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检索条件"任意字段=Conference on Wavelet Applications in Signal and Image Processing VI"

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A Multiplier-Less Level-3 Haar wavelet Transform Approximation Requiring Five Additions Only 15

A Multiplier-Less Level-3 Haar Wavelet Transform Approximati...

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15th IEEE Dallas Circuit and System conference (DCAS)

作者： da Rosa, Morgana M. A. Paim, Guilherme Seidel, Henrique B. Almeida, Sergio da Costa, Eduardo A. C. Bampi, Sergio Univ Catolica Pelotas UCPel Grad Program Elect Engn & Comp Pelotas RS Brazil Fed Univ Rio Grande do Sul UFRGS Grad Program Microelect PGMICRO Porto Alegre RS Brazil

ISBN: (纸本)9781665498852

Approximate transform kernels have emerged as an alternative design to reduce the hardware complexity of digital signal processors by exploiting the intrinsic error resilience of many embedded consumer electronics applications such as signal, image and video processing, and computer vision. The approximate level-4 Haar wavelet transform (AxHWT-4) is the state-of-the-art approximate transform for ECG (electrocardiogram) signal processing and reduces more than nine times the energy consumption compared to the exact transform. In this work, we propose an approximate level-3 Haar wavelet transform (AxHWT-3) for increasing the hardware design efficiency with related to the AxHWT-4 while obtaining higher R-peak detection accuracy in the electrocardiogram signal. Our AxHWT-3 approximation is performed in the Haar matrix kernel to eliminate its multiplications which in the hardware design results in savings of energy consumption and VLSI hardware area. Our AxHWT-3 proposal achieves an R-peak detection accuracy of more than 99% and reduces the energy consumption by 15.98 times and the VLSI area by 12.64 times compared to the exact solution (i.e., the original level-3 Haar). In addition, the proposed AxHWT-3 outperforms the state-of-the-art AxHWT-4 with a savings of about 32% in power dissipation and 19.5% in VLSI area.

关键词： Approximate Computing Approximate Transforms Haar wavelet ECG Digital signal processing Hardware Design

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Comparative Study on Classification of images in Compressed and Uncompressed Domains

Comparative Study on Classification of Images in Compressed ...

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International conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Ashwini K Perla Sree Neha S Jaiadityan Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amrita school of computing Chennai India

ISBN: (数字)9798350350845

ISBN: (纸本)9798350350852

image Classification is the basis of Computer vision. Classification with image data finds a variety of applications in various fields. A comparative study of Classifying the images in the compressed and uncompressed domain is exploited in this research. A garbage classification dataset with 6 different categories is employed for this purpose. Naitve Convolutional neural network is utilized as the classifier. Compressed classification is achieved by compressing the images using discrete wavelet transform of three levels and by feeding the compressed data to the model for classification. The accuracy achieved by classification in the uncompressed domain is about 82.1% with a loss of 0.892. The accuracy achieved in the compressed domain by the three levels of the discrete wavelet transform is 81.7%,81.2%, and 74.6% with losses of 0.793, 0.891, and 1.071 respectively. There is minimal difference in accuracy achieved between uncompressed and compressed domains. image compression by discrete wavelet transform overperforms the uncompressed domain by several factors like size, elapsed time and memory space.

关键词： Wireless communication image coding wavelet domain Memory management Transforms signal processing Data models

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wavelet-Based Data Compression for Remote Sensing and image processing

Wavelet-Based Data Compression for Remote Sensing and Image ...

引用

Optimization Computing and Wireless Communication (ICOCWC), International conference on

作者： Dhyan Chandra Yadav Atul Dadhich Ananya Saha Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electrical Engineering Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

wavelet-based totally records compression is a form of information compression used to method far flung sensing and picture processing. This technique makes use of wavelets, that are mathematical features that divide a signal into separate frequency components, and gift the signal as a sum of the components. wavelet-primarily based compression permits for compression of statistics into a smaller, extra efficient form without sizeable lack of best. furthermore, it could additionally produce precise reconstructions of the original sign or image, making it a feasible tool for remote sensing and image processing. This approach is utilized in a selection of applications including medical imaging, television transmission, satellite tv for pc imagery and far flung sensing. via utilising wavelets, more efficient and special records may be received. moreover, due to its low time complexity, wavelet-based compression is ideal for processing big quantities of data speedy and efficaciously.

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3rd International conference on Machine vision and Augmented Intelligence, MAI 2023

3rd International Conference on Machine Vision and Augmented...

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3rd International conference on Machine vision and Augmented Intelligence, MAI 2023

ISBN: (纸本)9789819743582

The proceedings contain 76 papers. The special focus in this conference is on Machine vision and Augmented Intelligence. The topics include: Survey on Robustness of Deep Learning Techniques on Adversarial Attacks in WBAN;synergizing Collaborative and Content-Based Filtering for Enhanced Movie Recommendations;exploring Transformer-Based Approaches for Hyperspectral image Classification: A Comparative Analysis;deep Learning for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition;tracking of Ship and Plane in Satellite videos Using a Convolutional Regression Network with Deep Features;Tumor Detection and Analysis from Brain MRI images Using Deep Learning;software Maintenance Prediction Using Stack Ensemble Deep Learning Algorithms;resource Allocation in 6G Network for High-Speed Train Using D2D Outband Communication;controlling the Band-to-Band Tunneling Effect in Charge Plasma Based Dopingless Transistor;Comparison of Different CIC Filter Architectures on the Basis of a Novel Parameter Called Noise Factor for Sigma-Delta Based ADCs;the Scientific Analysis on Effective Yoga Posture Recognition Techniques;impact of Gamma Rays on Emerging Devices for Photonic applications;shaft Rotation Monitoring Using Radar signal processing and wavelet Transform;gysel Power Divider Miniaturization Using an Inter-Digital Capacitor-Based Slow-Wave Structure;noise Estimation and Removal in Fundus images Using Pyramid Real image Denoising Network;evaluation of Hybrid Encryption Method to Secure Healthcare Data;multimodal Face Recognition System Using Hybrid Deep Learning Feature;Classification of Copy and Move image by Using HELM-FSK Method: An Efficient Approach;analysis of Energy Efficient Smart Home Based on IoT System;role of Explainable Artificial Intelligence Approaches in Cybersecurity.

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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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image Enhancement Using HSV Color Space, DWT, and BiHE Techniques 6

Image Enhancement Using HSV Color Space, DWT, and BiHE Techn...

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6th Iraqi International conference on Engineering Technology and its applications, IICETA 2023

作者： Obaida, Tameem Hameed Kadum, Salman Abd Najjar, Fallah H. Al-Jawahry, Hassan M. Technical Institute of Najaf Al-Furat Al-Awsat Technical University Najaf54001 Iraq School of Computing University Technology of Malaysia Faculty of Engineering Johar Bahru Malaysia The Islamic University Faculty of Engineering Najaf54001 Iraq

ISBN: (纸本)9798350303339

image enhancement is vital in computer vision and image processing applications. This study introduces an innovative approach combining HSV color space, Discrete wavelet Transforms (DWT), and Bi Histogram Equalization (BiHE) techniques. By converting the image from RGB to HSV, we separate color and intensity information, allowing us to enhance the intensity component for improved visual quality. The DWT is then applied to decompose the image into frequency sub bands, enabling targeted manipulation of specific frequency components to enhance image details and overall appearance. Additionally, the BiHE technique enhances contrast while preserving local image details. The proposed method was evaluated on various images, including tissue, football, Lena, and yellow lily. The results show that the proposed method achieved high Peak signal-to-Noise Ratio (PSNR) values, with tissue achieving 32.1853 dB, football achieving 30.5452 dB, Lena achieving 27.1462 dB, and yellow lily achieving 25.2015 dB. The Mean Squared Error (MSE) values were also low, with tissue having an MSE of 0.0006, football having an MSE of 0.0009, Lena having an MSE of 0.0022, and yellow lily having an MSE of 0.0031. These results demonstrate the effectiveness of the proposed method in enhancing the visual quality, details, and contrast of the images while maintaining naturalness and avoiding artifacts. Experimental results demonstrate that our method outperforms traditional techniques, significantly enhancing visual quality, details, and contrast while maintaining naturalness and avoiding artifacts. © 2023 IEEE.

关键词： image enhancement

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wavelet Analysis of Photoplethysmogram in Zebrafish Larvaе Cardiovascular System Study

Wavelet Analysis of Photoplethysmogram in Zebrafish Larvaе ...

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Digital signal processing and its applications (DSPA), International conference on

作者： Artem E. Slavin Anastasia V. Guryleva Valeria I. Bukova Leonid A. Shuman Alexander B. Burlakov Faculty of Space Research Lomonosov Moscow State University Moscow Russia Acoustooptical Spectroscopy Lab Scientific and Technological Centre of Unique Instrumentation of the Russian Academy of Sciences Moscow Russia Department of Human and Animal Anatomy and Physiology Tyumen State University Tyumen Russia Faculty of Biology Lomonosov Moscow State University Moscow Russia

The work is aimed on the development of advanced algorithms for analyzing the photoplethysmogram signal, which characterizes cardiac activity. We have designed an algorithm that separates individual pulse waveforms from the photoplethysmogram, even in the presence of artifacts caused by pathological changes in zebrafish cardiac activity, due to cadmium exposure. The proposed algorithm is based on digital image processing and discrete wavelet transform; it enables reliable separation of individual pulse waveforms. The data obtained can then be processed using conventional techniques, providing accurate pulse wave characteristics for various applications, including toxicology studies, drug development and developmental biology research.

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Identity Recognition Using wavelet-Based Neural Networks: Review and Comparative Study

Identity Recognition Using Wavelet-Based Neural Networks: Re...

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Decision Aid Sciences and Application (DASA), International conference on

作者： Eliganti Ramalakshmi Dama Pranati Sreya Tanguturi Poojitha Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

wavelet neural networks are a unique combination of wavelet analysis and neural network architectures, thus providing the benefits of advanced signal processing. Unlike traditional methods, WNNs offer a multi-resolution approach that outshines for nonstationary signals; therefore, these are very useful in applications related to areas such as biomedical signal analysis, image processing, and time-series forecasting. Architecture of WNNs enables the replacement of activation functions with wavelet functions for the extraction of localized features, making both processing and interpretation of data much more efficient. Some of the recent improvements in designing WNNs improve its computational efficiency to suit the requirements of real-time applications and also to incorporate it into deep learning frameworks, but still, one of the main challenges faced in WNNs such as computational complexity and scalability need to be addressed. As the historical development, core contributions, and evolving architecture of WNNs is done, this paper represents those along with filling up certain existing research gaps. This research paper addresses future directions regarding hybrid models that combine synergies of CNNs with RNNs as well as WNNs and therefore posits opening its way for enhanced performance across various domains. Researching such an area ensures and guarantees further expansion of applications in WNNs besides strengthening their importance in signal processing in the modern arena.

关键词： wavelet transforms Neural networks Computer architecture Thermal sensors wavelet analysis Feature extraction Real-time systems Computational efficiency Internet of Things Noise measurement

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wavelet Based Random Noise Removal from Color images Using Python

Wavelet Based Random Noise Removal from Color Images Using P...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International conference on

作者： Devanand Bhonsle K K Saxena Ruhi Uzma Sheikh Anil Kumar Sahu Pushpendra Singh Tanu Rizvi Department of Electrical Engg. Shri Shankaracharya Technical Campus Bhilai India Department of Electrical Engg. Govt. Engineering College Bilaspur India Department of Electrical Engineering Anjuman College of Engg. & Technology Nagpur India Department of Electronics and Communication Engg. Siddhartha Institute of Technology & Sciences Narapally India Department of Electronics and Telecommunication Engineering Bhilai Institute of Technology Durg India

image de-noising is an essential field in image processing, encompassing a wide range of applications. This is pre-processing task in which unwanted noise signals are removed using different techniques. Noise are unwanted signals which deteriorate the useful information from the image. These information may be edges, ridges, contours are other fine structures. For different applications these details are important. Noise signals may contaminate the image partially or completely. It depends upon the type of noise and its level. Noise may be categories according to its characteristics. The most frequent types of noise signals encountered in image processing include Additive White Gaussian Noise, Speckle noise, salt and pepper noise, Rician noise, random noise, and more. Noise signals introduced in the images during data acquisition, transmission or due to faulty location. Additive white Gaussian noise is one of the most common noise signal which affect almost all the images in a certain extent. In this chapter we apply de-noising technique which is based on wavelet thresholding. wavelet transform is widely recognized as one of the most popular transforms in signal and image processing. It is used in various image processing applications. Thresholding is an essential component in wavelet transform, and it is commonly classified into two types: hard thresholding and soft thresholding. In the chapter we apply soft thresholding technique which outperforms hard thresholding technique.

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Feature-Driven Fusion of Thermal and visible images for Face Analysis

Feature-Driven Fusion of Thermal and Visible Images for Face...

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Artificial Intelligence and signal processing (AISP), INDIA, International conference on

作者： Devansh Tikariha Palani Thanaraj Krishnan T. Sunil Kumar School of Computer Science & Engineering Vellore Institute of Technology Chennai Campus India Department of Electrical Engineering Mathematics and Science University of Gävle Sweden

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

This study presents a feature-driven fusion method for combining thermal and visible images in facial analysis, leveraging morphological and gradient operations to enhance image quality and information content. By integrating diverse features, including structural components and edge details, the proposed fusion technique offers a comprehensive representation of input images, surpassing traditional methods like Discrete wavelet Transform (DWT) based image fusion. Performance evaluation using metrics such as Peak signal-to-Noise Ratio (PSNR) and Spatial Frequency (SF) demonstrates the superior quality and enhanced texture details achieved through the feature fusion approach. This proposed fusion technique not only enhances visual quality but also enriches the fused images with detailed information, highlighting its potential for various applications in image processing and analysis.

关键词： Performance evaluation visualization PSNR image edge detection Face recognition Transforms signal processing Thermal analysis Discrete wavelet transforms image fusion

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