A Simplified Min-Sum Decoding Algorithm for Non-Binary LDPC Codes

Non-binary low-density parity-check codes are robust to various channel impairments. However, based on the existing decoding algorithms, the decoder implementations are expensive because of their excessive computational complexity and memory usage. Based on the combinatorial optimization, we present an approximation method for the check node processing. The simulation results demonstrate that our scheme has small performance loss over the additive white Gaussian noise channel and independent Rayleigh fading channel. Furthermore, the proposed reduced-complexity realization provides significant savings on hardware, so it yields a good performance-complexity tradeoff and can be efficiently implemented.Comment: Partially presented in ICNC 2012, International Conference on Computing, Networking and Communications. Accepted by IEEE Transactions on Communication

Haina Storage: A Decentralized Secure Storage Framework Based on Improved Blockchain Structure

Although the decentralized storage technology based on the blockchain can effectively realize secure data storage on cloud services. However, there are still some problems in the existing schemes, such as low storage capacity and low efficiency. To address related issues, we propose a novel decentralized storage framework, which mainly includes four aspects: (1) we proposed a Bi-direction Circular Linked Chain Structure (BCLCS), which improves data's storage capacity and applicability in decentralized storage. (2) A Proof of Resources (PoR) decision model is proposed. By introducing the network environment as an essential evaluation parameter of storage right decision, the energy and time consumption of decision-making are reduced, and the fairness of decision-making is improved. (3) A chain structure dynamic locking mechanism (CSDLM) is designed to realize anti-traverse and access control. (4) A Bi-directional data Access Mechanism (BDAM) is proposed, which improves the efficiency of data access and acquisition in decentralized storage mode. The experimental results show that the framework has significantly improved the shortcomings of the current decentralized storage.Comment: 24 pages, 21 figure

Investigation of the stability of the anti-islanding detection in multi-DGs system

U radu je predstavljen poboljšani model multi-DGs mikro rešetki za analizu stabilnosti sustava tijekom vezivanja s rešetkom. DGs u sustavu opremljeni su Sandia frequency shift (SFS) shemom kao načinom anti-islanding zaštite. Uvođenjem dužine linije distribucijske mreže, pozitivnog porasta povratne sprege SFSa i distribuiranog dovoda energije, parametri izlazne snage za poboljšanje matematičkog modela mikro energetskih rešetki uspostavljeni su u tri vrste parametara i odnosu između margine stabilnosti mikro energetske rešetke za postizanje stabilnosti sustava praga dužine linije energetske mreže, i stabilnosti granične vrijednosti napona izlazne snage distribuirane istosmjerne struje. Taj postupak omogućuje projektantima i inženjerima obnovljivih energetskih sustava optimiziranje sustava i osiguranje stabilnosti. Konačno, uzimajući u obzir nekoliko potvrđivanja simulacija, u radu se daje poboljšani model koji može utjecati na aktualnu implementaciju analize distribuirane mikro energetske rešetke, te se tako može donijeti zaključak o stabilnosti kritičkog praga parametara sustava. Na temelju tih analiza slučaja, pokazalo se da je stabilnost sustava vrlo važna za stabilnost mikrorešetki mnogih distribuiranih multi-DGs, koji su korisni za projektiranje i implementaciju novih energetskih sustava.This paper presents an improved model of multi-DGs microgrids for analysing system stability during grid-connections. The DGs-in the system are equipped with the Sandia frequency shift (SFS) scheme as an anti-islanding protection technique. By introducing a distribution network line length, SFS positive feedback gain and distributed power supply, power output parameters to improve the micro power grid mathematical model are established in three kinds of parameters and the relationship between micro power grid stability margin, to obtain stability of the system of power line length threshold, and stability of the distributed power dc output voltage threshold. This process allows the designers and engineers of renewable energy systems to optimize the system and ensure stability. Finally, in view of the several common simulation validations, this paper puts forward an improved model that can affect actual implementation of distributed micro power grid analysis, whereby the stability of the system parameters’ critical threshold may be deduced. Based on these case studies, system stability is shown to be very important to the stability of many distributed multi-DGs microgrids, which are useful for the design and implementation of new energy systems

Decision Fusion Network with Perception Fine-tuning for Defect Classification

Surface defect inspection is an important task in industrial inspection. Deep learning-based methods have demonstrated promising performance in this domain. Nevertheless, these methods still suffer from misjudgment when encountering challenges such as low-contrast defects and complex backgrounds. To overcome these issues, we present a decision fusion network (DFNet) that incorporates the semantic decision with the feature decision to strengthen the decision ability of the network. In particular, we introduce a decision fusion module (DFM) that extracts a semantic vector from the semantic decision branch and a feature vector for the feature decision branch and fuses them to make the final classification decision. In addition, we propose a perception fine-tuning module (PFM) that fine-tunes the foreground and background during the segmentation stage. PFM generates the semantic and feature outputs that are sent to the classification decision stage. Furthermore, we present an inner-outer separation weight matrix to address the impact of label edge uncertainty during segmentation supervision. Our experimental results on the publicly available datasets including KolektorSDD2 (96.1% AP) and Magnetic-tile-defect-datasets (94.6% mAP) demonstrate the effectiveness of the proposed method

The Screening of the Protective Antigens of Aeromonas hydrophila Using the Reverse Vaccinology Approach: Potential Candidates for Subunit Vaccine Development

The threat of bacterial septicemia caused by Aeromonas hydrophila infection to aquaculture growth can be prevented through vaccination, but differences among A. hydrophila strains may affect the effectiveness of non-conserved subunit vaccines or non-inactivated A. hydrophila vaccines, making the identification and development of conserved antigens crucial. In this study, a bioinformatics analysis of 4268 protein sequences encoded by the A. hydrophila J-1 strain whole genome was performed based on reverse vaccinology. The specific analysis included signal peptide prediction, transmembrane helical structure prediction, subcellular localization prediction, and antigenicity and adhesion evaluation, as well as interspecific and intraspecific homology comparison, thereby screening the 39 conserved proteins as candidate antigens for A. hydrophila vaccine. The 9 isolated A. hydrophila strains from diseased fish were categorized into 6 different molecular subtypes via enterobacterial repetitive intergenic consensus (ERIC)-PCR technology, and the coding regions of 39 identified candidate proteins were amplified via PCR and sequenced to verify their conservation in different subtypes of A. hydrophila and other Aeromonas species. In this way, conserved proteins were screened out according to the comparison results. Briefly, 16 proteins were highly conserved in different A. hydrophila subtypes, of which 2 proteins were highly conserved in Aeromonas species, which could be selected as candidate antigens for vaccines development, including type IV pilus secretin PilQ (AJE35401.1) and TolC family outer membrane protein (AJE35877.1). The present study screened the conserved antigens of A. hydrophila by using reverse vaccinology, which provided basic foundations for developing broad-spectrum protective vaccines of A. hydrophila

REPOFUSE: Repository-Level Code Completion with Fused Dual Context

The success of language models in code assistance has spurred the proposal of repository-level code completion as a means to enhance prediction accuracy, utilizing the context from the entire codebase. However, this amplified context can inadvertently increase inference latency, potentially undermining the developer experience and deterring tool adoption - a challenge we termed the Context-Latency Conundrum. This paper introduces REPOFUSE, a pioneering solution designed to enhance repository-level code completion without the latency trade-off. REPOFUSE uniquely fuses two types of context: the analogy context, rooted in code analogies, and the rationale context, which encompasses in-depth semantic relationships. We propose a novel rank truncated generation (RTG) technique that efficiently condenses these contexts into prompts with restricted size. This enables REPOFUSE to deliver precise code completions while maintaining inference efficiency. Through testing with the CrossCodeEval suite, REPOFUSE has demonstrated a significant leap over existing models, achieving a 40.90% to 59.75% increase in exact match (EM) accuracy for code completions and a 26.8% enhancement in inference speed. Beyond experimental validation, REPOFUSE has been integrated into the workflow of a large enterprise, where it actively supports various coding tasks