This paper presents a formal approach,FSPD(Formal Specifications for Protocols of Decoders),to specify decoder communication protocols.Based on axiomatic,FSPD is a precise language with which programmers could use onl...This paper presents a formal approach,FSPD(Formal Specifications for Protocols of Decoders),to specify decoder communication protocols.Based on axiomatic,FSPD is a precise language with which programmers could use only one suitable driver to handle various types of decoders.FSPD is helpful for programmers to get high adaptability and reusability of decoder-driver software.展开更多
On an internet of video things(IoVT), an encoder needs to collect a large number of signal samples to improve the reconstruction quality. It is challenging to some occasions where the resources of an encoder are extre...On an internet of video things(IoVT), an encoder needs to collect a large number of signal samples to improve the reconstruction quality. It is challenging to some occasions where the resources of an encoder are extremely limited. The distributed video compressive sensing(DVCS) can save a lot of resources for the encoder. For the skip-block coding at such an encoder, this paper proposes a motion-adaptive adjacent-reference skipping(MAS) algorithm for DVCS with general decoders. The proposed algorithm makes full use of the spatial-temporal correlation between consecutive frames, and the reconstruction quality can be improved significantly. What’s more, the skipping ratio of non-keyframes is adaptive to the difference of their motion-speeds. The proposed algorithm does not need to change any decoder, so it can be easily applied to general decoders. The simulation results show that under different skipping ratios, the proposed algorithm can achieve better reconstruction quality than other existing algorithms, and thus improve the energy-efficiency of the encoder.展开更多
Benefiting from strong decoding capabilities,soft-decision decoding has been used to replace hard-decision decoding in various communication systems,and NAND flash memory systems are no exception.However,soft-decision...Benefiting from strong decoding capabilities,soft-decision decoding has been used to replace hard-decision decoding in various communication systems,and NAND flash memory systems are no exception.However,soft-decision decoding relies heavily on accurate soft information.Owing to the incremental step pulse programming(ISPP),program errors(PEs)in multi-level cell(MLC)NAND flash memory have different characteristics compared to other types of errors,which is very difficult to obtain such accurate soft information.Therefore,the characteristics of the log-likelihood ratio(LLR)of PEs are investigated first in this paper.Accordingly,a PE-aware statistical method is proposed to determine the usage of PE mitigation schemes.In order to reduce the PE estimating workload of the controller,an adaptive blind clipping(ABC)scheme is proposed subsequently to approximate the PEs contaminated LLR with different decoding trials.Finally,simulation results demonstrate that(1)the proposed PE-aware statistical method is effective in practice,and(2)ABC scheme is able to provide satisfactory bit error rate(BER)and frame error rate(FER)performance in a penalty of negligible increasing of decoding latency.展开更多
Modern satellite communication systems require on-board processing(OBP)for performance improvements,and SRAM-FPGAs are an attractive option for OBP implementation.However,SRAM-FPGAs are sensitive to radiation effects,...Modern satellite communication systems require on-board processing(OBP)for performance improvements,and SRAM-FPGAs are an attractive option for OBP implementation.However,SRAM-FPGAs are sensitive to radiation effects,among which single event upsets(SEUs)are important as they can lead to data corruption and system failure.This paper studies the fault tolerance capability of a SRAM-FPGA implemented Viterbi decoder to SEUs on the user memory.Analysis and fault injection experiments are conducted to verify that over 97%of the SEUs on user memory would not lead to output errors.To achieve a better reliability,selective protection schemes are then proposed to further improve the reliability of the decoder to SEUs on user memory with very small overhead.Although the results are obtained for a specific FPGA implementation,the developed reliability estimation model and the general conclusions still hold for other implementations.展开更多
Quantum error correction technology is an important solution to solve the noise interference generated during the operation of quantum computers.In order to find the best syndrome of the stabilizer code in quantum err...Quantum error correction technology is an important solution to solve the noise interference generated during the operation of quantum computers.In order to find the best syndrome of the stabilizer code in quantum error correction,we need to find a fast and close to the optimal threshold decoder.In this work,we build a convolutional neural network(CNN)decoder to correct errors in the toric code based on the system research of machine learning.We analyze and optimize various conditions that affect CNN,and use the RestNet network architecture to reduce the running time.It is shortened by 30%-40%,and we finally design an optimized algorithm for CNN decoder.In this way,the threshold accuracy of the neural network decoder is made to reach 10.8%,which is closer to the optimal threshold of about 11%.The previous threshold of 8.9%-10.3%has been slightly improved,and there is no need to verify the basic noise.展开更多
In this paper,we innovatively associate the mutual information with the frame error rate(FER)performance and propose novel quantized decoders for polar codes.Based on the optimal quantizer of binary-input discrete mem...In this paper,we innovatively associate the mutual information with the frame error rate(FER)performance and propose novel quantized decoders for polar codes.Based on the optimal quantizer of binary-input discrete memoryless channels(BDMCs),the proposed decoders quantize the virtual subchannels of polar codes to maximize mutual information(MMI)between source bits and quantized symbols.The nested structure of polar codes ensures that the MMI quantization can be implemented stage by stage.Simulation results show that the proposed MMI decoders with 4 quantization bits outperform the existing nonuniform quantized decoders that minimize mean-squared error(MMSE)with 4 quantization bits,and yield even better performance than uniform MMI quantized decoders with 5 quantization bits.Furthermore,the proposed 5-bit quantized MMI decoders approach the floating-point decoders with negligible performance loss.展开更多
Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics.While MRI-based automatic brain tumor segmentation technology reduces the b...Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics.While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information,existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors.In order to address these challenges and maximize the performance of brain tumor segmentation,this research introduces a novel SwinUNETR-based model by integrating a new decoder block,the Hierarchical Channel-wise Attention Decoder(HCAD),into a powerful SwinUNETR encoder.The HCAD decoder block utilizes hierarchical features and channelspecific attention mechanisms to further fuse information at different scales transmitted from the encoder and preserve spatial details throughout the reconstruction phase.Rigorous evaluations on the recent BraTS GLI datasets demonstrate that the proposed SwinHCAD model achieved superior and improved segmentation accuracy on both the Dice score and HD95 metrics across all tumor subregions(WT,TC,and ET)compared to baseline models.In particular,the rationale and contribution of the model design were clarified through ablation studies to verify the effectiveness of the proposed HCAD decoder block.The results of this study are expected to greatly contribute to enhancing the efficiency of clinical diagnosis and treatment planning by increasing the precision of automated brain tumor segmentation.展开更多
The development of non-invasive brain-computer interfaces(BCIs)relies on multidisciplinary integration across neuroscience,artificial intelligence,flexible electronics,and systems engineering.Recent advances in deep l...The development of non-invasive brain-computer interfaces(BCIs)relies on multidisciplinary integration across neuroscience,artificial intelligence,flexible electronics,and systems engineering.Recent advances in deep learning have significantly improved the accuracy and robustness of neural signal decoding.Parallel progress in electrode design—particularly through the use of flexible and stretchable materials like nanostructured conductors and novel fabrication strategies—has enhanced wearability and operational stability.Nevertheless,key challenges persist,including individual variability,biocompatibility limitations,and susceptibility to interference in complex environments.Further validation and optimization are needed to address gaps in generalization capability,long-term reliability,and real-world operational robustness.This review systematically examines the representative progress in neural decoding algorithms and flexible bioelectronic platforms over the past decade,highlighting key design principles,material innovations,and integration strategies that are poised to advance non-invasive BCI capabilities.It also discusses the importance of multimodal data fusion,hardware-software co-optimization,and closed-loop control strategies.Furthermore,the review discusses the application potential and associated engineering challenges of this technology in clinical rehabilitation and industrial translation,aiming to provide a reference for advancing non-invasive BCIs toward practical and scalable deployment.展开更多
Differential pulse-position modulation(DP PM)can achieve a good compromise between power and bandwidth requirements.However,the output sequence has undetectable insertions and deletions.This paper proposes a successiv...Differential pulse-position modulation(DP PM)can achieve a good compromise between power and bandwidth requirements.However,the output sequence has undetectable insertions and deletions.This paper proposes a successive cancellation(SC)decoding scheme based on the weighted levenshtein distance(WLD)of polar codes for correcting insertions/deletions in DPPM systems.In this method,the WLD is used to calculate the transfer probabilities recursively to obtain likelihood ratios,and the low-complexity SC decoding method is built according to the error characteristics to match the DPPM system.Additionally,the proposed SC decoding scheme is extended to list decoding,which can further improve error correction performance.Simulation results show that the proposed scheme can effectively correct insertions/deletions in the DPPM system,which enhances its reliability and performance.展开更多
In order to explore the gaps between decoders' interpretations and encoders' designing intentions with respect to the same multimodal discourses, thirty linguistic and thirty art graphic participants were chos...In order to explore the gaps between decoders' interpretations and encoders' designing intentions with respect to the same multimodal discourses, thirty linguistic and thirty art graphic participants were chosen as decoders and encoders, respectively.The participants were required to interpret the same research data in terms of the best and the worst major colors, as well as the best and the worst synergetic patterns formed by major modes.It was found that the complete unanimity in terms of both color and spatial arrangements among the interpretations between participants only reached 43.3%.The unanimity in the interpretations from the perspective of color alone reached 46.7%.Moreover, the interpretations from the perspective of spatial arrangements present high unanimity, with a rate up to 70%.It is concluded that there are both differences and similarities between the interpretations made by encoders and decoders.The possible reasons underlying both differences and similarities are probed in the present study as well.展开更多
Space laser communication(SLC)is an emerging technology to support high-throughput data transmissions in space networks.In this paper,to guarantee the reliability of high-speed SLC links,we aim at practical implementa...Space laser communication(SLC)is an emerging technology to support high-throughput data transmissions in space networks.In this paper,to guarantee the reliability of high-speed SLC links,we aim at practical implementation of low-density paritycheck(LDPC)decoding under resource-restricted space platforms.Particularly,due to the supply restriction and cost issues of high-speed on-board devices such as analog-to-digital converters(ADCs),the input of LDPC decoding will be usually constrained by hard-decision channel output.To tackle this challenge,density-evolution-based theoretical analysis is firstly performed to identify the cause of performance degradation in the conventional binaryinitialized iterative decoding(BIID)algorithm.Then,a computation-efficient decoding algorithm named multiary-initialized iterative decoding with early termination(MIID-ET)is proposed,which improves the error-correcting performance and computation efficiency by using a reliability-based initialization method and a threshold-based decoding termination rule.Finally,numerical simulations are conducted on example codes of rates 7/8 and 1/2 to evaluate the performance of different LDPC decoding algorithms,where the proposed MIID-ET outperforms the BIID with a coding gain of 0.38 dB and variable node calculation saving of 37%.With this advantage,the proposed MIID-ET can notably reduce LDPC decoder’s hardware implementation complexity under the same bit error rate performance,which successfully doubles the total throughput to 10 Gbps on a single-chip FPGA.展开更多
Quantum computing has the potential to solve complex problems that are inefficiently handled by classical computation.However,the high sensitivity of qubits to environmental interference and the high error rates in cu...Quantum computing has the potential to solve complex problems that are inefficiently handled by classical computation.However,the high sensitivity of qubits to environmental interference and the high error rates in current quantum devices exceed the error correction thresholds required for effective algorithm execution.Therefore,quantum error correction technology is crucial to achieving reliable quantum computing.In this work,we study a topological surface code with a two-dimensional lattice structure that protects quantum information by introducing redundancy across multiple qubits and using syndrome qubits to detect and correct errors.However,errors can occur not only in data qubits but also in syndrome qubits,and different types of errors may generate the same syndromes,complicating the decoding task and creating a need for more efficient decoding methods.To address this challenge,we used a transformer decoder based on an attention mechanism.By mapping the surface code lattice,the decoder performs a self-attention process on all input syndromes,thereby obtaining a global receptive field.The performance of the decoder was evaluated under a phenomenological error model.Numerical results demonstrate that the decoder achieved a decoding accuracy of 93.8%.Additionally,we obtained decoding thresholds of 5%and 6.05%at maximum code distances of 7 and 9,respectively.These results indicate that the decoder used demonstrates a certain capability in correcting noise errors in surface codes.展开更多
Aiming at the problem that the bit error rate(BER)of asymmetrically clipped optical orthogonal frequency division multiplexing(ACO-OFDM)space optical communication system is significantly affected by different turbule...Aiming at the problem that the bit error rate(BER)of asymmetrically clipped optical orthogonal frequency division multiplexing(ACO-OFDM)space optical communication system is significantly affected by different turbulence intensities,the deep learning technique is proposed to the polarization code decoding in ACO-OFDM space optical communication system.Moreover,this system realizes the polarization code decoding and signal demodulation without frequency conduction with superior performance and robustness compared with the performance of traditional decoder.Simulations under different turbulence intensities as well as different mapping orders show that the convolutional neural network(CNN)decoder trained under weak-medium-strong turbulence atmospheric channels achieves a performance improvement of about 10^(2)compared to the conventional decoder at 4-quadrature amplitude modulation(4QAM),and the BERs for both 16QAM and 64QAM are in between those of the conventional decoder.展开更多
This paper investigates the uplink spectral efficiency of distributed cell-free(CF)massive multiple-input multiple-output(mMIMO)networks with correlated Rayleigh fading channels based on three different channel estima...This paper investigates the uplink spectral efficiency of distributed cell-free(CF)massive multiple-input multiple-output(mMIMO)networks with correlated Rayleigh fading channels based on three different channel estimation schemes.Specifically,each access point(AP)first uses embedded pilots to estimate the channels of all users based on minimum mean-squared error(MMSE)estimation.Given the high computational cost of MMSE estimation,the low-complexity element-wise MMSE(EW-MMSE)channel estimator and the least-squares(LS)channel estimator without prior statistical information are also analyzed.To reduce non-coherent and coherent interference during uplink payload data transmission,simple centralized decoding(SCD)and large-scale fading decoding(LSFD)are examined.Then,the closedform expressions for uplink spectral efficiency(SE)using MMSE,EW-MMSE,and LS estimators are developed for maximum ratio(MR)combining under LSFD,where each AP may have any number of antennas.The sum SE maximization problem with uplink power control is formulated.Since the maximization problem is non-convex and challenging,a block coordinate descent approach based on the weighted MMSE method is used to get the optimal local solution.Numerical studies demonstrate that LSFD and efficient uplink power control can considerably increase SE in distributed CF m MIMO networks.展开更多
In this paper,an improved error-rate sliding window decoder is proposed for spatially coupled low-density parity-check(SC-LDPC)codes.For the conventional sliding window decoder,the message retention mechanism causes u...In this paper,an improved error-rate sliding window decoder is proposed for spatially coupled low-density parity-check(SC-LDPC)codes.For the conventional sliding window decoder,the message retention mechanism causes unreliable messages along the edges of belief propagation(BP)decoding in the current window to be kept for subsequent window decoding.To improve the reliability of the retained messages during the window transition,a reliable termination method is embedded,where the retained messages undergo more reliable parity checks.Additionally,decoding failure is unavoidable and even causes error propagation when the number of errors exceeds the error-correcting capability of the window.To mitigate this problem,a channel value reuse mechanism is designed,where the received channel values are utilized to reinitialize the window.Furthermore,considering the complexity and performance of decoding,a feasible sliding optimized window decoding(SOWD)scheme is introduced.Finally,simulation results confirm the superior performance of the proposed SOWD scheme in both the waterfall and error floor regions.This work has great potential in the applications of wireless optical communication and fiber optic communication.展开更多
Neural machine translation(NMT)has advanced with deep learning and large-scale multilingual models,yet translating lowresource languages often lacks sufficient training data and leads to hallucinations.This often resu...Neural machine translation(NMT)has advanced with deep learning and large-scale multilingual models,yet translating lowresource languages often lacks sufficient training data and leads to hallucinations.This often results in translated content that diverges significantly from the source text.This research proposes a refined Contrastive Decoding(CD)algorithm that dynamically adjusts weights of log probabilities from strong expert and weak amateur models to mitigate hallucinations in lowresource NMT and improve translation quality.Advanced large language NMT models,including ChatGLM and LLaMA,are fine-tuned and implemented for their superior contextual understanding and cross-lingual capabilities.The refined CD algorithm evaluates multiple candidate translations using BLEU score,semantic similarity,and Named Entity Recognition accuracy.Extensive experimental results show substantial improvements in translation quality and a significant reduction in hallucination rates.Fine-tuned models achieve higher evaluation metrics compared to baseline models and state-of-the-art models.An ablation study confirms the contributions of each methodological component and highlights the effectiveness of the refined CD algorithm and advanced models in mitigating hallucinations.Notably,the refined methodology increased the BLEU score by approximately 30%compared to baseline models.展开更多
Action recognition,a fundamental task in the field of video understanding,has been extensively researched and applied.In contrast to an image,a video introduces an extra temporal dimension.However,many existing action...Action recognition,a fundamental task in the field of video understanding,has been extensively researched and applied.In contrast to an image,a video introduces an extra temporal dimension.However,many existing action recognition networks either perform simple temporal fusion through averaging or rely on pre-trained models from image recognition,resulting in limited temporal information extraction capabilities.This work proposes a highly efficient temporal decoding module that can be seamlessly integrated into any action recognition backbone network to enhance the focus on temporal relationships between video frames.Firstly,the decoder initializes a set of learnable queries,termed video-level action category prediction queries.Then,they are combined with the video frame features extracted by the backbone network after self-attention learning to extract video context information.Finally,these prediction queries with rich temporal features are used for category prediction.Experimental results on HMDB51,MSRDailyAct3D,Diving48 and Breakfast datasets show that using TokShift-Transformer and VideoMAE as encoders results in a significant improvement in Top-1 accuracy compared to the original models(TokShift-Transformer and VideoMAE),after introducing the proposed temporal decoder.The introduction of the temporal decoder results in an average performance increase exceeding 11%for TokShift-Transformer and nearly 5%for VideoMAE across the four datasets.Furthermore,the work explores the combination of the decoder with various action recognition networks,including Timesformer,as encoders.This results in an average accuracy improvement of more than 3.5%on the HMDB51 dataset.The code is available at https://github.com/huangturbo/TempDecoder.展开更多
Quantum key distribution is increasingly transitioning toward network applications,necessitating advancements in system performance,including photonic integration for compact designs,enhanced stability against environ...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 efficiency.In this letter,we propose an orthogonal polarization exchange reflector Michelson interferometer model to address quantum channel disturbances caused by environmental factors.Based on this model,we designed a Sagnac reflector-Michelson interferometer decoder and verified its performance through an interference system.The interference fringe visibility exceeded 98%across all four coding phases at 625 MHz.These results indicate that the decoder effectively mitigates environmental interference while supporting high-speed modulation frequencies.In 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.展开更多
Intrinsic decomposition,the process of decomposing an image into reflectance and shading,is widely used in virtual and augmented reality tasks.Reflectance and shading often exhibit large gradients at the object edges,...Intrinsic decomposition,the process of decomposing an image into reflectance and shading,is widely used in virtual and augmented reality tasks.Reflectance and shading often exhibit large gradients at the object edges,and the intrinsic properties on the same object tend to be similar.This spatial coherence is closely related to semantic consistency because objects within the same semantic category often exhibit similar intrinsic properties.Therefore,incorporating semantic segmentation into a deep intrinsic decomposition framework helps the network distinguish between different object instances and understand high-level scene structures.To this end,we design an intrinsic decomposition network jointly trained with a dedicated semantic segmentation module,allowing semantic cues to enhance the decomposition of reflectance and shading.The semantic module provides guidance during training but is removed during inference,improving performance without increasing the inference cost.Additionally,to capture the global contextual dependencies critical for intrinsic decomposition,we adopt a Transformer-based backbone.The proposed backbone enables the model to associate distant regions with similar material properties,thereby maintaining consistency in reflectance and learning smooth illumination patterns across a scene.A convolutional decoder is also designed to output predictions with improved details.Experiments demonstrate that our approach achieves state-of-the-art performance in the quantitative evaluations on the Intrinsic Images in the Wild(IIW)and Shading Annotations in the wild(SAW)datasets.展开更多
文摘This paper presents a formal approach,FSPD(Formal Specifications for Protocols of Decoders),to specify decoder communication protocols.Based on axiomatic,FSPD is a precise language with which programmers could use only one suitable driver to handle various types of decoders.FSPD is helpful for programmers to get high adaptability and reusability of decoder-driver software.
基金supported by the National Natural Science Foundation of China(No.62001099)。
文摘On an internet of video things(IoVT), an encoder needs to collect a large number of signal samples to improve the reconstruction quality. It is challenging to some occasions where the resources of an encoder are extremely limited. The distributed video compressive sensing(DVCS) can save a lot of resources for the encoder. For the skip-block coding at such an encoder, this paper proposes a motion-adaptive adjacent-reference skipping(MAS) algorithm for DVCS with general decoders. The proposed algorithm makes full use of the spatial-temporal correlation between consecutive frames, and the reconstruction quality can be improved significantly. What’s more, the skipping ratio of non-keyframes is adaptive to the difference of their motion-speeds. The proposed algorithm does not need to change any decoder, so it can be easily applied to general decoders. The simulation results show that under different skipping ratios, the proposed algorithm can achieve better reconstruction quality than other existing algorithms, and thus improve the energy-efficiency of the encoder.
基金This work was supported by Key Project of Sichuan Province(no.2017SZYZF0002)Marie Curie Fellowship(no.796426).
文摘Benefiting from strong decoding capabilities,soft-decision decoding has been used to replace hard-decision decoding in various communication systems,and NAND flash memory systems are no exception.However,soft-decision decoding relies heavily on accurate soft information.Owing to the incremental step pulse programming(ISPP),program errors(PEs)in multi-level cell(MLC)NAND flash memory have different characteristics compared to other types of errors,which is very difficult to obtain such accurate soft information.Therefore,the characteristics of the log-likelihood ratio(LLR)of PEs are investigated first in this paper.Accordingly,a PE-aware statistical method is proposed to determine the usage of PE mitigation schemes.In order to reduce the PE estimating workload of the controller,an adaptive blind clipping(ABC)scheme is proposed subsequently to approximate the PEs contaminated LLR with different decoding trials.Finally,simulation results demonstrate that(1)the proposed PE-aware statistical method is effective in practice,and(2)ABC scheme is able to provide satisfactory bit error rate(BER)and frame error rate(FER)performance in a penalty of negligible increasing of decoding latency.
基金supported in part by the National Key R&D Program(Grant No.2017YFE0121300)in part by the National Natural Science Foundation of China (Grant No. 61501321)+1 种基金in part by Tianjin science and technology program (Grant No. 17ZXRGGX00160)the support of the TEXEO project TEC201680339R funded by the Spanish Ministry of Economy and Competitivity
文摘Modern satellite communication systems require on-board processing(OBP)for performance improvements,and SRAM-FPGAs are an attractive option for OBP implementation.However,SRAM-FPGAs are sensitive to radiation effects,among which single event upsets(SEUs)are important as they can lead to data corruption and system failure.This paper studies the fault tolerance capability of a SRAM-FPGA implemented Viterbi decoder to SEUs on the user memory.Analysis and fault injection experiments are conducted to verify that over 97%of the SEUs on user memory would not lead to output errors.To achieve a better reliability,selective protection schemes are then proposed to further improve the reliability of the decoder to SEUs on user memory with very small overhead.Although the results are obtained for a specific FPGA implementation,the developed reliability estimation model and the general conclusions still hold for other implementations.
基金the National Natural Science Foundation of China(Grant Nos.11975132 and 61772295)the Natural Science Foundation of Shandong Province,China(Grant No.ZR2019YQ01)the Project of Shandong Province Higher Educational Science and Technology Program,China(Grant No.J18KZ012).
文摘Quantum error correction technology is an important solution to solve the noise interference generated during the operation of quantum computers.In order to find the best syndrome of the stabilizer code in quantum error correction,we need to find a fast and close to the optimal threshold decoder.In this work,we build a convolutional neural network(CNN)decoder to correct errors in the toric code based on the system research of machine learning.We analyze and optimize various conditions that affect CNN,and use the RestNet network architecture to reduce the running time.It is shortened by 30%-40%,and we finally design an optimized algorithm for CNN decoder.In this way,the threshold accuracy of the neural network decoder is made to reach 10.8%,which is closer to the optimal threshold of about 11%.The previous threshold of 8.9%-10.3%has been slightly improved,and there is no need to verify the basic noise.
基金financially supported in part by National Key R&D Program of China(No.2018YFB1801402)in part by Huawei Technologies Co.,Ltd.
文摘In this paper,we innovatively associate the mutual information with the frame error rate(FER)performance and propose novel quantized decoders for polar codes.Based on the optimal quantizer of binary-input discrete memoryless channels(BDMCs),the proposed decoders quantize the virtual subchannels of polar codes to maximize mutual information(MMI)between source bits and quantized symbols.The nested structure of polar codes ensures that the MMI quantization can be implemented stage by stage.Simulation results show that the proposed MMI decoders with 4 quantization bits outperform the existing nonuniform quantized decoders that minimize mean-squared error(MMSE)with 4 quantization bits,and yield even better performance than uniform MMI quantized decoders with 5 quantization bits.Furthermore,the proposed 5-bit quantized MMI decoders approach the floating-point decoders with negligible performance loss.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)under the Metaverse Support Program to Nurture the Best Talents(IITP-2024-RS-2023-00254529)grant funded by the Korea government(MSIT).
文摘Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics.While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information,existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors.In order to address these challenges and maximize the performance of brain tumor segmentation,this research introduces a novel SwinUNETR-based model by integrating a new decoder block,the Hierarchical Channel-wise Attention Decoder(HCAD),into a powerful SwinUNETR encoder.The HCAD decoder block utilizes hierarchical features and channelspecific attention mechanisms to further fuse information at different scales transmitted from the encoder and preserve spatial details throughout the reconstruction phase.Rigorous evaluations on the recent BraTS GLI datasets demonstrate that the proposed SwinHCAD model achieved superior and improved segmentation accuracy on both the Dice score and HD95 metrics across all tumor subregions(WT,TC,and ET)compared to baseline models.In particular,the rationale and contribution of the model design were clarified through ablation studies to verify the effectiveness of the proposed HCAD decoder block.The results of this study are expected to greatly contribute to enhancing the efficiency of clinical diagnosis and treatment planning by increasing the precision of automated brain tumor segmentation.
基金the National Natural Science Foundation of China for Distinguished Young Scholars(62325403)the National Natural Science Foundation of China(62504103 and 82002454)+4 种基金the Basic Research Program of Jiangsu(BK20251214)the Natural Science Foundation of Jiangsu Province(BK20230498)the China Postdoctoral Science Foundation under Grant Number 2025T180143 and 2025M770547the Medical Scientific Research Project of Jiangsu Health Commission(ZD2021011)the Jiangsu Funding Program for Excellent Postdoctoral Talent(2024ZB427)。
文摘The development of non-invasive brain-computer interfaces(BCIs)relies on multidisciplinary integration across neuroscience,artificial intelligence,flexible electronics,and systems engineering.Recent advances in deep learning have significantly improved the accuracy and robustness of neural signal decoding.Parallel progress in electrode design—particularly through the use of flexible and stretchable materials like nanostructured conductors and novel fabrication strategies—has enhanced wearability and operational stability.Nevertheless,key challenges persist,including individual variability,biocompatibility limitations,and susceptibility to interference in complex environments.Further validation and optimization are needed to address gaps in generalization capability,long-term reliability,and real-world operational robustness.This review systematically examines the representative progress in neural decoding algorithms and flexible bioelectronic platforms over the past decade,highlighting key design principles,material innovations,and integration strategies that are poised to advance non-invasive BCI capabilities.It also discusses the importance of multimodal data fusion,hardware-software co-optimization,and closed-loop control strategies.Furthermore,the review discusses the application potential and associated engineering challenges of this technology in clinical rehabilitation and industrial translation,aiming to provide a reference for advancing non-invasive BCIs toward practical and scalable deployment.
基金supported by National Natural Science Foundation of China(No.61801327).
文摘Differential pulse-position modulation(DP PM)can achieve a good compromise between power and bandwidth requirements.However,the output sequence has undetectable insertions and deletions.This paper proposes a successive cancellation(SC)decoding scheme based on the weighted levenshtein distance(WLD)of polar codes for correcting insertions/deletions in DPPM systems.In this method,the WLD is used to calculate the transfer probabilities recursively to obtain likelihood ratios,and the low-complexity SC decoding method is built according to the error characteristics to match the DPPM system.Additionally,the proposed SC decoding scheme is extended to list decoding,which can further improve error correction performance.Simulation results show that the proposed scheme can effectively correct insertions/deletions in the DPPM system,which enhances its reliability and performance.
文摘In order to explore the gaps between decoders' interpretations and encoders' designing intentions with respect to the same multimodal discourses, thirty linguistic and thirty art graphic participants were chosen as decoders and encoders, respectively.The participants were required to interpret the same research data in terms of the best and the worst major colors, as well as the best and the worst synergetic patterns formed by major modes.It was found that the complete unanimity in terms of both color and spatial arrangements among the interpretations between participants only reached 43.3%.The unanimity in the interpretations from the perspective of color alone reached 46.7%.Moreover, the interpretations from the perspective of spatial arrangements present high unanimity, with a rate up to 70%.It is concluded that there are both differences and similarities between the interpretations made by encoders and decoders.The possible reasons underlying both differences and similarities are probed in the present study as well.
基金supported by the National Key R&D Program of China(Grant No.2022YFA1005000)the National Natural Science Foundation of China(Grant No.62101308 and 62025110).
文摘Space laser communication(SLC)is an emerging technology to support high-throughput data transmissions in space networks.In this paper,to guarantee the reliability of high-speed SLC links,we aim at practical implementation of low-density paritycheck(LDPC)decoding under resource-restricted space platforms.Particularly,due to the supply restriction and cost issues of high-speed on-board devices such as analog-to-digital converters(ADCs),the input of LDPC decoding will be usually constrained by hard-decision channel output.To tackle this challenge,density-evolution-based theoretical analysis is firstly performed to identify the cause of performance degradation in the conventional binaryinitialized iterative decoding(BIID)algorithm.Then,a computation-efficient decoding algorithm named multiary-initialized iterative decoding with early termination(MIID-ET)is proposed,which improves the error-correcting performance and computation efficiency by using a reliability-based initialization method and a threshold-based decoding termination rule.Finally,numerical simulations are conducted on example codes of rates 7/8 and 1/2 to evaluate the performance of different LDPC decoding algorithms,where the proposed MIID-ET outperforms the BIID with a coding gain of 0.38 dB and variable node calculation saving of 37%.With this advantage,the proposed MIID-ET can notably reduce LDPC decoder’s hardware implementation complexity under the same bit error rate performance,which successfully doubles the total throughput to 10 Gbps on a single-chip FPGA.
基金Project supported by the Natural Science Foundation of Shandong Province,China(Grant No.ZR2021MF049)Joint Fund of Natural Science Foundation of Shandong Province(Grant Nos.ZR2022LLZ012 and ZR2021LLZ001)the Key R&D Program of Shandong Province,China(Grant No.2023CXGC010901)。
文摘Quantum computing has the potential to solve complex problems that are inefficiently handled by classical computation.However,the high sensitivity of qubits to environmental interference and the high error rates in current quantum devices exceed the error correction thresholds required for effective algorithm execution.Therefore,quantum error correction technology is crucial to achieving reliable quantum computing.In this work,we study a topological surface code with a two-dimensional lattice structure that protects quantum information by introducing redundancy across multiple qubits and using syndrome qubits to detect and correct errors.However,errors can occur not only in data qubits but also in syndrome qubits,and different types of errors may generate the same syndromes,complicating the decoding task and creating a need for more efficient decoding methods.To address this challenge,we used a transformer decoder based on an attention mechanism.By mapping the surface code lattice,the decoder performs a self-attention process on all input syndromes,thereby obtaining a global receptive field.The performance of the decoder was evaluated under a phenomenological error model.Numerical results demonstrate that the decoder achieved a decoding accuracy of 93.8%.Additionally,we obtained decoding thresholds of 5%and 6.05%at maximum code distances of 7 and 9,respectively.These results indicate that the decoder used demonstrates a certain capability in correcting noise errors in surface codes.
基金supported by the National Natural Science Foundation of China(No.12104141).
文摘Aiming at the problem that the bit error rate(BER)of asymmetrically clipped optical orthogonal frequency division multiplexing(ACO-OFDM)space optical communication system is significantly affected by different turbulence intensities,the deep learning technique is proposed to the polarization code decoding in ACO-OFDM space optical communication system.Moreover,this system realizes the polarization code decoding and signal demodulation without frequency conduction with superior performance and robustness compared with the performance of traditional decoder.Simulations under different turbulence intensities as well as different mapping orders show that the convolutional neural network(CNN)decoder trained under weak-medium-strong turbulence atmospheric channels achieves a performance improvement of about 10^(2)compared to the conventional decoder at 4-quadrature amplitude modulation(4QAM),and the BERs for both 16QAM and 64QAM are in between those of the conventional decoder.
基金supported by National Natural Science Foundation of China(NSFC No.62020106001)。
文摘This paper investigates the uplink spectral efficiency of distributed cell-free(CF)massive multiple-input multiple-output(mMIMO)networks with correlated Rayleigh fading channels based on three different channel estimation schemes.Specifically,each access point(AP)first uses embedded pilots to estimate the channels of all users based on minimum mean-squared error(MMSE)estimation.Given the high computational cost of MMSE estimation,the low-complexity element-wise MMSE(EW-MMSE)channel estimator and the least-squares(LS)channel estimator without prior statistical information are also analyzed.To reduce non-coherent and coherent interference during uplink payload data transmission,simple centralized decoding(SCD)and large-scale fading decoding(LSFD)are examined.Then,the closedform expressions for uplink spectral efficiency(SE)using MMSE,EW-MMSE,and LS estimators are developed for maximum ratio(MR)combining under LSFD,where each AP may have any number of antennas.The sum SE maximization problem with uplink power control is formulated.Since the maximization problem is non-convex and challenging,a block coordinate descent approach based on the weighted MMSE method is used to get the optimal local solution.Numerical studies demonstrate that LSFD and efficient uplink power control can considerably increase SE in distributed CF m MIMO networks.
基金supported by the National Natural Science Foundation of China (No.62275193)。
文摘In this paper,an improved error-rate sliding window decoder is proposed for spatially coupled low-density parity-check(SC-LDPC)codes.For the conventional sliding window decoder,the message retention mechanism causes unreliable messages along the edges of belief propagation(BP)decoding in the current window to be kept for subsequent window decoding.To improve the reliability of the retained messages during the window transition,a reliable termination method is embedded,where the retained messages undergo more reliable parity checks.Additionally,decoding failure is unavoidable and even causes error propagation when the number of errors exceeds the error-correcting capability of the window.To mitigate this problem,a channel value reuse mechanism is designed,where the received channel values are utilized to reinitialize the window.Furthermore,considering the complexity and performance of decoding,a feasible sliding optimized window decoding(SOWD)scheme is introduced.Finally,simulation results confirm the superior performance of the proposed SOWD scheme in both the waterfall and error floor regions.This work has great potential in the applications of wireless optical communication and fiber optic communication.
基金M.Faheem is supported by VTT Technical Research Center of Finland.
文摘Neural machine translation(NMT)has advanced with deep learning and large-scale multilingual models,yet translating lowresource languages often lacks sufficient training data and leads to hallucinations.This often results in translated content that diverges significantly from the source text.This research proposes a refined Contrastive Decoding(CD)algorithm that dynamically adjusts weights of log probabilities from strong expert and weak amateur models to mitigate hallucinations in lowresource NMT and improve translation quality.Advanced large language NMT models,including ChatGLM and LLaMA,are fine-tuned and implemented for their superior contextual understanding and cross-lingual capabilities.The refined CD algorithm evaluates multiple candidate translations using BLEU score,semantic similarity,and Named Entity Recognition accuracy.Extensive experimental results show substantial improvements in translation quality and a significant reduction in hallucination rates.Fine-tuned models achieve higher evaluation metrics compared to baseline models and state-of-the-art models.An ablation study confirms the contributions of each methodological component and highlights the effectiveness of the refined CD algorithm and advanced models in mitigating hallucinations.Notably,the refined methodology increased the BLEU score by approximately 30%compared to baseline models.
基金Shanghai Municipal Commission of Economy and Information Technology,China (No.202301054)。
文摘Action recognition,a fundamental task in the field of video understanding,has been extensively researched and applied.In contrast to an image,a video introduces an extra temporal dimension.However,many existing action recognition networks either perform simple temporal fusion through averaging or rely on pre-trained models from image recognition,resulting in limited temporal information extraction capabilities.This work proposes a highly efficient temporal decoding module that can be seamlessly integrated into any action recognition backbone network to enhance the focus on temporal relationships between video frames.Firstly,the decoder initializes a set of learnable queries,termed video-level action category prediction queries.Then,they are combined with the video frame features extracted by the backbone network after self-attention learning to extract video context information.Finally,these prediction queries with rich temporal features are used for category prediction.Experimental results on HMDB51,MSRDailyAct3D,Diving48 and Breakfast datasets show that using TokShift-Transformer and VideoMAE as encoders results in a significant improvement in Top-1 accuracy compared to the original models(TokShift-Transformer and VideoMAE),after introducing the proposed temporal decoder.The introduction of the temporal decoder results in an average performance increase exceeding 11%for TokShift-Transformer and nearly 5%for VideoMAE across the four datasets.Furthermore,the work explores the combination of the decoder with various action recognition networks,including Timesformer,as encoders.This results in an average accuracy improvement of more than 3.5%on the HMDB51 dataset.The code is available at https://github.com/huangturbo/TempDecoder.
基金supported by the National Natural Science Foundation of China under Grant No.62001440。
文摘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 efficiency.In this letter,we propose an orthogonal polarization exchange reflector Michelson interferometer model to address quantum channel disturbances caused by environmental factors.Based on this model,we designed a Sagnac reflector-Michelson interferometer decoder and verified its performance through an interference system.The interference fringe visibility exceeded 98%across all four coding phases at 625 MHz.These results indicate that the decoder effectively mitigates environmental interference while supporting high-speed modulation frequencies.In 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.
基金Supported by Science and Technology Innovation 2030:Major Project of“New Generation Artificial Intelligence”(No.2022ZD0115901)the National Natural Science Foundation of China(No.62332003).
文摘Intrinsic decomposition,the process of decomposing an image into reflectance and shading,is widely used in virtual and augmented reality tasks.Reflectance and shading often exhibit large gradients at the object edges,and the intrinsic properties on the same object tend to be similar.This spatial coherence is closely related to semantic consistency because objects within the same semantic category often exhibit similar intrinsic properties.Therefore,incorporating semantic segmentation into a deep intrinsic decomposition framework helps the network distinguish between different object instances and understand high-level scene structures.To this end,we design an intrinsic decomposition network jointly trained with a dedicated semantic segmentation module,allowing semantic cues to enhance the decomposition of reflectance and shading.The semantic module provides guidance during training but is removed during inference,improving performance without increasing the inference cost.Additionally,to capture the global contextual dependencies critical for intrinsic decomposition,we adopt a Transformer-based backbone.The proposed backbone enables the model to associate distant regions with similar material properties,thereby maintaining consistency in reflectance and learning smooth illumination patterns across a scene.A convolutional decoder is also designed to output predictions with improved details.Experiments demonstrate that our approach achieves state-of-the-art performance in the quantitative evaluations on the Intrinsic Images in the Wild(IIW)and Shading Annotations in the wild(SAW)datasets.
