Results 51 to 60 of about 2,435,702 (285)

Joint Iterative Decoding of Trellis-Based VQ and TCM

, 2007
A joint video and channel coded system employing an iteratively decoded serial concatenation of a Vector Quantization (VQ) based video codec and a Trellis-Coded Modulation (TCM) scheme is proposed.
Yang, L.-L., Kliewer, J., Ng, S. X., Wang, J., Hanzo, L., Maunder, R. G.   +5 more
core   +1 more source

Improved multi-bits flipping algorithm for high-speed LDPC decoding

Tongxin xuebao, 2014
In order to improve the decoding speed of the RRWBF algorithm, a multiple-bits selection mechanism was proposed to accelerate the decoding convergence speed of the RRWBF algorithm.
Ke-xiang MA, Ji-cheng SUN, Meng WANG, Yu-ming ZHANG, Hai-lin ZHANG   +4 more
doaj   +2 more sources

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

A New List Decoding Algorithm for Short-Length TBCCs With CRC

IEEE Access, 2018
In this paper, a new list decoding algorithm for tail-biting convolutional codes (TBCCs) with a cyclic redundancy check (CRC) is proposed, where the CRC is considered as a concatenated outer code.
Jae-Won Kim, Jun-Woo Tak, Hee-youl Kwak, Jong-Seon No   +3 more
doaj   +1 more source

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Advanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch, Maximilian Holland, Klaus Drechsler, Johannes Schilp   +3 more
wiley   +1 more source

Symbol-Flipping Based Decoding of Generalized Low-Density Parity-Check Coded Constructed over GF(q)

, 2006
An efficient symbol-flipping based decoding algorithm designed for nonbinary Generalized Low-Density Parity-Check (GLDPC) codes is proposed. By extending the concept of the Weighted Bit Flip Voting (WBFV) algorithm designed for binary Hamming-code based ...
Kuo, F-C., Hanzo, L.
core   +1 more source

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

Advanced Engineering Materials, EarlyView.
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle, Cesar Alvarez‐Llamas, Vincent Motto‐Ros, Christophe Dujardin, Jérémie Margueritat, David Rodney   +5 more
wiley   +1 more source

Weighted symbol-flipping decoding for non-binary LDPC codes based on average probability and stopping criterion

Tongxin xuebao, 2016
In order to improve the decoding performance and reduce the decoding complexity of weighted symbols flipping decoding algorithm for nonbinary low density parity-check(LDPC)code, a weighted symbols flipping decoding for nonbinary LDPC codes based on ...
Rui GUO, Chun-yu LIU, Mei-jie WANG, Peng PAN, Jian-rong BAO, Chun-sheng GUO   +5 more
doaj   +2 more sources

A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys

Advanced Engineering Materials, EarlyView.
High‐entropy alloys offer vast potential for various applications, including electrocatalysis; however, their compositional complexity challenges conventional screening. We introduce an inverse‐design framework combining two neural networks to determine optimal compositions and reconstruct nanoparticle geometry from targeted properties and conventional
Mikael Takoutsin, Marta Campolucci, Nicolas de Andrade Ishiki, Nathan Mirgot, Nadir Zhamantay, Frédéric Kanoufi, Jennifer Peron, Jean Charlety, Emiliano Fonda, Valérie Briois, Marco Faustini, Maria‐Letizia De Marco, Christine Goyhenex, Ovidiu Ersen, Hervé Bulou   +14 more
wiley   +1 more source

Improved Adaptive Successive Cancellation List Decoding of Polar Codes

Entropy, 2019
Although the adaptive successive cancellation list (AD-SCL) algorithm and the segmented-CRC adaptive successive cancellation list (SCAD-SCL) algorithm based on the cyclic redundancy check (CRC) can greatly reduce the computational complexity of the ...
Xiumin Wang, Jinlong He, Jun Li, Zhuoting Wu, Liang Shan, Bo Hong   +5 more
doaj   +1 more source