WNet: Refined Double UNet for COVID-19 Lung CT Lesion Segmentation

codes for the paper "W-NetPan: Double-U Network for Inter-sensor Self-supervised Pan-sharpening"

Underwater target detection algorithm based on feature enhancement and feature fusion

this project is for PMRID's training and testing

A ML tool to decompose seismic data into earthquake and ambient noise signals.

This code is to separate teleseismic earthquake signals and noise signals.

BSS methods used for denoise 1D signals

This project focuses on denoising seismic data from the Apollo lunar mission and InSight Mars mission to isolate significant seismic signals from background noise. It enhances data clarity, aiding …

A hybrid neural network combining CNN and LSTM layers enhances ECG signal classification by capturing both spatial (waveform amplitude, shape) and temporal (event sequence) features. This approach…

Cluster Analysis of Trimmed Spectrograms: framework for detection and denoising of sparse signals in time-frequency domain.

Correlated and Signal-Dependent Denoising

Deep Learning in Quantitative Finance: Transformer Networks for Time Series Prediction

This repository includes code for the paper: Lithology Identification Based on One-dimensional Convolutional Neural Network and Recurrent Neural Network with Attention Mechanism.

Using CNN & RNN & GRU & LSTM & BiRNN & BiGRU & BiLSTM & Transformers for Emotions Sentimental Analysis

CNN-BiGRU-Attention模型

DCAE-CEST: : The DCAE-CEST method can learn the most important features of the CEST Z-spectrum and provide the most effective denoising solution with high fidelity of the data

Epilepsy Prediction with CNN-BiLSTM | BSc dissertation project

Decode auditory attention from EEG within the context of a two-speaker cocktail party paradigm

Two scripts designed to (1) pre-process raw pupil time-series data and (2) visualize baseline-normalized individual trial-level and average pupil size data across 3 uncertainty conditions in a pre-…

Attention recognition using EEG signals - The EEG signals are pre-processed and further used for recognizing attention.

This code analyzes traveling waves in EEG signals. It can be apply to any dataset, but it is based on the data available here: https://osf.io/pn784/. For details, please refer to this paper: "Disti…

The MATLAB code implements a Transformer model, a recent innovation in deep neural networks. It includes modules for multi-head attention and feed-forward layers, enabling advanced sequence modelin…

A home-brewed MATLAB Library for NNets - LSTMs with Attention

This MATLAB script defines a custom attention layer class `attentionLayer` that can be used in deep learning models, particularly for sequence-to-sequence tasks or transformer-based architectures.

BILSTM，GRU，LSTM

Two radial basis functions that were used to find an underlying signal in noisy 10 dimentional data. The initial RBF is fairly standard, with the second RBF having the ability to grow and prune neu…

Approximation and Classification example problems solved utilizing MLP (Multi Layer Perceptron) and RBF (Radial Basis Function) Neural Networks.

Codes for creating and training MLP and RBF ANNs

Code for the paper "Energy-stable global radial basis function methods on summation-by-parts form"