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WDMIR: Wavelet-Driven Multimodal Intent Recognition
Weiyin Gong, Kai Zhang, Yanghai Zhang, Qi Liu, Xinjie Sun, Junyu Lu, Linbo Zhu
https://arxiv.org/abs/2506.10011

WDMIR: Wavelet-Driven Multimodal Intent Recognition
Multimodal intent recognition (MIR) seeks to accurately interpret user intentions by integrating verbal and non-verbal information across video, audio and text modalities. While existing approaches prioritize text analysis, they often overlook the rich semantic content embedded in non-verbal cues. This paper presents a novel Wavelet-Driven Multimodal Intent Recognition(WDMIR) framework that enhances intent understanding through frequency-domain analysis of non-verbal information. To be more spe…

Novel software for continuous wavelet analysis enable EEG real-time analysis on portable computers
Shoichiro Nakanishi
https://arxiv.org/abs/2506.07793 htt…

Novel software for continuous wavelet analysis enable EEG real-time analysis on portable computers
Continuous Wavelet Transform (CWT) is frequently used for waveform analysis. For example, in the field of neuroscience research, CWT is performed to analyze electroencephalograms (EEG) and calculate the index of brain activity. Recent advancements in computer technology, such as general-purpose computing on Graphics Processing Units (GPGPU), have enabled the application of CWT to real-time waveform analysis. However, the computational complexity of CWT is large, and it is challenging to employ …

Evidence for Dual Periodic Signatures in PKS 0805-07 from Multi-technique Time Series Analysis
Sikandar Akbar, Zahir Shah, Ranjeev Misra, Sajad Boked, Naseer Iqbal
https://arxiv.org/abs/2506.09602

Evidence for Dual Periodic Signatures in PKS 0805-07 from Multi-technique Time Series Analysis
We report the identification of two statistically significant quasi-periodic oscillations in the weekly binned $γ$-ray light curve of the flat-spectrum radio quasar PKS 0805$-$07, observed by Fermi-LAT over the period MJD 59047.5-59740.5. By applying a suite of complementary time-series analysis techniques, we identify periodic signatures at approximately 255 and 112 days. These techniques include the Lomb-Scargle periodogram (LSP), Weighted Wavelet Z-transform (WWZ), REDFIT, Date-Compensated …

Title: What's Wrong with the Internet Now...
Topics: #Internet #Bloat #WebBloat
Channel: @…

PWD: Prior-Guided and Wavelet-Enhanced Diffusion Model for Limited-Angle CT
Yi Liu, Yiyang Wen, Zekun Zhou, Junqi Ma, Linghang Wang, Yucheng Yao, Liu Shi, Qiegen Liu
https://arxiv.org/abs/2507.05317

PWD: Prior-Guided and Wavelet-Enhanced Diffusion Model for Limited-Angle CT
Generative diffusion models have received increasing attention in medical imaging, particularly in limited-angle computed tomography (LACT). Standard diffusion models achieve high-quality image reconstruction but require a large number of sampling steps during inference, resulting in substantial computational overhead. Although skip-sampling strategies have been proposed to improve efficiency, they often lead to loss of fine structural details. To address this issue, we propose a prior informat…

Wavelet based solutions to the Poisson and the Helmholtz equations on the $n$-dimensional unit sphere
Ilona Iglewska-Nowak, Piotr Stefaniak
https://arxiv.org/abs/2507.03451

Wavelet based solutions to the Poisson and the Helmholtz equations on the $n$-dimensional unit sphere
We present a method of solving partial differential equations on the $n$-dimensional unit sphere using methods based on the continuous wavelet transform derived from approximate identities. We give an explicit analytical solution to the Poisson equation and to the Helmholtz equations. For the first one and for some special values of the parameter in the latter one, we derive a closed formula for the generalized Green function.

Integration of Wavelet Transform Convolution and Channel Attention with LSTM for Stock Price Prediction based Portfolio Allocation
Junjie Guo
https://arxiv.org/abs/2507.01973

Integration of Wavelet Transform Convolution and Channel Attention with LSTM for Stock Price Prediction based Portfolio Allocation
Portfolio allocation via stock price prediction is inherently difficult due to the notoriously low signal-to-noise ratio of stock time series. This paper proposes a method by integrating wavelet transform convolution and channel attention with LSTM to implement stock price prediction based portfolio allocation. Stock time series data first are processed by wavelet transform convolution to reduce the noise. Processed features are then reconstructed by channel attention. LSTM is utilized to predi…

Replaced article(s) found for eess.IV. https://arxiv.org/list/eess.IV/new/
[1/1]:
Variable Rate Learned Wavelet Video Coding using Temporal Layer Adaptivity

MEGANet-W: A Wavelet-Driven Edge-Guided Attention Framework for Weak Boundary Polyp Detection
Zhe Yee Tan
https://arxiv.org/abs/2507.02668 https://

MEGANet-W: A Wavelet-Driven Edge-Guided Attention Framework for Weak Boundary Polyp Detection
Colorectal polyp segmentation is critical for early detection of colorectal cancer, yet weak and low contrast boundaries significantly limit automated accuracy. Existing deep models either blur fine edge details or rely on handcrafted filters that perform poorly under variable imaging conditions. We propose MEGANet-W, a Wavelet Driven Edge Guided Attention Network that injects directional, parameter free Haar wavelet edge maps into each decoder stage to recalibrate semantic features. Our two ma…

Tunable Wavelet Unit based Convolutional Neural Network in Optical Coherence Tomography Analysis Enhancement for Classifying Type of Epiretinal Membrane Surgery
An Le, Nehal Mehta, William Freeman, Ines Nagel, Melanie Tran, Anna Heinke, Akshay Agnihotri, Lingyun Cheng, Dirk-Uwe Bartsch, Hung Nguyen, Truong Nguyen, Cheolhong An
https://

Tunable Wavelet Unit based Convolutional Neural Network in Optical Coherence Tomography Analysis Enhancement for Classifying Type of Epiretinal Membrane Surgery
In this study, we developed deep learning-based method to classify the type of surgery performed for epiretinal membrane (ERM) removal, either internal limiting membrane (ILM) removal or ERM-alone removal. Our model, based on the ResNet18 convolutional neural network (CNN) architecture, utilizes postoperative optical coherence tomography (OCT) center scans as inputs. We evaluated the model using both original scans and scans preprocessed with energy crop and wavelet denoising, achieving 72% acc…