A task scheduling algorithm based on value optimization is proposed for phased array radar. Firstly, the schedulability of tracking tasks is obtained through feasibility analysis and selecting operation on the task queue, using the proposed schedulability parameters. Then, a dynamic task value function about the actual execution time is established according to the peak value and value changing slope of tasks. A value optimization model for tracking task scheduling is constructed based on the task value function. Timeliness can be better achieved while adopting this model to assign execution time for tasks. Finally, searching tasks are scheduled using the idle time intervals between tracking tasks which are going to be executed. Simulation results show that proposed algorithm reduces the average time shift ratio, and improves the value achieving ratio compared with the traditional scheduling algorithms.
Network coding is widely used in wireless multicast networks in recent years due to its high transmission efficiency. To address the low efficiency of automatic retransmission caused by packet loss in wireless multicast network, a new Coding Scheduling strategy based on Arriving Time (CSAT) in virtual queue is proposed. For improving encoding efficiency, virtual queues are used to store packets that are initially generated and not received by all receivers. Considering the stability of the queue, CSAT strategy chooses to send packet from the primary and secondary queue at a certain ratio. Both encoding and non-encoding methods are combined to send in the secondary queue. According to the arrival sequence of packets in the queue, the sending method that makes more packets participate in encoding is selected. Simulation results show that the proposed CSAT not only effectively improves packet transmission efficiency, but also improves network throughput and reduces average wait delay.
For the problem that the classifier is less considered to be combined with the brain's cognitive process in the Brain-Computer Interface (BCI) system, a Chernoff-weighted based classifier integrated frame method is proposed and used in synchronous motor imagery BCI. In the method, the statistic characteristics of ElectroEncephaloGraphy (EEG) are obtained by analyzing in each time point of synchronous BCI, and then the probability model is established to compute the Chernoff error bound, which is adopted as the weight of common classifier to take the discriminant process. The test experiments are based on the datasets from BCI competitions, and the proposed frame method is employed to compose with LDA, SVM, ELM respectively. The experimental results demonstrate that the proposed frame method shows competitive performance compared with other methods.
For the problem of spectrum allocation in the multiplexing of cellular user spectrum resources by Device-to-Device (D2D) communication in heterogeneous networks, a D2D communication resource allocation mechanism based on improved discrete Pigeon-Inspired Optimization(PIO) algorithm is proposed. The user's Quality of Service (QoS) is guaranteed by setting the Signal-to-Interference plus Noise Ratio (SINR) threshold, the transmitting power is set for users by power control algorithms. To allocate resources for D2D users, the Binary discrete PIO based on Motion Weight (MWBPIO) algorithm is used. To ensure the communication quality of edge users, the D2D communication technology and relay technology are used to establish D2D relay links, so then the performance of system can be maximized. Simulation results show that the proposed scheme can effectively suppress the interference caused by the introduction of D2D users in heterogeneous communication systems. Moreover, the proposed scheme can effectively improve the communication quality of edge users, and improve the utilization of spectrum resources and the performance of the system.
For the radio frequency stealth control measure of radar intermittent radiation, the relationship between radiation time ratio and positioning performance is studied which takes cross location with two stations as an example. Firstly, the control method of radar intermittent radiation is analyzed. Then, under the assumption of uniform linear motion of the carrier aircraft, the influence model of radiation time ratio on positioning accuracy is established by using the Cramer-Rao Lower Bound (CRLB). Finally, the solution steps of the model are given and verified by simulation. The simulation results show that different radiation time ratios have different effects on the location performance. When the initial distance is 100 km and the radiation time ratio is less than 0.5, the location convergence time exceeds 10 s, which can effectively reduce the performance of cross location with two stations.
To deal with the estimation problem of non-stationary channel in massive Multiple-Input Multiple-Output (MIMO) up-link, the 2D channels’ sparse structure information in temporal-spatial domain is used, to design an iterative channel estimation algorithm based on Dirichlet Process (DP) and Variational Bayesian Inference (VBI), which can improve the accuracy under a lower pilot overhead and computation complexity. On account of that the stationary channel models is not suitable for massive MIMO systems anymore, a non-stationary channel prior model utilizing Dirichlet Process is constructed, which can map the physical spatial correlation channels to a probabilistic channel with the same sparse temporal vector. By applying VBI technology, a channel estimation iteration algorithm with low pilot overhead and complexity is designed. Experiment results show the proposed channel method has a better performance on the estimation accuracy than the state-of-art method, meanwhile it works robustly against the dynamic system key parameters.
Ultra-Dense Networks (UDNs) shorten the distance between terminals and nodes, which improve greatly the spectral efficiency and expand the system capacity. But the performance of cell edge users is seriously degraded. Reasonable planning of Virtual Cell (VC) can only reduce the interference of moderate scale UDNs, while the interference of users under overlapped base stations in a virtual cell needs to be solved by cooperative user clusters. A user clustering algorithm with Interference Increment Reduction (IIR) is proposed, which minimizes the sum of intra-cluster interference and ultimately maximizes system sum rate by continuously switching users with maximum interference between clusters. Compared with K-means algorithm, this algorithm, no need of specifying cluster heads, avoids local optimum without increasement of the computation complexity. The simulation results show that the system sum rate, especially the throughput of edge users, can be effectively improved when the network is densely deployed.
In the Orthogonal Frequency Division Multiplexing (OFDM) system, the receiver often needs to know the channel state information, because the frequency selective fading channel will generate inter-symbol interference in the data transmission. In the case of maritime communication, the method of channel estimation is often needed to detect the channel subjected to the interference of various external factors. In order to improve the estimation performance, the Fast Bayesian Matching Pursuit based on singular-value-decomposition for Optimizing observation matrix (FBMPO) is proposed, which fully considers not only the sparse channel of maritime communication, but also reduces the influence of uncertainty of the unpredictable channel. Computer simulation shows, compared with traditional channel estimation algorithms, the proposed algorithm can effectively improve the accuracy of channel estimation.
To solve the problem of inaccurate feature representation caused by indistinctive appearance difference in person re-identification domain, a new Matrix Metric Learning algerithm based on Bidirectional Reference (BRM2L) set is proposed. Firstly, reciprocal-neighbor reference sets in different camera views are respectively constructed by the reciprocal-neighbor scheme. To ensure the robustness of reference sets, the reference sets in different camera views are jointly considered to generate the Bidirectional Reference Set (BRS). With hard samples which are mined by the BRS to represent feature descriptors, accurate appearance difference representations could be obtained. Finally, these representations are utilized to conduct more effective matrix metric learning. Experimental results on several public datasets demonstrate the superiority of the proposed method.
Based on the study of the spur-line, a novel spurs-line structure is proposed. The design of a novel Ultra-WideBand (UWB) power divider is described based on the novel spur line structure for the 2.5～13.2 GHz frequency range. The designed device is compact and has a simple structure and good frequency response in the band. Its return loss insertion is less than –12 dB and its insertion loss is less than 3.5 dB. The equations used for the design are based on the concept of odd-even modes and transmission line analysis. The Beetle Antennae Search (BAS) algorithm is used to improve the efficiency and accuracy of the power divider design. In order to verify the accuracy of the design, a UWB power divider is designed by using material RO4003C as substrate. The results validate the feasibility of the spur line-based design and demonstrat that the BAS algorithm has a shortened running time and improved precision compared to other optimization methods. It can be widely used in UWB power divider design.
Satellite health monitoring is an important concern for satellite security, for which satellite telemetry data is the only source of data. Therefore, accurate prediction of missing data of satellite telemetry is an important forward-looking approach for satellite health diagnosis. For the high-dimensional structure formed by the satellite multi-component system, multi-instrument and multi-monitoring index, the Tensor Factorization based Prediction (TFP) algorithm for missing telemetry data is proposed. The proposed algorithm surpasses most existing methods, which can only be applied to low-dimensional data or specific dimension. The proposed algorithm makes accurate predictions by modeling the telemetry data as a Tensor to integrally utilize its high-dimensional feature; Computing the component matrixes via Tensor Factorization to reconstruct the Tensor which gives the predictions of the missing data; An efficient optimization algorithm is proposed to implement the related tensor calculations, for which the optimal parameter settings are strictly theoretically deduced. Experiments show that the proposed algorithm has better prediction accuracy than the most existing algorithms.
Compared with the traditional high-order Finite Difference Time Domain(FDTD) Method, an improved high-order FDTD optimization method is proposed in this paper. This algorithm is based on Ampere’s law of circuits and finds a set of optimal coefficients through computer technology to minimize the global dispersion error of the FDTD method.The simulation of point source radiation with different resolutions shows that this method still has very low phase error in the case of lower resolution. It provides an effective solution to the problem of numerical dispersion in the modeling of large size structures.
In order to study the subtle feature recognition of Identification Foe or Friend (IFF) radiation source signals, this paper proposes an IFF individual recognition method based on ensemble intrinsic time-scale decomposition to solve the problem of insufficient research on individual identification of IFF radiation source in complex noise environment. In this algorithm, the Ensemble Intrinsic Time-scale Decomposition (EITD) is applied to dividing the sampled signals into several practical signal components and obtaining the energy distribution diagram of the IFF radiation source signals in time-frequency domain. Through the texture analysis of time-frequency energy spectrum, the unintentional modulation feature of the radiation source signals is represented by the texture features of the image, which are sent to the Support Vector Machine (SVM) for classification and recognition. Experiments show that the proposed method is more accurate than the Hilbert-Huang Transform (HHT) and Inherent Time scale Decomposition (ITD) based method.
A full digital feedforward Time-Interleaved Analog-to-Digital Converter (TIADC) time skew calibration algorithm is presented, the time skew estimation adopts the feedforward extraction method of the improved derivative module of time skew function, which can greatly improve the accuracy of skew estimation when the input signal frequency is high. At the same time, the time skew function is based on subtraction, in order to reduce the complexity of skew estimation unit. Finally, the time skew is corrected by using first-order Taylor compensation. The simulation results show that when the input signal is a multi-frequency signal, the Spurious-Free Dynamic Range (SFDR) increases from 48.6 dB to 80.7 dB, after adopting the proposed time skew correction for a 4-channal 14-bit TIADC system. Compared with the traditional feedforward calibration structure based on correlation operation, the effective calibration input signal bandwidth can be increased from 0.19 to 0.39, which greatly increases the application range of the calibration algorithm.
In the Non-Orthogonal Multiple Access (NOMA) based cellular network with Vehicle-to-Vehicle (V2V) communication, to mitigate the co-channel interference between V2V users and cellular users as well as the power allocation problem based on the NOMA principle, an energy efficiency dynamic resource allocation algorithm is proposed. Firstly, a stochastic optimization model is established to maximize the energy efficiency by considering subchannel scheduling, power allocation and congestion control, in order to guarantee the delay and reliability of V2V users while satisfying the rate of cellular users. Then, leveraging on the Lyapunov stochastic optimization method, the traffic queues can be stabilized by admitting as much traffic data as possible to avoid network congestion, and the radio resource can be allocated dynamically according to the real-time network traffic and thus a suboptimal subchannel matching algorithm is designed to obtain the user scheduling scheme. Furthermore, the power allocation policy is obtained by utilizing successive convex optimization theory and Lagrange dual decomposition method. Finally, the simulation results show that the proposed algorithm can improve the system energy efficiency and ensure the Quality of Service (QoS) requirements of different users and network stability.
Dual satellite TDOA/FDOA localization is achieved by the TDOA hyperboloid and FDOA hyperboloid. The accuracy of localization is affected by TDOA/FDOA accuracy. In order to measure accurately the TDOA/FDOA, a method of TDOA/FDOA measurement based on short synthetic aperture is presented. This method improves the measurement accuracy by using a certain length of synthetic aperture. For narrowband signals, the method has the ability to estimate a single satellite Doppler frequency, and the frequency difference can be obtained from the results estimated by the two satellites. For wideband signals, high-precision estimates of frequency differences can be obtained by dual satellite data interference. For short-term stable radar signals, the processing results of STK simulation data confirm the effectiveness of the proposed method.
For LTE-V2X(Long Term Evolution-Vehicle to Everything) system, the cellular link and the SideLink (SL) are usually unstable in the handover process, and the situation is even deteriorating when the SL is employed to assist the normal handover process. To solve these problems, an SL-assisted joint handover scheme is proposed for vehicles in the network, which mainly includes: joint handover procedure design, signaling design, and the joint handover decision algorithm. Firstly, the SL is established for the vehicles that are about to request for handover. The SL is set up between the pair of vehicles with the best channel quality to ensure the link reliability. Secondly, in order to tackle the perplexing problem of SL being vulnerable in the fast changing radio environment, the joint handover signaling procedure is optimized with respect to two different realistic circumstances. Finally, the vehicle’s moving direction is further included in making the handover decision, thus reducing unnecessary handover operations. Simulation results illustrate that the SL-assisted joint handover scheme can effectively ameliorate the handover success rate and reduce significantly the number of LTE handovers.
For cases with small samples, the estimated noise subspace obtained from sample covariance matrix deviates from the true one, which results in MUltiple SIgnal Classification (MUSIC) Direction-Of-Arrival (DOA) estimation performance breakdown. To deal with this problem, an iterative algorithm is proposed to improve the MUSIC performance by modifying the signal subspace in this paper. Firstly, the DOAs are roughly estimated based on the noise subspace obtained from sample covariance matrix. Then, considering the sparsity of signals and the low-rank property of steering matrix, a new signal subspace is got from the steering matrix consisting of estimated DOAs and their adjacent angles. Finally, the signal subspace is modified by solving an optimization problem. Simulation results demonstrate the proposed algorithm can improve the subspace estimation accuracy and furtherly improve the MUSIC DOA estimation performance, especially in small sample cases.
According to the HyperSonic Vehicle (HSV) borne radar platform system, a multi-channel SAR-GMTI clutter suppression method is presented based on hypersonic platform forward squint mode. First, range walk correction and range compression are completed in the time domain, and the distance envelope is aligned simultaneously with phase error compensation. Then, the Doppler extended signal is compressed by three-order azimuth Chirp Fourier Transform (CFT), and the azimuth envelope of the echo is aligned with phase error compensation simultaneously. Next, the Digital Beam Forming (DBF) technology is applied to the range time-azimuth CFT domain by nulling the clutter and its ambiguous components to achieve Space-Time Adaptive Processing (STAP). The stationary clutter and its ambiguous components can be suppressed effectively and the echo signs of the moving target without blurring can be extracted.
The improvement of time-frequency resolution plays a crucial role in the analysis and reconstruction of multi-component non-stationary signals. For traditional time-frequency analysis methods with fixed window, the time-frequency concentration is low and hardly to distinguish the multi-component signals with fast-varying frequencies. In this paper, by adopting the local information of the signal, an adaptive synchrosqueezing transform is proposed for the signals with fast-varying frequencies. The proposed method is with high time-frequency resolution, superior to existing synchrosqueezing methods, and particularly suitable for multi-component signals with close and fast-varying frequencies. Meanwhile, by using the separability condition, the adaptive window parameters are estimated by local Rényi entropy. Finally, experiments on synthetic and real signals demonstrate the correctness of the proposed method, which is suitable to analyze and recover complex non-stationary signals.
Based on the network structure and training methods of the Extreme Learning Machine (ELM), Correntropy-based Fusion Extreme Learning Machine (CF-ELM) is proposed. Considering the problem that the fusion of representation level features is insufficient in most classification methods, the kernel mapping and coefficient weighting are combined to propose a Fusion Extreme Learning Machine (F-ELM), which can effectively fuse the representation level features. On this basis, the Mean Square Error (MSE) loss function is replaced by the correntropy-based loss function. A correntropy-based cycle update formula for training the weight matrices of the F-ELM is derived to enhance classification ability and robustness. Extensive experiments are performed on Caltech 101, MSRC and 15 Scene datasets respectively. The experimental results show that CF-ELM can further fuse the representation level features to improve the classification accuracy.
Due to the advantages such as the worst-case hardness assumption, lattice-based cryptography is believed to the most promising research direction in post-quantum cryptography. As one of the two main hard problems commonly used in lattice-based cryptography, Learning With Errors (LWE) problem is widely used in constructing numerous cryptosystems. In order to improve the efficiency of lattice-based cryptosystems, Zhang et al. (2019) introduced the Asymmetric LWE (ALWE) problem. In this paper, the relation between the ALWE problem and the standard LWE problem is studied, and it shows that for certain error distributions the two problems are polynomially equivent, which paves the way for constructing secure lattice-based cryptosystems from the ALWE problem.
Even attacks by quantum computer can be theoretically discovered if utilizing quantum communication protocols. Compared with entangled states, the Continuous Variable (CV) Gaussian coherent state is easier to be prepared. The schemes of quantum communication network based on coherent state will be more economical and practical. A Measurement-Device-Independent (MDI) Cluster state quantum communication network scheme by using coherent state is proposed. Quantum Secret Sharing (QSS) and Quantum Conference (QC) protocols can be implemented in this network. A linear Cluster state scheme is poposed to implement t-out-of-n QSS protocol, a star Cluster state scheme to implement four-user QSS protocol and QC protocol. The entanglement-based CV MDI scheme is used to analyze the relationship between the key rates and transmission distance for each symmetric and asymmetric protocol. The presented schemes provide a concrete reference for establishing CV MDI quantum QSS and QC protocol in quantum networks by using coherent state.
In view of the great success of quantum cryptography in key distribution, people also try to utilize the quantum mechanics to construct many other cryptographic protocols. Anonymous authenticated key exchange is exactly one kind of cryptographic tasks whose practical quantum solution is still awaited so far. To solve this problem, a quantum anonymous authenticated exchange protocol is proposed based on a quantum oblivious key transfer scheme. It not only realizes user anonymity and mutual authentication of the user and server, but also establishes a secure session key between the two parties. Besides, the attacks of the server either fail or can be discriminated with outside eavesdropping (the server is thus caught as a cheater), so the server generally will not cheat at the risk of gaining a bad reputation.
With the integration of information technology such as industrial Internet of Things (IoT), cloud computing and Industrial Control System (ICS), the security of industrial data is at enormous risk. In order to protect the confidentiality and integrity of data in such a complex distributed environment, a communication scheme is proposed based on Attribute-Based Encryption (ABE) algorithm, which integrates data encryption, access control, decryption outsourcing and data verification. In addition, it has the characteristics of constant ciphertext length. Finally, the scheme is analyzed in detail from three aspectsie correctness, security and performance overhead. The simulation results show that the algorithm has the advantage of low decryption overhead.
Learning unsupervised representations from multivariate medical signals, such as multi-modality polysomnography and multi-channel electroencephalogram, has gained increasing attention in health informatics. In order to solve the problem that the existing models do not fully incorporate the characteristics of the multivariate-temporal structure of medical signals, an unsupervised multi-Context deep Convolutional AutoEncoder (mCtx-CAE) is proposed in this paper. Firstly, by modifying traditional convolutional neural networks, a multivariate convolutional autoencoder is proposed to extract multivariate context features within signal segments. Secondly, semantic learning is adopted to auto-encode temporal information among signal segments, to further extract temporal context features. Finally, an end-to-end multi-context autoencoder is trained by designing objective function based on shared feature representation. Experimental results conducted on two public benchmark datasets (UCD and CHB-MIT) show that the proposed model outperforms the state-of-the-art unsupervised feature learning methods in different medical tasks, demonstrating the effectiveness of the learned fusional features in clinical settings.
Attribute-Based Group Signature(ABGS) is a new variant of group signature, and it allows group members with certain specific attributes to sign messages on behalf of the whole group anonymously; Once any dispute arises, an opening authority can effectively reveal and track the real identity information of the singer. For the problem that the first lattice-based attribute-based group signature scheme with verifier-local revocation has a long bit-size of group public-key, and thus a low space efficiency, a compact identity-encoding technique which only needs a fixed number of matrices is adopted to encode the identity information of group members, so that the bit-size of group public-key is independent of the number of group members. Moreover, a new Stern-like statistical zero-knowledge proofs protocol is proposed, which can effectively prove the member’s signature privilege, and its revocation-token is bound to a one-way and injective learning with errors function.
The security of classical symmetric cryptography is facing severe challenges in quantum environment, which has prompted researchers to explore cryptography algorithms that are secure in both classical and quantum environments. Post-quantum symmetric cryptography research emerges. Research in this field is still at its primary stage and has not formed a complete system. This paper categorizes the existing research results, and introduces the research status from four aspects, including quantum algorithm, cryptographic analysis method, security analysis, provable security. Based on the analysis of the research status, the development trend of post-quantum symmetric cryptography is predicted, which provides reference for the analysis and design of symmetric cryptography in quantum environment.
Regev introduced the Learning With Errors (LWE) problem in 2005, which has close connections to random linear code decoding and has found wide applications to cryptography, especially to post-quantum cryptography. The LWE problem is originally introduced in random access model, and there are evidences that indicate the hardness of this problem. It is well known that the LWE problem is vulnerable if the attacker is allowed to choose samples. However, to the best of the author’s knowledge, a complete algorithm has not been published. In this paper, the LWE problem in query samples access model is analyzed. The technique is to relate the problem to the hidden number problem, and then Fourier learning method is applied to the list decoding.
In order to explore the correlation between face and audio in the field of speaker recognition, a novel multimodal Generative Adversarial Network (GAN) is designed to map face features and audio features to a more closely connected common space. Then the Triplet-loss is used to constrain further the relationship between the two modals, with which the intra-class distance of the two modals is narrowed, and the inter-class distance of the two modals is extended. Finally, the cosine distance of the common space features of the two modals is calculated to judge whether the face and the voice are matched, and Softmax is used to recognize the speaker identity. Experimental results show that this method can effectively improve the accuracy of speaker recognition.
For the low-rate speech encoding problem, an information hidden algorithm based on the G.723.1 coding standard is proposed. In the pitch prediction coding process, by controlling the search range of the closed-loop pitch period (adaptive codebook), combined with the Random Position Selection (RPS) method and the Matrix Coding Method (MCM), the secret information is embedded, which is implemented in the speech coding process. The adoption of the RPS method reduces the correlation between the carrier code-words, and the adoption of the MCM method reduces the rate of change of the carrier. The experimental results show that the average PESQ (Perceptual Evaluation of Speech Quality) deterioration rate under the algorithm is 1.63%, and the concealment is good.
Considering the problem that the existing superpixel methods are usually unable to set an appropriate number of generated superpixels automatically and unable to adhere to image boundaries effectively, a new superpixel method is proposed in this paper, which utilizes local information to perform multi-level simple linear iterative clustering to generate superpixels. First, original image is initially segmented by Simple Liner Iterative Clustering based on Local Information (LI-SLIC). Then, each superpixel is segmented iteratively until its color standard deviation is lower than a predefined threshold. Finally, the over-segmented superpixels are merged based on the color differences between adjacent superpixels. Experiments on Berkeley, Pascal VOC and 3Dircadb databases, as well as comparison with other methods indicate that the proposed method can adhere to image boundaries more accurately, and can prevent over- and under- segmentations more effectively.
The lattice-based signature schemes are promising quantum-resistant replacements for classical signature schemes based on number theoretical hard problems. An important approach to construct lattice-based signature is utilizing the Fiat-Shamir transform and rejection sampling techniques. There are two Fiat-Shamir signatures among five lattice signature schemes submitted to the post-quantum project initiated by National Institute of Standards and Technology. One of them is called Dilithium, which is based on Module-Learning-With-Errors (MLWE) problem, it features on its simple design in the signing algorithm by using uniform sampling. The Dilithium is built on the generic lattices, to make the size of public key more compact, Dilithium adopts compression technique. On the other hand, schemes using NTRU lattices outperform schemes using generic lattices in efficiency and parameter sizes. This paper devotes to designing an efficient NTRU variant of Dilithium, by combining the advantage of NTRU and uniform rejection sampling, this scheme enjoys a concise structure and gains performance improvement over other lattice-based Fiat-Shamir signature without using extra compression techniques.
Generalized Feistel Schemes (GFS) are important components of symmetric ciphers, which have been extensively researched in classical setting. However, the security evaluations of GFS in quantum setting are rather scanty. In this paper, more improved polynomial-time quantum distinguishers are presented on Type-1 GFS in quantum Chosen-Plaintext Attack (qCPA) setting and quantum Chosen-Ciphertext Attack (qCCA) setting. In qCPA setting, new quantum polynomial-time distinguishers are proposed on
Autonomous vehicles are equipped with multiple on-board sensors to achieve self-driving functions. However, a tremendous amount of data is generated by autonomous vehicles, which significantly challenges the real-time task processing. Through multiple-vehicle cooperation, which makes the best of vehicle onboard computing resources, autonomous and cooperative driving becomes a promising candidate to solve the aforementioned problem. In this case, it is vital for autonomous and cooperative driving to form a driving platoon and allocate driving tasks efficiently. In this paper, a more general analytical model is developed based on G/G/1 queueing theory to model the topology of platoons. Next, Support Vector Machine (SVM) method is adopted to classify the “idle” and “busy” categories of the vehicles in the platoon based on their computing load and task processing capacity. Finally, based on the analysis above, an efficient task balancing strategy of platoons in autonomous and cooperative driving called Classification based Greed Balancing Strategy (C-GBS) is proposed, in order to balance the task burden among vehicles and cooperate more efficiently. Extensive simulations demonstrate that the proposed technique can reduce the processing delay of driving tasks in platoons with high computing load, which will improve the processing efficiency in autonomous vehicles.
In radio monitoring and target location applications, the received signals are often affected by complex electromagnetic environment, such as impulsive noise and cochannel interference. Traditional signal processing methods based on second-order statistics often fail to work properly. The signal processing methods based on fractional lower order statistics also encounter difficulties due to their dependence on prior knowledge of signals and noises. In recent years, the theory and method of correntropy and cyclic correntropy signal processing, which are widely concerned in the field of signal processing, are put forward. They are effective technical means to solve the problems of signal analysis and processing, parameter estimation, target location and other applications to complex electromagnetic environment. They promote greatly the development of the theory and application of non-Gaussian and non-stationary signal processing. This paper reviews systematically the basic theory and methods of correntropy and cyclic correntropy signal processing, including the background, definition, properties and characteristics of correntropy and cyclic correntropy, as well as their mathematical and physical meanings. This paper introduces also the applications of correntropy and cyclic correntropy signal processing to many fields, hoping to benefit the research and application of non-Gaussian and non-stationary statistical signal processing.
With the rapid development of intelligent transportation, vehicle terminals generate a large number of data messages that need to be processed in real time. Competition on limited resources will increase the delay of message processing and energy consumption for terminal equipment. For the equilibrium relationship between delay and energy loss, this paper proposes a content-aware classification offloading algorithm based on Mobile Edge Computing (MEC). Firstly, the security message is prioritized according to the analytic hierarchy process, and then the optimal task unloading model of delay and energy loss is established. The relational model is established by assigning different weight coefficients to delay and energy loss. The Lagrangian relaxation method is used to transform the non-convex problem into a convex problem, which combines the sub-gradient projection method and the greedy algorithm to obtain the feasible solution. The performance evaluation results show that the algorithm improves the message processing delay and energy loss to some extent.
Automatic Target Recognition(ATR) is an important research area in the field of radar information processing. Because the deep Convolution Neural Network(CNN) does not need to carry out feature engineering and the performance of image classification is superior, it attracts more and more attention in the field of radar automatic target recognition. The application of CNN to radar image processing is reviewed in this paper. Firstly, the related knowledges including the characteristics of the radar image is introduced, and the limitations of traditional radar automatic target recognition methods are pointed out. The principle, composition, development of CNN and the field of computer vision are introduced. Then, the research status of CNN in radar automatic target recognition is provided. The detection and recognition method of SAR image are presented in detail. The challenge of radar automatic target recognition is analyzed. Finally, the new theory and model of convolution neural network, the new imaging technology of radar and the application to complex environments in the future are prospected.
Quantum walks are raised for teleporting qubit or qudit. Based on quantum walk teleportation, an arbitrated quantum signature scheme with quantum walks on regular graphs is suggested, in which the entanglement source does not need preparing ahead. In the initial phase, the secret keys are generated via quantum key distribution system. In the signing phase, the signature for the transmitted message is created by the signer. Teleportation of quantum walks on regular graphs is applied to teleporting encrypted message copy from the signer to the verifier. Concretely, the sender encodes the ciphertext of message copy on coin state. Then two-step quantum walks are performed on the initial system state engendering the necessary entangled state for quantum teleportation, which can be the basis of signature generation and verification. In the verifying phase, the verifier verifies the validity of the completed signature under the aid of an arbitrator. Additionally, the applications of random number and public board deter the verifier’s existential forgery and repudiation attacks before the verifier accepts the true message. Analyses show that the suggested arbitrated quantum signature algorithm satisfies the general two requirements, i.e., impossibility of disavowal from the signer and the verifier and impossibility of forgery from anyone. The discussions demonstrate that the scheme may not prevent disavowal attack from the signer and that the corresponding improvements are presented. The scheme may be realizable because quantum walks have experimentally proven to be implementable in different physical systems.
Securely sharing and publishing location trajectory data relies on support of location privacy protection technology. Prior to the advent of differential privacy, K-anonymity and its derived models provide a means of quantitative assessment of location-trajectory privacy protection. However, its security relies heavily on the background knowledge of the attacker, and the model can not provide perfect privacy protection when a new attack occurs. Differential privacy effectively compensates for the above problems, and it proves the level of privacy protection based on rigorous mathematical theory and is increasingly used in the field of trajectory data privacy publishing. Therefore, the trajectory privacy protection technology based on differential privacy theory is studied and analyzed, and the methods of spatial statistical data publishing are introduced such as location histogram and trajectory histogram, the method of trajectory data set publishing and the model of continuous real-time location release privacy protection. At the same time, the existing methods are compared and analyzed, the key development directions are put forward in the future.
To improve the efficiency of the triangularization of ideal lattice basis, a fast algorithm for triangularizing an ideal lattice basis is proposed by studying the polynomial structure, which runs in time O(n3log2B), where n is the dimension of the lattice, B is the infinity norm of lattice basis. Based on the algorithm, a deterministic algorithm for computing the Smith Normal Form (SNF) of ideal lattice is given, which has the same time complexity and thus is faster than any previously known algorithms. Moreover, for a special class of ideal lattices, a method to transform such triangular bases into Hermite Normal Form (HNF) faster than previous algorithms will be present.
In the complex electromagnetic environment, multipath clutter in passive radar may be nonstationary and has jump characteristics. In order to suppress this kind of non-stationary clutter, a clutter suppression method is proposed based on channel segmentation and smoothing, which combines the Orthogonal Frequency Division Multiplexing (OFDM) modulation of the transmitting signal. First, the temporal domain signal model of the jumping clutter is established. Then it is transformed into subcarrier-domain by using the OFDM structure. After channel estimation of each OFDM symbol and smoothing the segmented channel estimation, the non-stationary clutter can be suppressed by the smoothed channel estimation and reference signal in each segment. Simulation and experiment data show that the proposed method can effectively suppress the non-stationary clutter with jumping characteristic.
The high-speed movement of vehicles inevitably leads to frequent data migration between edge servers and increases communication delay, which brings great challenges to the real-time computing service of edge servers. To solve this problem, a real-time reinforcement learning method based on Deep Q-learning Networks according to vehicle motion Trajectory Process (DQN-TP) is proposed. The proposed algorithm separates the decision-making process from the training process by using two neural networks. The decision neural network obtains the network state in real time according to the vehicle’s movement track and chooses the migration method in the virtual machine migration and task migration. At the same time, the decision neural network uploads the decision records to the memory replay pool in the cloud. The evaluation neural network in the cloud trains with the records in the memory replay pool and periodically updates the parameters to the on-board decision neural network. In this way, training and decision-making can be carried out simultaneously. At last, a large number of simulation experiments show that the proposed algorithm can effectively reduce the latency compared with the existing methods of task migration and virtual machine migration.
For the problem of vehicle positioning in Vehicular Ad-hoc NETworks (VANETs), in order to improve the positioning accuracy and real-time performance, a high-precision and real-time localization algorithm for automatic driving vehicles is proposed, including two technologies based on Matrix Pencil (MP) and Non-Linear Fitting (NLF), and visual perception. The MP-NLF technology uses joint TOA/AOA estimation to locate vehicles with a single station, and introduces high resolution estimation technology to improve the estimation accuracy. The visual perception based technology completes the localization by extracting the feature information of visual perceptual images in positioning area, carries on the unscented Kalman filter combined with the inertial sensor information to further improve the positioning accuracy. The simulation results show that, compared with the traditional multipath fingerprinting algorithm, the proposed algorithm has better performance even in the case of low Signal-to-Noise Ratio (SNR).
Some current works on intelligent and connected transportation system are presented, particularly focusing on the state of the art of the framework and key technologies in China or internationally, and the research development in some critical directions are elaborated including external environment perception, autonomous decision of vehicles, control execution and cooperative vehicle infrastructure system. On the basis of analyzing and summarizing the existing literature, the scheme of the future intelligent and connected transportation system and its working principle are described. The future intelligent and connected transportation system have the function of full path planning and precise, and the Real-Time Kinematic (RTK) and Synthetic Aperture Radar (SAR) technologies are used to detect and locate moving or non-moving objects, including those without GPS. And the continuity of the detection signal can be guaranteed in the environment where GPS signals are weak or non-signaled (e.g., tunnel, indoor) and the situation of close-range and non-visual. The Mobile Edge Computing (MEC) theory can also be used in the system to solve the key problems such as low latency and large-scale network access, and the big data, cloud computing, Internet of Things (IoTs) and mobile communication technologies are used to realize the global and networked intelligent and connected transportation system.
For the problem that the traditional traffic accident risk prediction algorithm can not automatically discriminate data features, and the model expression ability is poor, a traffic accident risk prediction algorithm based on deep learning is proposed. The algorithm firstly extracts multi-dimensional features by using the convolutional neural network established in the edge server for a large amount of traffic data collected in the edge network of vehicles. After normalization, de-equalization and other pre-processing, the new variables are input into the convolutional layer and the pooling layer for training. Finally, based on the output discrimination value of the fully connected layer, the risk of traffic accidents can be predicted by simulation. The simulation results show that the algorithm is validated to predict the risk of traffic accidents, and has lower loss and higher prediction accuracy than the traditional machine learning BP neural network algorithm and Logical Regression algorithm.
In vehicular networks, high mobility and complicated behaviors of vehicles fully manifest the uniqueness of characteristics of vehicular communications. In such a scenario, the data is generated in real-time, the traffic is distributed unevenly across the city and the communication patterns are revealed in various ways. All these characteristics make a fact that the traditional vehicular network deployment and resource management schemes can not satisfy the diverse quality of service requirements. Therefore, it is urgent to design intelligent heterogeneous vehicular networks with ubiquitous interconnection of "vehicle-person-road-cloud". How to make behavior prediction and assist the diversified and differentiated high-quality communication requirements in vehicular networks by using data analysis is still an open problem. This paper reviews the researches on vehicle behavior analysis, network deployment and access, and resource management, then focuses on the enabling technologies for intelligent vehicular networks. Firstly, by adopting advanced artificial intelligence and data analysis techniques, the spatial and temporal distribution characteristics of vehicle behaviors are explored, and general prediction models for these behaviors are then established. Based on the prediction models, efficient and intelligent network deployments, multiple network access schemes, as well as resource management schemes are completed, meeting the high-capacity and high-efficiency demands of future vehicular networks are designed.
This paper analyzes the security communication performance of secondary user communication pairs in Cognitive Radio Non-Orthogonal Multiple Access (CR-NOMA) networks, where interference sources and eavesdropping nodes are randomly distributed. The stochastic geometry theory is used to model the eavesdropping nodes and the interfering nodes as a homogeneous Poisson Point Processes (PPP). Firstly, to ensure the reliability of the primary user communication pairs, the power allocation coefficient set of the sender is obtained, and the closed expressions of the connection outage probability and the secrecy outage probability of the secondary user are further obtained. Then, the variation of the power distribution coefficient with the constraint of the primary user’s reliability is analyzed. Finally, the relationship between outage probability of secondary user communication pairs and the density of the eavesdropping nodes and the transmission power is studied. The research shows that the enhancement of interfering signal reduces the reliability of the system, but brings about a significant improvement of security performance. The simulation results verify the correctness of the theoretical analysis.
In order to improve the ability of noise elimination and channel equalization of strong non-linear signals, a Multi-scale Kernels learning Affine Projection filtering Algorithm based on Surprise Criterion (SC-MKAPA) is proposed on the basis of kernel learning adaptive filtering method. Based on the kernel affine projection filtering algorithm, the structure of the kernel combination function is improved, and the bandwidths of several different Gaussian kernels are taken as variable parameters to participate in the update of the filter together with the weighted coefficients.The calculation results are sparsed by using the surprise criterion, and the surprise measure is improved according to the constraints of the affine projection algorithm, which simplifies the variance term and reduces the calculation complexity. The algorithm is applied to noise cancellation, channel equalization, and Mackey Glass (MG) time series prediction The simulation results are compared with the traditional adaptive filtering algorithm and the kernel learning adaptive filtering algorithm, it proves the superiority of the proposed algorithm.
Indoor positioning technology based on Channel State Information (CSI) receives much attention in recent years. The existing indoor positioning solution is continuously innovative and improved in terms of deployment implementation and positioning accuracy. This paper proposes a passive one-transmitter two-receivers fingerprint indoor positioning system. The CSI data is collected by two fixed receiving end-devices. In the signal preprocessing stage, the CSI amplitude is singular value removed and low pass filtered, and the CSI phase is corrected by a linear fitting method, and the CSI amplitude and phase information obtained by the two receiving ends is collectively used as a fingerprint. The fingerprint samples are finally trained through the fully connected neural network, and matched with the collected real-time data. Experiments show that the matching recognition rate reaches 98% by using two receivers and the combination of amplitude and phase positioning, and the positioning accuracy is 0.69 m. It proves that the system can accurately and effectively achieve indoor positioning.
For the problems of sparse planar array optimization with side-lobe concave nulls constraints and premature algorithm, a Hybrid Trigonometric Mutation Differential Evolution (HTMDE) algorithm is proposed based on the idea of parameter adaptation. By introducing side-lobe concave nulls constraints matrix, adaptive penalty function is constructed. Time-varying weight combination mutation strategy and crossover strategy improve the initial global search ability and late convergence ability of the algorithm. The constrained optimization of the planar array with peak side lobe level and side-lobe concave nulls is finally realized. The simulation results show that, compared with the algorithm before the hybrid trigonometric mutation strategy, the algorithm not only optimizes the peak side-lobe level of sparse array, but also designs concave nulls in specified side-lobe area to reduce the influence of active interference.
To effectively detect sea surface targets by the passive interferometric microwave technology considered as an important complement for the space-based early warning system of China, a detection algorithm is proposed based on the Passive Interferometric Microwave Images (PIMI). First, the mathematical model of PIMI is established for the sea background and sea surface target. Second, the detection algorithm is introduced in detail, and numerical simulations are performed to demonstrate the feasibility of the proposed algorithm. Finally, the air-borne experiments are also carried out. Both theoretical and experimental results demonstrate that the proposed algorithm is feasible, can effectively detect sea surface targets, and shows good performance. That also exhibits that the moving metal vessels on the sea surface show a “hot” and “low” characteristic in PIMI, which can be used to improve the detection probability. The proposed detection algorithm can provide a reference for space-based PIMI to detect sea surface targets.
In the view of the integrity verification problem of data sharing on the cloud platform, a Shared Data auditing scheme supports efficient Revocation of group Members via multi-participation (SDRM) is proposed. First, through the Shamir secret sharing method, multiple group members participate in revoking the illegal group members, ensuring the equal rights between the group members. Second, this scheme combined with algebraic signature technology, the file identifier identifies the data owner’s upload data record and the normal group member’s access record, enabling the data owner to efficiently update all of its data. Finally, theoretical analysis and experimental verification of the correctness, safety and effectiveness of the scheme show that the scheme meets the requirement of efficient cancellation of group members, at the same time, as the number of data owners increases, the efficiency of updating data in this scheme is significantly higher than that of NPP.
The monolithic signal processing circuit system for Light Detection And Ranging (LIDAR)measurement has significant practical values in terms of improving LIDAR measurement accuracy and data rate, shortening measurement time, and reducing equipment size and power consumption. As the environment interface problem is less considered, the appropriate input interface model must be established to break through the technology difficulty to associate circuit system with photodetectors, die chip, package, transmission line, test board and so on in the operating frequency range. By the combination of theoretical analysis and model simulation, the real working environment of circuit systemfor LIDAR signal processing can be simulated reasonably. Furthermore, based on CMOS technology, the signal processing circuit chip is tested with different photodetector parasitic capacitances. The well agreements between simulation and the testing results validate the feasibility of the input interface model.
For online dynamic radio resources optimization for network slices in H-CRAN, by comprehensively considering traffic admission control, congestion control, resource allocation and reuse, the problem is formulated as a stochastic optimization programming which maximizes network average total throughput subject to Base Station (BS) transmit power, system stability, Quality of Service (QoS) requirements of different slices and resource allocation constraints. Then, a joint congestion control and resource allocation dynamic scheduling algorithm is proposed which will dynamically allocate resources to users in network slices with distinct performance requirements within each resource scheduling time slot. The simulation results show that the proposed algorithm can improve the network overall throughput while satisfying the QoS requirement of each slice user and maintaining network stability. Besides, it could also flexibly strike a dynamic balance between delay and throughput by simply tuning an introduced control parameter.
In order to study the bursting oscillations and its formation mechanism of memristor-based system, a multi-timescale memristor-based S-M system is established by introducing a memristor device and two slowly changing periodic excitations into the Shimizu-Morioka (S-M) system. Firstly, the bursting behavior and bifurcation mechanism of S-M system under single excitation are studied, and a symmetric bursting pattern of "sub-Hopf/sub-Hopf" is obtained. Then the multi-frequency excitation system is transformed into single frequency excitation system by using De Moivre formula, and the influence of additional excitation amplitude and frequency on "sub Hopf / sub Hopf" bursting mode is analyzed by using the Fast-slow analysis method. As a result, two new bursting patterns named as twisted “sub-Hopf/sub-Hopf” bursting and nested “sub-Hopf/sub-Hopf” are found under different amplitudes of the additional excitation. The corresponding bursting mechanisms are analyzed with time history diagram, bifurcation diagram and transformation phase diagram. Finally, Multisim simulation results, which are in good agreement with the numerical simulation results, are provided to verify the validity of the study.
In order to solve the problem of inaccurate location in insulator target detection, this paper proposes an insulator orientation recognition algorithm based on deep learning. By adding angle information to the axis alignment detection frame, it can effectively solve the problem that conventional deep learning algorithm can not accurately locate the target. First, the angular rotation parameters are introduced into the axially aligned rectangular detection frame to form a directional detection frame. Then the parameter offset is added as the fifth parameter to the loss function for iterative regression. At the same time, in order to improve the detection accuracy, Adam algorithm is used to replace Stochastic Gradient Descent (SGD) to optimize the loss function. Finally, the insulator directional detection model can be obtained. The experimental results show that the orientation detection frame with rotation angle can effectively locate the insulator target accurately.
The searchable encryption technology enables users to encrypt data and store it in the cloud, and can directly retrieve ciphertext data. Most of the existing searchable encryption schemes are single-to-single mode, and the searchable encryption scheme in some multi-user environments is based on public key cryptography or identity-based public key cryptosystem. Such schemes have certificate management and key escrow issues and scheme are vulnerable to suffer internal keyword guessing attacks. This paper combines public key authentication encryption and proxy re-encryption technology, and proposes an efficient certificateless authentication searchable encryption scheme for multi-user environment. The scheme uses proxy re-encryption technology to re-encrypt portion of ciphertexts, so that authorized users can generate trapdoor with the keywords to query ciphertext. In the random oracle model, we prove that our scheme has the ability to resist the internal keyword guessing of two type attackers in the certificateless public key cryptosystem, and the calculation and communication efficiency of the scheme is better than the similar scheme.
In order to reduce the serious mutual coupling effect across the elements of the existing collocated vector sensor array and further improve the parameter estimation accuracy, a Sparsely Stretched L-shaped Polarization Sensitive Array (SSL-PSA) is proposed in this paper, and a novel method for estimating the azimuth-elevation angles as well as polarization parameters is presented accordingly. Firstly, the signal model of SSL-PSA is established. Then, the SSL-PSA is divided into 6 subarrays, thus the ESPRIT algorithm can be utilized to estimate the Rotational Invariant Factors (RIFs). On this basis, a set of fine but ambiguous estimates and four sets of unambiguous coarse estimates of direction cosine are obtained by a series of mathematical operations. Then, four corresponding steering vectors can be reconstructed and the correct coarse direction-cosine estimation can be determined according to the orthogonality of the steering vector and the noise subspace. Finally, the estimates of Direction-Of-Arrival (DOA) and polarization can be achieved by the existing disambiguate method. Compared to the existing polarization sensitive array consists of collocated vector sensor, the proposed one has no collocated configuration, which can reduce the mutual coupling effect. Additionally, the proposed method can also extend the spatial aperture and refine the direction-finding accuracy without adding any redundant antennas. Simulations are carried out to verify the effectiveness of the proposed method.
As the key object in the process of template analysis, power traces have the characteristics of high dimension, less effective dimension and unaligned. Before effective preprocessing, template attack is difficult to work. Based on the characteristics of energy data, a global alignment method based on manifold learning is proposed to preserve the changing characteristics of power traces, and then the dimensionality of data is reduced by linear projection. The method is validated in Panda 2018 challenge1 standard datasets respectively. The experimental results show that the feature extraction effect of this method is superior than that of traditional PCA and LDA methods. Finally, the method of template analysis is used to recover the key, and the recovery success rates can reach 80% with only two traces.
In the presence of parasitic elements, fading memory may occur in charge controlled memristors. The effects of parasitic resistance and capacitance on the dynamic characteristics of memristor are studied by using the dynamic route map and simulation method. The oretical and simulation analysis shows that the ideal charge controlled (current controlled) memristor does not have fading memory when the parasitic resistance or capacitance exists alone under the excitation of DC and AC, but fading memory occurs when the parasitic resistance and capacitance exist at the same time. The mechanism is that the parasitic elements form discharge path, which leads to fading memory of the charge controlled memristor.
In order to overcome shortcomings of the traditional fuzzy clustering algorithm for image segmentation, such as this easily affected by noise, sensitive to the initial value of clustering center, easily falling into local optimum, and inadequate ability of fuzzy information processing, an intuitionistic fuzzy clustering image segmentation algorithm is proposed based on flower pollination optimization with nearest neighbor searching. Firstly, a novel extraction strategy of image spatial information is proposed, and then an intuitionistic fuzzy clustering objective function with image spatial information is constructed to improve the algorithm’s robustness against noise and enhance the ability of the algorithm to process the image fuzzy information. In order to overcome the defects of sensitivity to clustering centers and easily falling into local optimum, a flower pollination algorithm based on nearest neighbor learning search mechanism is proposed. Experimental results show that the proposed method can get satisfactory segmentation results on a variety of noisy images.
As a new type of neural network, Extreme Learning Machine (ELM) has extremely fast training speed and good generalization performance. Considering the problem that the Extreme Learning Machine has high computational complexity and huge memory demand when dealing with high dimensional data, a Batch inheritance Extreme Learning Machine (B-ELM) algorithm is proposed. Firstly, the dataset is divided into different batches, and the automatic encoder network is used to reduce the dimension of each batch. Secondly, the inheritance factor is introduced to establish the relationship between adjacent batches. At the same time, the Lagrange optimization function is constructed by combining the regularization framework to realize the mathematical modeling of batch ELM. Finally, the MNIST, NORB and CIFAR-10 datasets are used for the test experiment. The experimental results show that the proposed algorithm not only has higher classification accuracy, but also reduces effectively computational complexity and memory consumption.
In through-the-wall scene, due to the serious attenuation of signal caused by wall, the energy of target reflection signal in the received signal decreases significantly and the received signal is submerged in the direct signal of the transceiver and the reflection signal of indoor furniture, making the target behind wall is hard to be detected. In view of the above problems, a novel Through-the-Wall Multiple human targets Detection algorithm(TWMD) based on multidimensional signal features fusion is proposed. Firstly, the received Channel State Information(CSI) is preprocessed to eliminate the phase error and amplitude noise, and the multidimensional signal features are fully extracted from the correlation coefficient matrix by using time correlation and subcarrier correlation of CSI. Finally, the mapping between features and detection results is established by BP neural network. The experimental results show that the recognition accuracy of this algorithm in the environment with glass wall, brick wall and concrete wall is above 0.98, 0.90, 0.85, respectively. According to the detection results of 4000 samples, compared with the existing detection algorithms based on single signal feature, the proposed algorithm achieves an average accuracy improvement of 0.45 in the detection of different number of moving targets.
Image-to-image translation is a method to convert images in different domains. With the rapid development of the Generative Adversarial Network(GAN) in deep learning, GAN applications are increasingly concerned in the field of image-to-image translation. However, classical algorithms have disadvantages that the paired training data is difficult to obtain and the convert effect of generation image is poor. An improved Cycle-consistent Generative Adversarial Network(CycleGAN++) is proposed. New algorithm removes the loop network, and cascades the prior information of the target domain and the source domain in the image generation stage, The loss function is optimized as well, using classification loss instead of cycle consistency loss, realizing image-to-image translation without training data mapping. The evaluation of experiments on the CelebA and Cityscapes dataset show that new method can reach higher precision under the two classical criteria—Amazon Mechanical Turk perceptual studies(AMT perceptual studies) and Full-Convolutional Network score(FCN score), than the classical algorithms such as CycleGAN, IcGAN, CoGAN, and DIAT.
A novel design of nonlinear transformation function for the signal detection in impulsive noise is proposed. The proposed method takes the advantage of adjustable fading factors of the exponential function, it can be effective for different models of impulsive noise. By introducing the efficacy as the objective function, nonlinear design is converted into the problem of optimizing the threshold and bottom parameters to maximize the efficacy. Since the efficacy is continuous, derivative, and unimodal, the optimization problem can be easily solved by the traditional optimization methods, such as the Nelder-Mead simplex method. Analysis shows that the proposed design can obtain the optimal performance in the widely-used models of impulsive noise, including the symmetric α-stable model, the Class A model, and the Gaussian mixture model. Simulation on real atmospheric noise demonstrates that the proposed design is obviously better than the traditional clipper and blanker. Thus, this paper proposes an optimal and uniform solution for suppressing impulsive noise of various models.
In order to solve the problem of geomagnetic interference and model nonlinearity in the tracking process of magnetic dipole under geomagnetic background Monte Carlo Kalman Filter (MCKF) tracking method based on differential magnetic anomaly is proposed in this paper. The new tracking method takes the difference of magnetic field measured by sensor array as the observation signal, and uses Monte Carlo Kalman Filtering (MCKF) algorithm to solve the nonlinear problem of the model to realize the real-time tracking of magnetic dipole targets. The simulation results show that the proposed method is more accurate than the traditional Extended Kalman Filter (EKF) or Untracked Kalman Filter (UKF) in the stable tracking process. The results of real geomagnetic background tracking experiments show that the proposed algorithm has better tracking performance under low SNR.
The SIMON block cipher receives extensive attention since its proposed. With respect to integral attacks, some integral attacks on SIMON32 and SIMON48 are presented by Wang, Fu and Chu et al. In this paper, on the basis of the existing analysis results, the integral attacks on SIMON64 are further studied. Based on known 18-round integral distinguisher presented by Xiang et al., the integral attacks on 25-round SIMON64/128 are presented using meet-in-the-middle and partial-sum techniques. Then the amount of subkeys that need to be guessed during the attack is further reduced by equivalent-subkey technique, and the improved integral attacks on 26-round SIMON64/128 are also presented. Through further analysis, we find that the higher version of SIMON algorithm has better resistance to integral analysis.
An observer is placed on the airborne in the multistatic passive radar localization system. The error in observer position may seriously affect the localization accuracy. An algebraic closed-form solution is proposed for 3D localization of multistatic passive radar in the presence of sensor position errors. Firstly, the nonlinear Bistatic Range Difference (BRD) measurement equations are linearized by proper additional parameters and a pseudo-linear estimation model is given accordingly. Then a modified Two-Step Weighted Least Squares (TS-WLS) algorithm is developed with considering the statistic characteristics of the observer position measurement noises. Finally the Cramer-Rao Lower Bound (CRLB) and the theoretical error of the algorithm are derived. Simulation results show that the proposed algorithm can achieve the CRLB in a moderate level of noises.
A new ensemble TSK fuzzy classifier (i,e. IK-D-TSK) is proposed. First, all zero-order TSK fuzzy sub-classifiers are organized in a parallel way, then the output of each sub-classifier is augmented to the original (validation) input space, finally, the proposed Iterative Fuzzy C-Means (IFCM) clustering algorithm generates dictionary data on augmented validation dataset, and then KNN is used to predict the result for test data. IK-D-TSK has the following advantages: the output of each zero-order TSK subclassifier is augmented to the original input space to open the manifold structure in parallel, according to the principle of stack generalization, the classification accuracy can be improved; Compared with traditional TSK fuzzy classifiers which trains sequentially, IK-D-TSK train all the sub-classifiers in parallel, so the running speed can be effectively guaranteed; Because IK-D-TSK works based on dictionary data obtained by IFCM & KNN, it has strong robustness. The theoretical and experimental results show that IK-D-TSK has high classification performance, strong robustness and high interpretability.
To improve bandwidth, efficiency and linearity of Envelope Tracking (ET) architecture, it is necessary to optimize the performance of envelope supply modulator and linearize nonlinear behavior of the ET system. The optimization procedure of the supply modulator is proposed based on the equivalent circuit model. The frequency compensation network is used to improve the bandwidth and linearity of the modulator circuit. An envelope enhanced memory polynomial digital pre-distortion model is introduced to address the nonlinear distortion of the ET system. The practical circuit mentioned above is fabricated and the overall experimental system is set up. Measurement results show that the ET PA at S-band obtains measured efficiency 61%, 54%, 44% and Error Vector Magnitude (EVM) 1% for 6.7 dB PAPR signals with 5 MHz/10 MHz/20 MHz modulation bandwidths, respectively. The ET system exhibits competitive bandwidth, efficiency and linearity, which verifies the proposed optimization and linearization methodology.
Spoofing misleads the receiver to generate the wrong position information by trans-mitting signals similar to authentic satellite signals, which has great harm. In this paper, a single-antenna spoofing mitigation algorithm based on signal reconstruction is proposed for meaconing. Firstly, the carrier frequency and code phase of spoofing signal are obtained by parameter estimation method, and then the orthogonal projection matrix of spoofing signal subspace is constructed to suppress spoofing. The simulation results show that the algorithm has a good suppression effect on spoofing and ensure the receiver can locate effectively in the interference environment,the algorithm also has lower computational complexity.
Aiming at the problem of co-frequency base station interference in passive radar based on Long Term Evolution (LTE) signal, an algorithm based on blind source separation using second statistics is proposed. The presented algorithm is based on convolution mixed model, and achieves the minimum correlation among separated signals through multi-channel Least-Mean-Square (LMS) algorithm. Without statistical correlation among the signals of each transmitting base station, the separation of the observed signals is completed when the separated signals achieve the minimum correlation. On this base, the traditional signal processing for passive radar is improved. The steps of separating co-frequency interference clutter consisting of both direct-path and multipath clutter are added, which can suppress the clutter interference of co-channel base station. Simulation and analysis verify the effectiveness of the algorithm. The algorithm provides a reference for data processing of passive radar based on LTE signal.
For the non-linear and non-stationary characteristics of motor imagery ElectroEncephaloGram(EEG) signals, an EEG signal recognition method based on Conditional Empirical Mode Decomposition (CEMD) and Serial Parallel Convolutional Neural Network (SPCNN) is proposed. In the CEMD process, the correlation coefficient between the Intrinsic Mode Functions (IMFs) and the original signal is used as the first condition to select IMFs. Based on this, the relative energy occupancy rates between the IMFs are proposed as the second condition to select IMFs. Further, to consider the characteristics between the EEG signal channels and highlight the features in each EEG signal channel, a SPCNN model is proposed to classify the processed EEG signals. The experimental results show that the average recognition rate reaches 94.58% on the dataset collected by ourselves. And the average recognition rate reaches 82.13% on the BCI competition IV 2b dataset, which is 3.85% higher than the average recognition rate of convolutional neural network. Finally, the online control experiments are carried out on the designed intelligent wheelchair platform, which proves the effectiveness of the proposed algorithm for EEG signals recognition.
A novel image encryption algorithm is proposed based on a chaos set which consists of discrete chaotic systems and continuous chaotic systems. The chosen combination of chaotic system is dependent on the encryption intensity. The pixel mean value and pixel coordinate value of images are exploited to control the generation of key, thus enhancing the relationship between chaotic key and plain text. In addition, the octet of cipher text pixel is divided into three parts, and then hided into a processed public image, which can promote the external characteristics of cipher text. The image histogram analysis, correlation analysis, and information entropy analysis methods are adopted to identify the security performance, which indicates the effectiveness of the proposed image encryption algorithm and potential application in the image security transmission.
In view of shortcomings of dark channel prior dehazing methods, such as transmission in sky areas is small and halo effects are prone to occur in the edges, this paper proposes a novel and efficient dehazing algorithm. Firstly, the fan-shaped model with dark channel map of haze-free image is established by geometric analysis. Then a new Gaussian mean function is set to estimate the boundary values of the model and its standard deviation is adaptive processing. Mean-value unequal relationship is also introduced to approximate the two-sided boundary, which is used to fit the most excellent dark channel map of haze-free, further obtains the best transmission. At the same time the local atmospheric light is improved to recover the final result. Experimental results show that the proposed method can be widely applied to all kinds of images compared with other classical algorithms. The degree of dehazing is thorough, final result is clear and natural. More importantly, it is favorable for real-time processing that has low time complexity.
A micro-motion gesture recognition method based on multi-channel Frequency Modulated Continuous Wave (FMCW) millimeter wave radar is proposed, and an optimal radar parameter design criterion for feature extraction of micro-motion gestures is presented. The time-frequency analysis process is performed on the radar echo reflected by the hand, and the range Doppler spectrum, the range spectrum, the Doppler spectrum and the horizontal direction angle spectrum of the target are estimated. Then the range-Doppler-time-map feature is designed, range-time-map feature, Doppler-time-map feature, horizontal-angle-time-map feature, and three-joint feature with fixed frame time length are used to characterize the 7 classes micro-motion gestures, respectively. And these gesture features are captured and aligned according to the difference in amplitude and speed of the gesture motion process. Then a five-layer lightweight convolutional neural network is designed to classify the gesture features. The experimental results show that, the range-Doppler-time-map feature designed in this paper characterize the micro-motion gesture more accurately and has a better generalization ability for untrained test objects compared with other features.
A simple two-memristor chaotic circuit without inductance (only five electronic components) is designed by using a non-ideal active voltage control memristor and a flux-controlled smooth cubic nonlinear memristor. When the circuit parameters change, the basic dynamic behaviors of the system are studied in detail by the means of conventional nonlinear analysis, such as the analysis of equilibrium stability, phase diagram, Lyapunov exponent spectrum and bifurcation diagram. With the parameters changing, the proposed system can produce various phenomena of dynamics such as multi-scrolls, multi-wings and transient transition behaviors. Furthermore, the multistability characteristics of the system are also studied in the condition of changing the initial state of two memristors in system respectively, and some meaningful results are obtained. In order to verify the feasibility and stability of the circuit, the analog equivalent circuit of each memristor is constructed, and it is applied to the proposed chaotic circuit. The experimental results of the hardware circuit and the circuit simulation results of the Multisim are in good agreement with the theoretical analysis.
Wireless Local Area Network (WLAN) indoor intrusion detection technique is one of the current research hotspots in the field of intelligent detection, but the conventional database construction based intrusion detection technique does not consider the time-variant property of WLAN signal in the complicated indoor environment, which results in the low robustness of WLAN indoor intrusion detection system. To address this problem, a Multiple Kernel Maximum Mean Discrepancy (MKMMD) transfer learning based WLAN indoor intrusion detection approach is proposed. First of all, the offline labeled and online pseudo-labeled Received Signal Strength (RSS) features are used to construct source and target domains respectively. Second, the optimal transfer matrix is constructed to minimize the MKMMD of the joint distributions of RSS features in source and target domains. Third, a classifier trained from the transferred RSS features and the corresponding labels in source domain is used to classify the transferred RSS features in target domain, and meanwhile the label set corresponding to target domain is obtained. Finally, the label set corresponding to target domain is updated in an iterative manner until the proposed algorithm converges, and then the intrusion detection in target environment is achieved. The experimental results indicate that the proposed approach is able to preserve high detection accuracy as well as overcome the impact of time-variant signal property on the detection performance.
In view of the fact that worm viruses can only infect specific operating systems, the virus propagation rule and security performance optimization strategy in multi-operating system heterogeneous network are studied in this paper. First, considering that most viruses can only spread in link between the same operation system, the parameters of heterogeneous edges ratio are introduced into the SIRS (Susceptible Infected Remove Susceptible) virus transmission model, and the influence of heterogeneous edges and network security performance on the single system virus transmission is studied through system equilibrium solution and basic regeneration number analysis. Secondly, according to the moving target defense thought and technology, the network security optimization strategies is designed for non-isomeric random interrupt, non-isomeric random reconnecting and single operating system random node migration, and analyzes the variation of the same ratio and the basic number of regenerated Numbers in the three strategies and the impact on the safety of the network. Finally, the correctness of the virus propagation model is verified by simulation, and the network security performance optimization effects of the three strategies are analyzed.
In existing null broadening algorithm, the taper matrix does not contain phase information, and when it is used to against strong directional and large deviation angle interference, the null depth becomes shallow and the interference suppression performance drops seriously. An adaptive null broadening algorithm for sidelobe canceller is proposed based on dense disturbance in virtual airspace. The algorithm reconstructs the self-covariance matrix of the auxiliary array data and the co-covariance matrix of the main and auxiliary array data at the same time to realize the adaptive control of the null region. The taper matrix is only related to the position and width of the array elements, and it can be generated offline without disturbing information and occupying no computing resources of the system. The simulation results show that this method can achieve adaptive broadening of the null region and improve the robustness of non-stationary interference suppression.
Protograph Low Density Parity Check (P-LDPC) code has been widely used in various communication systems. In order to meet the requirements of error correction performance, hardware resource loss and power consumption in different application scenarios, further design optimization of P-LDPC codes is needed. This paper focuses on the properties of Joint Source-Channel Coding (JSCC) system based on Double P-LDPC (DP-LDPC) codes in standard channel environment, the optimization of code design and performance behavior, etc. The design and optimization for the system environment in recent years is summarized. It shows that the design optimization work has significantly improved the system performance, which provides some ideas for the research of II-oriented LDPC code. Finally, the future research work is discussed for the reference and promotion of interested scholars.
The millimeter wave radar is robust against various environments such as rain, fog, snow. It huge potentials in applications such as automotive radars, intelligent robots. At the same time, the rapid development of silicon technology improves the cut off frequency of the transistor, which make it possible to implement low cost millimeter wave radar SoCs in silicon. Recently a lot of research has been dedicated to improve the performance of the silicon based millimeter wave SoCs from both system level and key building blocks level. The current research status and future trends of the silicon based millimeter wave radar SoCs are reviewed in this paper.
In order to achieve a higher level of autonomy for Unmanned Combat Air Vehicle (UCAV) in autonomous air combat, an autonomous maneuvering decision system is established in this paper. Firstly, the factor function of maneuvering decision-making is established by using fuzzy logic, and then the prediction model of enemy aircraft maneuvering is designed. The air combat game is regarded as a Markov process, and the air combat situation is effectively calculated by using Bayesian inference (BI) theory. Finally, the whole air combat maneuvering decision-making process is carried out by Moving Horizon Optimization (MHO) method. Modeling and Simulation of short-range air combat are carried out. The results show that the proposed method can effectively improve the situation advantages of UCAV and has obvious advantages.
The massive high-dimensional measurements accumulated by distributed control systems bring great computational and modeling complexity to the traditional fault diagnosis algorithms, which fail to take advantage of the higher-order information for online estimation. In view of its powerful ability of representation learning and analyzing, deep learning based fault diagnosis is extensively studied, both in academia and in industry, making intelligent process control more automated and effective. In this paper, deep learning based fault diagnosis is reviewed and summarized as four parts, i.e., stacked auto-encoder based fault diagnosis, deep belief network based fault diagnosis, convolutional neural network based fault diagnosis, and recurrent neural network based fault diagnosis. Furthermore, some new trends, "integrated innovation", "data + knowledge" and "information fusion", are discussed from the view of data preprocessing, network design and decision module, which suggests the necessity and potential of further research.
Insect radar is the most effective tool for insect migration observation. In order to realize target recognition of insect radar, it is important to study the RCS characteristics of insects. This paper will analyze the static and dynamic Radar Cross Section (RCS) characteristics of insects. Firstly, based on the measured X-band fully-polarimetric RCS data, the static RCS characteristics of insects are analyzed, including the variations of horizontal and vertical polarization RCS with body weight respectively, and the variation of insect polarization pattern with body weight. Secondly, the dielectrics and geometric models currently used to study the RCS characteristics of insects are summarized by electromagnetic simulation. Twelve dielectric models consisting of four dielectrics (including water, spinal cord, dry skin, and chitin and hemolymph mixture) and three geometric models (including equivalent size prolate spheroid, equivalent mass prolate spheroid and triaxial prolate spheroid) are compared, and it be found that the RCS characteristics of equivalent mass prolate spheroid are closest to that of the real insects. Then, the fluctuation characteristics of insect dynamic RCS are analyzed based on the insect echo data measured in field by a Ku-band high-resolution insect radar. The measured insect dynamic RCS fluctuation data are fitted with four classical RCS fluctuation distribution models (χ2, Log-normal, Weibull and Gamma distribution), respectively. It can be seen from the least square error of fitting and goodness of fit test that Gamma distribution gives the best description of the statistical characteristics of insect RCS fluctuations. Finally, the application of insect RCS characteristics to insect orientation, mass and body length measurements for insect radars is summarized.
Polar Sine Transform (PST) is used to detect Copy-move forgeries in the paper, and the image to be detected is transformed into gray scale image and feature extraction is carried out by PST. Improved PatchMatch, a fast approximate nearest neighbor search algorithm, is used to match feature descriptors to overcome the problem of long time consuming caused by matching global descriptors. Experiments show that the proposed method is not only effective for linear Copy-move forgeries and rotation interference forgeries, but also robust to noise and JPEG compression interference forgeries. Finally, the experimental results of synthetic interference forgeries show that the accuracy can reach 98.0% when the synthetic forgeries range is small.
Ground Penetrating Radar (GPR), as a non-destructive technology, has been widely used to detect, locate, and characterize subsurface objects. Example applications include underground utility mapping and bridge deck deterioration assessment. However, manually interpreting the GPR scans to detect buried objects and estimate their positions is time-consuming and labor-intensive. Hence, the automatic detection of targets is necessary for practical application. To this end, this paper discusses the feasibility of using GPR to estimate target positions, and reviewed the progress made by domestic and international scholars on automatic hyperbolic signature detection in GPR scans. Thereafter, this paper summarizes and compares the processing methods for target detection. It is concluded that future research should focus on developing deep-learning based method to automatically detect and estimate subsurface features for on-site applications.
Pattern recognition algorithms can discover valuable information from mass data of biomedical images as guide for basic research and clinical application. In recent years, with improvement of the theory and practice of pattern recognition and machine learning, especially the appearance and application of deep learning, the crossing research among artificial intelligence, pattern recognition, and biomedicine become a hotspot, and achieve many breakthrough successes in related fields. This review introduces briefly the common framework and algorithms of image pattern recognition, summarizes the applications of these algorithms to biomedical image analysis including fluorescence microscopic images, histopathological images, and medical radiological images, and finally analyzes and prospect several potential research directions.
Due to the limitation of energy and bandwidth in Wireless Sensor Networks(WSN), the direct transmission of analog signals in the network is greatly restricted. Therefore, quantization of analog signals is an important means to save network energy and ensure effective bandwidth. To this end, based on the principle of minimum absolute mean reconstruction error a network quantization and energy optimization method is designed in this paper. Firstly, for single sensor, the optimal quantization bit number is derived under the condition of fixed energy and the optimal energy distribution is derived under the condition of fixed quantization bit number. Secondly, on the basis of single sensor, the optimal quantization bit number and optimal energy allocation are further deduced in multi-sensor case. In both cases, the sensor measurement noise and channel fading loss are considered. Finally, the numerical simulation results show that the proposed method is correct and better than the equal energy distribution.
The radio frequency fingerprinting of the emitter is complex, and the performance of Specific Emitter Identification (SEI) is subjected to the present expertise. To remedy this shortcoming, this paper presents a novel SEI algorithm based on signal trajectory image, which realizes joint extraction of multiple complex fingerprints using deep learning architecture. First, this paper analyses the visual characteristics of multiple emitter imperfections in the signal trajectory image. Thereafter, signal trajectory grayscale image is used as the signal representation. Finally, a deep residual network is constructed to learn the visual characteristics reflected in the images. The proposed method overcomes the limitations of existing knowledge, and combines high information integrity with low computational complexity. Simulation results demonstrate that, compared with the existing algorithms, the proposed one can remarkably improve the SEI performance with a gain of about 30%.
It is of great significance to optimize emergency resource schedule for earthquake emergency rescue. The conflicting multiple schedule goals, such as time, fairness, and cost, should be taken into consideration together in an earthquake emergency resource schedule. A three-objective optimization model with constraints is constructed according to earthquake emergency resource schedule problems. An Adaptive MultiObjective Particle Swarm Optimization (PSO) based on Evolutionary State Evaluation (AMOPSO/ESE) is proposed to optimize this model for obtaining the Pareto optimal set. At the same time, based on the decision behavior pattern of "macro first and micro later", the two-level optimal solution sets consisting of an interest optimal solution set and their neighborhood optimal solution sets are proposed to represent the Pareto front roughly, which can simplify the decision-making process. The simulation results show that the multiobjective resource schedules can be effectively obtained by the AMOPSO/ESE algorithm, and the performance of the proposed algorithm is better than that of the chosen competed algorithms in terms of convergence and diversity.
Considering the interference problem of overlapping areas of cells, the service continuity of mobile users and the utilization of spectrum resources in the 5G network. In this paper, we proposed an Energy Efficient resource allocation scheme for the Inactive user(EEI). Firstly, a user-centered virtual cell is generated based on the notification area of the inactive users, and the intra-cell next-generation NodeBs (gNBs) could cooperatively provide communication services for users to improve the communication quality, lower the inter-cell interference, and reduce the handover signaling overhead. Secondly, Lyapunov optimization method is used to maximize the energy efficiency of the network, while ensuring the stability of the data queue. To make the optimization problem tractable, the scheme is decomposed into two sub-problems: the optimal transmission resource allocation and optimal transmission power allocation. Notice that, the optimal solutions are local optimal, which are based on the relaxed optimization problem. The simulation results show that the proposed energy efficiency resource allocation scheme based on the inactive users could achieve a better performance than the comparison algorithms,in the price of higher computational complexity.
Existing Point-Of-Interest (POI) recommendation algorithms lack adaptability for users with different check-in features. To solve this problem, an adaptive POI recommendation method based on User Check-in Activity (UCA) feature and Temporal-Spatial (TS) probabilistic models UCA-TS is proposed. The user check-in activity is extracted using a probabilistic statistical analysis method, and a calculation method of user's inactive and active membership is given. On this basis, one-dimensional power law function and two-dimensional Gaussian kernel density estimation combined with time factor are used to calculate the probability for inactive and active features respectively, and the popularity of POI is incorporated to recommend. This method can adapt to the users' check-in features and reflect the users' check-in temporal-spatial preferences more accurately. The experiments show that the proposed method can effectively improve the recommendation precision and recall.
The current Synthetic Aperture Radar (SAR) target detection methods based on Convolutional Neural Network (CNN) rely on a large amount of slice-level labeled train samples. However, it takes a lot of labor and material resources to label the SAR images at slice-level. Compared to label samples at slice-level, it is easier to label them at image-level. The image-level label indicates whether the image contains the target of interest or not. In this paper, a semi-supervised SAR image target detection method based on CNN is proposed by using a small number of slice-level labeled samples and a large number of image-level labeled samples. The target detection network of this method consists of region proposal network and detection network. Firstly, the target detection network is trained using the slice-level labeled samples. After training convergence, the output slices constitute the candidate region set. Then, the image-level labeled clutter samples are input into the network and then the negative slices of the output are added to the candidate region set. Next, the image-level labeled target samples are input into the network as well. After selecting the positive and negative slices in the output of the network, they are added to the candidate region set. Finally, the detection network is trained using the updated candidate region set. The processes of updating candidate region set and training detection network alternate until convergence. The experimental results based on the measured data demonstrate that the performance of the proposed method is similar to the fully supervised training method using a much larger set of slice-level samples.
Cyclic Redundancy Check (CRC) is used in cascade with channel coding to improve the convergence of the decoding. In the new generation of wireless communication systems, such as 5G, both code length and code rate are diverse. To improve the decoding efficiency of cascaded systems, a CRC parallel algorithm with variable computing width is proposed in this paper. Based on the existing fixed bit-width parallel algorithm, this algorithm combines the parallel calculation of feedback data and input data in the formula recursive method, realizing a highly parallel CRC check architecture with variable bit-width CRC calculation. Compared with the existing parallel algorithms, the merged algorithm saves the overhead of circuit resources. When the bit-width is fixed, the resource saving effect is obvious, and at the same time, the feedback delay is also optimized by nearly 50%. When the bit-width is variable, the use of resources is also optimized accordingly.
Compressed Sensing (CS) theory is one of the most active research fields in electronic information engineering. CS theory overcomes the limits dictated by Nyquist sampling theorem. Compared to the required minimum sampling quantity, CS proves that the original signal can be restored with high probability by fewer measurements, which saves the time cost of data acquisition and processing without losing information features. CS theory can essentially be regarded as a tool for dealing with linear signal recovery problems, so it has obvious advantages in solving inverse problems of signals and images. Image degradation is one of them, and the process of restoring high-quality images is image optimization. In order to promote the academic research and practical application of CS theory, the basic principle of CS is introduced. Based on the previous research, this paper studies on CS-based image optimization technology in three main aspects: denoising, deblurring and super resolution. Finally, the problems and challenges are discussed, and the current trends are analyzed to provide reference and help for future work.
Image thresholding methods based on the rough entropy segment the images without prior information except the images. There are two problems to be considered in the rough entropy based thresholding methods, i.e., measuring the incompleteness of knowledge about an image and granulating the image. In this paper, reciprocal rough entropy, a new form of rough entropy, is defined to measure the incompleteness of the image information. In order to granulate the image effectively, a granule size selection method based on the homogeneity histogram is employed. The proposed reciprocal rough entropy is simple in expression and calculation. The experimental results verify the effectiveness of the proposed algorithm.
In order to reduce the delay of computing tasks and the total cost of the system, Mobile Eedge Computing (MEC) technology is applied to vehicular networks to improve further the service quality. The delay problem of vehicular networks is studied with the consideration of computing resources. In order to improve the performance of the next generation vehicular networks, a multi-platform offloading intelligent resource allocation algorithm is proposed to allocate the computing resources. In the proposed algorithm, the K-Nearest Neighbor (KNN) algorithm is used to select the offloading platform (i.e., cloud computing, mobile edge computing, local computing) for computing tasks. For the computing resource allocation problem and system complexity in non-local computing, reinforcement learning is used to solve the optimization problem of resource allocation in vehicular networks using the mobile edge computing technology. Simulation results demonstrate that compared with the baseline algorithms (i.e., all tasks offload to the local or MEC server), the proposed multi-platform offloading intelligent resource allocation algorithm achieves a significant reduction in latency cost, and the average system cost can be saved by 80%.
For the problem that mobile robot can not avoid large concave obstacles during navigation, this paper proposes a multi-state integrated navigation algorithm. The algorithm classifies the running state of mobile robot into running state, switching state and obstacle avoidance state according to different moving environment, and defines the state double switching conditions based on the running speed and running time of the mobile robot. The Artificial Potential Field Method (APFM) is used to navigate and observe the geometric configuration of adjacent obstacles in real time. When encountering an obstacle, the switching state is used to determine whether the state switching condition is satisfied, and the obstacle avoidance algorithm is executed to enter the obstacle avoidance state and enter the obstacle avoidance state to implement the obstacle avoidance algorithm. After the obstacle avoidance is completed, the state automatically switches back to the running state to continue the navigation task. The proposal of multi-state can solve the problem of local oscillation of traditional artificial potential field method in the process of avoiding large concave obstacles. Furthermore, the double-switching condition determination algorithm based on running speed and running time can realize smooth switching between states and optimize the path. The experimental results show that the algorithm can not only solve the local oscillation problem, but also reduce the obstacle avoidance time and improve the efficiency of the navigation algorithm.
Deep learning has shown excellent performance in the field of artificial intelligence. In the supervised identification task, deep learning algorithms can achieve unprecedented recognition accuracy by training massive tagged data. However, owing to the high cost of labeling massive data and the difficulty of obtaining massive data of rare categories, it is still a serious problem how to identify unknown class that is rarely or never seen during training. In view of this problem, the researches of Zero-Shot Learning (ZSL) in recent years was reviewed and illustrated from the aspects of research background, model analysis, data set introduction and performance analysis in this article. Some solutions of mainstream problem and prospects of future research are provided. Meanwhile, the current technical problems of ZSL was analyzed, which can offer some references to beginners and researchers of ZSL.
A high-resolution controllable magnification method for visual saliency object based on virtual optics is proposed in this paper. The original image is placed on the virtual object plane. Firstly, the diffractive wave of the original image on the virtual diffraction plane is obtained by inverse diffraction calculation, and then the forward diffraction calculation is carried out after the virtual diffraction wave is irradiated by spherical wave. The original images with different magnification can be reconstructed by changing the position of the observation plane. The simulation results show that compared with the general interpolation method, the magnified image shows a good visual perception effect, especially in the saliency region. When the degraded face image is used as the signal to be reconstructed, the significant areas such as eyes and nose are clearer than the general method. The local salient region in the original image is segmented by the level set method combined with salient map, and the magnification and contour extraction are performed. The contours show good smoothness.
To solve the problem of asynchronous sampling and communication delay of sensor network in space target tracking, an Asynchronous Distributed algorithm based on Information Filtering (ADIF) is proposed. First, local state information and measurement information with sampling time are transmitted between local sensor and adjacent nodes in a certain topology structure. Then, the local sensor sorts the received asynchronous information by time, and uses ADIF algorithm to calculate the target state respectively. This method is simple to implement, the frequency of communication between sensors is small, and it supports the real-time change of network topology, which is suitable for multi-target tracking. In this paper, single target and multi-target tracking are simulated respectively. The results show that the algorithm can effectively solve the problem of asynchronous sensor filtering, and the distributed filtering accuracy converges to the centralized result.
In the heterogeneous wireless networks, the parameter weight is difficult to determine for the vertical handover algorithm considering the parameters of the network and the user, at the same time, the vertical handover algorithm based on fuzzy logic has high complexity. Considering this problem, a hierarchical vertical handover algorithm based on fuzzy logic is proposed. Firstly, the Received Signal Strength (RSS), bandwidth and delay are input into the first-level fuzzy logic system. Combining with the rule adaptive matching, the QoS fuzzy value is inferred, and the network is initially filtered by the QoS fuzzy value to obtain the candidate network set; Then, the second-level fuzzy logic system is triggered by the trigger mechanism, and the QoS fuzzy value, network load rate and user access cost of the candidate network are input into the second-level fuzzy logic system. At the same time, the output decision value is obtained by combining the adaptive rule matching, so as to select the best access network. Finally, the experimental results show that the algorithm can guarantee the network performance while reducing the time cost of the system.
The wide-swath interferometric altimeter working at near-nadir is a newly developed ocean surface topography measurement technology in recent years. Different from land elevation measurement, for the dynamic ocean surface waves, they move randomly all the time and this brings bias in Synthetic Aperture Radar (SAR) imaging and interferometric processes and leads to the final height measurement errors. For the requirement of centimeter-level precision, this error is the main source of measurement errors. The errors due to the characteristics of ocean surface and their impact on near-nadir InSAR’s precision are investigated. The motion error theoretical model is established combining the characteristics of the ocean surface and InSAR working mechanism, and the electromagnetic bias and layover bias are also taken into consideration. The error models in different SAR modes under various sea states are simulated. The error model is validated by the interferometric SAR full-link experimental simulation and the simulation results are consistent with the theoretical values. The results show that the errors are approximately linear changing with the Doppler centroid frequency and are proportional to the radial velocity of targets modulated by scattering. The errors are not only related to the characteristics of the waves, but also related to system parameters. This work can provide the feasible suggestions for future system design, error budget and data processing.
Unbalanced load on the edge computing server will seriously affect service capabilities, a task scheduling strategy Reinforced Q-learning-Automatic Intent Picking (RQ-AIP) for edge computing scenarios is proposed. Firstly, the load balance of the entire network is measured based on the load distribution of the server. By combining the reinforcement learning method, the appropriate edge server is matched for the task to meet the resource differentiation needs of sensor node tasks. Then, a mapping relationship between task delay and terminal transmit power is constructed to satisfy the constraints of the physical domain. Combining the social attributes of terminal, the appropriate relay terminal is continuously selected for the task to achieve the load balancing of network by terminal-assisted scheduling. Simulation results show that compared with other load balancing strategies, the proposed strategy can effectively alleviate the load between the edge servers and the traffic of the core network, reduce task processing latency.
In order to solve the Virtual Network Function (VNF) migration optimization problem caused by the dynamicity of service requests on the 5G network slicing architecture, firstly, a stochastic optimization model based on Constrained Markov Decision Process (CMDP) is established to realize the dynamic deployment of multi-type Service Function Chaining (SFC). This model aims to minimize the average sum operating energy consumption of general servers, and is subject to the average delay constraint for each slicing as well as the average cache, bandwidth resource consumption constraints. Secondly, in order to overcome the issue of having difficulties in acquiring the accurate transition probabilities of the system states and the excessive state space in the optimization model, a VNF intelligent migration learning algorithm based on reinforcement learning framework is proposed. The algorithm approximates the behavior value function by Convolutional Neural Network (CNN), so as to formulate a suitable VNF migration strategy and CPU resource allocation scheme for each network slicing according to the current system state in each discrete time slot. The simulation results show that the proposed algorithm can effectively meet the QoS requirements of each slice while reducing the average energy consumption of the infrastructure.
Considering at the security risks and privacy leaks in the process of data and reward in the Mobile CrowdSensing (MCS), a distributed security delivery model based on Tangle network is proposed. Firstly, in the data perception stage, the local outlier factor detection algorithm is used to eliminate the anomaly data, cluster the perception data and determine the trusted participant. Then, in the transaction writing stage, Markov Monte Carlo algorithm is used to select the transaction and verify its legitimacy. The anonymous identity data is uploaded by registering with the authentication center, and the transaction is synchronously written to the distributed account book. Finally, combined with Tangle network cumulative weight consensus mechanism, when the security of transaction reaches its threshold, task publishers can safely deliver data and rewards. The simulation results show that the model not only protects user privacy, but also enhances the ability of secure delivery of data and reward. Compared with the existing sensing platform, the model reduces the time complexity and task publishing cost.
Linear tapered slot antennas have significant advantages over traditional horn antennas, dielectric rod antenna when used as feed elements in Focal Plane Arrays (FPA) of Passive MilliMeter Wave(PMMW) imaging. In this paper, a novel Antipodal Linear Tapered Slot Antenna(ALTSA) is designed and optimized. The proposed antenna, the gain of which is improved by loading metamaterial structure, is fed by the Substrate Integrated Waveguide(SIW). Simulation and measure analysis show that the good impedance characteristics, low sidelobe levels, high and smooth gain are all achieved in a wide frequency band. Meanwhile, the designed antenna has a smaller aperture width and is easier to form a denser feed array in the focal plane to improve the spatial resolution of passive millimeter wave imaging.
Since the echo characteristics of moving targets are different from that of stationary targets, the traditional reconstruction filter bank algorithm, i.e., the reconstruction filter algorithm, is not applicable. In this paper, a novel reconstruction approach of the moving target for a multichannel in azimuth High-Resolution Wide-Swath (HRWS) Synthetic Aperture Radar (SAR) system is proposed. The approach firstly analyzes the echo characteristics of the moving target for the multi-channel in azimuth SAR system and gives the main reason for the failure of the traditional reconstruction method in contrast to the form of the stationary target echo. By introducing the radial velocity of the moving target, the spectrum reconstruction of the uniform moving target is effectively realized, and the azimuth ambiguities of the uniform moving target for the multi-channel in azimuth SAR system is well suppressed. Space-borne simulated results confirm the effectiveness of the proposed reconstruction approach.
Live migration of Virtual Machines(VMs) across WANs is an important support for multi-datacenter cloud computing environments. The current live migration of VMs across WANs faces many technical challenges due to the limitations of small bandwidth and no shared storage, such as ensuring the security and consistency of image data migration. Therefore, a method for VM live migration across datacenters based on HashGraph is proposed in this paper, decentralized ideas are used to achieve reliable and efficient image distribution between datacenters. The Merkle DAG of HashGraph improves the deficiencies of deduplication when migrating images across datacenters. Compared with existing methods, it can reduce total migration time.
In order to completely remove the spurious-peak side effect in the undersampling based wide-band spectral analysis, this paper proposes a high-performance co-prime spectral analysis method based on paralleled all-phase point-pass filtering. On basis of a deep analysis on the mechanism of the classical co-prime spectral analysis, this paper discovers that this spurious-peak side effect arises from those redudant overlapping boundary-bands related to distinct polyphase filtering branches between the up data path and the down data path. Therefore, through replacing the prototype filters in the classical co-prime spectral analysis by the all-phase point-pass filtering banks, this paper derives a novel co-prime analysis dataflow based on paralleled all-phase point-pass filtering. Both theoretic analysis and numerical simulation show that the proposed spectral analysis method achieves remarkable performance improvement: it can not only completely remove the spurious-peak side effect, but also obtains a much higher spectral resolution than the classical co-prime analysis, thereby possessing another merit of distinguishing dense spectral components. The proposed spectral analysis method possesses vast potentials in the software-defined radio, radar detection, passive positioning and marine wireless communication etc.
To improve performance of denoising and edge preservation of the total variational image denoising model, a curvature difference driven minimal surface filter is proposed. Firstly, the presented filter model is constructed by adding an adaptive edge detection function of curvature difference to the mean curvature filter model. After that, from the perspective of differential geometry theory, the physical meaning of the energy functional model and the method of reducing the average curvature energy are elaborated. Finally, in the discrete image domain, the surface in the neighborhood of each pixel of the image is iteratively evolved to the minimal surface to minimize the average curvature energy of the energy functional, so that the total energy of the energy functional is also minimized. Experiments show that the filter can not only remove Gauss noise and salt and pepper noise, but also remove the mixed noise composed of these two kinds of noise. And its performance of noise reduction and edge preservation is better than the other five total variational algorithms of the same kind.
RGB-D saliency detection identifies the most visually attentive target areas in a pair of RGB and Depth images. Existing two-stream networks, which treat RGB and Depth data equally, are almost identical in feature extraction. As the lower layers Depth features with a lot noise, it causes image features not be well characterized. Therefore, a multi-modal feature-fused supervision of RGB-D saliency detection network is proposed, RGB and Depth data are studied independently through two-stream , double-side supervision module is used respectively to obtain saliency maps of each layer, and then the multi-modal feature-fused module is used to later three layers of the fused RGB and Depth of higher dimensional information to generate saliency predicted results. Finally, the information of lower layers is fused to generate the ultimate saliency maps. Experiments on three open data sets show that the proposed network has better performance and stronger robustness than the current RGB-D saliency detection models.
The application of Dynamic Voltage Scaling (DVS) technique in real-time system energy management will result in the decrease of system reliability. A dynamic energy management method based on Improved Bird Swarm Algorithm (IoBSA) is proposed in this paper. Firstly, the population is initialized uniformly with the principle of good point set, so as to improve the quality of initial solution and increase the diversity of population effectively. Secondly, in order to better balance the global and local search ability of BSA algorithm, the nonlinear dynamic adjustment factor is proposed. Then, a power consumption model with time and reliability constraints is established for the dynamic adjustment of processor frequency in embedded real-time systems. On the premise of ensuring real-time performance and stability, the proposed IoBSA algorithm is used to find the solution with minimum energy consumption. The experimental results show that compared with the traditional BSA algorithm and other common algorithms, the improved bird swarm algorithm has a strong advantage in solving the minimum energy consumption and a fast processing speed energy management.
In view of the problems of low attack detection rate and high false positive rate caused by poor accuracy and robustness of the extracted traffic features in network traffic anomaly detection, a network traffic anomaly detection method based on deep features learning is proposed, which is combined with Stacked Denoising Autoencoders (SDA) and softmax. Firstly, a two-stage optimization algorithm is designed based on particle swarm optimization algorithm to optimize the structure of SDA, the number of hidden layers and nodes in each layer is optimized successively based on the traffic detection accuracy, and the optimal structure of SDA in the search space is determined, improving the accuracy of traffic features extracted by SDA. Secondly, the optimized SDA is trained by the mini-batch gradient descent algorithm, and the traffic features with strong robustness are extracted by minimizing the difference between the reconstruction vector of the corrupted data and the original input vector. Finally, softmax is trained by the extracted traffic features to construct an anomaly detection classifier for detecting traffic attacks with high performance. The experimental results show that the proposed method can adjust the structure of SDA based on the experimental data and its classification tasks, extract traffic features with a higher accuracy and robustness, and detect traffic attacks with high detection rate and low false positive rate.
Focusing on the problem of rather large estimation error in the traditional Direction Of Arrival (DOA) estimation algorithm induced by finite subsampling, a robust DOA estimation method based on low rank recovery is proposed in this paper. Following the low-rank matrix decomposition method, the received signal covariance matrix is firstly modeled as the sum of the low-rank noise-free covariance matrix and sparse noise covariance one. After that, the convex optimization problem associated with the signal and noise covariance matrix is constructed on the basis of the low rank recovery theory. Subsequently, a convex model of the estimation error of the sampling covariance matrix can be formulated, and this convex set is explicitly included into the convex optimization problem to improve the estimation performance of signal covariance matrix such that the estimation accuracy and robustness of DOA can be enhanced. Finally, with the obtained optimal noiseless covariance matrix, the DOA estimation can be implemented by employing the Minimum Variance Distortionless Response (MVDR) method. In addition, exploiting the statistical characteristics of the sampling covariance matrix estimation error subjecting to the asymptotic normal distribution, an error parameter factor selection criterion is deduced to reconstruct the noise-free covariance matrix preferably. Compared with the traditional Conventional BeamForming (CBF), Minimum Variance Distortionless Response(MVDR), MUltiple SIgnal Classification (MUSIC) and Sparse and Low-rank Decomposition based Augmented Lagrange Multiplier(SLD-ALM) algorithms, numerical simulations show that the proposed algorithm has higher DOA estimation accuracy and better robustness performance under finite sampling snapshot.
Based on the in-depth research on the S-box constitution arithmetic of composite, An area optimized generic low-entropy higher-order masking scheme is proposed in this paper. The low entropy masking method is introduced on GF(24), and the partial module reusing design is adopted, which effectively reduces the number of multiplications based on the S-box inversion operation of the composite. The algorithm can be applied to any order masking scheme of arbitrary S-box composed of inversion operation, and further applies this scheme to AES, gives detailed simulation results and optimizes the layout area, compared with the traditional masking scheme. Effectively reduce the use of logical resources, in addition, the security is theoretically proved.
Orthogonal Frequency Division Multiplexing(OFDM) has been widely used in wireless communication systems, and its data transmission security has certain practical significance. A double encryption scheme is proposed which enhances the confidentiality of the OFDM communication system and can prevent brute force attacks significantly. Specifically, the first encryption is achieved by using neural network to generate the scrambling matrix, and the second encryption is implemented by chaotic sequence generating by composite discrete chaotic system based on Logistic mapping and Sine mapping. Moreover, it has larger secret key space compared with the single one-dimensional Logistic mapping chaotic system. The performance of double encryption is measured by verifying its chaotic characteristics and randomness (Lyapunov exponent and NIST) as well as its security performance in simulation. The results show that Lyapunov index is increased to 0.9850, and the maximum P-value in the NIST test is 0.9995 by using the proposed double encryption in this paper. It indicates such double encryption significantly improve the confidentiality of the OFDM communication system without affecting the transmission performance.
Link prediction considers to discover the unknown or missing links of complex networks by using the existing topology or other information. Resource Allocation index can achieve a good performance with low complexity. However, it ignores the path effectiveness of resource transmission process. The resource transmission process is an important internal driving force for the evolution of the network. By analyzing the effectiveness of the topology around the resource transmission path between nodes, a link prediction method based on topological effectiveness of resource transmission paths is proposed. Firstly, the influence of potential resource transmission paths between nodes on resource transmission is analyzed, and a quantitative method for resource transmission path effectiveness is proposed. Then, based on the effectiveness of the resource transmission path, after studying the two-way resource transmission amount between two nodes, the transmission path effectiveness index is proposed. The experimental results of 12 real networks show that compared with other link prediction methods, the proposed method can achieve higher prediction accuracy under the AUC and Precision metrics.
A memristive high-pass filter circuit is presented, which is composed of an active high-pass RC filter parallelly coupling with a memristor emulator of diode-bridge cascaded by LC oscillator. The circuit equations and system model are established. Based on bifurcation diagram, phase plane plot, and Poincaré mapping, bifurcation analysis with the feedback gain as adjustable parameter is performed, from which bursting oscillating behaviors including quasi-period, chaotic-torus, chaos, and multiple period that exist in such a memristive high-pass filter circuit are disclosed. Furthermore, through fast-slow analysis method, Hopf bifurcation set of the fast sub-system is derived, with which the formation mechanism of slow passage effect in the memristive high-pass filter circuit is expounded. Finally, the numerical simulation results are validated based on Multisim circuit simulations.
The traditional range-extended target detection is usually completed under the condition of scattering point density or scattering point number priori. The detection performance will be greatly reduced when the scattering point information of the target is completely unknown. To solve this problem, a Range Spread Target Detection method based on Online Estimation of Strong Scattering(OESS-RSTD) points is proposed. Firstly, the unsupervised clustering algorithm in machine learning is used to estimate the number of strong scattering points and the first detection threshold adaptively. Then, the second detection threshold is determined according to false alarm rate. Finally, the existence of the target is determined through two detection thresholds. In this paper, the simulation data and the measured data are used to verify and compare with other algorithms. By comparing the Signal-to-Noise Ratio (SNR) -detection probability curves of various methods with a given false alarm probability, it is verified that the proposed method in this paper has higher robustness than the traditional algorithm, and the method does not need any priori information of target scattering points.
In the emerging vehicular networks, the task of the car terminal requesting offloading has more stringent requirements for network bandwidth and offload delay, and the proposed Mobile Edge Computing (MEC) in the new communication network research solves better this challenge. This paper focuses on matching the offloaded objects when the car terminal performs the task offloading. By introducing the Software-Defined in-Vehicle Network (SDN-V) to schedule uniformly global variables, which realizes resource control management, device information collection and task information analysis. Based on the differentiated nature of user tasks, a model of importance is defined. On this basis, task priority is divided by designing the task to offload the priority mechanism. For the multi-objective optimization model, the non-convex optimization model is solved by the multiplier method. The simulation results show that compared with other offloading strategies, the proposed offloading mechanism has obvious effects on delay and energy consumption optimization, which can guarantee the benefit of users to the greatest extent.
Existing few-shot methods have problems that feature extraction scale is single, the learned class representations are inaccurate, the similarity calculation still rely on standard metrics. In order to solve the above problems, multi-level attention feature network is proposed. Firstly, the multiple scale images are obtained by scale processing, the features of multiple scale images are extracted and the image-level attention features are obtained by the image-level attention mechanism to fusion them. Then, class-level attention features are learned by using the class-level attention mechanism. Finally, the classification is performed by using the network to compute the similarity scores between features. The proposed method is evaluated on the Omniglot dataset and the MiniImagenet dataset. The experimental results show that multi-level attention feature network can further improve the classification accuracy under small sample conditions compared to the single-scale image features and average prototypes.
The decision on computation offloading to Mobile Edge Computing (MEC) may expose user’s characteristics and cause the user to be locked. A privacy-aware computation offloading method based on Lyapunov optimization is proposed in this paper. Firstly, the privacy of task is defined, and privacy restrictions are introduced to minimize the cumulative privacy of each MEC node; then, the fake task mechanism is proposed to balance the terminal energy consumption and privacy protection, reducing the cumulative privacy of MEC node by generating a fake task non-feature task when offloading is not performed due to privacy restrictions; finally, the privacy-aware computing offloading decision is modeled and solved based on the Lyapunov optimization. Simulation results validate that the Lyapunov optimization-based Privacy-aware Offloading Algorithm (LPOA) can stabilize user’s privacy near zero, and the total offloading frequency is consistent with the decision that don’t consider privacy, effectively protecting user’s privacy while maintaining a low average energy consumption.
In Software-Defined Networking (SDN) with distributed control plane, network expansion problems arise due to network domain management. To address this issue, a Traffic Engineering-based control Resource Optimization (TERO) mechanism of SDN is proposed. It analyzes the control resource consumption of flow requests processing with different path characteristics, and points out that the control resource consumption can be reduced by changing the association relationship between controllers and switches. The controller association mechanism is divided into two phases: firstly, a minimum set cover algorithm is designed to solve the controller association problem efficiently in large-scale network. Then, a coalitional game strategy is introduced to optimize the controller association relationship to reduce both control resource consumption and control traffic overhead. The simulation results demonstrate that while keeping control traffic overhead low, mechanism which in this paper can reduce control resource consumption by about 28% in comparison with the controller proximity mechanism.
As one of the Key 5G technologies, Non-Orthogonal Multiple Access (NOMA) can improve spectrum efficiency and increase the number of user connections by utilizing the resources in a non-orthogonal manner. In the uplink grant-free NOMA system, the Compressive Sensing (CS) and generalized Orthogonal Matching Pursuit (gOMP) algorithm are introduced in active user and data detection, to enhance the system performance. The gOMP algorithm is literally generalized version of the Orthogonal Matching Pursuit (OMP) algorithm, in the sense that multiple indices are identified per iteration. Meanwhile, the optimal number of indices selected per iteration in the gOMP algorithm is addressed to obtain the optimal performance. Simulations verify that the gOMP algorithm with optimal number of indices has better recovery performance, compared with the greedy pursuit algorithms and the Gradient Projection Sparse Reconstruction (GPSR) algorithm. In addition, given different system configurations in terms of the number of active users and subcarriers, the proposed gOMP with optimal number of indices also exhibits better performance than that of the other algorithms mentioned in this paper.
Considering the problem that the traditional circularly polarized microstrip antenna has narrow Axial Ratio (AR) bandwidth and small system capacity, a new type of broadband dual circularly polarized printed antenna is proposed. The antenna structure is simple with a dual port microstrip feed mode, consisting of only two radiating patches and an improved ground plane, and the entire size of antenna is 48 mm× 48 mm× 1 mm. By optimizing the shape of the radiating patch and adding a circular structure on the ground plane, the impedance bandwidth and the axial ratio bandwidth of the antenna can be effectively increased, achieving the dual circular polarization characteristics. The design process of antenna is given, and the circular polarization mechanism of the antenna is analyzed from the surface current distributions. The simulated and measured results show that the antenna has a very wide impedance bandwidth and axial ratio bandwidth. The working frequency band of the antenna is 1.9～9.6 GHz (the relative bandwidth is 133.9%), and the 3 dB AR bandwidth is 1.9～6.6 GHz (the relative bandwidth is 110.6%). The radiation performance and gain characteristics of the antenna are measured. The measured results agree well with the simulated results, which proves the effectiveness of the antenna. The antenna can be applied to Ultra WideBand (UWB) wireless communication systems and satellite communication systems.
Monthly Journal Founded in 1979
The Source Journal of EI Compendex The Source Journal of ESCI Database
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ISSN 1009-5896 CN 11-4494/TN
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