Results 21 to 30 of about 294 (197)

Iterative Decoding of Serial Concatenated Convolutionally Encoded Signals over Frequency Selective Fading Channels

Journal of Engineering and Sustainable Development, 2007
Serial concatenated convolutional coding with iterative decoding is examined for data transmission employing BFSK (binary frequency shift keying) modulation and non-coherent detection receiver.
Maha George Zia
doaj  

Multi‐View Biomedical Foundation Models for Molecule‐Target and Property Prediction

Advanced Science, EarlyView.
Molecular foundation models can provide accurate predictions for a large set of downstream tasks. We develop MMELON, an approach that integrates pre‐trained graph, image, and text foundation models and validate our multi‐view model on over 120 tasks, including GPCR binding.
Parthasarathy Suryanarayanan, Yunguang Qiu, Shreyans Sethi, Diwakar Mahajan, Hongyang Li, Yuxin Yang, Elif Eyigoz, Aldo Guzmán‐Sáenz, Daniel E. Platt, Timothy H. Rumbell, Kenney Ng, Sanjoy Dey, Myson Burch, Bum Chul Kwon, Pablo Meyer, Feixiong Cheng, Jianying Hu, Joseph A. Morrone   +17 more
wiley   +1 more source

Some Extended Results on the Design of Punctured Serially Concatenated Convolutional Codes

Journal of Communications Software and Systems, 2006
The aim of this paper is twofold. On one hand, it presents the results of the search for good punctured systematicrecursive convolutional encoders suitable for application in serially concatenated convolutional codes (SCCCs) operating in two different ...
Massimiliano Laddomada, Bartolo Scanavino   +1 more
doaj  

AI‐Based D‐Amino Acid Substitution for Optimizing Antimicrobial Peptides to Treat Multidrug‐Resistant Bacterial Infection

Advanced Science, EarlyView.
This study constructed the first D‐amino acid antimicrobial peptide dataset and developed an AI model for efficient screening of substitution sites, with 80% of candidate peptides showing enhanced activity. The lead peptide dR2‐1 demonstrated potent antimicrobial activity in vitro and in vivo, high stability, and low toxicity.
Yinuo Zhao, Qingzhou Kong, Haifan Gong, Lixiang Li, Jialu Fu, Boyao Wan, Peizhu Wang, Xiaojuan Li, Yue Wang, Jinghui Zhang, Yanbo Yu, Xiaoyun Yang, Xiuli Zuo, Haina Wang, Yanqing Li   +14 more
wiley   +1 more source

Analysis and Comparison of Some Recent Classes of Turbo Like Codes for the Upcoming DVB Standards

Journal of Communications Software and Systems, 2008
In this paper, a number of powerful recent classes of turbo like codes are analyzed as possible candidates for theupcoming DVB Standards. The final selection is justified in terms of the best tradeoff between complexity and performance.
Alexandre Graell, Guido Montorsi, Francesca Vatta   +2 more
doaj  

High‐Conductivity Electrolytes Screened Using Fragment‐ and Composition‐Aware Deep Learning

Advanced Science, EarlyView.
We present a new deep learning framework that hierarchically links molecular and functional unit attributions to predict electrolyte conductivity. By integrating molecular composition, ratios, and physicochemical descriptors, it achieves accurate, interpretable predictions and large‐scale virtual screening, offering chemically meaningful insights for ...
Xiangwen Wang, Muyang Chen, Gengyi Bao, Yan Lai, Jinghe Cao, Xinhua Liu, Rui Tan   +6 more
wiley   +1 more source

Synthetic Electrocardiogram Spectrogram Generation Using Generative Adversarial Network‐Based Models: A Comparative Study

Advanced Intelligent Systems, EarlyView.
Cardiovascular diseases are leading death causes; electrocardiogram (ECG) analysis is slow, motivating machine learning and deep learning. This study compares deep convolutional generative adversarial network, conditional GAN, and Wasserstein GAN with gradient penalty (WGAN‐GP) for synthetic ECG spectrograms; Fréchet Inception Distance (FID) and ...
Giovanny Barbosa‐Casanova, Norelli Schettini, Winston Percybrooks, Begoña García‐Zapirain   +3 more
wiley   +1 more source

A Generalizable Transformer Framework for Gene Regulatory Network Inference from Single‐Cell Transcriptomes

Advanced Intelligent Systems, EarlyView.
FTGRN introduces an LLM‐enhanced framework for gene regulatory network inference through a two‐stage workflow. It combines a Transformer‐based model, pretrained on GPT‐4 derived gene embeddings and regulatory knowledge, with a fine‐tuning stage utilizing single‐cell RNA‐seq data.
Guangzheng Weng, Hyobin Kim, Patrick Martin, Junil Kim, Tae‐Hyung Kim, Gi‐Hoon Nam, Dongha Kim, Kyoung Jae Won   +7 more
wiley   +1 more source

Multivariate Contrastive Predictive Coding with Sliding Windows for Disease Prediction from Electronic Health Records

Advanced Intelligent Systems, EarlyView.
Adaptive multi‐indicator contrastive predictive coding is introduced as a self‐supervised pretraining framework for multivariate EHR time series. An adaptive sliding‐window algorithm and 2D convolutional neural network encoder capture localized temporal patterns and global indicator dependencies, enabling label‐efficient disease prediction that ...
Hongxu Yuan, Xiaozhu Jing, Yuzheng Yan, Wuman Luo   +3 more
wiley   +1 more source

Upsampling DINOv2 Features for Unsupervised Vision Tasks and Weakly Supervised Materials Segmentation

Advanced Intelligent Systems, EarlyView.
Feature from recent image foundation models (DINOv2) are useful for vision tasks (segmentation, object localization) with little or no human input. Once upsampled, they can be used for weakly supervised micrograph segmentation, achieving strong results when compared to classical features (blurs, edge detection) across a range of material systems.
Ronan Docherty, Antonis Vamvakeros, Samuel J. Cooper   +2 more
wiley   +1 more source