Efficient error correction in high-speed communication networks, such as the 50G passive optical network (50G-PON), is paramount. This Letter focuses on optimizing a layered non-surjective finite alphabet iterative decoder (LNS-FAID) for 50G-PON, with an emphasis on high-throughput and low-power consumption. We propose using a distinct lookup table (LUT) for each iteration to enhance decoding performance and lower error floors. Additionally, we improve the 2-bit LNS-FAID architecture by adding operational states and a sign backtracking (SBT) strategy. This paper also introduces a hybrid precision model that merges 3-bit and 2-bit LNS-FAIDs, which balances error correction with computational efficiency. Our simulation results show that these approaches significantly improve the performance of the LDPC code in 50G-PON.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1364/OL.520880 | DOI Listing |
BMC Health Serv Res
January 2025
School of Humanities and Social Sciences, Beihang University, No. 37 Xueyuan Road, Beijing, 100191, China.
Background: To address the health inequity caused by decentralized management, China has introduced a provincial pooling system for urban employees' basic medical insurance. This paper proposes a research framework to evaluate similar policies in different contexts. This paper adopts a mixed-methods approach to more comprehensively and precisely capture the causal effects of the policy.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Oncology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China.
Exploring the potential of advanced artificial intelligence technology in predicting microsatellite instability (MSI) and Ki-67 expression of endometrial cancer (EC) is highly significant. This study aimed to develop a novel hybrid radiomics approach integrating multiparametric magnetic resonance imaging (MRI), deep learning, and multichannel image analysis for predicting MSI and Ki-67 status. A retrospective study included 156 EC patients who were subsequently categorized into MSI and Ki-67 groups.
View Article and Find Full Text PDFSci Rep
January 2025
College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy.
View Article and Find Full Text PDFSci Rep
January 2025
Ministry of Higher Education, Mataria Technical College, Cairo, 11718, Egypt.
The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
Regional Institute of Ophthalmology, Indira Gandhi Institute of Medical Sciences, Patna, 800025, Bihar, India.
Background And Objectives: Hypertensive Retinopathy (HR) is a retinal manifestation resulting from persistently elevated blood pressure. Severity grading of HR is essential for patient risk stratification, effective management, progression monitoring, timely intervention, and minimizing the risk of vision impairment. Computer-aided diagnosis and artificial intelligence (AI) systems play vital roles in the diagnosis and grading of HR.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!