Results 61 to 70 of about 1,096,634 (365)

Human papillomavirus (HPV) prediction for oropharyngeal cancer based on CT by using off‐the‐shelf features: A dual‐dataset study

open access: yesJournal of Applied Clinical Medical Physics, EarlyView.
Abstract Background This study aims to develop a novel predictive model for determining human papillomavirus (HPV) presence in oropharyngeal cancer using computed tomography (CT). Current image‐based HPV prediction methods are hindered by high computational demands or suboptimal performance.
Junhua Chen   +3 more
wiley   +1 more source

Extrinsic Information Transfer Charts for Characterizing the Iterative Decoding Convergence of Fully Parallel Turbo Decoders

open access: yesIEEE Access, 2015
Fully parallel turbo decoders (FPTDs) have been shown to offer a more-than-sixfold processing throughput and latency improvement over the conventional logarithmic Bahl-Cocke-Jelinek-Raviv (Log-BCJR) turbo decoders.
Hoang Anh Ngo   +2 more
doaj   +1 more source

ON TURBO CODES AND OTHER CONCATENATED SCHEMES IN COMMUNICATION SYSTEMS [PDF]

open access: yes, 2000
The advent of turbo codes in 1993 represented a significant step towards realising the ultimate capacity limit of a communication channel, breaking the link that was binding very good performance with exponential decoder complexity.
AMBROZE, MARCEL ADRIAN
core   +2 more sources

Geometric and dosimetric evaluation of a commercial AI auto‐contouring tool on multiple anatomical sites in CT scans

open access: yesJournal of Applied Clinical Medical Physics, EarlyView.
Abstract Current radiotherapy practices rely on manual contouring of CT scans, which is time‐consuming, prone to variability, and requires highly trained experts. There is a need for more efficient and consistent contouring methods. This study evaluated the performance of the Varian Ethos AI auto‐contouring tool to assess its potential integration into
Robert N. Finnegan   +6 more
wiley   +1 more source

Consolidate Overview of Ribonucleic Acid Molecular Dynamics: From Molecular Movements to Material Innovations

open access: yesAdvanced Engineering Materials, EarlyView.
Molecular dynamics simulations are advancing the study of ribonucleic acid (RNA) and RNA‐conjugated molecules. These developments include improvements in force fields, long‐timescale dynamics, and coarse‐grained models, addressing limitations and refining methods.
Kanchan Yadav, Iksoo Jang, Jong Bum Lee
wiley   +1 more source

Understanding Turbo Codes: A Signal Processing Study [PDF]

open access: yesarXiv, 2023
In this paper, we study turbo codes from the digital signal processing point of view by defining turbo codes over the complex field. It is known that iterative decoding and interleaving between concatenated parallel codes are two key elements that make turbo codes perform significantly better than the conventional error control codes.
arxiv  

Iterative Decoding Threshold Analysis for LDPC Convolutional Codes

open access: yesIEEE Transactions on Information Theory, 2010
An iterative decoding threshold analysis for terminated regular LDPC convolutional (LDPCC) codes is presented. Using density evolution techniques, the convergence behavior of an iterative belief propagation decoder is analyzed for the binary erasure ...
M. Lentmaier   +3 more
semanticscholar   +1 more source

Performance of LDPC Codes Under Faulty Iterative Decoding [PDF]

open access: yesIEEE Transactions on Information Theory, 2008
Departing from traditional communication theory where decoding algorithms are assumed to perform without error, a system where noise perturbs both computational devices and communication channels is considered here.
L. Varshney
semanticscholar   +1 more source

Permutation Decoding and the Stopping Redundancy Hierarchy of Cyclic and Extended Cyclic Codes

open access: yes, 2008
We introduce the notion of the stopping redundancy hierarchy of a linear block code as a measure of the trade-off between performance and complexity of iterative decoding for the binary erasure channel.
Hehn, Thorsten   +3 more
core   +1 more source

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

open access: yesAdvanced Engineering Materials, EarlyView.
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani   +4 more
wiley   +1 more source

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