Traditional Graphics Processing Unit(GPU) executing PGrid index K-Nearest Neighbor(KNN) query method has problems such as large query granularity,redundant calculation,and unstable performance.Therefore,facing spatial KNN relation queries,a KNN query strategy based on fine-grained partition search is proposed.Based on the Euclidean distance trigonometric inequality feature,the dynamic query range expansion based on Cell is constructed to realize the fine-grained partition and expansion of the query range with respect to each boundary distance of the Cell,and the optimum grid scales of object numbers for a given K value scale are theoretically analyzesd.Experimental results show that compared with the traditional KNN query method,the query strategy has obvious performance advantages under different K values and grid division scales.
For the detection of abnormal electricity behavior by users,power companies usually adopt manual inspection methods,however,this method requires a lot of manpower and material resources,and is influened by subjective factors.Therefore,an abnormal electricity behavior recognition algorithm based on ridge regression model is proposed.By collecting user electric data,the model is trained and the trained model is used for automatic detection of abnormal electricity behavior.In order to capture the sample information of unknown users’ electricity behavior categories,the graph regularization term is introduced on the basis of the ridge regression model.Taking into account the non-linear distribution characteristics of electricity data,the original data is mapped to high-dimensional Hilbert spaces through kernel functions,and a nonlinear ridge regression model based on graph regularity is gained.Experimental results show that compared with least squares,ridge regression,and graph regularization ridge regression models,this algorithm has higher recognition rate.
To solve the problems that some similar users will be missed and the basis for selecting nearest neighbors is single while user clustering,a recommendation algorithm based on clusterings of item rating and type rating is proposed.The user-item rating matrix and the user-item type rating matrix according to user’s rating records are firstly generated.The fuzzy C-means clustering is carried out by using the above two matrices and the improved the distance measurement method.Then,the nearest neighbor is selected according to the membership degree matrix generated by the clustering.Finally,the prediction rating is generated by the parameter weighting.Experimental results on MovieLens dataset show that the proposed algorithm can reflect the user’s rating accurately and improve the accuracy of the recommendation system effectively.
Aiming at the problem that the accuracy of the multi-dimensional continuous data is too low for traditional naive Bayesian classification algorithm,an improved classification algorithm based on attribute association is proposed.Directed against the multidimensional continuous data set with different attribute classes,it discretizes the data set by Gaussian segmentation,which is improved by using Laplace calibration,attribute association and weighted attribute.Experimental results show that,compared with improved algorithms by Laplace calibration or attribute weighting,the proposed algorithm can improve the accuracy of classification results,and its amplitude increase is increased with the increase of the number of attributes in a certain range,which is suitable for the classification of multidimensional continuous data.
In order to solve the problem of low connection efficiency after the Hash distribution table is converted into a random distribution table,a parallel connection operation algorithm for Hash tables in Massively Parallel Processor(MPP) database is presented.According to the data block distribution characteristics of the hash distribution table in the shared storage environment,combining with the scanning advantage of random reading,data multiple copies distributed storage is used to improve the local reading rate without losing the characteristics of data hash distribution.TPC-H standard test results show that compared with the traditional parallel connection algorithm,this algorithm can effectively improve the connection operation efficiency and reduce the response time of the connection query up to 30%.
Cooperative spectrum sensing algorithm requires a control channel when detecting the spectrum of primary user.However,the limitation of control channel bandwidth is the bottleneck of application of cooperative spectrum sensing when the number of cognitive users becomes very large.In order to solve this problem,this paper proposes a multi-threshold cooperative spectrum detection algorithm.Based on the establishment of a spectrum detection model,cognitive users are clustered with different application weight requirements to avoid congestion in the control channel.Results of theoretical analysis and simulation on the Matlab platform show that,the proposed algorithm can reduce the network delay and improve the detection probability of the system in Rayleigh channels.
It is difficult to maintain the connectivity when Primary User(PU) is active in Cognitive Radio Network(CRN).To solve the above problem,this paper presents an efficient scheme to build a bi-channel connected network with the minimum number of required channels combining power control and channel assignment.Firstly,basic topology is generated by using graph coloring to assign channels to every secondary user to achieve bi-channel connectivity.Secondly,considering local spanning tree is not connected after deleting nodes,it applies the improved MPH algorithm to give priority to link on these nodes which have the large path weights,which means they are passing by shortest paths.Then,aiming at the problem that the topology is divided into two parts after deleting the nodes,it adds node at the middle of the shortest link between all these edges,which can connect the two parts.Theoretical analysis and simulation results show that,the proposed scheme can maintain the connectivity of network,meanwhile reducing the mumber of required channels and network costs.
As a novel Non-Orthogonal Multiple Access(NOMA) technology,Sparse Code Multiple Access(SCMA) can meet the performance requirements of massive connections for 5G,but the Message Passing Algorithm(MPA) has the problems of slow convergence and high complexity.In this paper,a multi-user detection algorithm for SCMA system with low complexity is proposed to slove above problems.By narrowing down the search range of superposed codeword constellation points and introducing the weighting factor,it changes the initial probability of superpoed codeword constellation points in the search range and makes the decoding process faster and more accurate.Theoretical analysis and simulation results show that the proposed algorithm not only reduces the complexity,but also accelerates the convergence speed of the iterative process.
In cognitive radio network,spectrum sensing performance tends to be reflected by the system throughput.Based on the traditional perceptual frame structure,this paper redefines a new perceptual frame structure by introducing the cooperative spectrum prediction and spectrum segmentation.By combining the Hidden Markov Model(HMM) cooperative spectrum prediction algorithm based on DBSCAN,the accuracy of spectrum prediction is improved,the consumption of cooperative prediction bandwidth is reduced,and the effect of improving system throughput is achieved.Simulation results show that the system throughput under the improved frame structure is improved compared with that of the frame structure without the cooperation module and the frame structure with the cooperation module but without spectrum segmentation.
There are problems of waveform distortion,instability and even losing efficacy on condition that the beamforming algorithm of traditional two dimensional angular domain is applied to Three Dimensional Multiple Input Multiple Output(3D MIMO) scene.To solve the problem,on the basis of the traditional Two Dimensional Minimum Mean Square Error(2D MMSE) algorithm,an improved 3D MMSE angle domain beamforming algorithm is proposed,which applies the method of the eigen-decomposition to decompose the signal correlation matrix,removes the fluctuating factors in small eigenvalues associated with noise,and solves the issue of waveform distorting and losing efficacy.Simulation results show that the proposed algorithm can implement the beam forming in 3D angular domain,so that the MSE of array output is smaller and the Signal to Interference and Noise Ratio(SINR) of array output is higher than the 3D MVDR algorithm and the ZF algorithm.
Based on the inherent high sidelobe problem in Orthogonal Frequency-Devision Multiplexing (OFDM) system,a sidelobe suppression method for OFDM system is proposed based on subcarrier weighting method.The subcarrier weights are determined in such a way that the sidelobes of the transmission signal are minimized according to an optimization algorithm which allows several optimization constraints.To more effectively suppress the out-of-band radiation,the time domain windowing is introduced on the basis of the subcarrier weighting.The emission signal is multiplied by a window function which is different from the conventional rectangular window.Simulation results show that,the subcarrier weighting method can suppress the radiation intensity on average not less than 10 dB of OFDM sidelobe in the average without requiring the transmission of any side information,after the weighted transmission signal is windowed,the suppression effect of the sidelobe of the OFDM signal can reach about 25 dB.
The nodes importance ranking of complex networks is very important for network survivability research.Most of the existing nodes importance ranking methods do not consider the influence of network structure change and important neighbor nodes.To solve this problem,on the basis of multi-attribute evaluation and nodes deletion,an improved ranking method for complex network nodes is proposed.According to the local network nodes attributes,global network properties and network location attribute,the evaluation index is selected,and the initial nodes important degree is evaluated by the ideal point method.Followed by deleting the most important node,important degree of remaining nodes are evaluated,the influence of network structure and important neighbor nodes are reduced in this way,and the important network nodes ranking is finally get.Experimental results show that,compared with other methods,such as ideal point method,NICCM method,the proposed method is more accurate for the recognition and ranking of important nodes.
In Wireless Sensor Network(WSN),the current Energy-efficient Reliable Opportunistic Routing(EROR) algorithm based on network coding has shortcoming in balancing the energy consumption of nodes and prolonging the lifetime of network,so an optimized opportunistic routing algorithm based on network coding named OPEROR is proposed.It uses the channel bit error rate and packet loss rate to calculate the probability of failure of the received encode packets,reducing the retransmitted number of encode packets.The nodes in the forwarding set broadcast the cost packet directly instead of acknowledgement packet when it collects enough encode packets,reducing the network overhead and the waiting time of primary forwarding nodes.The neighbors of the primary forwarding nodes update their own forwarding cost based on the received cost packet,and decide whether to become assistant forwarding node,thus can prevent the neighbors with large forwarding cost from forwarding encode packets.Simulation results show that compared with EROR algorithm,the performance of OPEROR algorithm is improved in prolonging the lifetime of network and reducing the average energy consumption.
The Low Duty Cycle-Wireless Sensor Network(LDC-WSN) greatly extends the lifetime of the network by making the node at a low duty cycle,but makes the sleep delay in the network longer.To solve this problem,a Sleep Scheduling algorithm based on Multiple Wake-up(SSMW) is proposed,which balances energy consumption and time.Through dynamic sensing of residual energy and the multiple wake-up mechanism,a lower bound of delay for any topological structure is given.Simulation results show that,compared with LES algorithm and TOSS algorithm,the proposed algorithm has a significant performance improvement in the sleep delay,which can balance the energy consumption and prolong the network lifetime effectively.
In order to solve the hot pot problem which is caused by uneven load energy of cluster head node in Wireless Sensor Network(WSN),this paper proposes a partition routing protocol based on multiple sinks.It deploys multiple sinks and makes a reasonable partition in monitoring area,and uses the bat optimization algorithm which leads into variable scale chaos strategy to select the appropriate cluster head nodes to avoid the local optimization of bat algorithm.In data transmission phase,the cluster head node chooses the remaining energy greater than the average energy and the nearest node from the base station as the next hop.Simulation results show that compared with LEACH protocol and DEBUC protocol,the proposed protocol can reduce and balance energy consumption,increase the amount of packet reception,and extend the network lifetime.
Faster-than-Nyquist(FTN) signaling,which is promising in 5G mobile communication,has higher information rate and spectrum efficiency.However,symbol interval’s compression resulted in infinite Inter-Symbol Interferences (ISI),which would deteriorate the performance of carrier synchronization.To solve the problem,the new carrier synchronization algorithm is proposed.The frequency can be obtained by the spectrum peak index,which can be obtained by the quadratic polynomial fitting for periodgram main lobe.In addition,the algorithm is improved by increasing the length of FFT,combining the interpolation and fitting algorithm.Simulation results indicate that improved algorithm can adapt to the FTN signaling character better than Candan’s algorithm and the adjacent symbol phase division algorithm.And the estimation performance can approach to Cramer-Rao Bound (CRB) better.
Bandwidth utilization is a core index in network transmission capability evaluation.Too large bandwidth utilization will cause node congestion and make the performance of the network drop dramatically.Aiming at this problem,based on bandwidth utilization,a bandwidth utilization routing algorithm with exponential function is proposed,named EBURM.It firstly puts forward an objective function in mathematics,then studies and calculated several key elements and factors in the objective function,and gives the the theoretical values and calculation formula of transmission of efficiency,bandwith utilzation and step factor.In the current popular enterprise intranet network architecture,EBURM is compared with classical OSPF routing mechanism by using simulation tools.Experimental result shows that the EBURM has excellent linear characteristics under the condition and the number of EBURM path is less than five.EBURM can adjust the traffic transmission strategy of the source node in the network to reduce the cost in the traffic transmission.
In order to improve the efficiency and security of data upload for cloud storage users,a cloud storage channel hiding algorithm based on multiple bit repeating data deletion is proposed.A framework for cross user covert channels including cloud storage providers,victims and attackers is built,and the use of the message selection upload mechanism eliminates the need for “0” file upload,so as to reduce the number of uploaded files.At the same time,in order to realize error free decoding of multiple bit covert channel,a new synchronization technique is proposed,which sorts files with time stamp,so as to improve the order of data transmission.Finally,in the cloud storage service SugarSync and BaiduYun,the performance of the proposed algorithm is tested,and the results verify the effectiveness and security of the algorithm.
In order to study the influence of different edge immunization strategies on the spread of rumors and synthesize the effects of different types of triple group structure on the nodes,a new edge immunization strategy,called triple group immunization strategy is proposed.Simulations are performed on real online social networks and artificially synthesized scale-free networks,respectively,and degree-degree edge immunization,mediation-mediacy edge immunization,feature vector-feature vector edge immunization,edge inversion immunization and triple group edge immunization equalization immune strategies are compared.Simulation results show that when the probability of infection of the rumors is low,the effect of the triple edge immunization is not significant,when the infection probability is high,the triple groupd edge immunization effect is better,and the effect is second only to degree-degree edge immunization.
The two-dimensional code is only suitable for the transmission of single privilege information,cannot meet the needs of different permission users to gain the information.Using hierarchical encryption mode,the two-dimensional code information is encrypted according to the permission of secret information.The Hash function is used to generate the private key corresponding to different permission secret information automatically,to meet the acquisition needs of low permission information by high permission users.Using the attribute encryption algorithm,the access tree is generated according to the attribute set corresponding to the permission and access privilege of different information.The access permissions corresponding to different user attributes is calculated and the user private key is generated.The privilege of secret information and user attribute permissions are matched to complete the hierarchical encryption of two- dimensional code.Experimental results show that two dimensional code classification encryption algorithm based on attribute encryption can meet the needs of different permission users for different privilege information.
To solve the problem of Electronic Control Unit(ECU) in the Controller Area Network(CAN) bus of intra-vehicle network,which is easy to be tampered and faked,this paper puts forward an authentication protocol based on one-time pad in the vehicle network.Firstly,it uses secure storage module TA in the Gateway ECU(GECU) to verify the legal identity of the ECU.Then according to the vehicle power supply voltage,it achieves the random number,generates the session key,and simplifies the key management of TA to ECU.At last,it periodically updates the session key when connecting and releasing external devices and effectively prevents the replay attack.The experimental result shows that this protocol can be applied to the vehicle environment efficiently,which significantly reduces the bus load and improves the communication efficiency.
According to previous studies,the selection of the number of hidden nodes in the 5-levels neural network structure is not clear.To solve the problem,an improved 5-levels Deep Belief Networks(DBN) structure design and optimization method of nodes number is proposed.The number of the first hidden layer and the second hidden layer nodes is estimated to be 1/3 to 2/3 between the number of the first layer nodes.The number of the third hidden layer and the fourth hidden layer nodes equals the first of the number of hidden layer and input layer nodes,and then the first hidden layer and the second hidden layer nodes number is optimized by spline interpolation method.The structure features only 2-layers of weight before pre-training,which simplifies the Restricted Boltzmann Machine(RBM) pre-training method of DBN.Experimental results on the MNINST dataset verify the efficiency and high accuracy of the network structure.
Traditional super-pixels segmentation method has problems of poor anti-noise performance,inaccurate merging and other issues.To solve these problems,a method of segmentation of side-scan sonar images by super-pixel clustering is proposed.The fast bilateral filter is used to perform the noise reduction processing to reduce the difficulty of subsequent segmentation.The luminance and texture features of the de-noised side-scan sonar images are extracted,and the similarities of the two features are calculated.These similarities are combined with weights to give the distance metric between the pixels and the cluster centers to generate the super-pixels.The saliency super-pixels are labeled based on the luminance feature,and all super-pixels are clustered by the maximum flow and minimum cut method.The proportions of the super-pixels with saliency within the clusters are calculated,and they are compared with the preset threshold.Clusters with proportion which is larger than the threshold are marked as foreground;otherwise,they are marked as background.As a result,the final segmentation is obtained.Experimental results show that compared with Fuzzy Local Information C-Means (FLICM) algorithm and Simple Linear Iterative Clustering (SLIC) algorithm,the segmentation accuracy of the proposed algorithm is high,and over-division rate and under-division rate are low.
Head pose estimation is the key to detect identity and understand behavior in many intelligent systems,but it is influenced by illumination,occlusion and resolution.Aiming at the problem that the accuracy of head pose estimation for color 2-D images is not high enough,based on the analysis of existing face pose estimation methods,a head pose estimation approach based on facial feature point localization is proposed.This approach combines the Adaboost algorithm and the ellipse skin color model to detect the human face,which provides the accurate face area in the picture.Hough circle detection method is used to locate the eyes and nostrils.By using the position information of the eyes and nostrils,the eye and nose positioning results are compared with the eye nose in the face head posture,so that the different head posture is rough estimated.Experimental results show that this approach can achieve a recognition rate of 93.53% on 6 different head poses face outside.
At present,commercial wireless chips can only realize the communication function of the industrial wireless network standard,while leaving the functions like the slot scheduling and synchronization to rely on timing interrupt by the software.Therefore,previous solutions increase the difficulty of development and restrict the popularization of industrial wireless protocols such as WIA-PA.To solve the above problems,in this paper,a system level industrial wireless chip for industrial wireless network is designed,named WIASoC2400,which supports the physical layer protocol of the TSCH mode.It bases on the TSCH mode of the IEEE802.15.4e protocol and the ARM Cortex-M3 kernel,and integrates 2.4 GHz WIA-PA wireless communication module.In addition,in order to ensure the security of data transmission,it includes AES-128 encryption and decryption module,which is consistent with the security specification of IEEE802.15.4-2006 protocol CCM* mode.Simulation results demonstrate that WIASoC2400 can satisfy the requirements of time slot synchronization accuracy and frequency hopping in TSCH mode,and has the advantages of precise timing,fast processing and simple implementation.
For the traditional Spatio Temporal Context target tracking(STC) algorithm,no change of tracking’s window for a long time leads to learning space context model does not have a targeted problem when the target scale changes.This paper proposes a Spatio Temporal Context target tracking algorithm for Adaptive Learning(STC-AL).The Scale Invariant Feature Transform(SIFT) is extracted from the front and back output windows and used to eliminate false matches.After analyzing the matching point set,the output window is adjusted,and the learning and updating of the traditional spatial model is improved.Experimental results show that STC-AL algorithm can adapt to changes in the target scale,and tracking is more accurate compared with that of STC algorithm,CT algorithm and KCF algorithm.
The main factors that influence the recommendation results in location recommendation system include location,personal interest,social relationship and time cycle.In order to effectively integrate 4 factors to personalized location recommendation,the corresponding selection probability model is constructed for each factor.The influence of 4 factors on user selection is analyzed.Finally,the heuristic recommendation algorithm is proposed by combining 4 factors.Experimental results show that,compared with the traditional location-based recommendation algorithm,the proposed algorithm has better performance and the recommended results are more acceptable to users.
Based on the characteristics of Uyghur language,a method of identifying Uyghur language event coreference relationship based on Stacked Denoising Autoencoder(SDAE) is proposed.This paper divides the Uyghur events to the candidate event pairs,extracted the nine features,basic characteristics of the event,the trigger word and the event distance.At the same time,the word embedding is used to calculate the semantic similarity of Uyghur events trigger words,taking semantic similarity as one of the features.And then training SDAE model,using softmax to complete the identification task of Uyghur language event coreference relationship.Experimental results show that SDAE is more suitable for the identification task than Support Vector Machine(SVM),the shallow machine learning model,and the use of word embedding further enhances the identification performance.
In order to analyze the exhaust emission regularity under the actual traffic conditions of urban roads,the MCD and STCA models of cellular automaton are improved,on this basis,a model based on the speed-acceleration query table coupled with traffic flow cellular automaton is built for vehicle emission statistics.The coupling model is implemented under different traffic flow service levels,and the speed,acceleration and working conditions are obtained.The distribution of the vehicle’s working conditions,three typical exhaust emission processes,and the total emissions of 1 h and 1 km are statistically analyzed.The results show that the coupling mechanism of the model is clear,and it can be applied to analyze the discharge regularity of single vehicle and vehicle flow under the actual traffic service levels.
In multi-target tracking system,some targets will be lost in the tracking when the targets are crossing or closed to each other used by Gaussian Mixture-Probability Hypothesis Density(GM-PHD).In order to solve this problem,an improved algorithm for scenario with crossing targets is put forward.After update,the estimated Gauss label is managed.If the estimated number of targets is reduced,it is needed to determine whether the targets are crossing or closed to each other.If these trajectories are closed to each other,the followed step is label management and weight reset of Gaussian items,and target states and tracks are re-estimated.Otherwise,the reduction of target will be regarded as a normal target extinction phenomenon,and track management is directly carried out.Experimental results show that the improved algorithm can solve the missed detection caused by the crossing targets successfully,and has good stability.