Results 11 to 20 of about 294 (197)

Iterative decoding of serially concatenated convolutionalcodes

Electronics Letters, 1996
Serial concatenation of convolutional codes separated by an interleaver has recently been shown, through the use of upper bounds to the maximum likelihood performance, to be competitive with parallel concatenated coding schemes known in the literature as ‘turbo codes’.
S. BENEDETTO, MONTORSI, Guido
openaire   +2 more sources

Serial Concatenation of Space-Time and Recursive Convolutional Codes [PDF]

ETRI Journal, 2003
We propose a new serial concatenation scheme for space-time and recursive convolutional codes, in which a space-time code is used as the outer code and a single recursive convolutional code as the inner code. We discuss previously proposed serial concatenation schemes employing multiple inner codes and compare them with the new one. The proposed method
Young-Jo Ko, Jung-Im Kim
openaire   +1 more source

The Geometry of Layer 2/3 Cortical Sound Processing in Slow Wave Sleep

Advanced Science, EarlyView.
Sleep is associated with a sensory disconnection whose mechanisms remain elusive. Large neuronal population recordings in the auditory cortex revealed that, in NREM sleep, the neural code for sounds is highly similar to wakefulness, but coordinated modulations of neuron responsiveness intermittently disconnect the local cortical networks from sensory ...
Allan Muller, Anton Filipchuk, Sophie Bagur, Brice Bathellier   +3 more
wiley   +1 more source

Diffusion‐MRI‐Based Estimation of Cortical Architecture via Machine Learning (DECAM) in Primate Brains

Advanced Science, EarlyView.
We present Diffusion‐MRI‐based Estimation of Cortical Architecture via Machine Learning (DECAM), a deep‐learning framework for estimating primate brain cortical architecture optimized with best response constraint and cortical label vectors. Trained using macaque brain high‐resolution multi‐shell dMRI and histology data, DECAM generates high‐fidelity ...
Tianjia Zhu, Minhui Ouyang, Shufang Tan, Jianlin Guo, Ziqin Zhang, Xuan Liu, Risheng Liu, Hao Huang   +7 more
wiley   +1 more source

DualPG‐DTA: A Large Language Model‐Powered Graph Neural Network Framework for Enhanced Drug‐Target Affinity Prediction and Discovery of Novel CDK9 Inhibitors Exhibiting in Vivo Anti‐Leukemia Activity

Advanced Science, EarlyView.
This study introduces DualPG‐DTA, a framework integrating two pre‐trained models to generate molecular and protein representations. It constructs dual graphs processed by specialized neural networks with dynamic attention for feature fusion, achieving superior benchmark performance.
Yihao Chen, Jindi Huang, Cong Liu, Shipeng Zhang, Xinze Li, Zhang Zhang, Tie‐Gen Chen, Ling Wang   +7 more
wiley   +1 more source

HiST: Histological Images Reconstruct Tumor Spatial Transcriptomics via MultiScale Fusion Deep Learning

Advanced Science, EarlyView.
HiST, a multiscale deep learning framework, reconstructs spatially resolved gene expression profiles directly from histological images. It accurately identifies tumor regions, captures intratumoral heterogeneity, and predicts patient prognosis and immunotherapy response.
Wei Li, Dong Zhang, Eryu Peng, Shijun Shen, Hamid Alinejad‐Rokny, Yao Liu, Junke Zheng, Cizhong Jiang, Youqiong Ye   +8 more
wiley   +1 more source

Pathomics Signature for Prognosis and CA19‐9 Interception in Pancreatic Ductal Adenocarcinoma: A Real‐Life, Multi‐Center Study

Advanced Science, EarlyView.
This study develops a deep learning‐based pathomics model to predict survival outcomes in pancreatic cancer patients. The CrossFormer architecture analyzes routine H&E‐stained tissue slides, identifying key prognostic features including stromal patterns, cellular characteristics, and immune infiltration.
Qiangda Chen, Zhihang Xu, Yiping Zou, Zhenlai Jiang, Yecheng Li, Taochen He, Hanlin Yin, Jiali Li, Yanfei An, Jiande Han, Yuqi Xie, Wei Gan, Yaolin Xu, Wenquan Wang, Junyi He, Haibo Wang, Wenchuan Wu, Zhenyu Ye, Wenhui Lou, Jihui Hao, Liang Liu, Jun Yu, Ning Pu   +22 more
wiley   +1 more source

Accurate Identification of Protein Binding Sites for All Drug Modalities Using ALLSites

Advanced Science, EarlyView.
ALLSites is a unified sequence‐based framework for identifying proteome‐wide binding sites across all drug modalities. It integrates a gated convolutional network with a transformer architecture to capture residue interactions directly from the sequence.
Minjie Mou, Mingkun Lu, Zhimeng Zhou, Yanlin Ren, Xinyuan Yu, Ziqi Pan, Yuan Zhou, Hao Yang, Lingyan Zheng, Shukai Gu, Yang Zhang, Wei Hu, Fengcheng Li, Haibin Dai, Feng Zhu   +14 more
wiley   +1 more source

Reed-Solomon Hybrid Codes for Optical Communications

Journal of Engineering and Sustainable Development, 2013
The astonishing performance of concatenated codes attracted many researchers and this has resulted in an explosive amount of literature since their introduction few years ago.
Awatif Ali Jafaar
doaj  

Deformation Prediction of 4D‐Printed Active Composite Structures Based on Data Mining

Advanced Science, EarlyView.
A curvature‐driven sequence point generation (CSPG) algorithm based on data mining is proposed to predict the deformation of double‐layer voxelized composite structures of arbitrary lengths. In addition, the CSPG algorithm is applied to predict the deformation of 2D and 3D structures assembled from beam elements, and its effectiveness is validated ...
Mengtao Wang, Yifan Xu, Zaiyang Liu, Hidemitsu Furukawa, Zhongkui Wang, Ren Xu, Lin Meng   +6 more
wiley   +1 more source