In this paper, a modified FPGA scheme for the convolutional encoder and Viterbi decoder based on the IEEE 802.11a standards of WLAN is presented in OFDM baseband processing systems. The proposed design supports a gene...In this paper, a modified FPGA scheme for the convolutional encoder and Viterbi decoder based on the IEEE 802.11a standards of WLAN is presented in OFDM baseband processing systems. The proposed design supports a generic, robust and configurable Viterbi decoder with constraint length of 7, code rate of 1/2 and decoding depth of 36 symbols. The Viterbi decoder uses full-parallel structure to improve computational speed for the add-compare-select (ACS) modules, adopts optimal data storage mechanism to avoid overflow and employs three distributed RAM blocks to complete cyclic trace-back. It includes the core parts, for example, the state path measure computation, the preservation and transfer of the survivor path and trace-back decoding, etc. Compared to the general Viterbi decoder, this design can effectively decrease the 10% of chip logic elements, reduce 5% of power consumption, and increase the encoder and decoder working performance in the hardware implementation. Lastly, relevant simulation results using Verilog HDL language are verified based on a Xinlinx Virtex-II FPGA by ISE 7.1i. It is shown that the Viterbi decoder is capable of decoding (2, 1, 7) convolutional codes accurately with a throughput of 80 Mbps.展开更多
The encoding/decoding scheme based on Fiber Bragg Grating (FBG) for Optical Code Division Multiple Access (OCDMA) system is analyzed and the whole process from transmitting end to receiving end is researched in detail...The encoding/decoding scheme based on Fiber Bragg Grating (FBG) for Optical Code Division Multiple Access (OCDMA) system is analyzed and the whole process from transmitting end to receiving end is researched in detail. The mathematical mode including signal transmission, summing, receiving and recovering are established respectively. One of the main sources of Bit Error Rate (BER) of OCDMA system based on FBGs is the unevenness of signal power spectrum, which leads to the chip powers unequal with each other. The Signal to Interfere Ratio (SIR) and BER performance of the system are studied and simulated at the case with uneven distribution of chips' powers.展开更多
With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions...With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.展开更多
With the growing adoption of automated guided vehicles(AGVs)in various industries,the integrated production and transportation scheduling problem(IPTSP)has emerged as a critical research focus.The IPTSP is classified ...With the growing adoption of automated guided vehicles(AGVs)in various industries,the integrated production and transportation scheduling problem(IPTSP)has emerged as a critical research focus.The IPTSP is classified as a strongly NP-hard problem due to the simultaneous scheduling of two resources:machines and transportation equipment.Meta-heuristic algorithms are one of the most popular and effective approaches to solving this problem.However,their effectiveness heavily depends on the choice of solution representation,which influences both the algorithm’s search space and convergence speed.This paper reviews the existing encoding and decoding methods and proposes a novel active decoding approach.Based on different combinations of encoding and decoding methods,six solution representations are identified,among which the newly proposed representation offers a trade-off between the search space and the algorithm’s efficiency.Specifically,four scenarios of IPTSP under different assumptions are first analyzed.Next,the variations in the six solution representations across unused scenarios and different layouts,as well as their respective encoding spaces and qualities,are summarized.Subsequently,the search efficiency of the six solution representations is evaluated using a genetic algorithm to analyze their performance under different scenarios,layouts,time ratios,and number of AGVs.Finally,the advantages,disadvantages and applicable scenes for each solution representation are summarized based on the experimental results and analysis.These findings provide valuable insights for designing more efficient algorithms to address the IPTSP.展开更多
Ancient villages in Lingnan serve as crucial carriers of Lingnan culture.Their abundant cultural symbols now face the dual task of inheritance and innovation in the digital era.Drawing on Stuart Hall’s encoding/decod...Ancient villages in Lingnan serve as crucial carriers of Lingnan culture.Their abundant cultural symbols now face the dual task of inheritance and innovation in the digital era.Drawing on Stuart Hall’s encoding/decoding theory,this study explores how representative cultural symbols of Lingnan’s ancient villages are digitally translated and disseminated.By analyzing specific cases,it elucidates the logic of audience interaction and consumption during the decoding of these digital cultural symbols.This study aims to offer valuable insights for revitalizing ancient village culture and informing its sustainable industrial development.展开更多
Rail surface damage is a critical component of high-speed railway infrastructure,directly affecting train operational stability and safety.Existing methods face limitations in accuracy and speed for small-sample,multi...Rail surface damage is a critical component of high-speed railway infrastructure,directly affecting train operational stability and safety.Existing methods face limitations in accuracy and speed for small-sample,multi-category,and multi-scale target segmentation tasks.To address these challenges,this paper proposes Pyramid-MixNet,an intelligent segmentation model for high-speed rail surface damage,leveraging dataset construction and expansion alongside a feature pyramid-based encoder-decoder network with multi-attention mechanisms.The encoding net-work integrates Spatial Reduction Masked Multi-Head Attention(SRMMHA)to enhance global feature extraction while reducing trainable parameters.The decoding network incorporates Mix-Attention(MA),enabling multi-scale structural understanding and cross-scale token group correlation learning.Experimental results demonstrate that the proposed method achieves 62.17%average segmentation accuracy,80.28%Damage Dice Coefficient,and 56.83 FPS,meeting real-time detection requirements.The model’s high accuracy and scene adaptability significantly improve the detection of small-scale and complex multi-scale rail damage,offering practical value for real-time monitoring in high-speed railway maintenance systems.展开更多
The translation activity is a process of the interlinguistic transmission of information realized by the information encoding and decoding.Encoding and decoding,cognitive practices operated in objective contexts,are i...The translation activity is a process of the interlinguistic transmission of information realized by the information encoding and decoding.Encoding and decoding,cognitive practices operated in objective contexts,are inevitably of selectivity ascribing to the restriction of contextual reasons.The translator as the intermediary agent connects the original author(encoder)and the target readers(decoder),shouldering the dual duties of the decoder and the encoder,for which his subjectivity is irrevocably manipulated by the selectivity of encoding and decoding.展开更多
In order to make the information transmission more efficient and reliable in a digital communication channel with limited capacity, various encoding-decoding techniques have been proposed and widely applied in many br...In order to make the information transmission more efficient and reliable in a digital communication channel with limited capacity, various encoding-decoding techniques have been proposed and widely applied in many branches of the signal processing including digital communications, data compression,information encryption, etc. Recently, due to its promising application potentials in the networked systems(NSs), the analysis and synthesis issues of the NSs under various encoding-decoding schemes have stirred some research attention. However, because of the network-enhanced complexity caused by the limited network resources, it poses new challenges to the design of suitable encoding-decoding procedures to meet certain control or filtering performance for the NSs. In this survey paper, our aim is to present a comprehensive review of the encoding-decodingbased control and filtering problems for different types of NSs.First, some basic introduction with respect to the coding-decoding mechanism is presented in terms of its engineering insights,specific properties and theoretical formulations. Then, the recent representative research progress in the design of the encodingdecoding protocols for various control and filtering problems is discussed. Some possible further research topics are finally outlined for the encoding-decoding-based NSs.展开更多
Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding ...Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding phase.This paper presents a medical image segmentation model based on SAM with a local multi-scale feature encoder(LMSFE-SAM)to address the issues above.Firstly,based on the SAM,a local multi-scale feature encoder is introduced to improve the representation of features within local receptive field,thereby supplying the Vision Transformer(ViT)branch in SAM with enriched local multi-scale contextual information.At the same time,a multiaxial Hadamard product module(MHPM)is incorporated into the local multi-scale feature encoder in a lightweight manner to reduce the quadratic complexity and noise interference.Subsequently,a cross-branch balancing adapter is designed to balance the local and global information between the local multi-scale feature encoder and the ViT encoder in SAM.Finally,to obtain smaller input image size and to mitigate overlapping in patch embeddings,the size of the input image is reduced from 1024×1024 pixels to 256×256 pixels,and a multidimensional information adaptation component is developed,which includes feature adapters,position adapters,and channel-spatial adapters.This component effectively integrates the information from small-sized medical images into SAM,enhancing its suitability for clinical deployment.The proposed model demonstrates an average enhancement ranging from 0.0387 to 0.3191 across six objective evaluation metrics on BUSI,DDTI,and TN3K datasets compared to eight other representative image segmentation models.This significantly enhances the performance of the SAM on medical images,providing clinicians with a powerful tool in clinical diagnosis.展开更多
Many monographs point out that differential encoding and decoding is necessary for ef- fectual information transmission against phase ambiguity while seldom discuss the reason why phase ambiguity will emerge inevitabl...Many monographs point out that differential encoding and decoding is necessary for ef- fectual information transmission against phase ambiguity while seldom discuss the reason why phase ambiguity will emerge inevitably.Available algorithms are specially designed for certain modulation scheme;these algorithms cannot satisfy the requirement of soft-defined radio,which perhaps demands a uniform algorithm for different modulations.This paper proposes a new opinion on phase ambiguity from the view of probability.This opinion believes that modulating symbol sequence can affect,at optimum sampling epoch,the modulated waveform as oscillating carrier has done,and so the stochastic sequence leads to phase ambiguity.Based on a general signal model,this paper also puts forward a novel universal algorithm,which is suitable for different signals,even some new ones,by configuring several parameters.展开更多
文摘In this paper, a modified FPGA scheme for the convolutional encoder and Viterbi decoder based on the IEEE 802.11a standards of WLAN is presented in OFDM baseband processing systems. The proposed design supports a generic, robust and configurable Viterbi decoder with constraint length of 7, code rate of 1/2 and decoding depth of 36 symbols. The Viterbi decoder uses full-parallel structure to improve computational speed for the add-compare-select (ACS) modules, adopts optimal data storage mechanism to avoid overflow and employs three distributed RAM blocks to complete cyclic trace-back. It includes the core parts, for example, the state path measure computation, the preservation and transfer of the survivor path and trace-back decoding, etc. Compared to the general Viterbi decoder, this design can effectively decrease the 10% of chip logic elements, reduce 5% of power consumption, and increase the encoder and decoder working performance in the hardware implementation. Lastly, relevant simulation results using Verilog HDL language are verified based on a Xinlinx Virtex-II FPGA by ISE 7.1i. It is shown that the Viterbi decoder is capable of decoding (2, 1, 7) convolutional codes accurately with a throughput of 80 Mbps.
基金Supported by the Natural Science Research Foundation of Jiangsu Higher-Learning Insti-tution (No.04jkb510057).
文摘The encoding/decoding scheme based on Fiber Bragg Grating (FBG) for Optical Code Division Multiple Access (OCDMA) system is analyzed and the whole process from transmitting end to receiving end is researched in detail. The mathematical mode including signal transmission, summing, receiving and recovering are established respectively. One of the main sources of Bit Error Rate (BER) of OCDMA system based on FBGs is the unevenness of signal power spectrum, which leads to the chip powers unequal with each other. The Signal to Interfere Ratio (SIR) and BER performance of the system are studied and simulated at the case with uneven distribution of chips' powers.
文摘With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.
基金Supported by National Key R&D Program of China(Grant No.2022YFB3302700)National Natural Science Foundation of China(Grant No.U21B2029)Fundamental Research Funds for the Central Universities(Grant No.2024BRA004).
文摘With the growing adoption of automated guided vehicles(AGVs)in various industries,the integrated production and transportation scheduling problem(IPTSP)has emerged as a critical research focus.The IPTSP is classified as a strongly NP-hard problem due to the simultaneous scheduling of two resources:machines and transportation equipment.Meta-heuristic algorithms are one of the most popular and effective approaches to solving this problem.However,their effectiveness heavily depends on the choice of solution representation,which influences both the algorithm’s search space and convergence speed.This paper reviews the existing encoding and decoding methods and proposes a novel active decoding approach.Based on different combinations of encoding and decoding methods,six solution representations are identified,among which the newly proposed representation offers a trade-off between the search space and the algorithm’s efficiency.Specifically,four scenarios of IPTSP under different assumptions are first analyzed.Next,the variations in the six solution representations across unused scenarios and different layouts,as well as their respective encoding spaces and qualities,are summarized.Subsequently,the search efficiency of the six solution representations is evaluated using a genetic algorithm to analyze their performance under different scenarios,layouts,time ratios,and number of AGVs.Finally,the advantages,disadvantages and applicable scenes for each solution representation are summarized based on the experimental results and analysis.These findings provide valuable insights for designing more efficient algorithms to address the IPTSP.
基金2025 Institutional-Level Scientific Research Project of South China Business College,Guangdong University of Foreign Studies,“Digital Dissemination and Consumer Behaviour Research:Cultural Symbols of Lingnan Heritage Villages”(Project No.:25-005C)。
文摘Ancient villages in Lingnan serve as crucial carriers of Lingnan culture.Their abundant cultural symbols now face the dual task of inheritance and innovation in the digital era.Drawing on Stuart Hall’s encoding/decoding theory,this study explores how representative cultural symbols of Lingnan’s ancient villages are digitally translated and disseminated.By analyzing specific cases,it elucidates the logic of audience interaction and consumption during the decoding of these digital cultural symbols.This study aims to offer valuable insights for revitalizing ancient village culture and informing its sustainable industrial development.
基金supported in part by the National Natural Science Foundation of China under Grant 6226070954Jiangxi Provincial Key R&D Programme under Grant 20244BBG73002.
文摘Rail surface damage is a critical component of high-speed railway infrastructure,directly affecting train operational stability and safety.Existing methods face limitations in accuracy and speed for small-sample,multi-category,and multi-scale target segmentation tasks.To address these challenges,this paper proposes Pyramid-MixNet,an intelligent segmentation model for high-speed rail surface damage,leveraging dataset construction and expansion alongside a feature pyramid-based encoder-decoder network with multi-attention mechanisms.The encoding net-work integrates Spatial Reduction Masked Multi-Head Attention(SRMMHA)to enhance global feature extraction while reducing trainable parameters.The decoding network incorporates Mix-Attention(MA),enabling multi-scale structural understanding and cross-scale token group correlation learning.Experimental results demonstrate that the proposed method achieves 62.17%average segmentation accuracy,80.28%Damage Dice Coefficient,and 56.83 FPS,meeting real-time detection requirements.The model’s high accuracy and scene adaptability significantly improve the detection of small-scale and complex multi-scale rail damage,offering practical value for real-time monitoring in high-speed railway maintenance systems.
文摘The translation activity is a process of the interlinguistic transmission of information realized by the information encoding and decoding.Encoding and decoding,cognitive practices operated in objective contexts,are inevitably of selectivity ascribing to the restriction of contextual reasons.The translator as the intermediary agent connects the original author(encoder)and the target readers(decoder),shouldering the dual duties of the decoder and the encoder,for which his subjectivity is irrevocably manipulated by the selectivity of encoding and decoding.
基金supported in part by the Royal Society of the UK,the Nationa Natural Science,Foundation of China(61329301,61374039)the Program for Capability Construction of Shanghai Provincial Universities(15550502500)the Alexander von Humboldt Foundation of Germany
文摘In order to make the information transmission more efficient and reliable in a digital communication channel with limited capacity, various encoding-decoding techniques have been proposed and widely applied in many branches of the signal processing including digital communications, data compression,information encryption, etc. Recently, due to its promising application potentials in the networked systems(NSs), the analysis and synthesis issues of the NSs under various encoding-decoding schemes have stirred some research attention. However, because of the network-enhanced complexity caused by the limited network resources, it poses new challenges to the design of suitable encoding-decoding procedures to meet certain control or filtering performance for the NSs. In this survey paper, our aim is to present a comprehensive review of the encoding-decodingbased control and filtering problems for different types of NSs.First, some basic introduction with respect to the coding-decoding mechanism is presented in terms of its engineering insights,specific properties and theoretical formulations. Then, the recent representative research progress in the design of the encodingdecoding protocols for various control and filtering problems is discussed. Some possible further research topics are finally outlined for the encoding-decoding-based NSs.
基金supported by Natural Science Foundation Programme of Gansu Province(No.24JRRA231)National Natural Science Foundation of China(No.62061023)Gansu Provincial Science and Technology Plan Key Research and Development Program Project(No.24YFFA024).
文摘Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding phase.This paper presents a medical image segmentation model based on SAM with a local multi-scale feature encoder(LMSFE-SAM)to address the issues above.Firstly,based on the SAM,a local multi-scale feature encoder is introduced to improve the representation of features within local receptive field,thereby supplying the Vision Transformer(ViT)branch in SAM with enriched local multi-scale contextual information.At the same time,a multiaxial Hadamard product module(MHPM)is incorporated into the local multi-scale feature encoder in a lightweight manner to reduce the quadratic complexity and noise interference.Subsequently,a cross-branch balancing adapter is designed to balance the local and global information between the local multi-scale feature encoder and the ViT encoder in SAM.Finally,to obtain smaller input image size and to mitigate overlapping in patch embeddings,the size of the input image is reduced from 1024×1024 pixels to 256×256 pixels,and a multidimensional information adaptation component is developed,which includes feature adapters,position adapters,and channel-spatial adapters.This component effectively integrates the information from small-sized medical images into SAM,enhancing its suitability for clinical deployment.The proposed model demonstrates an average enhancement ranging from 0.0387 to 0.3191 across six objective evaluation metrics on BUSI,DDTI,and TN3K datasets compared to eight other representative image segmentation models.This significantly enhances the performance of the SAM on medical images,providing clinicians with a powerful tool in clinical diagnosis.
基金Supported by Henan Prominent Talents Innovation Foundation (No.0421000100).
文摘Many monographs point out that differential encoding and decoding is necessary for ef- fectual information transmission against phase ambiguity while seldom discuss the reason why phase ambiguity will emerge inevitably.Available algorithms are specially designed for certain modulation scheme;these algorithms cannot satisfy the requirement of soft-defined radio,which perhaps demands a uniform algorithm for different modulations.This paper proposes a new opinion on phase ambiguity from the view of probability.This opinion believes that modulating symbol sequence can affect,at optimum sampling epoch,the modulated waveform as oscillating carrier has done,and so the stochastic sequence leads to phase ambiguity.Based on a general signal model,this paper also puts forward a novel universal algorithm,which is suitable for different signals,even some new ones,by configuring several parameters.
