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检索条件"任意字段=International Computer Conference on Wavelet Active Media Technology and Information Processing"

共 6383 条记录，以下是11-20 订阅

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2020 17th international computer conference on wavelet active media technology and information processing, ICCWAMTIP 2020

2020 17th International Computer Conference on Wavelet Activ...

引用

17th international computer conference on wavelet active media technology and information processing, ICCWAMTIP 2020

ISBN: (纸本)9781665405058

The proceedings contain 90 papers. The topics discussed include: MLP-based power analysis attacks with two-point joint feature selection;a cascading incremental training approach for largescale distributed data based on support vector machine;a tensor modeling for video rain streaks removal approach based on the main direction of rain streaks;automatic room temperature control system using Arduino UNO R3 and DHT11 sensor;3DCNN: three-layers deep convolutional neural network architecture for breast cancer detection using clinical image data;classification of surface natural resources based on U-Net and GF-1 satellite images;an improved kubernetes scheduling algorithm for deep learning platform;the application research on military Internet of Things;and fake news detection using deep recurrent neural networks.

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2015 12th international computer conference on wavelet active media technology and information processing, ICCWAMTIP 2015

2015 12th International Computer Conference on Wavelet Activ...

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12th international computer conference on wavelet active media technology and information processing, ICCWAMTIP 2015

ISBN: (纸本)9781467382663

The proceedings contain 105 papers. The topics discussed include: a self-adaptive algorithm of carrier frequency offset estimate in wireless network;robust matching algorithm based on surf;a modified genetic algorithm for resource allocation in OFDM-based cognitive radio systems;analysis on impact factors of travel speed and their countermeasures for Shanghai urban rail transit network;direction finding based on single channel array using block-sparse recovery;adaptive comblining multi-branch frequency-domain detector for underwater acoustic cooperative communication;a new single-phase specific harmonic detection algorithm without phase-locked loop;a new super-twisting algorithm with state extension of sliding variable toward fast convergence of high accuracy;motion artifacts removal for FNIRs data based on independent component analysis;a method of w a VEFORMs classification based on cascaded neural networks;and ant colony optimization of virtual machine placement for data latency minimization in cloud systems.

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2022 19th international computer conference on wavelet active media technology and information processing, ICCWAMTIP 2022

2022 19th International Computer Conference on Wavelet Activ...

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19th international computer conference on wavelet active media technology and information processing, ICCWAMTIP 2022

ISBN: (纸本)9781665493871

The proceedings contain 138 papers. The topics discussed include: a novel and efficient sequential learning-based malware classification model;source-free unsupervised domain adaptation via denoising mutual learning;swot analysis and strategies of high-quality career guidance education in universities in the context of big data;an intelligent and transparent inference: spiking neural network for causal reasoning;relation heterogeneous graph neural network;sparse sampled logistic chaos-based frequency modulated signals for MIMO radar;a polarization-independent transmissive metalens in the microwave band;multi-agent reinforcement learning with contribution-based assignment online routing in SDN;lattice-based anonymous signcryption for smart grid;domain adaptation method for document object detection;spatio-temporal graph-TCN neural network for traffic flow prediction;object cluster position using reinforcement learning;enhancing the adversarial transferability of vision transformers through perturbation invariance;and target identification technology of millimeter-wave radar based on feature template.

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wavelet active media technology And information processing (In 2 Volumes) - Proceedings Of The international computer conference 2006

引用

2006年

作者： Jian Ping Li Simon X Yang

ISBN: (数字)9789812772763

ISBN: (纸本)9789812700421

wavelet analysis has gained recognition as a useful tool for analyzing time-frequency, and is playing an important role in signal and information processing. wavelet analysis is not only based on a bright new idea, but also on concepts that already existed in various forms in many different fields. The formalization and emergence of this wavelet theory is the result of a multidisciplinary effort that has brought together of varied disciplines. This book captures the essence of the state of the art in wavelet analysis and active media technology.

关键词： computer Science

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Research on Digital Watermarking Algorithm Based on wavelet Transform Fused with Visual Model 24

Research on Digital Watermarking Algorithm Based on Wavelet ...

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8th international conference on Electronic information technology and computer Engineering, EITCE 2024

作者： Yu, Fei School of Electrical and Electronic Engineering Guilin Institute of Information Technology Guilin China

ISBN: (纸本)9798400710094

This paper aims to develop a digital watermarking algorithm based on wavelet transform and the characteristics of the human visual system (HVS). The main objective is to leverage the masking characteristics of HVS and the time-frequency localization properties of wavelet transform to achieve efficient watermark embedding. The goal is to ensure that the watermark is imperceptible to the human eye while maintaining the robustness and embedding strength of the watermark. This research contributes to enhancing the practicality and reliability of digital image processing and information security technologies. © 2024 Copyright held by the owner/author(s).

关键词： Embeddings

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A wavelet-based Diffusion Model for Low-light Image Enhancement in Raw Space 16

A Wavelet-based Diffusion Model for Low-light Image Enhancem...

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2024 16th international conference on Graphics and Image processing, ICGIP 2024

作者： Zhang, Xin-Yu Xu, Yang Wei, Zhi-Hui Wu, Ze-Bin School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Low-light image enhancement (LLIE) in Raw space has posed a challenge in the field of image processing and computational photography. Different from image processing in sRGB space, Raw images store more image information and thus have more space for image processing. Existing methods often struggle to approximate real images in terms of image detail enhancement and color restoration. Drawing inspiration from the latest advancements in denoising diffusion models, we introduce an innovative framework that leverages wavelet transform and diffusion models to tackle the low light image enhancement challenge in Raw space. In our method, the input low-light image is first decomposed into different frequency sub-bands by discrete wavelet transform (DWT) from both image and feature levels. Then a redesigned diffusion model is trained to adaptively denoise and enhance the different frequency sub-bands. In order to achieve better results in human eye perception, we also improved the loss function during training process. Finally, the enhanced result of the low-light image is finally reconstructed through inverse wavelet transform (IWT) and subsequent processing, transitioning from Raw space to sRGB space. Extensive experiments on SID and ELD datasets show that our refined method outperforms several existing methods, delivering superior performance both quantitatively and qualitatively. © 2025 SPIE.

关键词： Discrete wavelet transforms

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Research on Noise Reduction of Engine Vibration Signal Based on RLMD- Improved wavelet Threshold Algorithm 24

Research on Noise Reduction of Engine Vibration Signal Based...

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8th international conference on Electronic information technology and computer Engineering, EITCE 2024

作者： Wang, Jun Zhu, Zhifeng Cai, Bohua Yao, Yong School of Electrical and Information Engineering Anhui University of Technology Anhui Ma’anshan China Anhui Fcar Electronic Technology Co. Ltd. Anhui Ma’anshan China

ISBN: (纸本)9798400710094

Due to the high speed of vehicle travel and road bumps, the vibration signals of engines during operation are characterized by nonstationarity and low signal-to-noise ratios, making it challenging to diagnose faults based on signal feature extraction. A signal denoising algorithm utilizing robust local mean decomposition (RLMD) and improved wavelet thresholding has been designed. The signal is decomposed into several product functions using RLMD. Then, improved wavelet thresholding (IWT) is applied to each product function. Finally, the denoised signal is obtained by reconstructing these functions. The IWT function reduces the shortcomings of traditional methods through smooth transitions, better preserving signal characteristics. Analysis is done through simulation experiments and actual diesel engine signal measurements. This paper compares the proposed algorithm with the traditional empirical mode decomposition (EMD) and wavelet threshold (WT) method, showing significant improvements in denoising algorithm. This method can better balance noise removal and signal preservation when processing complex non-stationary signals, offering strong practical application value. © 2024 Copyright held by the owner/author(s).

关键词： Noise abatement

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Linking information Needs: Enhancing Crowdsourcing Action through New media and Online Documentation in Crisis Situations 24

Linking Information Needs: Enhancing Crowdsourcing Action th...

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5th international conference on computer Science and Management technology, ICCSMT 2024

作者： Chen, Feng Zhang, Nan Business School Ludong University Shandong Yantai China School of Public Administration and Society Jiangsu Normal University Jiangsu Xuzhou China

ISBN: (纸本)9798400709999

During crises, social media has emerged as a crucial platform for the dissemination of crisis information;however, it also presents challenges such as information overload, demand matching, and rapid processing. This study focuses specifically on the Henan rainstorm rescue case to analyze the information requirements and dissemination behavior of governmental bodies, social organizations, and netizens. Additionally, it applies crowdsourcing theory to examine bystander behavior during such crises. The findings reveal that bystanders publish a significantly greater number of microblogs compared to government entities and other rescue organizations. Furthermore, online documents play an indispensable role in promoting multi-party participation by leveraging cloud computing technology. Consequently, this enhances crisis management efficiency by mobilizing netizens and the general public to actively engage in crisis information governance. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Disaster prevention

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Global Color-Aware Arbitrary Style Transfer with Discrete wavelet Transform 20th

Global Color-Aware Arbitrary Style Transfer with Discrete W...

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20th IFIP WG 10.3 international conference on Network and Parallel Computing, NPC 2024

作者： Chen, Bochao Tan, Junhao Li, Xiaoqiang Pei, Songwen School of Computer Engineering and Science Shanghai University Shanghai200444 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9789819628292

The purpose of image arbitrary style transfer is to apply a given artistic or photorealistic style to a target content image. While existing methods can effectively transfer style information, the variability in color brightness features between the style and content images can significantly impact the quality of the style transfer. Specifically, traditional arbitrary style transfer relies on an encoder-decoder architecture of spatial-aware neural networks operating in the RGB domain, which typically preserves only local color brightness information and lacks a global color perception compared to the wavelet frequency domain. In this paper, we address the issue of insufficient global color perception in images from the perspective of image enhancement. We propose Global Color-aware Arbitrary Style Transfer with Discrete wavelet Transform (GCAST), which achieves a balance between the global color characteristics of style images and the local details of content images. Our approach involves an encoder that integrates preliminary feature encoding through cross-layer attention fusion, while the decoder modifies frequency features using a wavelet-based adjustment block. To maintain a balance between content and style in the stylized images, we design a comprehensive loss function that incorporates multiple restoration loss, double style loss, and noise regularization loss. Extensive experiments comparing our method with state-of-the-art techniques demonstrate that our proposed model generates stylized images with superior results in terms of global color perception and human perceptual studies. © IFIP international Federation for information processing 2025.

关键词： Decoding

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Multiresolution Encoder-Decoder Convolutional Neural Network for Magnetic Resonance Image Segmentation

Multiresolution Encoder-Decoder Convolutional Neural Network...

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2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Tarafdar, Kishore K. Meher, Aaditya Shah, Mirat Saifee, Qutubuddin Kumar, Dushyant Nimkar, Anant V. Jayasundar, Rama Gadre, Vikram M. Department of Electrical Engineering Indian Institute of Technology Bombay India Department of Information Technology India Department of Computer Engineering India Sardar Patel Institute of Technology Mumbai India India Department of NMR All India Institute of Medical Sciences New Delhi India

ISBN: (纸本)9798350368741

In image segmentation by deep learning, encoder-decoder Convolutional Neural Network (CNN) architectures are fundamental for creating and learning representations. However, with many filters in these models, one often loses the tractability of these models and interprets them as black boxes. In this paper, we describe the design of a multiresolution encoder-decoder CNN having a distinct low-frequency processing encoder and mixed-frequency processing decoder. The encoder of the proposed architecture first creates a localized spatial-frequency representation of the feature space using a framelet system and then learns representations using trainable convolution layers while converging to segment structures. The decoder takes in the encoder output, including distinct low and high-frequency skip connections and reconstructs the Region of Interest (RoI) with accurate boundaries. Spatial attention layers for low frequencies and a wavelet-based self-attention for high frequencies in the decoder successively aid the reconstruction of the desired segment. The described network architecture is overall lossless by design, despite information loss in wavelet pooling layers because of the initial subband decomposition and separate low and high-frequency paths. The model is tested separately using T1-weighted brain Magnetic Resonance (MR) images with two classes and accurately segments large and small RoIs. The trained model also successfully segments MR volumes acquired by our group. © 2025 IEEE.

关键词： Magnetic Resonance Imaging multiresolution analysis semantic segmentation wavelet attention wavelet pooling

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