21 papers · ranked by Valyu relevance
Julia Lieb, Joachim Rosenthal
It is well known that there is a correspondence between convolutional codes and discrete-time linear systems over finite fields. In this paper, we employ the linear systems representation of a convolutional code to develop a decoding algorithm for convolutional codes over the erasure channel. In this kind of channel…
Ted Hurley
Convolutional codes are constructed, designed and analysed using row and/or block structures of unit algebraic schemes. Infinite series of such codes and of codes with specific properties are derived. Properties are shown algebraically and algebraic decoding methods are derived. For a given rate and given…
Paridhi Latawa, Nuh Aydın
| 1 | Abstract | | 2 | | --- | --- | --- | --- | | 2 | | Introduction | 2 | | 3 | | Convolutional Codes | 3 | | | 3.1 | Encoding of Binary Convolutional Codes | 3 | | | 3.2 | Decoding Convolutional Codes | 11 | | | 3.3 | Truncated Viterbi Decoding | 13 | | 4 | | DNA Codes | 17 | | | 4.1 | Constraints for the…
Francisco Revson F. Pereira, Giuliano G. La Guardia, Francisco de Assis
'Francisco de Assis'] In this paper, we construct new families of convolutional codes. Such codes are obtained by means of algebraic geometry codes. Additionally, more families of convolutional codes are constructed by means of puncturing, extending, expanding and by the direct product code construction applied to…
Parag Dhounde, Avinash Bhute
- This paper explores the design of convolutional codes for varying constraint lengths, focusing on their role in error correction in digital communication systems. Convolutional codes are essential in achieving reliable data transmission across noisy channels. The constraint length, which determines the memory of the…
Lu Xu, Yixin Ma, Rui Shi, Juanjuan Li + 2 more
'Antonio Guerrieri'] The accurate identification of channel-coding types plays a crucial role in wireless communication systems. The recognition of convolutional codes presents challenges, primarily due to their strong temporal dependencies, varying constraint lengths, and additional contamination from noise. However…
Lu Xu, Xu Chen, Yixin Ma, Rui Shi + 4 more
Due to the critical role of channel coding, convolutional code recognition has attracted growing interest, particularly in non-cooperative communication scenarios such as spectrum surveillance. Deep learning-based approaches have emerged as promising techniques, offering improved classification performance. However…
Anastasia Kurmukova, Fedor Ivanov, Victor Zyablov
—In this paper, we provide a new approach to the analytical estimation of the bit-error rate (BER) for convolutional codes for Viterbi decoding in the binary symmetric channel (BSC). The expressions we obtained for lower and upper BER bounds are based on the active distances of the code and their distance spectrum. The…
Mr. Atul Singh Kushwah, Ms. Priya Rathore, Mr. Ramsewak Kumar
- **In this paper we estimate the performance of 24 MIMO-MC-CDMA system using convolution code in MATLAB which highly reduces BER. MC-CDMA (Multi Carrier Code Division for Multiple Access) is a multi-user and multiple access system which is formed by the combination of OFDM and CDMA and convolution encoding scheme is…
Sebastian Nagel, Martin Spüler
In this paper, we present a Brain-Computer Interface (BCI) that is able to reach an information transfer rate (ITR) of more than 1200 bit/min using non-invasively recorded EEG signals. By combining the EEG2Code method with deep learning, we present an extremely powerful approach for decoding visual information from…
Yuwei Ma, Yingke Lei, Changming Liu, Wei Wang + 6 more
Facing heterogeneous signals increasing in dynamic spectrum, cognitive radio urgently needs blind channel coding identification. This technology addresses the core challenge of unknown coding schemes in non-cooperative communications. Existing methods are typically restricted to specific coding types and suffer from…
Jing-Yi Li, Shen Jin, Xin-Ming Tu, Yang Ding + 1 more
Motif identification is among the most common and essential computational tasks for bioinformatics and genomics. Here we proposed a novel convolutional layer for deep neural network, named Variable Convolutional (vConv) layer, for effective motif identification in high-throughput omics data by learning kernel length…
Wai Hoh Tang, Shao Ren Sim, Daniel Ying Kia Aik, Ashwin Venkata Subba Nelanuthala + 3 more
Imaging Fluorescence Correlation Spectroscopy (Imaging FCS) is a powerful tool to extract information on molecular mobilities, actions and interactions in live cells, tissues and organisms. Nevertheless, several limitations restrict its applicability. First, FCS is data hungry, requiring 50,000 frames at 1 ms time…
Mu Li, Kede Ma, Jane You, David Zhang + 1 more
—Precise estimation of the probabilistic structure of natural images plays an essential role in image compression. Despite the recent remarkable success of end-to-end optimized image compression, the latent codes are usually assumed to be fully statistically factorized in order to simplify entropy modeling. However…
Siwei Wang, Benjamin Hoshal, Elizabeth A de Laittre, Olivier Marre + 2 more
Much of sensory neuroscience focuses on presenting stimuli that are chosen by the experimenter because they are parametric and easy to sample and are thought to be behaviorally relevant to the organism. However, it is not generally known what these relevant features are in complex, natural scenes. This work focuses on…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
Authors not listed
The automatic generation of image captions in natural language is a critical and challenging task, particularly in the context of environmental monitoring and control. This paper presents a novel deep learning-driven image captioning system designed for real-time monitoring and predictive control of pollutant gas…
Yimeng Zhang, Tai Sing Lee, Ming Li, Fang Liu + 1 more
In this study, we evaluated the convolutional neural network (CNN) method for modeling V1 neurons of awake macaque monkeys in response to a large set of complex pattern stimuli. CNN models outperformed all the other baseline models, such as Gabor-based standard models for V1 cells and various variants of generalized…
Zachary Humphreys, Xenophon Evangelopoulos, Stavros Gerolymatos, Edward O. Pyzer-Knapp + 1 more
Graph neural networks have recently met huge success in various inference tasks including materials property prediction amongst many others. Nevertheless, having an inherently locally-based representation capacity as they do, global representation of materials' structures can only only be achieved by expanding the…
Jie Chen, Hengrui Zhang, Carolin Wahl, Wei Liu + 4 more
A bottleneck in high-throughput nanomaterials discovery is the pace at which new materials can be structurally characterized. Although current machine learning (ML) methods show promise for the automated processing of electron diffraction patterns (DPs), they fail in high-throughput experiments where DPs are collected…
Jian-Xue Huang, Ya-Lin Huang, Chia-Ying Hsieh, Chun-Shu Wei
Recently, decoding human electroencephalographic (EEG) data using convolutional neural network (CNN) has driven the state-of-the-art recognition of motor-imagery EEG patterns for brain-computer interfacing (BCI). While a variety of CNN models have been used to classify motor-imagery EEG data, it is unclear if…