potential in reshaping memory-restricted signal processing, redefining paradigms.

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An Unsupervised Domain Adversarial wavelet Learning Method for registration of Optical and SAR Heterogeneous Remote Sensing images

An Unsupervised Domain Adversarial Wavelet Learning Method f...

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signal, Information and Data processing (ICSIDP), IEEE International Conference on

作者： Hongze Ye Shenfu Zhang Liangzhi Li Xiangchao Meng School of Information Science and Engineering Ningbo University Ningbo China College of Geological Engineering and Geomatics Chang’an University Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Optical images and synthetic aperture radar (SAR) remote sensing images are highly complementary information for Earth observation and applications. Accurate registration of these two data types is essential for subsequent collaboration processing and analysis. However, traditional methods struggle to achieve effective registration due to nonlinear radiometric disparities and geometric distortions. Meanwhile, supervised deep learning approaches require costly manual annotations. To address these issues, this paper proposes an unsupervised registration method based on domain adversarial wavelet learning. We innovatively design an inter-domain adversarial mutual transfer (IAT) module to alleviate cross-modal differences while preserving structural features. Additionally, wavelet transform is employed at different scales for feature matching, to mitigate radiometric disparities and enhance feature recognizability. By incorporating an attention mechanism, the network structure is optimized through spatial alignment and image similarity. The proposed method outperforms other state-of-the-art methods in both quantitative and qualitative aspects of benchmark datasets.

关键词： Optical filters wavelet transforms wavelet domain Optical distortion Radiometry Optical imaging Optical sensors Nonlinear optics Synthetic aperture radar Remote sensing

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A High-Speed Computational Pipeline Single MAC-Based VLSI Architecture for Real-Time signal and image processing 4

A High-Speed Computational Pipeline Single MAC-Based VLSI Ar...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Vaithiyanathan, Dhandapani James, Britto Pari Mariammal, Karuthapandian National Institute of Technology Delhi Delhi110036 India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Chennai600062 India Anna University Madras Institute of Technology Chennai600044 India

ISBN: (纸本)9798350343670

VLSI architectures play a vital role in most real-time applications because of their optimal performance. In this work, a single MAC (Multiply-Accumulate) based pipelined architecture is proposed for faster computation of the one-dimension (1-D) discrete wavelet transform (DWT). The main objective of this work is to reduce computational complexity by utilizing resource sharing and to enhance the speed of operation by employing a pipelining scheme. The designed single MAC-based architecture for computing DWT is suitable for infrasound data processing which employs a high-pass filter (HPF) and low-pass filter (LPF). The architecture efficiently implements a resource-sharing scheme by adopting only these two filters. In addition, in this realization, pipelining is utilized to achieve high-speed computation of DWT. Further, this design makes use of common elements several times which provide a convenient and suitable implementation. The proposed structure is coded using Verilog and implemented in FPGA (Field Programmable Gate Array) hardware with the Xilinx platform. Intending easily transported and real-time utilization, this architecture is realized using Xilinx Virtex-5 series FPGA. The area of the proposed architecture is reduced by about 44.8% when examining conventional parallel MAC architecture. Likewise, this pipelined design offers power conservation of about 31.97% over the parallel design. Further, the proposed architecture offers an operating speed of 276MHz with a power conservation of 200mW at 276MHz clock frequency. © 2024 IEEE.

关键词： DWT FPGA Device High-Speed Low-Power MAC Pipelining VLSI Architecture

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Exploring the potential of wavelets: In the field of image processing

Exploring the potential of wavelets: In the field of image p...

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作者： J. J. Wong Delft University of Technology

This research consists of two applications of image processing, namely, image compression and image denoising. image compression aims to reduce the size of an image without losing too many features. This is often used to store a large number of images such as fingerprints. Denoising is a technique for removing noise from an image while preserving as many of the edges and other detailed features as possible. This research studies the use of different discrete wavelets and the Dual Tree Complex wavelets in the image compression and denoising process. The wavelet transform decomposes the original image into approximation and detail coefficients, where the approximation coefficients are calculated by averaging and the detail coefficients by taking differences. The wavelet transform is also invertible so that the image can be reconstructed again using the approximation and detail coefficients. The compression and denoising method consists of three steps: decomposition, thresholding and reconstruction. The difference between compression and denoising lies in the threshold part. For image compression, a percentage of the detail coefficient is chosen as the threshold. The wavelets db6, sym5, coif3, bior4.4 and rbio1.5 are chosen to compress the images. The images are tested with compression rates ranging from 5:1 to 43:1. Based on the Structural Similarity Index Measurement (SSIM), the bior4.4 wavelet performs best. For denoising, the threshold is optimised to obtain a denoised image. The discrete wavelets used are db4, coif3, bior2.8. Of these, the bior2.8 wavelet performs the best on images used in this research. Therefore, the bior2.8 wavelet is compared with the Dual Tree Complex wavelet (DTCW). Based on the Peak signal-toNoise-Ratio (PSNR), the denoised image using the DTCW performs better than the bior2.8 wavelet. Overal, wavelets are a powerful tool in image processing. The different wavelets each have their own characteristics. The choice of the optimal wavelet depe

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Effect of Pre-processing on MST based Infrared and Visible image Fusion 3

Effect of Pre-processing on MST based Infrared and Visible I...

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3rd International Conference on signal processing and Communication (ICPSC)

作者： Budhiraja, Sumit Agrawal, Sunil Sohi, Balwinder S. Sharma, Neeraj Panjab Univ ECE UIET Chandigarh India Chandigarh Univ Mohali Punjab India

ISBN: (纸本)9781665428644

image fusion combines information from multiple images in a single image to increase the information processing capabilities. Infrared and visible image fusion is finding a lot of applications these days, as it brings complementary information from the source images in one image. The performance of image fusion is affected by noise and artifacts, which tend to creep in during fusion process. Pre-processing of source images is generally done to improve the quality of final fused image. In this paper, a general framework for fusion of infrared and visible image is proposed for investigating role of preprocessing on quality of image fusion. Various arrangements of image enhancement and denoising techniques are used to investigate the role of preprocessing in improving the efficiency of image fusion. The subjective and objective performance evaluations show that joint use of denoising and enhancement improves the performance of infrared and visible image fusion.

关键词： image Fusion image Denoising image Enhancement Discrete Stationary wavelet Transform

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