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2022 International Conference on Information Science and Communications Technologies, ICISCT 2022

作者： Alexandr, Sergeev Saint Petersburg State University of Aerospace Instrumentation Department of Computer Technology and Programming Saint Petersburg Russia

ISBN: (纸本)9781665472296

orthogonal and quasi-orthogonal matrices with a limited number of element values and structured in some way are of considerable interest for many technical applications related to image processing and signal coding. The paper classifies and considers cyclic matrices and matrices with cyclic blocks. Descriptions of noise-resistant encoding methods of images and their protective masking using such matrices are given. Based on the analysis of the autocorrelation functions of new code sequences obtained from strings of modified cyclic quasi-orthogonal matrices and the simulation performed, numerical values of the quality parameters of the new code sequences are given. It is shown that their application makes it possible to reduce the maximum level of the side lobe of the aperiodic autocorrelation function, which allows us to conclude that their application is promising and that signal coding algorithms can be revised. © 2022 IEEE.

关键词： signal encoding

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image Based Detection of Cotton Plant Diseases Using Discrete wavelet Transform Features

Image Based Detection of Cotton Plant Diseases Using Discret...

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IEEE International Conference on Intelligent Techniques in Control, Optimization and signal processing (INCOS)

作者： Shalini Mohan Lakshmanan Muthulakshmi Devaraj D Department of Biotechnology Kalasalingam Academy of Research and Education Srivilliputtur India Department of Electrical and Electronics Engineering Kalasalingam Academy of Research and Education Srivillliputur India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Cotton is one of the most vital cash crops cultivated around the globe and its yield directly influences the economy and the livelihood of huge number of people associated with it. The major loss of cotton occurs due to damage incurred by microbial pathogens that infect the cotton crop. The course of infection and their effect on the growth and yield of the plant varies depending upon the type of disease. Appropriate treatment for the specific type of disease could prevent the spread of infection and also reduce the use of pesticide that is usually done during the empirical treatment techniques. Identification of the type of cotton plant disease is critical and needs to be done at a faster pace. image processingbased machine learning approach is found to be a better option for this application. This article deals with development of the machine learning model capable of identifying the different types of cotton plant diseases using image processing. Foliar images of diseased cotton plants are collected from public databases and preprocessing operations like resizing, filtering and contrast enhancement are performed followed by k-means clustering for segmentation of diseased part of the leaf. image decomposition is performed using discrete wavelet transform with Daubechies wavelet at five levels and the mean, standard deviation, and entropy of the coefficients are used for building the machine learning model. Classification with Artificial Neural Network trained with backpropagation algorithm has a classification accuracy of 97% indicating the veracity of the model. Hence, the developed model could be used as for developing a user-friendly interface that could be tapped for real-time applications.

关键词： Plant diseases image segmentation Crops Machine learning Backpropagation algorithms Artificial neural networks Discrete wavelet transforms

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wavelet Knowledge Distillation for Efficient Stain Normalization in Digital Histopathology

Wavelet Knowledge Distillation for Efficient Stain Normaliza...

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IEEE signal processing and Communications applications (SIU)

作者： Sefa Keklik Murat Ekinci Elif Baykal Kablan Bilgisayar Mühendisliği Bölümü Karadeniz Teknik Üniversitesi Trabzon Türkiye Yazılım Mühendisliği Bölümü Karadeniz Teknik Üniversitesi Trabzon Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

In digital histopathology, color standardization, known as stain normalization, is widely used in computer-aided diagnosis (CAD) systems. This study details the adaptation and implementation of the wavelet Knowledge Distillation (WKD) method to CAD systems. The proposed method focuses on knowledge transfer between the teacher and student models within a specially designed Pix2Pix Generative Adversarial Network (GAN) for stain normalization in histopathology images. The student model, guided by the knowledge transferred by the teacher model using wavelet-based feature extraction, significantly improves the accuracy of stain normalization, which is crucial for preserving histological details and image quality. The WKD method has demonstrated high performance on the publicly available paired MITOS-ATYPIA dataset, outperforming state-of-the-art methods. Using the same settings, the Teacher model achieved a PSNR of 25.559, SSIM of 0.934, and RMSE of 7.270. Additionally, the student model used in the method yielded better results in the Fréchet Inception Uzaklığı (FID) metric compared to the teacher and baseline models.

关键词： Knowledge engineering Measurement image quality Solid modeling Adaptation models Histopathology Standardization

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High dimensional Bio medical images denoising using wavelet transform and modified bilateral filter 14

High dimensional Bio medical images denoising using wavelet ...

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14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)

作者： Jabbar, Muhammad Usama Waqar, Ali Awais, Muhammad Mushtaq, Zohaib Abbass, Muhammad Jamshed Rehman, Muhammad Abdul Saleem, Faisal Ahmed, Shehzad Jan, Ahmed Zubair Natl Univ Sci & Technol Dept Elect Engn Islamabad Pakistan Riphah Int Univ Fac Engn & Appl Sci Islamabad Pakistan Univ Napoli Parthenope Naples Italy Wroclaw Univ Sci & Technol Fac Mech Engn Wroclaw Poland

ISBN: (纸本)9781665460712

Biomedical images consist of salient information that helps in diagnosis of diseases. Significant amount of information is lost during process of transmission due to addition of noisy components especially for Computed Tomography (CT) scan, Ultrasound imaging and Magnetic Resonance Imaging (MRI). image denoising is an important and necessary pre-processing phase in image applications. Numerous techniques have been developed for denoising process over decades. The purpose is to remove noise from images. wavelet transform is current state of the art addition to analyze image denoising;able to achieve improved experimental results than from existing techniques. Generalized Gaussian distribution is considered to model noise. In this article, we introduce mix adaptive thresholding with modified bilateral filter. Simulation results demonstrate that attributes of images are improved. The Qualitative and quantitative assessment proves better denoised results for proposed method using parameters like Peak signal to Noise Ratio (PSNR), Mean Opinion Score (MOS) and Structural Similarity Index (SSIM) between the true and estimated biomedical images.

关键词： Gaussian noise wavelet thresholding biomedical images denoising PSN SSIM

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Research on Anomaly Test Data processing Method Based on wavelet Analysis

Research on Anomaly Test Data Processing Method Based on Wav...

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

作者： Kong Xiangqi Wang Zhongyuan Unit 41 of 92941 Troops PLA Huludao China

A large amount of test data and test data will be generated in the stages of equipment development, design, setting and use. Meanwhile, due to the easy impact of external environment in the process of data collection, the quality of collected data will be reduced. If the processing of abnormal data is not accurate enough, the analysis and evaluation of test results will be affected. Study the corresponding data processing methods to ensure the accuracy of test data and support the analysis and evaluation of test results. Aiming at the problems existing in traditional abnormal data processing methods, this paper optimizes the existing wavelet analysis based on theoretical research and mathematical calculation, and summarizes an effective method for processing signal abnormal data, which provides effective support for the analysis and interpretation of test results.

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Edge Detection of Noise images Based on Improved Canny Algorithm with Adaptive Threshold

Edge Detection of Noise Images Based on Improved Canny Algor...

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Advances in Electrical Engineering and Computer applications( AEECA), 2020 IEEE International Conference on

作者： Jing Zhang School of Modern Health and Regimen Industry Anhui Sanlian University Hefei China

ISBN: (纸本)9798350355260

Edge detection is a significant research direction in the fields of image processing and computer vision, widely applied in image segmentation, object detection, and other areas. The traditional Canny edge detection algorithm excels in noise resistance and edge localization, but it still faces challenges such as edge information loss and the need for manual setting of dual thresholds when processing noisy images. This paper proposes an improved Canny edge detection algorithm with adaptive thresholds, which effectively addresses the deficiencies of the traditional Canny algorithm in noise suppression and threshold setting by introducing a wavelet filter and an adaptive threshold determination method based on Otsu's method for maximizing the between-class variance. Experimental results on the BSD500 dataset demonstrate that the proposed algorithm performs excellently in edge detection of noisy images, with an accuracy improvement of approximately 5%-10% compared to the traditional Canny algorithm.

关键词： Location awareness image segmentation Accuracy image edge detection Noise reduction Manuals Approximation algorithms Discrete wavelet transforms Noise measurement signal to noise ratio

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Novel signal processing Techniques for High Angular Resolution Diffusion-Weighted Imaging

Novel Signal Processing Techniques for High Angular Resoluti...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Dushyanth V Babu R Praveen Kumar Thakur Shiv Shankar Shankar Department of Civil Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India ARKA JAIN University Jamshedpur Jharkhand India Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Excessive angular decision diffusion-weighted imaging (HARDWI) is an effective approach for visualizing tissue microstructures, which are otherwise hard to look at the usage of conventional MRI technology. To attain this, HARDWI relies on sign-processing techniques to reconstruct images from tremendously anisotropic diffusion-weighted alerts. Novel signal processing strategies advanced in current years have further advanced the abilities of HARDWI. These strategies leverage the sparsity of diffusion signal in more than one angular guideline, allowing the reconstruction of man or woman diffusion occasions within the specimen’s microstructure. wavelet rework-primarily based strategies allow for the advanced decision of the reconstructed images, extended evaluation, and stepped forward signal-to-noise ratio in the resulting photographs. Other strategies, inclusive of diffusion orientation transform, have been advanced to estimate the orientation of fiber tracts better. Furthermore, spatially-variable regularization techniques have been advanced for advanced estimation of the diffusion parameters at a voxel stage. The combination of these signal processing techniques has advanced the application of HARDWI to a spread of biomedical applications, such as neuroanatomy, tumor imaging, and small animal studies.

关键词： wavelet transforms Visualization image resolution Imaging Optical fiber networks Microstructure image reconstruction signal resolution Tumors signal to noise ratio

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The Impact of Feature Extraction on Classification Accuracy Examined by Employing a signal Transformer to Classify Hand Gestures Using Surface Electromyography signals

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SENSORS 2024年第4期24卷 1259页

作者： Moslhi, Aly Medhat Aly, Hesham H. ElMessiery, Medhat Arab Acad Sci Technol & Maritime Transport Fac Engn Smart Village CampusPOB 2033 Giza Egypt Cairo Univ Fac Engn POB 2033 Giza Egypt

Interest in developing techniques for acquiring and decoding biological signals is on the rise in the research community. This interest spans various applications, with a particular focus on prosthetic control and rehabilitation, where achieving precise hand gesture recognition using surface electromyography signals is crucial due to the complexity and variability of surface electromyography data. Advanced signal processing and data analysis techniques are required to effectively extract meaningful information from these signals. In our study, we utilized three datasets: NinaPro Database 1, CapgMyo Database A, and CapgMyo Database B. These datasets were chosen for their open-source availability and established role in evaluating surface electromyography classifiers. Hand gesture recognition using surface electromyography signals draws inspiration from image classification algorithms, leading to the introduction and development of the Novel signal Transformer. We systematically investigated two feature extraction techniques for surface electromyography signals: the Fast Fourier Transform and wavelet-based feature extraction. Our study demonstrated significant advancements in surface electromyography signal classification, particularly in the Ninapro database 1 and CapgMyo dataset A, surpassing existing results in the literature. The newly introduced signal Transformer outperformed traditional Convolutional Neural Networks by excelling in capturing structural details and incorporating global information from image-like signals through robust basis functions. Additionally, the inclusion of an attention mechanism within the signal Transformer highlighted the significance of electrode readings, improving classification accuracy. These findings underscore the potential of the signal Transformer as a powerful tool for precise and effective surface electromyography signal classification, promising applications in prosthetic control and rehabilitation.

关键词： surface electromyography EMG transformer feature extraction hand gesture recognition

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Design Exploration of DWT-Based Feature Extraction Using FPGA for High-Performance signal processing 16

Design Exploration of DWT-Based Feature Extraction Using FPG...

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16th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2025

作者： Trabes, Emanuel Zayed, Aymen Valderrama, Carlos Tarrillo, Jimmy Universidad Nacional de San Luis Department of Electronics Argentina University of Mons Service d'Électronique et de Microélectronique Mons Belgium Research Laboratory of Technology and Medical Imaging Ltim LR12ES06 Faculty of Medicine of Monastir Tunisia National Engineering School of Sousse University of Sousse Tunisia Universidad de Ingenieria y Tecnologia Electrical and Mechatronic Department Peru

ISBN: (纸本)9798331522124

The discrete wavelet transform (DWT) is commonly used for feature extraction in machine learning applications. Since these applications are frequently deployed in portable systems with limited computational resources, FPGA-based hybrid hardware/software solutions might be a viable choice. This article provides an analysis of various 4-level db4 DWT and feature extraction techniques implemented on the Zynq 7020 device. Alternative DWT versions include fixed-point and floating-point implementations, cascade and single-core reuse architectures, as well as designs in HDL and VHDL. The feature extraction process considers mean, energy, and entropy. It has also been implemented in an architecture that efficiently reuses these computational cores. These versions are compared in terms of accuracy, resources used, performance, and power consumption. © 2025 IEEE.

关键词： image reconstruction

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TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature signals

TCCT-Net: Two-Stream Network Architecture for Fast and Effic...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Vedernikov, Alexander Kumar, Puneet Chen, Haoyu Seppanen, Tapio Li, Xiaobai Univ Oulu Ctr Machine Vis & Signal Anal Oulu Finland Zhejiang Univ State Key Lab Blockchain & Data Secur Hangzhou Peoples R China

ISBN: (纸本)9798350365474

Engagement analysis finds various applications in healthcare, education, advertisement, services. Deep Neural Networks, used for analysis, possess complex architecture and need large amounts of input data, computational power, inference time. These constraints challenge embedding systems into devices for real-time use. To address these limitations, we present a novel two-stream feature fusion "Tensor-Convolution and Convolution-Transformer Network" (TCCT-Net) architecture. To better learn the meaningful patterns in the temporal-spatial domain, we design a "CT" stream that integrates a hybrid convolutional-transformer. In parallel, to efficiently extract rich patterns from the temporal-frequency domain and boost processing speed, we introduce a "TC" stream that uses Continuous wavelet Transform (CWT) to represent information in a 2D tensor form. Evaluated on the EngageNet dataset, the proposed method outperforms existing baselines, utilizing only two behavioral features (head pose rotations) compared to the 98 used in baseline models. Furthermore, comparative analysis shows TCCT-Net's architecture offers an order-of-magnitude improvement in inference speed compared to state-of-the-art image-based Recurrent Neural Network (RNN) methods. The code will be released at https: //***/vedernikovphoto/TCCT_Net.

关键词： Tensors

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