We present a novel architecture of asynchronous reconfigurable computing array (ARCA) to seek the balance between performance and versatility. Advancing the completion detector of control circuit, a modified structure of asynchronous micropipeline based on DSDCVSL is discussed. The analysis and simulation of our ARCA have both resulted in high-performance and low-power consumption, and it can be used as an IP module integrated into a system on chip to built the reconfigurable computing platform.
The research purpose of this paper is focused on investigating the performance of extra-large scale massive multiple-input multiple-output(XL-MIMO)systems with residual hardware impairments.The closed-form expression of the achievable rate under the match filter(MF)receiving strategy was derived and the influence of spatial non-stationarity and residual hardware impairments on the system performance was investigated.In order to maximize the signal-to-interference-plus-noise ratio(SINR)of the systems in the presence of hardware impairments,a hardware impairments-aware minimum mean squared error(HIA-MMSE)receiver was proposed.Furthermore,the stair Neumann series approximation was used to reduce the computational complexity of the HIA-MMSE receiver,which can avoid matrix inversion.Simulation results demonstrate the tightness of the derived analytical expressions and the effectiveness of the low complexity HIA-MMSE(LC-HIA-MMSE)receiver.
The manifold matrix of the received signals can be destroyed when the array is with the gain and phase errors,which will affect the performance of the traditional direction of arrival(DOA)estimation approaches.In this paper,a novel active array calibration method for the gain and phase errors based on a cascaded neural network(GPECNN)was proposed.The cascaded neural network contains two parts:signal-to-noise ratio(SNR)classification network and two sets of error estimation subnetworks.Error calibration subnetworks are activated according to the output of the SNR classification network,each of which consists of a gain error estimation network(GEEN)and a phase error estimation network(PEEN),respectively.The disadvantage of neural network topology architecture is changing when the number of array elements varies is addressed by the proposed group calibration strategy.Moreover,due to the data characteristics of the input vector,the cascaded neural network can be applied to arrays with arbitrary geometry without repetitive training.Simulation results demonstrate that the GPECNN not only achieves a better balance between calibration performance and calibration complexity than other methods but also can be applied to arrays with different numbers of sensors or different shapes without repetitive training.
This paper proposes a robust adaptive filter based on the exponent sin cost to improve the capability against Gaussian or multiple types of non-Gaussian noises of the adaptive filtering algorithm when dealing with time-varying/time-invariant linear systems function exponent sin(ExpSin).Then a variable step-size(VSS)-ExpSin algorithm is extended further.Besides,the stepsize,the convergence,and the steady-state performance of the proposed algorithm are validated experimentally.The Monte Carlo simulation results of linear system identification illustrate the principle and efficiency of this proposed adaptive filtering algorithm.Results suggest that the proposed adaptive filtering algorithm has superior performance when estimating the unknown linear systems under multiple-types measurement noises.
The factors like production accuracy and completion time are the determinants of the optimal scheduling of the complex products work-flow,so the main research direction of modern work-flow technology is how to assure the dynamic balance between the factors.Based on the work-flow technology,restraining the completion time,and analyzing the deficiency of traditional minimum critical path algorithm,a virtual iterative reduction algorithm(VIRA)was proposed,which can improve production accuracy effectively with time constrain.The VIRA with simplification as the core abstracts a virtual task that can predigest the process by combining the complex structures which are cyclic or parallel,finally,by using the virtual task and the other task in the process which is the iterative reduction strategy,determines a path which can make the production accuracy and completion time more balanced than the minimum critical path algorithm.The deadline,the number of tasks,and the number of cyclic structures were used as the factors affecting the performance of the algorithm,changing the influence factors can improve the performance of the algorithm effectively through the analysis of detailed data.Consequently,comparison experiments proved the feasibility of the VIRA.
Facial expression recognition(FER)is a vital application of image processing technology.In this paper,a FER model based on the residual network is proposed.The proposed model introduces the idea of the DenseNet,in which the outputs of the residual blocks are not simply added but are linked to the channel dimension.In addition,transfer learning is used to reduce training costs and accelerate training speed.The accuracy and robustness of the proposed FER model were tested by K-fold cross-validation.Experimental results show that the proposed method has competitive performances on FER2013,FER plus(FERPlus),and the real-world affective faces database(RAF-DB).
The sudden surge of various applications poses great challenges to the computation capability of mobile devices.To address this issue,computation offloading to multi-access edge computing(MEC)was proposed as a promising paradigm.This paper studies partial computation offloading scenario by considering time delay and energy consumption,where the task can be splitted into several blocks and computed both in local devices and MEC,respectively.Since the formulated problem is a nonconvex problem,this paper proposes an ant colony-based algorithm to achieve the suboptimal solution.Specifically,the proposed method first establish a multi-user one-MEC scenario,in which user devices are able to offload some part of the task to MEC server.Then,it develops an ant colony-based algorithm to decide the offloading parts and allocation strategy of MEC resources to minimize system cost.Finally,simulation results show the effectiveness of the proposed algorithm in terms of system cost and demonstrate that it outperforms other existing methods.
As a way of training a single hidden layer feedforward network(SLFN),extreme learning machine(ELM)is rapidly becoming popular due to its efficiency.However,ELM tends to overfitting,which makes the model sensitive to noise and outliers.To solve this problem,L2 1-norm is introduced to ELM and an L2 1-norm robust regularized ELM(L2,1-RRELM)was proposed.L2,1-RRELM gives constant penalties to outliers to reduce their adverse effects by replacing least square loss function with a non-convex loss function.In light of the non-convex feature of L2,1-RRELM,the concave-convex procedure(CCCP)is applied to solve its model.The convergence of L2,1-RRELM is also given to show its robustness.In order to further verify the effectiveness of L2,1-RRELM,it is compared with the three popular extreme learning algorithms based on the artificial dataset and University of California Irvine(UCI)datasets.And each algorithm in different noise environments is tested with two evaluation criterions root mean square error(RMSE)and fitness.The results of the simulation indicate that L2,1-RRELM has smaller RMSE and greater fitness under different noise settings.Numerical analysis shows that L2,1-RRELM has better generalization performance,stronger robustness,and higher anti-noise ability and fitness.
In intelligent education,most student-oriented learning path recommendation algorithms are based on either collaborative filtering methods or a 0-1 scoring cognitive diagnosis model.Unfortunately,they fail to provide a detailed report about the students'mastery of knowledge and skill and explain the recommendation results.In addition,they are unable to offer realistic learning path recommendations based on students'learning progress.Knowledge graph based memory recommendation algorithm(KGM-RA)was proposed to solve these problems.On the one hand,KGM-RA can provide more accurate diagnosis information by continuously fitting the students'knowledge and skill proficiency vector(SKSV)in a multi-level scoring cognitive diagnosis model.On the other hand,it also proposes the forgetting recall degree(FRD)according to the statistical results of the human forgetting phenomenon.It also calculates closeness centrality in the knowledge graph to achieve the recommended recall effect consistent with the human forgetting phenomenon.Experiments show that the KGM-RA can obtain the actual learning path recommendations for students,provides the adjustable ability of FRD,and has better reliability and interpretability.
In this paper,the resource allocation optimization for the simultaneous wireless information and power transfer(SWIPT)full-duplex(FD)relaying networks is investigated,in which the power-constrained relay scavenges energy from the source signal and assists information transmission by FD operation.Taking into account non-linear energy harvesting(EH)hardware circuit characteristics of the relay,the information rate maximization problem is developed by jointly optimizing the time-switching(TS)factor and transmission powers of the source in two different phases.However,the formulated optimization is highly non-convex and difficult to solve.To cope with this problem,the primary problem is decomposed into two sub-problems with respect to the TS factor and transmission powers.After solving these two sub-problems,the final sub-optimal solutions can be obtained by alternating search.Simulation results prove that the optimization of both TS factor and transmission powers can effectively enhance the information rate for the considered networks.
In this communication,a frequency-,radiation pattern-and polarization-reconfigurable antenna employing liquid metal is presented.Two crossed dipole antennas are surrounded by four independent reflectors and directors to realize multi-beam switching.The length of dipole arms can be adjusted by extracting the liquid metal from the needle tube to achieve frequency reconfiguration.The polarization can be switched by injecting liquid metal into different dipole microfluidic channels.It is simple in design and has multiple reconfigurable capabilities.An antenna with a relative frequency tuning range of 35.8％extending from 2.43 GHz to 3.49 GHz is fabricated.It also can perform 6 kinds of beam steering over a 360° coverage and switch between two different polarizations.The antenna has potential to employ cognitive radio(CR)and base station in wireless systems.
For classification problems,the traditional least squares twin support vector machine(LSTSVM)generates two nonparallel hyperplanes directly by solving two systems of linear equations instead of a pair of quadratic programming problems(QPPs),which makes LSTSVM much faster than the original TSVM.But the standard LSTSVM adopting quadratic loss measured by the minimal distance is sensitive to noise and unstable to re-sampling.To overcome this problem,the expectile distance is taken into consideration to measure the margin between classes and LSTSVM with asymmetric squared loss(aLSTSVM)is proposed.Compared to the original LSTSVM with the quadratic loss,the proposed aLSTSVM not only has comparable computational accuracy,but also performs good properties such as noise insensitivity,scatter minimization and re-sampling stability.Numerical experiments on synthetic datasets,normally distributed clustered(NDC)datasets and University of California,Irvine(UCI)datasets with different noises confirm the great performance and validity of our proposed algorithm.
A source enumeration method based on diagonal loading of eigenvalues and constructing second-order statistics is proposed,for the case that the antenna array observed signals are overlapped with spatial colored noise,and the number of antennas compared with the number of snapshots meet the requirement of general asymptotic regime.Firstly,the sample covariance matrix of the observed signals is obtained,the eigenvalues of the sample covariance matrix can be acquired by eigenvalue decomposition,and the eigenvalues are diagonally loaded,and a new formula for calculating the diagonal loading is presented.Based on the diagonal loaded eigenvalues,the difference values are calculated for the adjacent eigenvalues after diagonal loading,and the statistical variance of the difference values is calculated.On this basis,the second-order statistics of the difference values are constructed,and when the second-order statistics are minimized,the corresponding number of sources is estimated.The proposed method has wide applicability,which is suitable for both general asymptotic regime and classical asymptotic system,and is suitable for both white Gaussian noise environment and colored noise environment.The method makes up for the lack of source enumeration methods in the case of general asymptotic system and colored noise.
When the power of the mainlobe interference received by the receiver is at the same level as the power of the sidelobe interference power,the traditional eigen-projection interference suppression method has the problems of severe beam deformation and peak shift.Aiming at these problems,a beam pattern optimization method(BPOM)was proposed,which can suppress the interference well even when the mainlobe interference power is approximately equal to the sidelobe interference power.In the method,the mainlobe interference eigenvectors are firstly determined according to the correlation criterion.Then through the eigenvalue comparison,the sidelobe interference eigenvectors whose eigenvalues are approximately equal to the mainlobe interference eigenvalues are judged.After that,a projection matrix is constructed to filter out the mainlobe and sidelobe interference.Finally,the covariance matrix is reconstructed and the weight vector for beamforming is obtained.Simulation shows that BPOM has a better output performance than the existing algorithms in case that the power of the mainlobe interference is close to that of the sidelobe interference.
Aiming at the problem that the current encrypted traffic classification methods only use the single network framework such as convolutional neural network(CNN),recurrent neural network(RNN),and stacked autoencoder(SAE),and only construct a shallow network to extract features,which leads to the low accuracy of encrypted traffic classification,an encrypted traffic classification framework based on the fusion of vision transformer and temporal features was proposed.Bottleneck transformer network(BoTNet)was used to extract spatial features and bi-directional long short-term memory(BiLSTM)was used to extract temporal features.After the two sub-networks are parallelized,the feature fusion method of early fusion was used in the framework to perform feature fusion.Finally,the encrypted traffic was identified through the fused features.The experimental results show that the BiLSTM and BoTNet fusion transformer(BTFT)model can enhance the performance of encrypted traffic classification by fusing multi-dimensional features.The accuracy rate of a virtual private network(VPN)and non-VPN binary classification is 99.9％,and the accuracy rate of fine-grained encrypted traffic twelve-classification can also reach 97％.
Sharing of the electronic medical records among different hospitals raises serious concern of the leakage of individual privacy for the adoption of the semi trustworthiness of the medical cloud platform.The tracking and revocation of malicious users have become urgent problems.To solve these problems,this paper proposed a traceable and directly revocable medical data sharing scheme.In the scheme,a unique identity parameter(ID),which was generated and embedded in the private key generation phase by the medical service provider(MSP),is used to identify legal authorized user and trace malicious user.Only when attributes satisfy the access policy and user's ID is not in the revocation list can the user calculate the decryption key.Malicious user can be tracked and directly revoked by using the revocation list.Under the assumption of decision bilinear Diffie-Hellman(DBDH),this paper has proved that the scheme is able to achieve security against chosen-plaintext attack(CPA).The performance analysis demonstrates that the sizes of the public key and private key are shorter,and the time overhead is less than other schemes in the public-private key generation,data encryption and data decryption stages.
To achieve the confidentiality and retrievability of outsourced data simultaneously,a dynamic multi-keyword fuzzy ranked search scheme(DMFRS)with leakage resilience over encrypted cloud data based on two-level index structure was proposed.The first level index adopts inverted index and orthogonal list,combined with 2-gram and location-sensitive Hashing(LSH)to realize a fuzzy match.The second level index achieves user search permission decision and search result ranking by combining coordinate matching with term frequency-inverse document frequency(TF-IDF).A verification token is generated within the results to verify the search results,which prevents the potential malicious tampering by cloud service providers(CSP).The semantic security of DMFRS is proved by the defined leakage function,and the performance is evaluated based on simulation experiments.The analysis results demonstrate that DMFRS gains certain advantages in security and performance against similar schemes,and it meets the needs of storage and privacy-preserving for outsourcing sensitive data.
Joint sparse recovery(JSR)in compressed sensing(CS)is to simultaneously recover multiple jointly sparse vectors from their incomplete measurements that are conducted based on a common sensing matrix.In this study,the focus is placed on the rank defective case where the number of measurements is limited or the signals are significantly correlated with each other.First,an iterative atom refinement process is adopted to estimate part of the atoms of the support set.Subsequently,the above atoms along with the measurements are used to estimate the remaining atoms.The estimation criteria for atoms are based on the principle of minimum subspace distance.Extensive numerical experiments were performed in noiseless and noisy scenarios,and results reveal that iterative subspace matching pursuit(ISMP)outperforms other existing algorithms for JSR.
Knowledge tracking(KT)algorithm,which can model the cognitive level of learners,is a fundamental artificial intelligence approach to solve the personalized learning problem in the field of education.The recently presented separated self-attentive neural knowledge tracing(SAINT)algorithm has got a great improvement on predictingthe accuracy of students'answers in comparison with the present other methods.However there is still potential to enhance its performance for it fails to effectively utilize temporal features.In this paper,an optimization algorithm for SAINT based on Ebbinghaus'law of forgetting was proposed which took temporal features into account.The proposed algorithm used forgetting law-based data binning to discretize the time information sequences,so as to obtain the temporal featuresin accordance with people's forgetting pattern.Then the temporal features were used as input in the decoder of SAINT model to improve its accuracy.Ablation experiments and comparison experiments were performed on the EdNet dataset in order to verify the effectiveness of the proposed model.Seen in the experimental results,it achieved higher area under curve(AUC)values than the other present representative knowledge tracing algorithms.It demonstrates that temporal featuresare necessary for KT algorithms if it can be properly dealt with.
To solve the problem that the performance of the coverage,interference rate,load balance andweak power in the radio frequency identification(RFID)network planning.This paper proposes an elite opposition-based learning and Lévy flight sparrow search algorithm(SSA),which is named elite opposition-based learning and Levy flight SSA(ELSSA).First,the algorithm initializes the population by an elite opposed-based learning strategy to enhance the diversity of the population.Second,Lévy flight is introduced into the scrounger's position update formula to solve the situation that the algorithm falls into the local optimal solution.It has a probability that the current position is changed by Lévy flight.This method can jump out of the local optimal solution.In the end,the proposed method is compared with particle swarm optimization(PSO)algorithm,grey wolf optimzer(GWO)algorithm and SSA in the multiple simulation tests.The simulated results showed that,under the same number of readers,the average fitness of the ELSSA is improved respectively by 3.36％,5.67％and 18.45％.By setting the different number of readers,ELSSA uses fewer readers than other algorithms.The conclusion shows that the proposed method can ensure a satisfying coverage by using fewer readers and achieving higher comprehensive performance.
