In remote sensing imagery,approximately 67%of the data are affected by cloud cover,significantly increasing the difficulty of image classification,recognition,and other downstream interpretation tasks.To effectively a...In remote sensing imagery,approximately 67%of the data are affected by cloud cover,significantly increasing the difficulty of image classification,recognition,and other downstream interpretation tasks.To effectively address the randomness of cloud distribution and the non-uniformity of cloud thickness,we propose a coarse-to-fine thin cloud removal architecture based on the observations of the random distribution and uneven thickness of cloud.In the coarse-level declouding network,we innovatively introduce a multi-scale attention mechanism,i.e.,pyramid nonlocal attention(PNA).By integrating global context with local detail information,it specifically addresses image quality degradation caused by the uncertainty in cloud distribution.During the fine-level declouding stage,we focus on the impact of cloud thickness on declouding results(primarily manifested as insufficient detail information).Through a carefully designed residual dense module,we significantly enhance the extraction and utilization of feature details.Thus,our approach precisely restores lost local texture features on top of coarse-level results,achieving a substantial leap in declouding quality.To evaluate the effectiveness of our cloud removal technology and attention mechanism,we conducted comprehensive analyses on publicly available datasets.Results demonstrate that our method achieves state-of-the-art performance across a wide range of techniques.展开更多
The generation of high-quality,realistic face generation has emerged as a key field of research in computer vision.This paper proposes a robust approach that combines a Super-Resolution Generative Adversarial Network(...The generation of high-quality,realistic face generation has emerged as a key field of research in computer vision.This paper proposes a robust approach that combines a Super-Resolution Generative Adversarial Network(SRGAN)with a Pyramid Attention Module(PAM)to enhance the quality of deep face generation.The SRGAN framework is designed to improve the resolution of generated images,addressing common challenges such as blurriness and a lack of intricate details.The Pyramid Attention Module further complements the process by focusing on multi-scale feature extraction,enabling the network to capture finer details and complex facial features more effectively.The proposed method was trained and evaluated over 100 epochs on the CelebA dataset,demonstrating consistent improvements in image quality and a marked decrease in generator and discriminator losses,reflecting the model’s capacity to learn and synthesize high-quality images effectively,given adequate computational resources.Experimental outcome demonstrates that the SRGAN model with PAM module has outperformed,yielding an aggregate discriminator loss of 0.055 for real,0.043 for fake,and a generator loss of 10.58 after training for 100 epochs.The model has yielded an structural similarity index measure of 0.923,that has outperformed the other models that are considered in the current study for analysis.展开更多
Improving the detection accuracy of rail internal defects and the generalization ability of detection models are not only the main problems in the field of defect detection but also the key to ensuring the safe operat...Improving the detection accuracy of rail internal defects and the generalization ability of detection models are not only the main problems in the field of defect detection but also the key to ensuring the safe operation of high-speed trains.For this reason,a rail internal defect detection method based on an enhanced network structure and module design using ultrasonic images is proposed in this paper.First,a data augmentation method was used to extend the existing image dataset to obtain appropriate image samples.Second,an enhanced network structure was designed to make full use of the high-level and low-level feature information in the image,which improved the accuracy of defect detection.Subsequently,to optimize the detection performance of the proposed model,the Mish activation function was used to design the block module of the feature extraction network.Finally,the pro-posed rail defect detection model was trained.The experimental results showed that the precision rate and F1score of the proposed method were as high as 98%,while the model’s recall rate reached 99%.Specifically,good detec-tion results were achieved for different types of defects,which provides a reference for the engineering application of internal defect detection.Experimental results verified the effectiveness of the proposed method.展开更多
As a nanometer-level interconnection,the Optical Network-on-Chip(ONoC)was proposed since it was typically characterized by low latency,high bandwidth and power efficiency. Compared with a 2-Dimensional(2D)design,the 3...As a nanometer-level interconnection,the Optical Network-on-Chip(ONoC)was proposed since it was typically characterized by low latency,high bandwidth and power efficiency. Compared with a 2-Dimensional(2D)design,the 3D integration has the higher packing density and the shorter wire length. Therefore,the 3D ONoC will have the great potential in the future. In this paper,we first discuss the existing ONoC researches,and then design mesh and torus ONoCs from the perspectives of topology,router,and routing module,with the help of 3D integration. A simulation platform is established by using OPNET to compare the performance of 2D and 3D ONoCs in terms of average delay and packet loss rate. The performance comparison between 3D mesh and 3D torus ONoCs is also conducted. The simulation results demonstrate that 3D integration has the advantage of reducing average delay and packet loss rate,and 3D torus ONoC has the better performance compared with 3D mesh solution. Finally,we summarize some future challenges with possible solutions,including microcosmic routing inside optical routers and highly-efficient traffic grooming.展开更多
The secondary cell wall(SCW)is essential for plant growth and development in vascular plants,and its biosynthesis is mainly controlled by a complex hierarchical regulatory network involving multiple transcription fact...The secondary cell wall(SCW)is essential for plant growth and development in vascular plants,and its biosynthesis is mainly controlled by a complex hierarchical regulatory network involving multiple transcription factors(TFs)at the transcription level.However,TFs that specifically regulate secondary xylem have not been widely reported.In this study,we described a poplar KNOTTED1-like homeobox(KNOX)TF PtoKNAT3a1,which was mainly expressed in the expanding xylem cells of stems.PtoKNAT3a1 overexpression caused fiber SCW thickening and increased all measured SCW compositions by upregulating the expression of SCW-biosynthetic genes and-associated TFs,but had no effect on the vessels of SCW.The opposite phenotype was observed in the PtoKNAT3a1-knockout lines.Hence,we further demonstrated that Pto-KNAT3a1 could physically interact with the NAC master switches PtoWND2A/3A to enhance the expression of downstream MYB TFs and SCW biosynthetic genes(including PtoMYB20,PtoMYB21,PtoMYB90,PtoCoMT2,PtoGT43B and PtoCesA8).Meanwhile,the studies also demonstrate that the KNAT3 has functional differentiation in xylem development.Taken together,these data suggest that the KNAT3a1-WND2A/3A module positively regulates fiber development of the secondary xylem in poplar via the WND2A/3A-mediated hierarchical regulatory network,and supplies useful information for fiber SCW formation.The research not only deepens the understanding of the hierarchical regulatory network affecting SCW formation but also supplies genetic resources and molecular targets for plant fiber utilization.展开更多
In modern wireless communication and electromagnetic control,automatic modulationclassification(AMC)of orthogonal frequency division multiplexing(OFDM)signals plays animportant role.However,under Doppler frequency shi...In modern wireless communication and electromagnetic control,automatic modulationclassification(AMC)of orthogonal frequency division multiplexing(OFDM)signals plays animportant role.However,under Doppler frequency shift and complex multipath channel conditions,extracting discriminative features from high-order modulation signals and ensuring model inter-pretability remain challenging.To address these issues,this paper proposes a Fourier attention net-work(FAttNet),which combines an attention mechanism with a Fourier analysis network(FAN).Specifically,the method directly converts the input signal to the frequency domain using the FAN,thereby obtaining frequency features that reflect the periodic variations in amplitude and phase.Abuilt-in attention mechanism then automatically calculates the weights for each frequency band,focusing on the most discriminative components.This approach improves both classification accu-racy and model interpretability.Experimental validation was conducted via high-order modulationsimulation using an RF testbed.The results show that under three different Doppler frequencyshifts and complex multipath channel conditions,with a signal-to-noise ratio of 10 dB,the classifi-cation accuracy can reach 89.1%,90.4%and 90%,all of which are superior to the current main-stream methods.The proposed approach offers practical value for dynamic spectrum access and sig-nal security detection,and it makes important theoretical contributions to the application of deeplearning in complex electromagnetic signal recognition.展开更多
In complex networks, network modules play a center role, which carry out a key function. In this paper, we introduce the spatial correIation function to describe the relationships among the network modules. Our focus ...In complex networks, network modules play a center role, which carry out a key function. In this paper, we introduce the spatial correIation function to describe the relationships among the network modules. Our focus is to investigate how the network modules evolve, and what the evolution properties of the modules are. In order to test the proposed method, as the examples, we use our method to analyze and discuss the ER random network and scale-free network. Rigorous analysis of the existing data shows that the introduced correlation function is suitable for describing the evolution properties of network modules. Remarkably, the numerical simulations indicate that the ER random network and scale-free network have different evolution properties.展开更多
In wireless sensor network, the primary design is to save the energy consumption as much as possible while achieving the given task. Most of recent researches works have only focused on the individual layer issues and...In wireless sensor network, the primary design is to save the energy consumption as much as possible while achieving the given task. Most of recent researches works have only focused on the individual layer issues and ignore the importance of inter working between different layers in a sensor network. In this paper, we use a cross-layer approach to propose an energy-efficient and extending the life time of the sensor network. This protocol which uses routing in the network layer, and the data scheduling in MAC layer. The main ob-jective of this paper is to provide a possible and flexible approach to solve the conflicts between the require-ments of large scale, long life-time, and multi-purpose wireless sensor networks. This OEEXLM module gives better performance compared to all other existing protocols. The performance of OEEXLM module compared with S-MAC and directed diffusion protocol.展开更多
In this study, irrigation modules calculated in planning and actualized operational stage of the irrigation networks are examined. Irrigation module used irrigation networks is a constant discharge parameter, meeting ...In this study, irrigation modules calculated in planning and actualized operational stage of the irrigation networks are examined. Irrigation module used irrigation networks is a constant discharge parameter, meeting the needs of irrigation water requirement smonthly of crops in one hectare of irrigation area and it is a constant discharge flowing continuously for a month. Extent of the overlapping between the irrigation planning module and the operation module actualized during the operational stage of the irrigation network depends on changes in the cropping patterns, differences in the effects of field irrigation methods used by farmers on the capacity of the constructed system, the increases or decreases in the water demands depending on the irrigation period, as well as the extent of sustainability according to the selected operation method. A2 irrigation area of Aydin plain irrigation network, locating in the Büyük Menderes basin, Turkey is selected as study area, with an area of 2500 ha. Irrigation planning module calculated for this network is q = 1.16 l/s/ha and it has been designed as per the supply demand operation method. For the study;actualized irrigation module in the operation stage has been compared with the planning irrigation module by using Excel software and taking parameters such as actual crop pattern and percentage distributions, actualized irrigated areas, irrigation networks and water distribution, water intake of irrigation networks which have been calculated without operation losses, as well as with 5%, 10%, and 15% operation losses. The July operation module calculated for the examined irrigation network generally conforms to the planning module, as it has received the values close to or below the value of irrigation planning modules.展开更多
Analyzing the function of gene sets is a critical step in interpreting the results of high-throughput experiments in systems biology. A variety of enrichment analysis tools have been developed in recent years, but mos...Analyzing the function of gene sets is a critical step in interpreting the results of high-throughput experiments in systems biology. A variety of enrichment analysis tools have been developed in recent years, but most output a long list of significantly enriched terms that are often redundant, making it difficult to extract the most meaningful functions. In this paper, we present GOMA, a novel enrichment analysis method based on the new concept of enriched functional Gene Ontology (GO) modules. With this method, we systematically revealed functional GO modules, i.e., groups of functionally similar GO terms, via an optimization model and then ranked them by enrichment scores. Our new method simplifies enrichment analysis results by reducing redundancy, thereby preventing inconsistent enrichment results among functionally similar terms and providing more biologically meaningful results.展开更多
Deep Learning(DL)is such a powerful tool that we have seen tremendous success in areas such as Computer Vision,Speech Recognition,and Natural Language Processing.Since Automated Modulation Classification(AMC)is an imp...Deep Learning(DL)is such a powerful tool that we have seen tremendous success in areas such as Computer Vision,Speech Recognition,and Natural Language Processing.Since Automated Modulation Classification(AMC)is an important part in Cognitive Radio Networks,we try to explore its potential in solving signal modulation recognition problem.It cannot be overlooked that DL model is a complex model,thus making them prone to over-fitting.DL model requires many training data to combat with over-fitting,but adding high quality labels to training data manually is not always cheap and accessible,especially in real-time system,which may counter unprecedented data in dataset.Semi-supervised Learning is a way to exploit unlabeled data effectively to reduce over-fitting in DL.In this paper,we extend Generative Adversarial Networks(GANs)to the semi-supervised learning will show it is a method can be used to create a more dataefficient classifier.展开更多
To improve the recognition rate of signal modulation recognition methods based on the clustering algorithm under the low SNR, a modulation recognition method is proposed. The characteristic parameter of the signal is ...To improve the recognition rate of signal modulation recognition methods based on the clustering algorithm under the low SNR, a modulation recognition method is proposed. The characteristic parameter of the signal is extracted by using a clustering algorithm, the neural network is trained by using the algorithm of variable gradient correction （Polak-Ribiere） so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram. Simulation results show that the recognition rate based on this algorithm is enhanced over 30% compared with the methods that adopt clustering algorithm or neural network based on the back propagation algorithm alone under the low SNR. The recognition rate can reach 90% when the SNR is 4 dB, and the method is easy to be achieved so that it has a broad application prospect in the modulating recognition.展开更多
In this paper,we apply adaptive coded modulation (ACM) schemes to a wireless networked control system (WNCS) to improve the energy efficiency and increase the data rate over a fading channel.To capture the characteris...In this paper,we apply adaptive coded modulation (ACM) schemes to a wireless networked control system (WNCS) to improve the energy efficiency and increase the data rate over a fading channel.To capture the characteristics of varying rate, interference,and routing in wireless transmission channels,the concepts of equivalent delay (ED) and networked condition index (NCI) are introduced.Also,the analytic lower and upper bounds of EDs are obtained.Furthermore,we model the WNCS as a multicontroller switched system (MSS) under consideration of EDs and loss index in the wireless transmission.Sufficient stability condition of the closed-loop WNCS and corresponding dynamic state feedback controllers are derived in terms of linear matrix inequality (LMI). Numerical results show the validity and advantage of our proposed control strategies.展开更多
Automatic modulation classification(AMC)aims at identifying the modulation of the received signals,which is a significant approach to identifying the target in military and civil applications.In this paper,a novel dat...Automatic modulation classification(AMC)aims at identifying the modulation of the received signals,which is a significant approach to identifying the target in military and civil applications.In this paper,a novel data-driven framework named convolutional and transformer-based deep neural network(CTDNN)is proposed to improve the classification performance.CTDNN can be divided into four modules,i.e.,convolutional neural network(CNN)backbone,transition module,transformer module,and final classifier.In the CNN backbone,a wide and deep convolution structure is designed,which consists of 1×15 convolution kernels and intensive cross-layer connections instead of traditional 1×3 kernels and sequential connections.In the transition module,a 1×1 convolution layer is utilized to compress the channels of the previous multi-scale CNN features.In the transformer module,three self-attention layers are designed for extracting global features and generating the classification vector.In the classifier,the final decision is made based on the maximum a posterior probability.Extensive simulations are conducted,and the result shows that our proposed CTDNN can achieve superior classification performance than traditional deep models.展开更多
Software module clustering problem is an important and challenging problem in software reverse engineering whose main goal is to obtain a good modular structure of the software system. The large complex software syste...Software module clustering problem is an important and challenging problem in software reverse engineering whose main goal is to obtain a good modular structure of the software system. The large complex software system can be divided into some subsystems that are easy to understand and maintain through the software module clustering. Aiming at solving the problem of slow convergence speed, the poor clustering result, and the complex algorithm, a software module clustering algorithm using probability selection is proposed. Firstly, we convert the software system into complex network diagram, and then we use the operation of merger, adjustment and optimization to get the software module clustering scheme. To evaluate the effectiveness of the algorithm, a set of experiments was performed on 5 real-world module clustering problems. The comparison of the experimental results proves the simplicity of the algorithm as well as the low time complexity and fast convergence speed. This algorithm provides a simple and effective engineering method for software module clustering problem.展开更多
Significant progress has been made in computational imaging(CI),in which deep convolutional neural networks(CNNs)have demonstrated that sparse speckle patterns can be reconstructed.However,due to the limited“local”k...Significant progress has been made in computational imaging(CI),in which deep convolutional neural networks(CNNs)have demonstrated that sparse speckle patterns can be reconstructed.However,due to the limited“local”kernel size of the convolutional operator,for the spatially dense patterns,such as the generic face images,the performance of CNNs is limited.Here,we propose a“non-local”model,termed the Speckle-Transformer(SpT)UNet,for speckle feature extraction of generic face images.It is worth noting that the lightweight SpT UNet reveals a high efficiency and strong comparative performance with Pearson Correlation Coefficient(PCC),and structural similarity measure(SSIM)exceeding 0.989,and 0.950,respectively.展开更多
Web offers a very convenient way to access remote information resources,an important measurement of evaluating Web services quality is how long it takes to search and get information.By caching the Web server’s dynam...Web offers a very convenient way to access remote information resources,an important measurement of evaluating Web services quality is how long it takes to search and get information.By caching the Web server’s dynamic content,it can avoid repeated queries for database and reduce the access frequency of original resources,thus to improve the speed of server’s response.This paper describes the concept,advantages,principles and concrete realization procedure of a dynamic content cache module for Web server.展开更多
To promote reliable and secure communications in the cognitive radio network,the automatic modulation classification algorithms have been mainly proposed to estimate a single modulation.In this paper,we address the cl...To promote reliable and secure communications in the cognitive radio network,the automatic modulation classification algorithms have been mainly proposed to estimate a single modulation.In this paper,we address the classification of superimposed modulations dedicated to 5G multipleinput multiple-output(MIMO)two-way cognitive relay network in realistic channels modeled with Nakagami-m distribution.Our purpose consists of classifying pairs of users modulations from superimposed signals.To achieve this goal,we apply the higher-order statistics in conjunction with the Multi-BoostAB classifier.We use several efficiency metrics including the true positive(TP)rate,false positive(FP)rate,precision,recall,F-Measure and receiver operating characteristic(ROC)area in order to evaluate the performance of the proposed algorithm in terms of correct superimposed modulations classification.Computer simulations prove that our proposal allows obtaining a good probability of classification for ten superimposed modulations at a low signal-to-noise ratio,including the worst case(i.e.,m=0.5),where the fading distribution follows a one-sided Gaussian distribution.We also carry out a comparative study between our proposal usingMultiBoostAB classifier with the decision tree(J48)classifier.Simulation results show that the performance of MultiBoostAB on the superimposed modulations classifications outperforms the one of J48 classifier.In addition,we study the impact of the symbols number,path loss exponent and relay position on the performance of the proposed automatic classification superimposed modulations in terms of probability of correct classification.展开更多
As an alternative to satellite communications,multi-hop relay networks can be deployed for maritime long-distance communications.Distinct from terrestrial environment,marine radio signals are affected by many factors,...As an alternative to satellite communications,multi-hop relay networks can be deployed for maritime long-distance communications.Distinct from terrestrial environment,marine radio signals are affected by many factors,e.g.,weather conditions,evaporation ducting,and ship rocking caused by waves.To ensure the data transmission reliability,the block Markov superposition transmission(BMST)codes,which are easily configurable and have predictable performance,are applied in this study.Meanwhile,the physical-layer network coding(PNC)scheme with spatial modulation(SM)is adopted to improve the spectrum utilization.For the BMST-SMPNC system,we propose an iterative algorithm,which utilizes the channel observations and the a priori information from BMST decoder,to compute the soft information corresponding to the XORed bits constructed by the relay node.The results indicate that the proposed scheme outperforms the convolutional coded SM-PNC over fast-fading Rician channels.Especially,the performance can be easily improved in high spatial correlation maritime channel by increasing the memory m.展开更多
Based on Immune Programming(IP), a novel Radial Basis Function (RBF) networkdesigning method is proposed. Through extracting the preliminary knowledge about the widthof the basis function as the vaccine to form the im...Based on Immune Programming(IP), a novel Radial Basis Function (RBF) networkdesigning method is proposed. Through extracting the preliminary knowledge about the widthof the basis function as the vaccine to form the immune operator, the algorithm reduces thesearching space of canonical algorithm and improves the convergence speed. The application ofthe RBF network trained with the algorithm in the modulation-style recognition of radar signalsdemonstrates that the network has a fast convergence speed with good performances.展开更多
基金supported by the Fundamental Research Funds for the Central Universities(No.2572025BR14)the China Energy Digital Intelligence Technology Development(Beijing)Co.,Ltd.Science and Technology Innovation Project(No.YA2024001500).
文摘In remote sensing imagery,approximately 67%of the data are affected by cloud cover,significantly increasing the difficulty of image classification,recognition,and other downstream interpretation tasks.To effectively address the randomness of cloud distribution and the non-uniformity of cloud thickness,we propose a coarse-to-fine thin cloud removal architecture based on the observations of the random distribution and uneven thickness of cloud.In the coarse-level declouding network,we innovatively introduce a multi-scale attention mechanism,i.e.,pyramid nonlocal attention(PNA).By integrating global context with local detail information,it specifically addresses image quality degradation caused by the uncertainty in cloud distribution.During the fine-level declouding stage,we focus on the impact of cloud thickness on declouding results(primarily manifested as insufficient detail information).Through a carefully designed residual dense module,we significantly enhance the extraction and utilization of feature details.Thus,our approach precisely restores lost local texture features on top of coarse-level results,achieving a substantial leap in declouding quality.To evaluate the effectiveness of our cloud removal technology and attention mechanism,we conducted comprehensive analyses on publicly available datasets.Results demonstrate that our method achieves state-of-the-art performance across a wide range of techniques.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(*MSIT)(No.2018R1A5A7059549).
文摘The generation of high-quality,realistic face generation has emerged as a key field of research in computer vision.This paper proposes a robust approach that combines a Super-Resolution Generative Adversarial Network(SRGAN)with a Pyramid Attention Module(PAM)to enhance the quality of deep face generation.The SRGAN framework is designed to improve the resolution of generated images,addressing common challenges such as blurriness and a lack of intricate details.The Pyramid Attention Module further complements the process by focusing on multi-scale feature extraction,enabling the network to capture finer details and complex facial features more effectively.The proposed method was trained and evaluated over 100 epochs on the CelebA dataset,demonstrating consistent improvements in image quality and a marked decrease in generator and discriminator losses,reflecting the model’s capacity to learn and synthesize high-quality images effectively,given adequate computational resources.Experimental outcome demonstrates that the SRGAN model with PAM module has outperformed,yielding an aggregate discriminator loss of 0.055 for real,0.043 for fake,and a generator loss of 10.58 after training for 100 epochs.The model has yielded an structural similarity index measure of 0.923,that has outperformed the other models that are considered in the current study for analysis.
基金Supported by National Natural Science Foundation of China(Grant No.61573233)Guangdong Provincial Natural Science Foundation of China(Grant No.2021A1515010661)Guangdong Provincial Special Projects in Key Fields of Colleges and Universities of China(Grant No.2020ZDZX2005).
文摘Improving the detection accuracy of rail internal defects and the generalization ability of detection models are not only the main problems in the field of defect detection but also the key to ensuring the safe operation of high-speed trains.For this reason,a rail internal defect detection method based on an enhanced network structure and module design using ultrasonic images is proposed in this paper.First,a data augmentation method was used to extend the existing image dataset to obtain appropriate image samples.Second,an enhanced network structure was designed to make full use of the high-level and low-level feature information in the image,which improved the accuracy of defect detection.Subsequently,to optimize the detection performance of the proposed model,the Mish activation function was used to design the block module of the feature extraction network.Finally,the pro-posed rail defect detection model was trained.The experimental results showed that the precision rate and F1score of the proposed method were as high as 98%,while the model’s recall rate reached 99%.Specifically,good detec-tion results were achieved for different types of defects,which provides a reference for the engineering application of internal defect detection.Experimental results verified the effectiveness of the proposed method.
基金supported in part by the National Nat-ural Science Foundation of China(Grant Nos.61401082,61471109,61502075,61672123,91438110,U1301253)the Fundamental Research Funds for Central Universities(Grant Nos.N161604004,N161608001,N150401002,DUT15RC(3)009)Liaoning Bai Qian Wan Talents Program,and National High-Level Personnel Special Support Program for Youth Top-Notch Talent
文摘As a nanometer-level interconnection,the Optical Network-on-Chip(ONoC)was proposed since it was typically characterized by low latency,high bandwidth and power efficiency. Compared with a 2-Dimensional(2D)design,the 3D integration has the higher packing density and the shorter wire length. Therefore,the 3D ONoC will have the great potential in the future. In this paper,we first discuss the existing ONoC researches,and then design mesh and torus ONoCs from the perspectives of topology,router,and routing module,with the help of 3D integration. A simulation platform is established by using OPNET to compare the performance of 2D and 3D ONoCs in terms of average delay and packet loss rate. The performance comparison between 3D mesh and 3D torus ONoCs is also conducted. The simulation results demonstrate that 3D integration has the advantage of reducing average delay and packet loss rate,and 3D torus ONoC has the better performance compared with 3D mesh solution. Finally,we summarize some future challenges with possible solutions,including microcosmic routing inside optical routers and highly-efficient traffic grooming.
基金supported by grants from the Biological Breeding-National Science and Technology Major Project(Grant No.2023ZD0406803)the National Key Research and Development Program(Grant No.2021YFD2200204)+2 种基金the National Science Foundation of China(Grant No.32071791 and 32271835)the Chongqing Youth Top Talent Program(Grant No.CQYC201905028)Fundamental Research Funds for the Central Universities(Grant No.XDJK2020B036).
文摘The secondary cell wall(SCW)is essential for plant growth and development in vascular plants,and its biosynthesis is mainly controlled by a complex hierarchical regulatory network involving multiple transcription factors(TFs)at the transcription level.However,TFs that specifically regulate secondary xylem have not been widely reported.In this study,we described a poplar KNOTTED1-like homeobox(KNOX)TF PtoKNAT3a1,which was mainly expressed in the expanding xylem cells of stems.PtoKNAT3a1 overexpression caused fiber SCW thickening and increased all measured SCW compositions by upregulating the expression of SCW-biosynthetic genes and-associated TFs,but had no effect on the vessels of SCW.The opposite phenotype was observed in the PtoKNAT3a1-knockout lines.Hence,we further demonstrated that Pto-KNAT3a1 could physically interact with the NAC master switches PtoWND2A/3A to enhance the expression of downstream MYB TFs and SCW biosynthetic genes(including PtoMYB20,PtoMYB21,PtoMYB90,PtoCoMT2,PtoGT43B and PtoCesA8).Meanwhile,the studies also demonstrate that the KNAT3 has functional differentiation in xylem development.Taken together,these data suggest that the KNAT3a1-WND2A/3A module positively regulates fiber development of the secondary xylem in poplar via the WND2A/3A-mediated hierarchical regulatory network,and supplies useful information for fiber SCW formation.The research not only deepens the understanding of the hierarchical regulatory network affecting SCW formation but also supplies genetic resources and molecular targets for plant fiber utilization.
基金supported by the National Natural Science Foundation of China(No.62027801).
文摘In modern wireless communication and electromagnetic control,automatic modulationclassification(AMC)of orthogonal frequency division multiplexing(OFDM)signals plays animportant role.However,under Doppler frequency shift and complex multipath channel conditions,extracting discriminative features from high-order modulation signals and ensuring model inter-pretability remain challenging.To address these issues,this paper proposes a Fourier attention net-work(FAttNet),which combines an attention mechanism with a Fourier analysis network(FAN).Specifically,the method directly converts the input signal to the frequency domain using the FAN,thereby obtaining frequency features that reflect the periodic variations in amplitude and phase.Abuilt-in attention mechanism then automatically calculates the weights for each frequency band,focusing on the most discriminative components.This approach improves both classification accu-racy and model interpretability.Experimental validation was conducted via high-order modulationsimulation using an RF testbed.The results show that under three different Doppler frequencyshifts and complex multipath channel conditions,with a signal-to-noise ratio of 10 dB,the classifi-cation accuracy can reach 89.1%,90.4%and 90%,all of which are superior to the current main-stream methods.The proposed approach offers practical value for dynamic spectrum access and sig-nal security detection,and it makes important theoretical contributions to the application of deeplearning in complex electromagnetic signal recognition.
基金National Natural Science Foundation of China under Grant Nos.60634010 and 60776829New Century Excellent Talents in Universities under Grant No.NCET-06-0074the Key Project of the Ministry of Education of China under Grant No.107007
文摘In complex networks, network modules play a center role, which carry out a key function. In this paper, we introduce the spatial correIation function to describe the relationships among the network modules. Our focus is to investigate how the network modules evolve, and what the evolution properties of the modules are. In order to test the proposed method, as the examples, we use our method to analyze and discuss the ER random network and scale-free network. Rigorous analysis of the existing data shows that the introduced correlation function is suitable for describing the evolution properties of network modules. Remarkably, the numerical simulations indicate that the ER random network and scale-free network have different evolution properties.
文摘In wireless sensor network, the primary design is to save the energy consumption as much as possible while achieving the given task. Most of recent researches works have only focused on the individual layer issues and ignore the importance of inter working between different layers in a sensor network. In this paper, we use a cross-layer approach to propose an energy-efficient and extending the life time of the sensor network. This protocol which uses routing in the network layer, and the data scheduling in MAC layer. The main ob-jective of this paper is to provide a possible and flexible approach to solve the conflicts between the require-ments of large scale, long life-time, and multi-purpose wireless sensor networks. This OEEXLM module gives better performance compared to all other existing protocols. The performance of OEEXLM module compared with S-MAC and directed diffusion protocol.
文摘In this study, irrigation modules calculated in planning and actualized operational stage of the irrigation networks are examined. Irrigation module used irrigation networks is a constant discharge parameter, meeting the needs of irrigation water requirement smonthly of crops in one hectare of irrigation area and it is a constant discharge flowing continuously for a month. Extent of the overlapping between the irrigation planning module and the operation module actualized during the operational stage of the irrigation network depends on changes in the cropping patterns, differences in the effects of field irrigation methods used by farmers on the capacity of the constructed system, the increases or decreases in the water demands depending on the irrigation period, as well as the extent of sustainability according to the selected operation method. A2 irrigation area of Aydin plain irrigation network, locating in the Büyük Menderes basin, Turkey is selected as study area, with an area of 2500 ha. Irrigation planning module calculated for this network is q = 1.16 l/s/ha and it has been designed as per the supply demand operation method. For the study;actualized irrigation module in the operation stage has been compared with the planning irrigation module by using Excel software and taking parameters such as actual crop pattern and percentage distributions, actualized irrigated areas, irrigation networks and water distribution, water intake of irrigation networks which have been calculated without operation losses, as well as with 5%, 10%, and 15% operation losses. The July operation module calculated for the examined irrigation network generally conforms to the planning module, as it has received the values close to or below the value of irrigation planning modules.
基金supported by grants from the National Natural Science Foundation of China(No.60970091, 61171007, 11131009)
文摘Analyzing the function of gene sets is a critical step in interpreting the results of high-throughput experiments in systems biology. A variety of enrichment analysis tools have been developed in recent years, but most output a long list of significantly enriched terms that are often redundant, making it difficult to extract the most meaningful functions. In this paper, we present GOMA, a novel enrichment analysis method based on the new concept of enriched functional Gene Ontology (GO) modules. With this method, we systematically revealed functional GO modules, i.e., groups of functionally similar GO terms, via an optimization model and then ranked them by enrichment scores. Our new method simplifies enrichment analysis results by reducing redundancy, thereby preventing inconsistent enrichment results among functionally similar terms and providing more biologically meaningful results.
基金This work is supported by the National Natural Science Foundation of China(Nos.61771154,61603239,61772454,6171101570).
文摘Deep Learning(DL)is such a powerful tool that we have seen tremendous success in areas such as Computer Vision,Speech Recognition,and Natural Language Processing.Since Automated Modulation Classification(AMC)is an important part in Cognitive Radio Networks,we try to explore its potential in solving signal modulation recognition problem.It cannot be overlooked that DL model is a complex model,thus making them prone to over-fitting.DL model requires many training data to combat with over-fitting,but adding high quality labels to training data manually is not always cheap and accessible,especially in real-time system,which may counter unprecedented data in dataset.Semi-supervised Learning is a way to exploit unlabeled data effectively to reduce over-fitting in DL.In this paper,we extend Generative Adversarial Networks(GANs)to the semi-supervised learning will show it is a method can be used to create a more dataefficient classifier.
基金supported by the National Natural Science Foundation of China(6107207061301179)the National Science and Technology Major Project(2010ZX03006-002-04)
文摘To improve the recognition rate of signal modulation recognition methods based on the clustering algorithm under the low SNR, a modulation recognition method is proposed. The characteristic parameter of the signal is extracted by using a clustering algorithm, the neural network is trained by using the algorithm of variable gradient correction （Polak-Ribiere） so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram. Simulation results show that the recognition rate based on this algorithm is enhanced over 30% compared with the methods that adopt clustering algorithm or neural network based on the back propagation algorithm alone under the low SNR. The recognition rate can reach 90% when the SNR is 4 dB, and the method is easy to be achieved so that it has a broad application prospect in the modulating recognition.
基金National Outstanding Youth Founda-tion (No.60525303)National Natural Science Foundation of China(No.60404022,60704009)Natural Science Foundation of Hebei Province (No.F2005000390,F2006000270).
文摘In this paper,we apply adaptive coded modulation (ACM) schemes to a wireless networked control system (WNCS) to improve the energy efficiency and increase the data rate over a fading channel.To capture the characteristics of varying rate, interference,and routing in wireless transmission channels,the concepts of equivalent delay (ED) and networked condition index (NCI) are introduced.Also,the analytic lower and upper bounds of EDs are obtained.Furthermore,we model the WNCS as a multicontroller switched system (MSS) under consideration of EDs and loss index in the wireless transmission.Sufficient stability condition of the closed-loop WNCS and corresponding dynamic state feedback controllers are derived in terms of linear matrix inequality (LMI). Numerical results show the validity and advantage of our proposed control strategies.
基金supported in part by the National Natural Science Foundation of China under Grant(62171045,62201090)in part by the National Key Research and Development Program of China under Grants(2020YFB1807602,2019YFB1804404).
文摘Automatic modulation classification(AMC)aims at identifying the modulation of the received signals,which is a significant approach to identifying the target in military and civil applications.In this paper,a novel data-driven framework named convolutional and transformer-based deep neural network(CTDNN)is proposed to improve the classification performance.CTDNN can be divided into four modules,i.e.,convolutional neural network(CNN)backbone,transition module,transformer module,and final classifier.In the CNN backbone,a wide and deep convolution structure is designed,which consists of 1×15 convolution kernels and intensive cross-layer connections instead of traditional 1×3 kernels and sequential connections.In the transition module,a 1×1 convolution layer is utilized to compress the channels of the previous multi-scale CNN features.In the transformer module,three self-attention layers are designed for extracting global features and generating the classification vector.In the classifier,the final decision is made based on the maximum a posterior probability.Extensive simulations are conducted,and the result shows that our proposed CTDNN can achieve superior classification performance than traditional deep models.
基金Supported by the Science Foundation of Education Ministry of Shaanxi Province(15JK1672)the Industrial Research Project of Shaanxi Province(2016GY-089)the Innovation Fund of Xi’an University of Posts and Telecommunications(103-602080012)
文摘Software module clustering problem is an important and challenging problem in software reverse engineering whose main goal is to obtain a good modular structure of the software system. The large complex software system can be divided into some subsystems that are easy to understand and maintain through the software module clustering. Aiming at solving the problem of slow convergence speed, the poor clustering result, and the complex algorithm, a software module clustering algorithm using probability selection is proposed. Firstly, we convert the software system into complex network diagram, and then we use the operation of merger, adjustment and optimization to get the software module clustering scheme. To evaluate the effectiveness of the algorithm, a set of experiments was performed on 5 real-world module clustering problems. The comparison of the experimental results proves the simplicity of the algorithm as well as the low time complexity and fast convergence speed. This algorithm provides a simple and effective engineering method for software module clustering problem.
基金funding support from the Science and Technology Commission of Shanghai Municipality(Grant No.21DZ1100500)the Shanghai Frontiers Science Center Program(2021-2025 No.20)+2 种基金the Zhangjiang National Innovation Demonstration Zone(Grant No.ZJ2019ZD-005)supported by a fellowship from the China Postdoctoral Science Foundation(2020M671169)the International Postdoctoral Exchange Program from the Administrative Committee of Post-Doctoral Researchers of China([2020]33)。
文摘Significant progress has been made in computational imaging(CI),in which deep convolutional neural networks(CNNs)have demonstrated that sparse speckle patterns can be reconstructed.However,due to the limited“local”kernel size of the convolutional operator,for the spatially dense patterns,such as the generic face images,the performance of CNNs is limited.Here,we propose a“non-local”model,termed the Speckle-Transformer(SpT)UNet,for speckle feature extraction of generic face images.It is worth noting that the lightweight SpT UNet reveals a high efficiency and strong comparative performance with Pearson Correlation Coefficient(PCC),and structural similarity measure(SSIM)exceeding 0.989,and 0.950,respectively.
文摘Web offers a very convenient way to access remote information resources,an important measurement of evaluating Web services quality is how long it takes to search and get information.By caching the Web server’s dynamic content,it can avoid repeated queries for database and reduce the access frequency of original resources,thus to improve the speed of server’s response.This paper describes the concept,advantages,principles and concrete realization procedure of a dynamic content cache module for Web server.
文摘To promote reliable and secure communications in the cognitive radio network,the automatic modulation classification algorithms have been mainly proposed to estimate a single modulation.In this paper,we address the classification of superimposed modulations dedicated to 5G multipleinput multiple-output(MIMO)two-way cognitive relay network in realistic channels modeled with Nakagami-m distribution.Our purpose consists of classifying pairs of users modulations from superimposed signals.To achieve this goal,we apply the higher-order statistics in conjunction with the Multi-BoostAB classifier.We use several efficiency metrics including the true positive(TP)rate,false positive(FP)rate,precision,recall,F-Measure and receiver operating characteristic(ROC)area in order to evaluate the performance of the proposed algorithm in terms of correct superimposed modulations classification.Computer simulations prove that our proposal allows obtaining a good probability of classification for ten superimposed modulations at a low signal-to-noise ratio,including the worst case(i.e.,m=0.5),where the fading distribution follows a one-sided Gaussian distribution.We also carry out a comparative study between our proposal usingMultiBoostAB classifier with the decision tree(J48)classifier.Simulation results show that the performance of MultiBoostAB on the superimposed modulations classifications outperforms the one of J48 classifier.In addition,we study the impact of the symbols number,path loss exponent and relay position on the performance of the proposed automatic classification superimposed modulations in terms of probability of correct classification.
基金the National Key Research and Development Program of China(No.2017YFE0112600)the National Science Foundation of China[No.61971454,No.91438101&No.61771499]the National Science Foundation of Guangdong,China[No.2016A030308008].
文摘As an alternative to satellite communications,multi-hop relay networks can be deployed for maritime long-distance communications.Distinct from terrestrial environment,marine radio signals are affected by many factors,e.g.,weather conditions,evaporation ducting,and ship rocking caused by waves.To ensure the data transmission reliability,the block Markov superposition transmission(BMST)codes,which are easily configurable and have predictable performance,are applied in this study.Meanwhile,the physical-layer network coding(PNC)scheme with spatial modulation(SM)is adopted to improve the spectrum utilization.For the BMST-SMPNC system,we propose an iterative algorithm,which utilizes the channel observations and the a priori information from BMST decoder,to compute the soft information corresponding to the XORed bits constructed by the relay node.The results indicate that the proposed scheme outperforms the convolutional coded SM-PNC over fast-fading Rician channels.Especially,the performance can be easily improved in high spatial correlation maritime channel by increasing the memory m.
文摘Based on Immune Programming(IP), a novel Radial Basis Function (RBF) networkdesigning method is proposed. Through extracting the preliminary knowledge about the widthof the basis function as the vaccine to form the immune operator, the algorithm reduces thesearching space of canonical algorithm and improves the convergence speed. The application ofthe RBF network trained with the algorithm in the modulation-style recognition of radar signalsdemonstrates that the network has a fast convergence speed with good performances.
