GitHub

arieltarliman/Image-Denoising-with-Convolutional-Autoencoders-and-UNet

This repository presents a deep learning project focused on image denoising. The core task is to build and evaluate neural network models capable of removing artificially added Gaussian noise from ...
IEEE

SCALABLE CONVOLUTIONAL DICTIONARY LEARNING WITH CONSTRAINED RECURRENT SPARSE AUTO-ENCODERS

Abstract: Given a convolutional dictionary underlying a set of observed signals, can a carefully designed auto-encoder recover the dictionary in the presence of noise? We introduce an auto-encoder ...
GitHub

kersoan-College-work/convolutional-denoising-autoencoder

Using autoencoder, we are trying to remove the noise added in the encoder part and tent to get the output which should be same as the input with minimal loss. The dataset which is used is mnist ...
CoinTelegraph

What are convolutional neural networks?

What are convolutional neural networks in deep learning? Convolutional neural networks are used in computer vision tasks, which employ convolutional layers to extract features from input data.
Design-Reuse

Rate 1 3 802 16d turbo convolutional code encoder decoder IP Listing

This is a fully compatible 3GPP (UMTS and LTE) and 3GPP2 cdma2000 (1xEV-DV Release D and 1xEV-DO Release B) error control decoder. The decoder can be used to decode ...
EDN

The designer’s basic guide to WiMAX: Part 2

Part 1 of this article provided an overview of WiMAX: its architecture; the standards on which it is based; and general operating principles. Part 1 is available at The designer’s basic guide to WiMAX ...
IEEE

A Study on Detection and Classification of Semi-Supervised Malicious Encrypted Traffic with Variational Self-Encoder Based on Null Convolution

Abstract: With the rapid development of Internet technology, network traffic shows a significant growth trend in terms of quantity and type. In this context, the categorization and monitoring of ...