GitHub

convolutional-encoder-decoder

Add a description, image, and links to the convolutional-encoder-decoder topic page so that developers can more easily learn about it.
IEEE

Research on An-Based Decode of Tail-Biting Convolutional Codes and Their Performance Analyses Used in LTE System

Abstract: Tail-biting convolutional codes has been used in physical broadcast channel and physical downlink control channel of LTE, this paper introduces the architecture of the convolutional encoder ...
IEEE

On the Tail-Biting Convolutional Code Decoder for the LTE and LTE-A standards'

Abstract: In this paper, we consider the design of the Tail-Biting Convolutional Code Decoder (TB-CCD) for the Long Term Evolution (LTE) system. Tail-biting convolutional coding is used for channel ...
Frontiers

DTCNet: finger flexion decoding with three-dimensional ECoG data

School of Big Data, Zhuhai College of Science and Technology, Zhuhai, China ECoG signals are widely used in Brain-Computer Interfaces (BCIs) due to their high spatial resolution and superior signal ...
GitHub

Convolutional Logic Networks Code and RTL implementation

Hi, First of all great work. Wanted to know if the code for convolution LGN is going to be publicly available anytime soon? Also, I was quite curious about the adder tree structure that mimics ...
TV Technology

Magewell to Show Latest Pro Convert Device, Preview Xmit Encoder/Decoder at Infocomm

NANJING, China—Magewell will showcase the latest addition to its Pro Convert product line and introduce a new family of Pro Convert devices during InfoComm 2025, June 11-13, at the Orange County ...
Frontiers

Predictive modelling employing machine learning, convolutional neural networks (CNNs), and smartphone RGB images for non-destructive biomass estimation of pearl millet ...

Digital tools and non-destructive monitoring techniques are crucial for real-time evaluations of crop output and health in sustainable agriculture, particularly for precise above-ground biomass (AGB) ...
Scientific Research Publishing

Li, Z., Yang, W., Peng, S., et al. (2020) A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects.

ABSTRACT: Convolutional auto-encoders have shown their remarkable performance in stacking deep convolutional neural networks for classifying image data during the past several years. However, they are ...