19 papers · ranked by Valyu relevance
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…
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…
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…
Ted Hurley
A linear block code over a field can be derived from a unit scheme. Looking at codes as structures within a unit scheme greatly extends the availability of linear block and convolutional codes and allows the construction of the codes to required length, rate, distance and type. Properties of a code emanate from…
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…
Niklas Gassner, Julia Lieb, Abhinaba Mazumder, Michael Schaller
In this paper, we present a framework for generic decoding of convolutional codes, which allows us to do cryptanalysis of code-based systems that use convolutional codes as public keys. We then apply this framework to information set decoding, study success probabilities and give tools to choose variables. Finally, we…
Chengxiang Peng, Paul Annus, Marek Rist, Raul Land + 2 more
'Xiaoning Jiang'] Ultrasonic testing (UT) is a vital nondestructive testing (NDT) technique used to evaluate the integrity of materials and structures. However, conventional excitation signals often suffer from significant attenuation in highly attenuative materials, resulting in low signal energy and poor signal…
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…
Takumi Ando
Image recognition models have evolved tremendously. Despite the progress for general images, histopathological images are not easy targets. One of the reasons is that histopathological images can be 100000-200000px in height and width which are often too large for a deep neural network model to handle directly because…
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…
Xin Li
In this paper, we revisit the problem of computational modeling of simple and complex cells for an over-parameterized and direct-fit model of visual perception. Unlike conventional wisdom, we highlight the difference in parallel and sequential binding mechanisms between simple and complex cells. A new proposal for…
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…
Sinem Gümüş, Fatih Kamışlı
—This paper considers lossless image compression and presents a learned compression system that can achieve stateof-the-art lossless compression performance but uses only 59K parameters, which is more than 30x less than other learned systems proposed recently in the literature. The explored system is based on a learned…
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…
Andrew McNutt, Yanjing Li, Paul Francoeur, David Koes
Knowledge of the bound protein-ligand structure is critical to many drug discovery tasks. One tool for in silico bound structure elucidation is molecular docking, which samples and scores ligand binding conformations. Recent work has demonstrated that convolutional neural networks (CNNs) for protein-ligand pose scoring…