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

共 4564 条记录，以下是131-140 订阅

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Epileptic Activity Prediction Within Pre-ictal Epochs: Application on Selected Channels 7

Epileptic Activity Prediction Within Pre-ictal Epochs: Appli...

引用

7th International conference on image and signal processing and their applications, ISPA 2022

作者： Hasnaoui, Lyna Henaa Djebbari, Abdelghani University of Tlemcen Laboratory of Biomedical Engineering Faculty of Technology Tlemcen Algeria

ISBN: (纸本)9781665480420

Automatic epileptic seizure prediction (AESP) is a challenge in epileptology since it has the potential to improve seizure incidence control while also providing a better understanding of seizure origin. We created a system using the CHB-MIT Scalp EEG Database that utilizes the Discrete wavelet Transform (DWT) and a kNN classifier to investigate effective channels among the numerous channels within the CHB-MIT Scalp EEG database. We decomposed pre-ictal and normal signals with a 5- level DWT and then classified them using selected key features. We attained an accuracy of 98.27%, a sensitivity of 100%, and specificity of 96.66%, indicating the effectiveness of the deployed technique. Additionally, it outperforms previous results in classification accuracy, the computation time for selected channels, the pre-ictal period, and the time available for seizure avoidance techniques. © 2022 IEEE.

关键词： Discrete wavelet transforms

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Underwater image Fusion Enhancement Algorithm based on Color Correction and Sharpening 2

Underwater Image Fusion Enhancement Algorithm based on Color...

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2nd International conference on signal processing, Computer Networks and Communications, SPCNC 2023

作者： Ding, Zhongjun Li, Zhiyuan Li, Dewei College of Ocean Science and Engineering Shandong University of Science and Technology Qingdao266590 China National Deep Sea Center Qingdao266237 China

ISBN: (纸本)9798400716430

To address the challenges of low brightness, color distortion, and blurred details caused by complex subaqueous environments, a novel underwater image fusion and enhancement algorithm based on color correction and image sharpening has been proposed. Firstly, the ACE algorithm is used to correct and enhance the color, and then apply gamma correction to the v channel of the HSv three channels to improve brightness. Concurrently, the Laplace algorithm is used to sharpen the color-corrected image. Finally, we use a wavelet transform fusion algorithm to fuse the images. Experimental results on multiple underwater images under different scenarios show that the algorithm achieves good color correction and enhancement of details from a subjective perspective. From an objective perspective, the algorithm performs better than others in terms of the IE, UIQM, and UICQE mean evaluations. The algorithm improves the mean evaluation by 24.1%, 1.88 times, and more than 44.6% respectively compared with the raw images. These results indicate that the algorithm performs better than other algorithms in underwater image processing, with the advantage of maximizing the preservation of image information, making it suitable for various situations. © 2023 ACM.

关键词： image fusion

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Listless Block Cube Tree Coding for Low Resource Hyperspectral image Compression Sensors 5

Listless Block Cube Tree Coding for Low Resource Hyperspectr...

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5th International conference on Multimedia, signal processing and Communication Technologies (IMPACT)

作者： Chandra, Harshit Bajpai, Shrish Integral Univ Fac Engn & Informat Technol ECE Dept Lucknow Uttar Pradesh India

ISBN: (纸本)9781665476478

Hyperspectral (HS) image has rich spectral information content, which facilitates multiple applications including remote sensing. Due to the big data size of the HS image, compression is a required process for the efficiency of image storage and transmission. However, the complexity of the compression algorithms turns real-time compression into a very challenging task. A novel listless set partitioned hyperspectral image compression algorithm is proposed. The proposed compression algorithm uses zero block cube tree structure to exploit the inter and intra sub-band correlation to achieve the compression. From the result, it has been clear that the proposed compression algorithm has low coding complexity with at-par coding efficiency. Thus, it can be a suitable contender for low-resource hyperspectral image sensors.

关键词： Coding Complexity Compression Hyperspectral image Set Partitioned Hyperspectral image Compression wavelet Transform

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Research on finite element analysis and signal denoising based on electromagnetic acoustic emission 2

Research on finite element analysis and signal denoising bas...

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2nd International conference on signal image processing and Communication, ICSIPC 2022

作者： Yang, Chenglong Lai, Yushu Li, Qiuyue School of Electronic and information Engineering Chongqing Three Gorges University Chongqing404130 China

ISBN: (纸本)9781510657694

In order to investigate the feasibility of acoustic emission phenomenon aroused by electromagnetic excitation in metal specimens, electromagnetic acoustic emission model based on COMSOL software was established. On this basis, electromagnetic acoustic emission experiments were carried out and real signals with a lot of noise were obtained. To remove the noise of the electromagnetic acoustic emission signal, wavelet packet transform and complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) was introduced. Firstly, the noise signal of low-frequency was removed by the wavelet packet transform, and the retained node signal was reconstructed. Then the reconstructed signal was resolved by CEEMDAN, and the effective component signal was obtained by the variance contribution rate. Finally, to obtain denoised electromagnetic acoustic emission signal, the effective component signal was reconstructed. The result of simulation and experiment shows that the electromagnetic acoustic emission signal could be effectively obtained by loading electromagnetic excitation, which has greater practical value in engineering applications. © 2022 SPIE.

关键词： Empirical mode decomposition

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Design and Analysis of 4 Bit Multipliers using Pass Transistor Logic and Gate Diffusion Input Technique using 18nm FinFET Technology 7

Design and Analysis of 4 Bit Multipliers using Pass Transist...

引用

7th International conference on Devices, Circuits and Systems (ICDCS)

作者： Pavithara, P. Ramesh, M. Prabu, M. Kumar, K. Kavin Ponmurugan, P. Suriyan, v. Udaya Kongu Engn Coll Dept Elect & Commun Engn Erode India M Kumarasamy Coll Engn Dept Elect & Elect Engn Karur India Nandha Engn Coll Dept Elect & Elect Engn Erode India Meenakshi Acad Higher Educ Res Chennai Tamil Nadu India

ISBN: (数字)9798350350470

ISBN: (纸本)9798350350470;9798350350487

very Large-Scale Integration (vLSI) is a method for manufacturing integrated circuits, where an extensive number of transistors are intricately placed onto a single silicon chip. Complementary Metal Oxide Semiconductor (CMOS) stands out as a leading technology in computer chip design, while Pass-Transistor Logic (PTL) serves as a prevalent logic design technique, utilizing transmission gates or pass transistors instead of traditional inverters for logic gate implementation. The Gate Diffusion Input Technique (GDI) is known for speed and power efficiency. A 4 x 4 array multiplier (AM) using Ripple-Carry Adder (RCA) finds applications in digital signal processing and computing, particularly in tasks like digital filtering, signal compression, image processing, and video processing. The speed of the 4 x 4 AM using RCA is slower than the other multiplication methods that incorporate Carry-Look Ahead Adder (CLA). This work involves the design of 4 x 4 AM using FinFET technology with PTL logic. The parameters like propagation delay, power, and power delay product for 4 x 4 AM is estimated using the CADENCE virtuoso Tool at 18nm technology node with a supply voltage of 1.8v. The existing AM using RCA has the power consumption of 724.43 mu W and propagation delay of 30.26 ns. The proposed AM using CLA with PTL logic experiences a delay of 10.10 ns and power consumption of 185.07 mu W. This indicates that the proposed multiplier exhibits less delay and lower power consumption compared to other FinFET based multipliers. In addition, the proposed multiplier enables a maximum of 66.62 % improvement in speed and 79.12 % improvement in power when compared to its counterparts thereby showcasing its potential for improved efficiency in integrated circuit applications.

关键词： FinFET PTL GDI Array Multiplier Wallace tree multiplier Power Delay

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Automated CNN model for Indian Sign Language Gesture Recognition 10

Automated CNN model for Indian Sign Language Gesture Recogni...

引用

10th International conference on Communication and signal processing, ICCSP 2024

作者： Sambhav, v.K. Rajmohan, R. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350353068

This paper presents the development and evaluation of a Convolutional Neural Network (CNN)-based model for the real-time recognition of Indian Sign Language (ISL) gestures. With the objective of enhancing communication for the deaf and hard-of-hearing community, the study navigates through the process of dataset creation, model training, and deployment for real-time sign interpretation. Leveraging a comprehensive dataset sourced from Kaggle, the research focuses on a subset of ISL gestures, employing advanced data preprocessing techniques, model architecture design, and machine learning frameworks facilitated by Google Colab's computing resources. The performance of the developed model is rigorously assessed using a validation set, with results analyzed through a confusion matrix, and metrics such as accuracy, precision, recall, and F1 score. These evaluations underscore the model's efficacy in recognizing ISL gestures with high accuracy, offering promising implications for real-world applications as an assistive technology. © 2024 IEEE.

关键词： image recognition

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Rice Sheath Rot Disease Diagnosis Utilizing Deep Learning: A wavelet-Filtered Method for Feature Extraction and Analysis 4

Rice Sheath Rot Disease Diagnosis Utilizing Deep Learning: A...

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4th International conference on Intelligent Technologies, CONIT 2024

作者： Doma, Sreelakshmi Rinku, Dhruva R. Jyothi, v. Kumar, Puli vinay Atheeswaran, Athiraja Rajeswaran, N. Telangana Hyderabad India Cvr College of Engineering Department of Ece Telangana Hyderabad India Vardhaman College of Engineering Department of Ece Telangana Hyderabad India Malla Reddy Engineering College It Department Telangana Hyderabad India Malla Reddy College of Engineering Department of Cse Telangana Hyderabad India Malla Reddy College of Engineering Department of Ece Telangana Hyderabad India

ISBN: (纸本)9798350349900

Rice sheath rot disease is a serious threat to rice production that reduces both the amount and quality of one of the most important food crops farmed globally. This study provides a novel approach to detecting Rice Sheath Rot illness using state-of-the-art deep learning algorithms. It does this by employing a sizable dataset that was collected from several sources, including agricultural institutions, research centers, and first-hand field observations. Our main goal is to develop a trustworthy and efficient diagnostic tool by utilizing cutting edge machine learning and image processing techniques. Our method for pre-processing images starts with the use of wavelet filters. This technique is crucial for removing noise and enhancing the quality of the dataset, which consists of highdefinition images of rice plants suffering from the Sheath Rot disease. Next, sophisticated feature extraction algorithms are used to assess the ill patches' color, shape, and texture. These qualities are crucial for distinguishing damaged plant tissues from healthy ones. Our main diagnostic model is based on Convolutional Neural Networks (CNN), which have demonstrated effectiveness in image identification and classification applications. The CNN model is trained using the pre-processed and feature-enhanced information, which enables it to identify the intricate patterns and characteristics linked to Sheath Rot sickness. The model's architecture is designed to optimize efficiency and precision, ensuring prompt and precise diagnosis of illnesses. The suggested approach uses AI and deep learning to help farmers and agronomists detect infections early on and to significantly advance the field of agricultural disease diagnosis. When combined with waveletfiltered pre-processing and CNN-based analysis, our approach sets a new benchmark for the detection and management of Rice Sheath Rot disease. This might lead to more effective disease control strategies and better crop management practices. ©

关键词： Convolutional neural networks

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LCCM-vC: LEARNED CONDITIONAL CODING MODES FOR vIDEO COMPRESSION

LCCM-VC: LEARNED CONDITIONAL CODING MODES FOR VIDEO COMPRESS...

引用

IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Hadizadeh, Hadi Bajic, Ivan v. Quchan Univ Technol Quchan Iran Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9798350302615

End-to-end learning-based video compression has made steady progress over the last several years. However, unlike learning-based image coding, which has already surpassed its hand-crafted counterparts, the progress on learning-based video coding has been slower. In this paper, we present learned conditional coding modes for video coding (LCCM-vC), a video coding model that competes favorably against HEvC reference software implementation. Our model utilizes conditional - rather than residual - coding, and introduces additional coding modes to improve compression performance. The compression efficiency is especially good in the high-quality/high-bitrate range, which is important for broadcast and video-on-demand streaming applications. The implementation of LCCM-vC is available at https://***/hadihdz/lccm_vc

关键词： End-to-end learned video coding conditional coding augmented normalizing flows autoencoders

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Comparison of Widely-used image Restoration Methods for Additive and Multiplicative Noises

Comparison of Widely-used Image Restoration Methods for Addi...

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

作者： Mohammad Zedan Yury S. Bekhtin dept. Diagnostic Information Technologies National Research University “MPEI” Moscow Russian Federation dept. Automatics and Information Technologies in Control Ryazan State Radio Engieneering University named after V.F. Utkin Ryazan Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

This paper evaluates and compares the performance of various image denoising techniques, including wavelet transform, Wiener filtering, non-local means (NLM), denoising autoencoders (DAEs), and feedforward autoencoders. There are cases of additive and multiplicative noises considered where the Gaussian distribution with zero mean stands for the first case and an exponential distribution with unity mean describes the second case (speckle). The experiments have been conducted on the given dataset which contained both still test images and synthetic aperture radar (SAR) images with varying noise levels to analyze the effectiveness of these denoising approaches. The performance of each method is assessed using key image quality metrics: Peak signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Squared Error (MSE), and Normalized Absolute Error (NAK). The results demonstrate that denoising autoencoders DAEs provide superior performance in terms of structural preservation and noise reduction, particularly in low-noise conditions, while Wiener filtering proves to be computationally efficient and robust under high-noise conditions. These findings offer insights into the selection of effective denoising techniques for noisy images based on specific noise characteristics and application requirements.

关键词： wavelet transforms Wiener filters Additives PSNR Autoencoders Speckle Radar polarimetry Computational efficiency image restoration Noise level

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Peculiarities of wavelet Transform Application for EEG signal Analysis During vibration Influence

Peculiarities of Wavelet Transform Application for EEG Signa...

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

作者： Tatyana N. Matveeva Andrey N. Brysin Alexandra v. Torchinskaya Daniel G. Kornilov Egor I. Spivak School No. 6: Aerospace Moscow Aviation Institute (National Research University) (MAI Moscow Aviation Institute) Moscow Russia CS 1 «Data protection» MIREA - Russian Technological University (RTU MIREA Moscow Russia School No. 3: Control Systems Computer Science and Power Engineering Moscow Aviation Institute (National Research University) (MAI Moscow Aviation Institute) Moscow Russia «RZD-TECHSERVICE» LLC Moscow Russia «BOKSMETR» LLC Moscow Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The paper deals with the reaction of the vehicle operator's organism to external vibration and the development of a monitoring system at the early stages of vibration effects. The choice of wavelet transform as the main method of signal processing is justified. The zones of organism reaction to external overloads are singled out, and the application of Gaussian mother wavelets for electroencephalogram analysis, except Morlet wavelet, at vibration impact on the vehicle operator is evaluated. Experimental work was carried out on the EGv 10–100 stand of the vibromechanics laboratory of IMASH RAS.

关键词： wavelet transforms vibrations Digital signal processing wavelet analysis Organisms Electroencephalography signal analysis Monitoring

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