Multi-source network coding allows intermediate nodes to linearly combine packets from multiple sources, but it is vulnerable to pollution attacks which can cause multiple down- stream data to be polluted. To solve th...Multi-source network coding allows intermediate nodes to linearly combine packets from multiple sources, but it is vulnerable to pollution attacks which can cause multiple down- stream data to be polluted. To solve this problem, we take advan- tage of lattice signature and homomorphic property to build a se- cure multi-source network coding scheme. By means of the lattice basis delegation algorithms, our scheme can generate a public lattice for all source nodes and the homomorphic signatures can be calculated on this lattice. Consequently, the multi-source signature problem can be transformed into single-source signature problem only if all source nodes are considered as a whole. Scheme analy- sis shows the correctness and homomorphic property of the pro- posed scheme.展开更多
Recently,coded caching has been treated as a promising technique to alleviate the traffic burden in wireless networks.To support high efficient coded caching multicast transmissions,the time-varying heterogeneous chan...Recently,coded caching has been treated as a promising technique to alleviate the traffic burden in wireless networks.To support high efficient coded caching multicast transmissions,the time-varying heterogeneous channel conditions need to be considered.In this paper,a practical and novel multi-source spinal coding(MSSC)scheme is developed for coded caching multicast transmissions under heterogeneous channel conditions.By exploring joint design of network coding and spinal coding(SC),MSSC can achieve unequal link rates in multicast transmissions for different users.Moreover,by leveraging the rateless feature of SC in our design,MSSC can well adapt the link rates of all users in multicast transmissions without any feedback of time-varying channel conditions.A maximum likelihood(ML)based decoding process for MSSC is also developed,which can achieve a linear complexity with respect to the user number in the multicast transmission.Simulation results validate the effectiveness of the MSSC scheme.Compared to the existing scheme,the sum rate of MSSC in multicast transmissions is improved by about 20%.When applying MSSC in coded caching systems,the total transmission time can be reduced by up to 48% for time-varying channels.展开更多
Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.P...Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.展开更多
Quantum error correction is a technique that enhances a system’s ability to combat noise by encoding logical information into additional quantum bits,which plays a key role in building practical quantum computers.The...Quantum error correction is a technique that enhances a system’s ability to combat noise by encoding logical information into additional quantum bits,which plays a key role in building practical quantum computers.The XZZX surface code,with only one stabilizer generator on each face,demonstrates significant application potential under biased noise.However,the existing minimum weight perfect matching(MWPM)algorithm has high computational complexity and lacks flexibility in large-scale systems.Therefore,this paper proposes a decoding method that combines graph neural networks(GNN)with multi-classifiers,the syndrome is transformed into an undirected graph,and the features are aggregated by convolutional layers,providing a more efficient and accurate decoding strategy.In the experiments,we evaluated the performance of the XZZX code under different biased noise conditions(bias=1,20,200)and different code distances(d=3,5,7,9,11).The experimental results show that under low bias noise(bias=1),the GNN decoder achieves a threshold of 0.18386,an improvement of approximately 19.12%compared to the MWPM decoder.Under high bias noise(bias=200),the GNN decoder reaches a threshold of 0.40542,improving by approximately 20.76%,overcoming the limitations of the conventional decoder.They demonstrate that the GNN decoding method exhibits superior performance and has broad application potential in the error correction of XZZX code.展开更多
MORPAS is a special GIS (geographic information system) software system, based on the MAPGIS platform whose aim is to prospect and evaluate mineral resources quantificationally by synthesizing geological, geophysical,...MORPAS is a special GIS (geographic information system) software system, based on the MAPGIS platform whose aim is to prospect and evaluate mineral resources quantificationally by synthesizing geological, geophysical, geochemical and remote sensing data. It overlays geological database management, geological background and geological abnormality analysis, image processing of remote sensing and comprehensive abnormality analysis, etc.. It puts forward an integrative solution for the application of GIS in basic-level units and the construction of information engineering in the geological field. As the popularization of computer networks and the request of data sharing, it is necessary to extend its functions in data management so that all its data files can be accessed in the network server. This paper utilizes some MAPGIS functions for the second development and ADO (access data object) technique to access multi-source geological data in SQL Server databases. Then remote visiting and congruous management will be realized in the MORPAS system.展开更多
With the rapid development of the mobile Internet,users generate massive data in different forms in social network every day,and different characteristics of users are reflected by these social media data.How to integ...With the rapid development of the mobile Internet,users generate massive data in different forms in social network every day,and different characteristics of users are reflected by these social media data.How to integrate multiple heterogeneous information and establish user profiles from multiple perspectives plays an important role in providing personalized services,marketing,and recommendation systems.In this paper,we propose Multi-source&Multi-task Learning for User Profiles in Social Network which integrates multiple social data sources and contains a multi-task learning framework to simultaneously predict various attributes of a user.Firstly,we design their own feature extraction models for multiple heterogeneous data sources.Secondly,we design a shared layer to fuse multiple heterogeneous data sources as general shared representation for multi-task learning.Thirdly,we design each task’s own unique presentation layer for discriminant output of specific-task.Finally,we design a weighted loss function to improve the learning efficiency and prediction accuracy of each task.Our experimental results on more than 5000 Sina Weibo users demonstrate that our approach outperforms state-of-the-art baselines for inferring gender,age and region of social media users.展开更多
Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantita...Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.展开更多
Network Coding (NC) is a recent technique which is used to improve the transmission data rate and the power efficiency. These goals are obtained by combining data together before transmitting them, resulting to less t...Network Coding (NC) is a recent technique which is used to improve the transmission data rate and the power efficiency. These goals are obtained by combining data together before transmitting them, resulting to less transmitted data that carry the same amount of information. NC research work over the physical layer and the upper layers are popular and needed to be more investigated. In this paper, we propose a practical system of large-number of connected multi-source network coding (LMSNC), at the physical layer that exploits the broadcast nature of the wireless channel, using the practical and bandwidth-efficient schemes decode-and-forward (DF) and then compare it with Amplify and Forward (AF). The theoretical analysis and the simulation results show the effect of the noise when it cumulates in AF system and how DF is solving this severe default. Moreover, we consider the MSNC for Small-number of connected sources (SMSNC) and the two-way communication setup where two users exchange their information over an intermediate network node (ideally called Base Station), as two reference cases to compare with. With SMSNC, the number of necessary downlink transmissions from the intermediate node to the users is reduced, and thus the throughput is increased. Simulation results obtained using high-performance non-binary turbo codes, based on Partial Unit Memory (PUM) codes (4, 2, 1, 4) and (8, 4, 3, 8);confirm that combining PUM Turbo Code (PUMTC) and NC in the proposed MSNC setup gives almost the same BER performance as that for SMSNC at the small number of processing steps mainly when PUMTC (8, 4, 3, 8) is performed, which is required to retrieve the received coded messages. In the scenario of AF, combining packets results to cumulate the noise, which justifies the reason we decided to increase the number of transmitted coded messages in the network, i.e., the BER performance improves when sending extra coded messages. Finally, the possibility for a trade-off among BER, data rate and the number of transmitted coded messages is shown for LMSNC through graphics and simulation results.展开更多
In this work, the homomorphism of the classic linear block code in linear network coding for the case of binary field and its extensions is studied. It is proved that the classic linear error-control block code is hom...In this work, the homomorphism of the classic linear block code in linear network coding for the case of binary field and its extensions is studied. It is proved that the classic linear error-control block code is homomorphic network error-control code in network coding. That is, if the source packets at the source node for a linear network coding are precoded using a linear block code, then every packet flowing in the network regarding to the source satisfies the same constraints as the source. As a consequence, error detection and correction can be performed at every intermediate nodes of multicast flow, rather than only at the destination node in the conventional way, which can help to identify and correct errors timely at the error-corrupted link and save the cost of forwarding error-corrupted data to the destination node when the intermediate nodes are ignorant of the errors. In addition, three examples are demonstrated which show that homomorphic linear code can be combined with homomorphic signature, McEliece public-key cryptosystem and unequal error protection respectively and thus have a great potential of practical utility.展开更多
In this paper,a sparse graph neural network-aided(SGNN-aided)decoder is proposed for improving the decoding performance of polar codes under bursty interference.Firstly,a sparse factor graph is constructed using the e...In this paper,a sparse graph neural network-aided(SGNN-aided)decoder is proposed for improving the decoding performance of polar codes under bursty interference.Firstly,a sparse factor graph is constructed using the encoding characteristic to achieve high-throughput polar decoding.To further improve the decoding performance,a residual gated bipartite graph neural network is designed for updating embedding vectors of heterogeneous nodes based on a bidirectional message passing neural network.This framework exploits gated recurrent units and residual blocks to address the gradient disappearance in deep graph recurrent neural networks.Finally,predictions are generated by feeding the embedding vectors into a readout module.Simulation results show that the proposed decoder is more robust than the existing ones in the presence of bursty interference and exhibits high universality.展开更多
In mobile computing environments, most IoT devices connected to networks experience variable error rates and possess limited bandwidth. The conventional method of retransmitting lost information during transmission, c...In mobile computing environments, most IoT devices connected to networks experience variable error rates and possess limited bandwidth. The conventional method of retransmitting lost information during transmission, commonly used in data transmission protocols, increases transmission delay and consumes excessive bandwidth. To overcome this issue, forward error correction techniques, e.g., Random Linear Network Coding(RLNC) can be used in data transmission. The primary challenge in RLNC-based methodologies is sustaining a consistent coding ratio during data transmission, leading to notable bandwidth usage and transmission delay in dynamic network conditions. Therefore, this study proposes a new block-based RLNC strategy known as Adjustable RLNC(ARLNC), which dynamically adjusts the coding ratio and transmission window during runtime based on the estimated network error rate calculated via receiver feedback. The calculations in this approach are performed using a Galois field with the order of 256. Furthermore, we assessed ARLNC's performance by subjecting it to various error models such as Gilbert Elliott, exponential, and constant rates and compared it with the standard RLNC. The results show that dynamically adjusting the coding ratio and transmission window size based on network conditions significantly enhances network throughput and reduces total transmission delay in most scenarios. In contrast to the conventional RLNC method employing a fixed coding ratio, the presented approach has demonstrated significant enhancements, resulting in a 73% decrease in transmission delay and a 4 times augmentation in throughput. However, in dynamic computational environments, ARLNC generally incurs higher computational costs than the standard RLNC but excels in high-performance networks.展开更多
A network-coding-based multisource LDPC-coded cooperative MIMO scheme is proposed,where multiple sources transmit their messages to the destination with the assistance from a single relay.The relay cooperates with mul...A network-coding-based multisource LDPC-coded cooperative MIMO scheme is proposed,where multiple sources transmit their messages to the destination with the assistance from a single relay.The relay cooperates with multiple sources simultaneously via network-coding.It avoids the issues of imperfect frequency/timing synchronization and large transmission delay which may be introduced by frequency-division multiple access(FDMA)/code-division multiple access(CDMA)and time-division multiple access(TDMA)manners.The proposed joint″Min-Sum″iterative decoding is effectively carried out in the destination.Such a decoding algorithm agrees with the introduced equivalent joint Tanner graph which can be used to fully characterize LDPC codes employed by the sources and relay.Theoretical analysis and numerical simulation show that the proposed scheme with joint iterative decoding can achieve significant cooperation diversity gain.Furthermore,for the relay,compared with the cascade scheme,the proposed scheme has much lower complexity of LDPC-encoding and is easier to be implemented in the hardware with similar bit error rate(BER)performance.展开更多
Digital content such as games,extended reality(XR),and movies has been widely and easily distributed over wireless networks.As a result,unauthorized access,copyright infringement by third parties or eavesdroppers,and ...Digital content such as games,extended reality(XR),and movies has been widely and easily distributed over wireless networks.As a result,unauthorized access,copyright infringement by third parties or eavesdroppers,and cyberattacks over these networks have become pressing concerns.Therefore,protecting copyrighted content and preventing illegal distribution in wireless communications has garnered significant attention.The Intelligent Reflecting Surface(IRS)is regarded as a promising technology for future wireless and mobile networks due to its ability to reconfigure the radio propagation environment.This study investigates the security performance of an uplink Non-Orthogonal Multiple Access(NOMA)system integrated with an IRS and employing Fountain Codes(FCs).Specifically,two users send signals to the base station at separate distances.A relay receives the signal from the nearby user first and then relays it to the base station.The IRS receives the signal from the distant user and reflects it to the relay,which then sends the reflected signal to the base station.Furthermore,a malevolent eavesdropper intercepts both user and relay communications.We construct mathematical equations for Outage Probability(OP),throughput,diversity evaluation,and Interception Probability(IP),offering quantitative insights to assess system security and performance.Additionally,OP and IP are analyzed using a Deep Neural Network(DNN)model.A deeper comprehension of the security performance of the IRS-assisted NOMA systemin signal transmission is provided by Monte Carlo simulations,which are also carried out to confirm the theoretical conclusions.展开更多
The Base Station (BS) or access point is the building block of wireless networks, so, we propose exploiting it together with the Network Coding (NC) principle. NC suffers from the complexity of the decoding processes,...The Base Station (BS) or access point is the building block of wireless networks, so, we propose exploiting it together with the Network Coding (NC) principle. NC suffers from the complexity of the decoding processes, i.e., complicated Jordan Gaussian Elimination (JGE) processes. So, this paper proposes a deterministic NC algorithm to reduce the number of sequential network decoding steps, and hence minimizing the complexity of JGE process resulting to better time delay and processing time. We propose an algorithm that combines higher number of the transmitted packets resulting to better data-rate but worse Bet Error Rate (BER). However, using such strong Forward error correction channel code, which is Partial Unit Memory Turbo Code (PUMTC) results to minimize the losses in the BER to a very acceptable lever, in fact, in Decode-and-Forward (DF) BS, the BER can be regarded as minimum. Simulation results, for both Amplify-and-Forward (AF) and DF BS schemes using PUMTC based on (8, 4, 3, 8) component codes, confirm that using PUMTC mitigates the problem of noise aggregation resulting from applying NC in the proposed schemes.展开更多
Quantum error correction is a crucial technology for realizing quantum computers.These computers achieve faulttolerant quantum computing by detecting and correcting errors using decoding algorithms.Quantum error corre...Quantum error correction is a crucial technology for realizing quantum computers.These computers achieve faulttolerant quantum computing by detecting and correcting errors using decoding algorithms.Quantum error correction using neural network-based machine learning methods is a promising approach that is adapted to physical systems without the need to build noise models.In this paper,we use a distributed decoding strategy,which effectively alleviates the problem of exponential growth of the training set required for neural networks as the code distance of quantum error-correcting codes increases.Our decoding algorithm is based on renormalization group decoding and recurrent neural network decoder.The recurrent neural network is trained through the ResNet architecture to improve its decoding accuracy.Then we test the decoding performance of our distributed strategy decoder,recurrent neural network decoder,and the classic minimum weight perfect matching(MWPM)decoder for rotated surface codes with different code distances under the circuit noise model,the thresholds of these three decoders are about 0.0052,0.0051,and 0.0049,respectively.Our results demonstrate that the distributed strategy decoder outperforms the other two decoders,achieving approximately a 5%improvement in decoding efficiency compared to the MWPM decoder and approximately a 2%improvement compared to the recurrent neural network decoder.展开更多
Distribution networks denote important public infrastructure necessary for people’s livelihoods.However,extreme natural disasters,such as earthquakes,typhoons,and mudslides,severely threaten the safe and stable opera...Distribution networks denote important public infrastructure necessary for people’s livelihoods.However,extreme natural disasters,such as earthquakes,typhoons,and mudslides,severely threaten the safe and stable operation of distribution networks and power supplies needed for daily life.Therefore,considering the requirements for distribution network disaster prevention and mitigation,there is an urgent need for in-depth research on risk assessment methods of distribution networks under extreme natural disaster conditions.This paper accessesmultisource data,presents the data quality improvement methods of distribution networks,and conducts data-driven active fault diagnosis and disaster damage analysis and evaluation using data-driven theory.Furthermore,the paper realizes real-time,accurate access to distribution network disaster information.The proposed approach performs an accurate and rapid assessment of cross-sectional risk through case study.The minimal average annual outage time can be reduced to 3 h/a in the ring network through case study.The approach proposed in this paper can provide technical support to the further improvement of the ability of distribution networks to cope with extreme natural disasters.展开更多
<div style="text-align:justify;"> Polar codes using successive-cancellation decoding always suffer from high latency for its serial nature. Fast simplified successive-cancellation decoding algorithm im...<div style="text-align:justify;"> Polar codes using successive-cancellation decoding always suffer from high latency for its serial nature. Fast simplified successive-cancellation decoding algorithm improves the situation in theoretically but not performs well as expected in practical for the workload of nodes identification and the existence of many short blocks. Meanwhile, Neural network (NN) based decoders have appeared as potential candidates to replace conventional decoders for polar codes. But the exponentially increasing training complexity with information bits is unacceptable which means it is only suitable for short codes. In this paper, we present an improvement that increases decoding efficiency without degrading the error-correction performance. The long polar codes are divided into several sub-blocks, some of which can be decoded adopting fast maximum likelihood decoding method and the remained parts are replaced by several short codes NN decoders. The result shows that time steps the proposed algorithm need only equal to 79.8% of fast simplified successive-cancellation decoders require. Moreover, it has up to 21.2 times faster than successive-cancellation decoding algorithm. More importantly, the proposed algorithm decreases the hardness when applying in some degree. </div>展开更多
The multi-source and single-sink(MSSS) topology in wireless sensor networks(WSNs) is defined as a network topology,where all of nodes can gather,receive and transmit data to the sink.In energy-constrained WSNs with su...The multi-source and single-sink(MSSS) topology in wireless sensor networks(WSNs) is defined as a network topology,where all of nodes can gather,receive and transmit data to the sink.In energy-constrained WSNs with such a topology,the joint optimal design in the physical,medium access control(MAC) and network layers is considered for network lifetime maximization(NLM).The problem of integrating multi-layer information to compute NLM,which involves routing flow,link schedule and transmission power,is formulated as a nonlinear optimization problem.Specially under time division multiple access(TDMA) scheme,this problem can be transformed into a convex optimization problem.To solve it analytically we make use of the property that local optimization is global optimization in convex problem.This allows us to exploit the Karush-Kuhn-Tucker (KKT) optimality conditions to solve it and obtain analytical solution expression,i.e.,the globally optimal network lifetime(NL).NL is derived as a function of number of nodes,their initial energy and data rate arrived at them. Based on the analysis of analytical approach,it takes the influence of data rates,link access and routing method over NLM into account.Moreover,the globally optimal transmission schemes are achieved by solution set during analytical approach and applied to algorithms in TDMA-based WSNs aiming at NLM on OMNeT++ to compare with other suboptimal schemes.展开更多
In order to realize broadband and high-speed transmission in the last mile access network,the Coded Wavelength-Division Multiplexing(Coded-WDM)technique on PON(so-called CDM-PON)is presented on fiber-to-the-home acces...In order to realize broadband and high-speed transmission in the last mile access network,the Coded Wavelength-Division Multiplexing(Coded-WDM)technique on PON(so-called CDM-PON)is presented on fiber-to-the-home access network.In this paper,the codcd-WDM coder/decoder(codec}is configured on Optical Network Unit(ONU)and Optical Line Terminal(OLT)in Ethemet Passive Optical Network(E-PON).Here,network codecs are constructed with Arrayed Waveguide Grating(AWG)devices and the signature address code is employed as complementary Walsh code whose original code is for data bit'1'and complementary code is for data bit'0'respectively.It is shown that the simultaneous active user of proposed scheme using CWH code is improved 100%than using M-Sequence and conventional Walsh code for given bit error rate of 10^-9.In addition,the signal to noise ratio(SNR)performance is improved by 6dB compared with conventional scheme employed M-sequence and Walsh code.展开更多
<div style="text-align:justify;"> <p style="text-align:justify;background:white;"> <span style="font-size:10.0pt;font-family:" color:black;"="">This artic...<div style="text-align:justify;"> <p style="text-align:justify;background:white;"> <span style="font-size:10.0pt;font-family:" color:black;"="">This article has been retracted to straighten the academic record. In making this decision the Editorial Board follows COPE's </span><span><a href="http://publicationethics.org/files/retraction%20guidelines.pdf"><span style="font-size:10.0pt;font-family:;" "="">Retraction Guidelines</span></a></span><span style="font-size:10.0pt;font-family:" color:black;"="">. The aim is to promote the circulation of scientific research by offering an ideal research publication platform with due consideration of internationally accepted standards on publication ethics. The Editorial Board would like to extend its sincere apologies for any inconvenience this retraction may have caused.</span><span style="font-size:10.0pt;font-family:" color:black;"=""></span> </p> <p style="text-align:justify;background:white;"> <span style="font-size:10.0pt;font-family:" color:black;"="">Please see the </span><span><a href="https://www.scirp.org/journal/paperinformation.aspx?paperid=101825"><span style="font-size:10.0pt;font-family:;" "="">article page</span></a></span><span style="font-size:10.0pt;font-family:" color:black;"=""> for more details. </span><span><a href="https://www.scirp.org/pdf/opj_2020072814494052.pdf"><span style="font-size:10.0pt;font-family:;" "="">The full retraction notice</span></a></span><span style="font-size:10.0pt;font-family:" color:black;"=""> in PDF is preceding the original paper which is marked "RETRACTED". </span> </p> <br /> </div>展开更多
基金Supported by the National Natural Science Foundation of China(61571024,61272501)the National Basic Research Program of China(2012CB315905)the Research Promotion Grants-in-Aid for KUT Graduates of Special Scholarship Program and the Fundamental Research Funds for Central Universities(YWF15GJSYS059)
文摘Multi-source network coding allows intermediate nodes to linearly combine packets from multiple sources, but it is vulnerable to pollution attacks which can cause multiple down- stream data to be polluted. To solve this problem, we take advan- tage of lattice signature and homomorphic property to build a se- cure multi-source network coding scheme. By means of the lattice basis delegation algorithms, our scheme can generate a public lattice for all source nodes and the homomorphic signatures can be calculated on this lattice. Consequently, the multi-source signature problem can be transformed into single-source signature problem only if all source nodes are considered as a whole. Scheme analy- sis shows the correctness and homomorphic property of the pro- posed scheme.
基金supported by National Natural Science Foundation of China(No.61801290 and 61771312).
文摘Recently,coded caching has been treated as a promising technique to alleviate the traffic burden in wireless networks.To support high efficient coded caching multicast transmissions,the time-varying heterogeneous channel conditions need to be considered.In this paper,a practical and novel multi-source spinal coding(MSSC)scheme is developed for coded caching multicast transmissions under heterogeneous channel conditions.By exploring joint design of network coding and spinal coding(SC),MSSC can achieve unequal link rates in multicast transmissions for different users.Moreover,by leveraging the rateless feature of SC in our design,MSSC can well adapt the link rates of all users in multicast transmissions without any feedback of time-varying channel conditions.A maximum likelihood(ML)based decoding process for MSSC is also developed,which can achieve a linear complexity with respect to the user number in the multicast transmission.Simulation results validate the effectiveness of the MSSC scheme.Compared to the existing scheme,the sum rate of MSSC in multicast transmissions is improved by about 20%.When applying MSSC in coded caching systems,the total transmission time can be reduced by up to 48% for time-varying channels.
基金supported by Natural Science Foundation of China(Nos.62303126,62362008,author Z.Z,https://www.nsfc.gov.cn/,accessed on 20 December 2024)Major Scientific and Technological Special Project of Guizhou Province([2024]014)+2 种基金Guizhou Provincial Science and Technology Projects(No.ZK[2022]General149) ,author Z.Z,https://kjt.guizhou.gov.cn/,accessed on 20 December 2024)The Open Project of the Key Laboratory of Computing Power Network and Information Security,Ministry of Education under Grant 2023ZD037,author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024)Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2024B25),author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024).
文摘Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.
基金supported by the Natural Science Foundation of Shandong Province,China(Grant No.ZR2021MF049)the Joint Fund of Natural Science Foundation of Shandong Province,China(Grant Nos.ZR2022LL.Z012 and ZR2021LLZ001)the Key Research and Development Program of Shandong Province,China(Grant No.2023CXGC010901).
文摘Quantum error correction is a technique that enhances a system’s ability to combat noise by encoding logical information into additional quantum bits,which plays a key role in building practical quantum computers.The XZZX surface code,with only one stabilizer generator on each face,demonstrates significant application potential under biased noise.However,the existing minimum weight perfect matching(MWPM)algorithm has high computational complexity and lacks flexibility in large-scale systems.Therefore,this paper proposes a decoding method that combines graph neural networks(GNN)with multi-classifiers,the syndrome is transformed into an undirected graph,and the features are aggregated by convolutional layers,providing a more efficient and accurate decoding strategy.In the experiments,we evaluated the performance of the XZZX code under different biased noise conditions(bias=1,20,200)and different code distances(d=3,5,7,9,11).The experimental results show that under low bias noise(bias=1),the GNN decoder achieves a threshold of 0.18386,an improvement of approximately 19.12%compared to the MWPM decoder.Under high bias noise(bias=200),the GNN decoder reaches a threshold of 0.40542,improving by approximately 20.76%,overcoming the limitations of the conventional decoder.They demonstrate that the GNN decoding method exhibits superior performance and has broad application potential in the error correction of XZZX code.
文摘MORPAS is a special GIS (geographic information system) software system, based on the MAPGIS platform whose aim is to prospect and evaluate mineral resources quantificationally by synthesizing geological, geophysical, geochemical and remote sensing data. It overlays geological database management, geological background and geological abnormality analysis, image processing of remote sensing and comprehensive abnormality analysis, etc.. It puts forward an integrative solution for the application of GIS in basic-level units and the construction of information engineering in the geological field. As the popularization of computer networks and the request of data sharing, it is necessary to extend its functions in data management so that all its data files can be accessed in the network server. This paper utilizes some MAPGIS functions for the second development and ADO (access data object) technique to access multi-source geological data in SQL Server databases. Then remote visiting and congruous management will be realized in the MORPAS system.
基金This work is supported by State Grid Science and Technology Project under Grant No.520613180002,62061318C002the Fundamental Research Funds for the Central Universities(Grant No.HIT.NSRIF.201714)+4 种基金Weihai Science and Technology Development Program(2016DXGJMS15)Key Research and Development Program in Shandong Provincial(2017GGX90103)Sanming Science and Technology Project,Grant No.2015-G-6,Shandong province vocational education educational reform research project.Grant No.2017209Study and Development of Smart Agriculture Control System Based on Spark Big Data Decision(2017N0029)Jiangsu Province industrial Communication Technology Application Technology Innovation Team Project.
文摘With the rapid development of the mobile Internet,users generate massive data in different forms in social network every day,and different characteristics of users are reflected by these social media data.How to integrate multiple heterogeneous information and establish user profiles from multiple perspectives plays an important role in providing personalized services,marketing,and recommendation systems.In this paper,we propose Multi-source&Multi-task Learning for User Profiles in Social Network which integrates multiple social data sources and contains a multi-task learning framework to simultaneously predict various attributes of a user.Firstly,we design their own feature extraction models for multiple heterogeneous data sources.Secondly,we design a shared layer to fuse multiple heterogeneous data sources as general shared representation for multi-task learning.Thirdly,we design each task’s own unique presentation layer for discriminant output of specific-task.Finally,we design a weighted loss function to improve the learning efficiency and prediction accuracy of each task.Our experimental results on more than 5000 Sina Weibo users demonstrate that our approach outperforms state-of-the-art baselines for inferring gender,age and region of social media users.
基金supported by the National Natural Science Foundation of China(Nos.52279107 and 52379106)the Qingdao Guoxin Jiaozhou Bay Second Submarine Tunnel Co.,Ltd.,the Academician and Expert Workstation of Yunnan Province(No.202205AF150015)the Science and Technology Innovation Project of YCIC Group Co.,Ltd.(No.YCIC-YF-2022-15)。
文摘Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.
文摘Network Coding (NC) is a recent technique which is used to improve the transmission data rate and the power efficiency. These goals are obtained by combining data together before transmitting them, resulting to less transmitted data that carry the same amount of information. NC research work over the physical layer and the upper layers are popular and needed to be more investigated. In this paper, we propose a practical system of large-number of connected multi-source network coding (LMSNC), at the physical layer that exploits the broadcast nature of the wireless channel, using the practical and bandwidth-efficient schemes decode-and-forward (DF) and then compare it with Amplify and Forward (AF). The theoretical analysis and the simulation results show the effect of the noise when it cumulates in AF system and how DF is solving this severe default. Moreover, we consider the MSNC for Small-number of connected sources (SMSNC) and the two-way communication setup where two users exchange their information over an intermediate network node (ideally called Base Station), as two reference cases to compare with. With SMSNC, the number of necessary downlink transmissions from the intermediate node to the users is reduced, and thus the throughput is increased. Simulation results obtained using high-performance non-binary turbo codes, based on Partial Unit Memory (PUM) codes (4, 2, 1, 4) and (8, 4, 3, 8);confirm that combining PUM Turbo Code (PUMTC) and NC in the proposed MSNC setup gives almost the same BER performance as that for SMSNC at the small number of processing steps mainly when PUMTC (8, 4, 3, 8) is performed, which is required to retrieve the received coded messages. In the scenario of AF, combining packets results to cumulate the noise, which justifies the reason we decided to increase the number of transmitted coded messages in the network, i.e., the BER performance improves when sending extra coded messages. Finally, the possibility for a trade-off among BER, data rate and the number of transmitted coded messages is shown for LMSNC through graphics and simulation results.
基金supported by Natural Science Foundation of China (No.61271258)
文摘In this work, the homomorphism of the classic linear block code in linear network coding for the case of binary field and its extensions is studied. It is proved that the classic linear error-control block code is homomorphic network error-control code in network coding. That is, if the source packets at the source node for a linear network coding are precoded using a linear block code, then every packet flowing in the network regarding to the source satisfies the same constraints as the source. As a consequence, error detection and correction can be performed at every intermediate nodes of multicast flow, rather than only at the destination node in the conventional way, which can help to identify and correct errors timely at the error-corrupted link and save the cost of forwarding error-corrupted data to the destination node when the intermediate nodes are ignorant of the errors. In addition, three examples are demonstrated which show that homomorphic linear code can be combined with homomorphic signature, McEliece public-key cryptosystem and unequal error protection respectively and thus have a great potential of practical utility.
文摘In this paper,a sparse graph neural network-aided(SGNN-aided)decoder is proposed for improving the decoding performance of polar codes under bursty interference.Firstly,a sparse factor graph is constructed using the encoding characteristic to achieve high-throughput polar decoding.To further improve the decoding performance,a residual gated bipartite graph neural network is designed for updating embedding vectors of heterogeneous nodes based on a bidirectional message passing neural network.This framework exploits gated recurrent units and residual blocks to address the gradient disappearance in deep graph recurrent neural networks.Finally,predictions are generated by feeding the embedding vectors into a readout module.Simulation results show that the proposed decoder is more robust than the existing ones in the presence of bursty interference and exhibits high universality.
文摘In mobile computing environments, most IoT devices connected to networks experience variable error rates and possess limited bandwidth. The conventional method of retransmitting lost information during transmission, commonly used in data transmission protocols, increases transmission delay and consumes excessive bandwidth. To overcome this issue, forward error correction techniques, e.g., Random Linear Network Coding(RLNC) can be used in data transmission. The primary challenge in RLNC-based methodologies is sustaining a consistent coding ratio during data transmission, leading to notable bandwidth usage and transmission delay in dynamic network conditions. Therefore, this study proposes a new block-based RLNC strategy known as Adjustable RLNC(ARLNC), which dynamically adjusts the coding ratio and transmission window during runtime based on the estimated network error rate calculated via receiver feedback. The calculations in this approach are performed using a Galois field with the order of 256. Furthermore, we assessed ARLNC's performance by subjecting it to various error models such as Gilbert Elliott, exponential, and constant rates and compared it with the standard RLNC. The results show that dynamically adjusting the coding ratio and transmission window size based on network conditions significantly enhances network throughput and reduces total transmission delay in most scenarios. In contrast to the conventional RLNC method employing a fixed coding ratio, the presented approach has demonstrated significant enhancements, resulting in a 73% decrease in transmission delay and a 4 times augmentation in throughput. However, in dynamic computational environments, ARLNC generally incurs higher computational costs than the standard RLNC but excels in high-performance networks.
基金Supported by the Postdoctoral Science Foundation of China(2014M561694)the Science and Technology on Avionics Integration Laboratory and National Aeronautical Science Foundation of China(20105552)
文摘A network-coding-based multisource LDPC-coded cooperative MIMO scheme is proposed,where multiple sources transmit their messages to the destination with the assistance from a single relay.The relay cooperates with multiple sources simultaneously via network-coding.It avoids the issues of imperfect frequency/timing synchronization and large transmission delay which may be introduced by frequency-division multiple access(FDMA)/code-division multiple access(CDMA)and time-division multiple access(TDMA)manners.The proposed joint″Min-Sum″iterative decoding is effectively carried out in the destination.Such a decoding algorithm agrees with the introduced equivalent joint Tanner graph which can be used to fully characterize LDPC codes employed by the sources and relay.Theoretical analysis and numerical simulation show that the proposed scheme with joint iterative decoding can achieve significant cooperation diversity gain.Furthermore,for the relay,compared with the cascade scheme,the proposed scheme has much lower complexity of LDPC-encoding and is easier to be implemented in the hardware with similar bit error rate(BER)performance.
基金supported in part by Vietnam National Foundation for Science and Technology Development(NAFOSTED)under Grant 102.04-2021.57in part by Culture,Sports and Tourism R&D Program through the Korea Creative Content Agency grant funded by the Ministry of Culture,Sports and Tourism in 2024(Project Name:Global Talent Training Program for Copyright Management Technology in Game Contents,Project Number:RS-2024-00396709,Contribution Rate:100%).
文摘Digital content such as games,extended reality(XR),and movies has been widely and easily distributed over wireless networks.As a result,unauthorized access,copyright infringement by third parties or eavesdroppers,and cyberattacks over these networks have become pressing concerns.Therefore,protecting copyrighted content and preventing illegal distribution in wireless communications has garnered significant attention.The Intelligent Reflecting Surface(IRS)is regarded as a promising technology for future wireless and mobile networks due to its ability to reconfigure the radio propagation environment.This study investigates the security performance of an uplink Non-Orthogonal Multiple Access(NOMA)system integrated with an IRS and employing Fountain Codes(FCs).Specifically,two users send signals to the base station at separate distances.A relay receives the signal from the nearby user first and then relays it to the base station.The IRS receives the signal from the distant user and reflects it to the relay,which then sends the reflected signal to the base station.Furthermore,a malevolent eavesdropper intercepts both user and relay communications.We construct mathematical equations for Outage Probability(OP),throughput,diversity evaluation,and Interception Probability(IP),offering quantitative insights to assess system security and performance.Additionally,OP and IP are analyzed using a Deep Neural Network(DNN)model.A deeper comprehension of the security performance of the IRS-assisted NOMA systemin signal transmission is provided by Monte Carlo simulations,which are also carried out to confirm the theoretical conclusions.
文摘The Base Station (BS) or access point is the building block of wireless networks, so, we propose exploiting it together with the Network Coding (NC) principle. NC suffers from the complexity of the decoding processes, i.e., complicated Jordan Gaussian Elimination (JGE) processes. So, this paper proposes a deterministic NC algorithm to reduce the number of sequential network decoding steps, and hence minimizing the complexity of JGE process resulting to better time delay and processing time. We propose an algorithm that combines higher number of the transmitted packets resulting to better data-rate but worse Bet Error Rate (BER). However, using such strong Forward error correction channel code, which is Partial Unit Memory Turbo Code (PUMTC) results to minimize the losses in the BER to a very acceptable lever, in fact, in Decode-and-Forward (DF) BS, the BER can be regarded as minimum. Simulation results, for both Amplify-and-Forward (AF) and DF BS schemes using PUMTC based on (8, 4, 3, 8) component codes, confirm that using PUMTC mitigates the problem of noise aggregation resulting from applying NC in the proposed schemes.
基金Project supported by Natural Science Foundation of Shandong Province,China (Grant Nos.ZR2021MF049,ZR2022LLZ012,and ZR2021LLZ001)。
文摘Quantum error correction is a crucial technology for realizing quantum computers.These computers achieve faulttolerant quantum computing by detecting and correcting errors using decoding algorithms.Quantum error correction using neural network-based machine learning methods is a promising approach that is adapted to physical systems without the need to build noise models.In this paper,we use a distributed decoding strategy,which effectively alleviates the problem of exponential growth of the training set required for neural networks as the code distance of quantum error-correcting codes increases.Our decoding algorithm is based on renormalization group decoding and recurrent neural network decoder.The recurrent neural network is trained through the ResNet architecture to improve its decoding accuracy.Then we test the decoding performance of our distributed strategy decoder,recurrent neural network decoder,and the classic minimum weight perfect matching(MWPM)decoder for rotated surface codes with different code distances under the circuit noise model,the thresholds of these three decoders are about 0.0052,0.0051,and 0.0049,respectively.Our results demonstrate that the distributed strategy decoder outperforms the other two decoders,achieving approximately a 5%improvement in decoding efficiency compared to the MWPM decoder and approximately a 2%improvement compared to the recurrent neural network decoder.
文摘Distribution networks denote important public infrastructure necessary for people’s livelihoods.However,extreme natural disasters,such as earthquakes,typhoons,and mudslides,severely threaten the safe and stable operation of distribution networks and power supplies needed for daily life.Therefore,considering the requirements for distribution network disaster prevention and mitigation,there is an urgent need for in-depth research on risk assessment methods of distribution networks under extreme natural disaster conditions.This paper accessesmultisource data,presents the data quality improvement methods of distribution networks,and conducts data-driven active fault diagnosis and disaster damage analysis and evaluation using data-driven theory.Furthermore,the paper realizes real-time,accurate access to distribution network disaster information.The proposed approach performs an accurate and rapid assessment of cross-sectional risk through case study.The minimal average annual outage time can be reduced to 3 h/a in the ring network through case study.The approach proposed in this paper can provide technical support to the further improvement of the ability of distribution networks to cope with extreme natural disasters.
文摘<div style="text-align:justify;"> Polar codes using successive-cancellation decoding always suffer from high latency for its serial nature. Fast simplified successive-cancellation decoding algorithm improves the situation in theoretically but not performs well as expected in practical for the workload of nodes identification and the existence of many short blocks. Meanwhile, Neural network (NN) based decoders have appeared as potential candidates to replace conventional decoders for polar codes. But the exponentially increasing training complexity with information bits is unacceptable which means it is only suitable for short codes. In this paper, we present an improvement that increases decoding efficiency without degrading the error-correction performance. The long polar codes are divided into several sub-blocks, some of which can be decoded adopting fast maximum likelihood decoding method and the remained parts are replaced by several short codes NN decoders. The result shows that time steps the proposed algorithm need only equal to 79.8% of fast simplified successive-cancellation decoders require. Moreover, it has up to 21.2 times faster than successive-cancellation decoding algorithm. More importantly, the proposed algorithm decreases the hardness when applying in some degree. </div>
文摘The multi-source and single-sink(MSSS) topology in wireless sensor networks(WSNs) is defined as a network topology,where all of nodes can gather,receive and transmit data to the sink.In energy-constrained WSNs with such a topology,the joint optimal design in the physical,medium access control(MAC) and network layers is considered for network lifetime maximization(NLM).The problem of integrating multi-layer information to compute NLM,which involves routing flow,link schedule and transmission power,is formulated as a nonlinear optimization problem.Specially under time division multiple access(TDMA) scheme,this problem can be transformed into a convex optimization problem.To solve it analytically we make use of the property that local optimization is global optimization in convex problem.This allows us to exploit the Karush-Kuhn-Tucker (KKT) optimality conditions to solve it and obtain analytical solution expression,i.e.,the globally optimal network lifetime(NL).NL is derived as a function of number of nodes,their initial energy and data rate arrived at them. Based on the analysis of analytical approach,it takes the influence of data rates,link access and routing method over NLM into account.Moreover,the globally optimal transmission schemes are achieved by solution set during analytical approach and applied to algorithms in TDMA-based WSNs aiming at NLM on OMNeT++ to compare with other suboptimal schemes.
文摘In order to realize broadband and high-speed transmission in the last mile access network,the Coded Wavelength-Division Multiplexing(Coded-WDM)technique on PON(so-called CDM-PON)is presented on fiber-to-the-home access network.In this paper,the codcd-WDM coder/decoder(codec}is configured on Optical Network Unit(ONU)and Optical Line Terminal(OLT)in Ethemet Passive Optical Network(E-PON).Here,network codecs are constructed with Arrayed Waveguide Grating(AWG)devices and the signature address code is employed as complementary Walsh code whose original code is for data bit'1'and complementary code is for data bit'0'respectively.It is shown that the simultaneous active user of proposed scheme using CWH code is improved 100%than using M-Sequence and conventional Walsh code for given bit error rate of 10^-9.In addition,the signal to noise ratio(SNR)performance is improved by 6dB compared with conventional scheme employed M-sequence and Walsh code.
文摘<div style="text-align:justify;"> <p style="text-align:justify;background:white;"> <span style="font-size:10.0pt;font-family:" color:black;"="">This article has been retracted to straighten the academic record. In making this decision the Editorial Board follows COPE's </span><span><a href="http://publicationethics.org/files/retraction%20guidelines.pdf"><span style="font-size:10.0pt;font-family:;" "="">Retraction Guidelines</span></a></span><span style="font-size:10.0pt;font-family:" color:black;"="">. The aim is to promote the circulation of scientific research by offering an ideal research publication platform with due consideration of internationally accepted standards on publication ethics. The Editorial Board would like to extend its sincere apologies for any inconvenience this retraction may have caused.</span><span style="font-size:10.0pt;font-family:" color:black;"=""></span> </p> <p style="text-align:justify;background:white;"> <span style="font-size:10.0pt;font-family:" color:black;"="">Please see the </span><span><a href="https://www.scirp.org/journal/paperinformation.aspx?paperid=101825"><span style="font-size:10.0pt;font-family:;" "="">article page</span></a></span><span style="font-size:10.0pt;font-family:" color:black;"=""> for more details. </span><span><a href="https://www.scirp.org/pdf/opj_2020072814494052.pdf"><span style="font-size:10.0pt;font-family:;" "="">The full retraction notice</span></a></span><span style="font-size:10.0pt;font-family:" color:black;"=""> in PDF is preceding the original paper which is marked "RETRACTED". </span> </p> <br /> </div>
