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Anti-Interference High-Speed Modulation decoder for Quantum Key Distribution

Chinese Physics Letters 2025年第1期42卷 34-39页

作者： Hua-Xing Xu Shao-Hua Wang Chang-Lei Wang Ping Zhang National Engineering Research Center for Public Safety Risk Perception and Control by Big Data China Academic of Electronics and Information TechnologyBeijing 100041China CETC Academy of Electronics and Information Technology Group Co. Ltd.Beijing 100041China

Quantum key distribution is increasingly transitioning toward network applications,necessitating advancements in system performance,including photonic integration for compact designs,enhanced stability against environmental disturbances,higher key rates,and improved *** this letter,we propose an orthogonal polarization exchange reflector Michelson interferometer model to address quantum channel disturbances caused by environmental *** on this model,we designed a Sagnac reflector-Michelson interferometer decoder and verified its performance through an interference *** interference fringe visibility exceeded 98%across all four coding phases at 625 *** results indicate that the decoder effectively mitigates environmental interference while supporting high-speed modulation *** addition,the proposed anti-interference decoder,which does not rely on magneto-optical devices,is well-suited for photonic integration,aligning with the development trajectory for next-generation quantum communication devices.

关键词： decoder interferometer polarization

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Simplified Fast List PAC decoder for Broadcasting Services in 6G: Algorithm and Implementation

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IEEE TRANSACTIONS ON BROADCASTING 2025年

作者： Dai, Jingxin Yin, Hang Lv, Yansong Wang, Yuhuan Xu, Yin Lv, Rui Commun Univ China State Key Lab Media Convergence & Commun Beijing 100024 Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai 200240 Peoples R China

In the 6G network, integrating broadcasting and mobile networks will significantly improve the transmission capability. Considering the excellent error-correction performance, polarized-adjusted convolutional (PAC) codes are promising for ensuring reliable data transmission in 6G broadcasting services. However, the inherent high decoding latency of PAC codes poses challenges for seamless switching between broadcasting and mobile services. In this paper, we propose a simplified fast list (SFL) PAC decoder, which jointly exploits the node thresholds and adaptive path-pruning technology to reduce the decoding latency while maintaining high reliability. Firstly, we present a novel path expansion rule based on the node thresholds to avoid unnecessary computations. Secondly, the introduction of the adaptive path-pruning technology efficiently reduces the number of sorting operations. Moreover, we implement the proposed decoder on general purpose processors (GPPs) by software. Simulation results show that the proposed SFL decoding algorithm significantly reduces the decoding latency by up to 75.18% compared to the state-of-the-art (SOTA) work with no noticeable degradation in error-correction performance. Software implementation of the proposed decoder achieves an 18.80% improvement in throughput performance over the SOTA PAC software decoder.

关键词： Decoding Picture archiving and communication systems Software Codes 6G mobile communication Broadcasting Software algorithms Throughput Polar codes Signal processing algorithms PAC codes broadcasting decoding latency threshold decoder software

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A Critical-Set-Based Multi-Bit Successive Cancellation List decoder for Polar Codes: Algorithm and Implementation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年第3期72卷 1472-1485页

作者： Cao, Shan Chen, Shan Jiang, Limin Jiang, Zhiyuan Shanghai Univ Shanghai Inst Adv Commun & Data Sci Key Lab Specialty Fiber Opt & Opt Access Networks Shanghai 200444 Peoples R China Shanghai Univ Shanghai Inst Adv Commun & Data Sci Joint Int Res Lab Specialty Fiber Opt & Adv Commun Shanghai 200444 Peoples R China

With the evolution of wireless communication systems, there is a growing demand for high reliability and low latency in channel coding, particularly in 5G and beyond wireless systems used in applications such as autonomous driving and remote medical services. For the decoding of polar codes, the multi-bit successive cancellation list (MSCL) decoding technique was recently introduced to decrease the decoding latency by decoding several short inner codes in parallel, which preserves high reliability compared to the conventional successive cancellation list (SCL) decoding. However, as parallelism increases, the complexity of the decoding path sorting also increases significantly, which makes it resource-intensive for hardware implementation. To address this issue, this paper proposes a configurable critical-set-based multi-bit successive cancellation list (CS-MSCL) decoding algorithm, which first introduces critical sets to the MSCL decoding for the optimization of path pruning. Subsequently, an enhanced CS-MSCL algorithm is introduced for large list-size MSCL decoding, which can boost the error correction performance. Then, an area-efficient decoding architecture is introduced, which supports the cyclic redundancy check (CRC) and the CS-MSCL decoding compatible with the 5G standard. The proposed decoder is implemented in SMIC 40 nm CMOS technology with a parallelism degree of 8, which has a peak area efficiency of 4.64 Gbps/mm(2 )for list size 4 and 2.01 Gbps/mm(2) for list size 8. Compared to state-of-the-art SCL-based decoders, the normalized area efficiency is improved by 7.16% and 17.54% for list sizes 4 and 8, respectively.

关键词： Polar codes decoder decoder successive cancellation list decoding 5G 5G hardware implementation hardware implementation

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MUSTER: A Multi-Scale Transformer-Based decoder for Semantic Segmentation

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第1期9卷 202-212页

作者： Xu, Jing Shi, Wentao Gao, Pan Li, Qizhu Wang, Zhengwei Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China TikTok Pte Ltd Singapore 048583 Singapore ByteDance Shanghai 201103 Peoples R China

In recent works on semantic segmentation, there has been a significant focus on designing and integrating transformer-based encoders. However, less attention has been given to transformer-based decoders. We emphasize that the decoder stage is equally vital as the encoder in achieving superior segmentation performance. It disentangles and refines high-level cues, enabling precise object boundary delineation at the pixel level. In this paper, we introduce a novel transformer-based decoder called MUSTER, which seamlessly integrates with hierarchical encoders and consistently delivers high-quality segmentation results, regardless of the encoder architecture. Furthermore, we present a variant of MUSTER that reduces FLOPS while maintaining performance. MUSTER incorporates carefully designed multi-head skip attention (MSKA) units and introduces innovative upsampling operations. The MSKA units enable the fusion of multi-scale features from the encoder and decoder, facilitating comprehensive information integration. The upsampling operation leverages encoder features to enhance object localization and surpasses traditional upsampling methods, improving mIoU (mean Intersection over Union) by 0.4% to 3.2%. On the challenging ADE20K dataset, our best model achieves a single-scale mIoU of 50.23 and a multi-scale mIoU of 51.88, which is on-par with the current state-of-the-art model. Remarkably, we achieve this while significantly reducing the number of FLOPs by 61.3%.

关键词： Transformers Decoding Semantic segmentation Head Convolutional neural networks Semantics Computer architecture transformer decoder lightweight feature fusion

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Abstractive Text Summarization for Legal Documents Using Optimization-Based Bi-LSTM with Encoder-decoder Model

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2025年

作者： Aggarwal, Deepti Sharma, Arun Indira Gandhi Delhi Tech Univ Women IT Dept New Delhi 110006 Delhi India

In recent years, the amount of textual data has grown quickly, creating a useful resource for information extraction and analysis. Due to the high complexity and unstructured nature of legal documents, automated text summarizing (ATS) is a necessary but difficult process. ATS is a technique that uses computer power to summarize lengthy paragraphs quickly. Summarizing the extensive materials manually is a highly difficult and time-consuming operation for people. As a result, an optimization-based deep learning (DL) model for abstract summarization is presented in the paper (AS). The proposed working procedure is broken down into three stages. They are text pre-processing, feature extraction, and abstractive summary categorization. The dataset is first placed through a pre-processing process, including stop word removal, tokenization, lemmatization, and stemming. The words and phrases are then represented in vector format throughout the feature extraction phase, which is handled by the NumberBatch (a combination of Enhanced GloVe Model (EGM), Enhanced FastText (EFT), and word2vector). To produce the abstractive text summary, the features obtained from the Numberbatch are fed into the DL model Bi-LSTM-based encoder-decoder with attention model. The metaheuristic optimization Honey Badger algorithm (HBA) is employed to optimize the network weights. This would increase the effectiveness of creating summaries based on ROUGE scores, and the proposed Bi-LSTM-HBA model performs better than currently used methods.

关键词： Feature extraction pre-processing encoder decoder abstractive summary optimization Bi-LSTM attention mechanism

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Numerical Investigation of 4 x 2 Encoder and 2 x 4 decoder Using Phononic Crystal Based Ring Resonator for Acoustic Applications

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JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES 2025年第1期13卷 1-11页

作者： Bin, Arka Roy Yakkala, Bhaskarrao Rakshit, Jayanta Kumar Nagaraju, V. Hossain, Manjur Kumar, Dhiraj Natl Inst Technol Agartala Dept Elect & Instrumentat Engn Agartala 799046 India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Elect & Commun Engn Chennai 602105 India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Comp Sci & Engn Chennai 602105 India

PurposeThis study aims to propose a novel design for a 4 x 2 encoder and its corresponding 2 x 4 decoder using two-dimensional phononic crystal ring resonator (PnC-RR).MethodsThe proposed encoder/decoder structure is based on acoustic waveguides and crystal ring resonator cavities, operating at a specific frequency of 45.1 kHz. The two-dimensional design employs a square lattice configuration comprising a rectangular mercury base and cylindrical water rods. The performance of the design is evaluated using key parameters such as Contrast Ratio, Extinction Ratio, and Insertion Loss. All simulations and analyses were conducted using COMSOL Multiphysics *** design demonstrates effective performance metrics, making it suitable for integration into phononic circuits. The encoder and decoder structures are optimized to enhance signal processing capabilities at the specified *** design has potential applications in phononic integrated circuits, particularly in underwater acoustic systems and medical *** proposed 4 x 2 encoder and 2 x 4 decoder based on PnC-RR offers a promising solution for efficient signal processing in specialized environments, supported by robust performance parameters and reliable simulation results.

关键词： Phononic crystal Phononic band gap Acoustic ring resonator Encoder decoder

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REFD:recurrent encoder and fusion decoder for temporal knowledge graph reasoning

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APPLIED INTELLIGENCE 2025年第6期55卷 1-21页

作者： Liu, Qian Feng, Siling Huang, Mengxing Bhatti, Uzair Aslam Coll Informat & Commun Engn 58 Renmin Ave Haikou 570100 Hainan Peoples R China

Reasoning over Temporal Knowledge Graphs (TKGs) presents challenges in modeling the dynamic relationships and evolving behaviors of entities and relations over time. Traditional approaches often treat entities and relations separately, which limits their ability to capture their joint temporal evolution and interactions. To overcome these limitations, REFD (Recurrent Encoder and Fusion decoder) is proposed, a novel framework designed to improve TKG reasoning. The REFD framework consists of two primary components: a recurrent encoder and a fusion decoder. The recurrent encoder incorporates three key modules: (1) the full-domain multi-scale temporal recurrent encoder, which effectively captures temporal dependencies across varying time scales, (2) the entity-relation symbiotic temporal feature deep fusion engine, which integrates temporal features of both entities and relations, and (3) the intelligent temporal feature priority dynamic adjustment mechanism, which adaptively adjusts the importance of different features over time. The fusion decoder, particularly the entity-relation feature Fusion decoder, combines the temporal features of entities and relations to model their joint evolution, overcoming the limitations of previous methods that model them separately. By jointly capturing the evolving dynamics of entities and relations over time, REFD significantly enhances the accuracy of temporal reasoning tasks. Experimental results show that REFD outperforms existing approaches, offering superior prediction accuracy and better handling of the complexities in TKGs.

关键词： Temporal knowledge graph Knowledge graph Encoder decoder

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Design of a Universal decoder Model Based on DNA Winner-Takes-All Neural Networks

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IEEE TRANSACTIONS ON COMPUTERS 2025年第4期74卷 1267-1277页

作者： Huang, Chun Shao, Jiaying Peng, Baolei Guo, Qingshuang Li, Panlong Sun, Junwei Wang, Yanfeng Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China

DNA computing has proven to possess strong parallel processing capabilities, offering notable advantages for multi-objective computation. Traditional, complex nonlinear DNA molecular logic circuits require the pre-construction of basic logic gates, followed by their cascading to achieve logic functions. However, as the number of cascade levels increases, more DNA strands are required to amplify and recover signals, causing the system's reaction time to grow exponentially. This paper introduces a novel approach for building complex nonlinear digital logic circuits using DNA winner-take-all neural networks. The logic circuit comprises four computational modules: weight multiplication, summation, competitive annihilation, and reporting. First, an annihilation strand is designed to control the reaction rate between two competing signals, resolving the interference between weight multiplication and competitive annihilation. Second, new grouping strategies-complementary annihilation, equal annihilation, and denoise annihilation-are introduced. These strategies exponentially reduce the number of competitive strands and significantly decrease system reaction time. The effect becomes more pronounced as the number of input patterns increases. Finally, 2-4, 3-8, and 4-16 decoder circuits are built using the winner-take-all neural networks, and an n-2(n) universal decoder model is further developed. This study presents an effective method for implementing complex nonlinear logic circuits through DNA strand displacement reactions.

关键词： DNA Decoding Logic circuits Biological neural networks Logic gates Cancer Computers Fuels Computational modeling Signal restoration Complementary annihilation decoder DNA strand displacement denoise annihilation equal annihilation winner-take-all neural network

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Spiking Neural Belief Propagation decoder for Short Block Length LDPC Codes

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IEEE COMMUNICATIONS LETTERS 2025年第1期29卷 45-49页

作者： von Bank, Alexander Edelmann, Eike-Manuel Miao, Sisi Mandelbaum, Jonathan Schmalen, Laurent Karlsruhe Inst Technol Commun Engn Lab D-76187 Karlsruhe Germany

Spiking neural networks (SNNs) are neural networks that enable energy-efficient signal processing due to their event-based nature. This letter proposes a novel decoding algorithm for low-density parity-check (LDPC) codes that integrates SNNs into belief propagation (BP) decoding by approximating the check node update equations using SNNs. For the (273,191) and (1023,781) finite-geometry LDPC code, the proposed decoder outperforms sum-product decoder at high signal-to-noise ratios (SNRs). The decoder achieves a similar bit error rate to normalized sum-product decoding with successive relaxation. Furthermore, the novel decoding operates without requiring knowledge of the SNR, making it robust to SNR mismatch.

关键词： Decoding Neurons Energy efficiency Belief propagation Phase change materials Membrane potentials Signal to noise ratio Mathematical models Iterative decoding Spiking neural networks channel codes decoder message passing belief propagation decoding LDPC codes neuromorphic event-based computing event-based computing

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Images, normal maps and point clouds fusion decoder for 6D pose estimation

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INFORMATION FUSION 2025年 117卷

作者： Zhang, Hong-Bo Hong, Jia-Xin Liu, Jing-Hua Lei, Qing Du, Ji-Xiang Huaqiao Univ Coll Comp Sci & Technol Xiamen 361000 Fujian Peoples R China Fuzhou Technol & Business Univ Sch Technol Fuzhou 350715 Fujian Peoples R China Huaqiao Univ Fujian Key Lab Big Data Intelligence & Secur Xiamen 361000 Fujian Peoples R China

6D pose estimation plays a crucial role in enabling intelligent robots to interact with their environment by understanding 3D scene information. This task is challenging due to factors such as texture-less objects, illumination variations, and scene occlusions. In this work, we present a novel approach that integrates feature fusion from multiple data modalities-specifically, RGB images, normal maps, and point clouds-to enhance the accuracy of 6D pose estimation. Unlike previous methods that rely solely on RGB-D data or focus on either shallow or deep feature fusion, the proposed method uniquely incorporates both shallow and deep feature fusion across heterogeneous modalities, compensating for the information often lost in point clouds. Specifically, the proposed method includes an adaptive feature fusion module designed to improve the communication and fusion of shallow features between RGB images and normal maps. Additionally, a multi-modal fusion decoder is implemented to facilitate cross-modal feature fusion between image and point cloud data. Experimental results demonstrate that the proposed method achieves state-of-the-art performance, with 6D pose estimation accuracy reaching 97.7% on the Linemod dataset, 71.5% on the Occlusion Linemod dataset, and 95.8% on the YCB-Video dataset. These results underline the robustness and effectiveness of the proposed approach in complex environments.

关键词： 6D pose estimation Multi-modal fusion Normal maps Adaptive feature fusion decoder

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