In recent years, Machine Learning (ML) based spectrum sensing technology has provided a new solution in spectrum status identification for cognitive radio systems. Based on the large amount of spectrum observations captured by the Secondary User Equipment (SUE) in the Cellular Cognitive Radio Network (CCRN), this paper proposes a spectrum sensing scheme based on the Primary User (PU) transmission mode classification. Firstly, based on a variety of typical ML classification algorithms, the proposed scheme classifies the transmission mode of multiple Primary User Transmitters (PUTs) in the CCRN, and determines the joint operating state of all the PUTs in the CCRN. Subsequently, the SUE evaluates the possibility of accessing the licensed spectrum in the currently determined PUT transmission mode according to its geographical location or spectrum observation data. Since the actual locations of the PUTs in the network may be readily known in advance or unaware of at all, the proposed scheme solves the problem in three different methods. Theoretical derivation and experimental results show that compared with the traditional energy detection scheme, the proposed scheme not only remarkably improves the spectrum sensing performance, but also significantly increases the opportunities of dynamic accessing to the licensed spectrum for the SUEs. The proposed scheme can be used as an efficient and practical spectrum sensing solution in the CCRN.

For the problem that the three-dimensional positioning accuracy is not high and the positioning time is too long in indoor Visible Light Communication(VLC). An indoor visible light three-dimensional positioning system based on Improved IMmune Particle Swarm Optimization(IIMPSO) algorithm is proposed. By analyzing the indoor multipath effects, the fitter Field Of View (FOV) is selected to reduce the influence of the reflection. Meanwhile, the positioning model under the tilt state is improved. The Kalman filter algorithm is used to reduce the impact of environmental interference on the received power. On the basis, it is integrated with the improved immune particle swarm algorithm. Simulation results show the average positioning error of the indoor three-dimensional positioning system is 0.031 m, and the positioning time is 2.3 s in the indoor of 5 m × 5 m × 3 m. Compared with the existing three-dimensional positioning system, the positioning accuracy and convergence speed are significantly improved.

For the low iteration convergence rate and the disability to track the change of channels in hierarchical matching game, a new resource allocation strategy for wireless virtual networks, i.e., the channel’s price-based hierarchical matching/Stackelberg game is proposed in this paper. A three-level joint optimization model is established on each layer reward function based on stream’s bandwidth-based user’s satisfaction, the system’s bandwidth and the slice’s power. The hierarchical matching/Stackelberg game is adopted to solve the optimizing problem. In the lower layer of the hierarchical game, the

is defined to present Mobile Virtual Network Operator(MVNO) m-InPn and one-to-one matching game between it and UEs is constructed to displace the many-to-one matching game between UEs and MVNOs, where a price based on the global information of channels is given to speed up the identical convergence between the upper and the lower layer and make UEs select the optimal

adapting the channel. After proving the existing of equilibrium, the rejecting-receiving algorithm for one-to-one matching game is proposed. In the upper layer of the hierarchical game, a Stackelberg game between the InPs and many

is formed based on the connection between those users and

, and an optimized power pricing and allocation strategy based on local information of channel are given, which makes the optimal system utility and resource utilization based on channels. Finally, the process for the two-tier cycling is given and the stability of the hierarchical game is characterized. Simulation results show that the channel’s price-based hierarchical matching/Stackelberg game strategy outperforms the random pricing hierarchical matching/Stackelberg game and the conventional hierarchical matching game in the aspect of tracking channel’s changing and spectrum efficiency and system’s utility.

When designing a spread spectrum system, it is sometimes necessary to analyze the anti-interference ability of the system. However, the existing literature on the anti-wideband interference capability and the ability to resist partial frequency band interference of the Direct Sequence Spread Spectrum (DSSS) system are different, and the Bit Error Rate (BER) formulas provided are different, the general BER formula of direct sequence spread spectrum system under wideband interference and partial frequency band interference is given by theoretical derivation. The correctness of the formula is verified by computer simulation. Finally, the performance of the DSSS system with the interference frequency and the interference bandwidth is analyzed using the formula given in this paper.

Passive InterModulation (PIM) products are spurious frequency signals which occur in microwave and radio frequency communication system. And it is noticed that PIM levels have the characteristic of changing with time. In order to find out the relationship between PIM level and time, as the typical microwave component which more often causes PIM in communication system, coaxial connector is chosen and analyzed using chaotic method. Firstly, the third order PIM level time series of coaxial connector is obtained by PIM measurement system. Based on the experimental data, the phase space is reconstructed and the optimal embedding dimension m and delay time τ are confirmed. Secondly, the largest Lyapunov exponent is calculated by the method named the small data sets with embedding dimension m and delay time τ. And from the qualitative and quantitative perspective, it is verified that the passive intermodulation level time series have the characteristic of chaos. Lastly, the prediction of PIM level with chaotic method is performed on the basis of the largest Lyapunov exponent. And the maximum error between the theoretical prediction value and the experimental value is 2.61% within the maximum predictable scale, indicating that the chaotic prediction is an effective way. The method that predicts the PIM level of microwave components in the communication system discussed in this paper provides a new way of studying the PIM mitigation technique for communication system and provides a new idea for improving the performance of the communication system.

Circular SAR (CSAR) has the ability of 3-D imaging due to its special curve trajectory. Single-pass CSAR can theoretically obtain the resolution of the sub-wavelength level on the distance-azimuth plane, but its resolution at the elevation direction is very low. At the same time, CSAR 3-D imaging with Back Projection(BP) has high algorithm complexity and low imaging efficiency. A coherent 3-D imaging method for multi-circular SAR based on an improved 3-D back projection algorithm is proposed. For the problem of high time complexity of the imaging algorithm, an improved 3-D BP algorithm for CSAR based on constructing geometric interpolation kernel is proposed. 3-D interpolation operations are transformed into 1-D interpolation operations and distance vector searching operations. The final imaging result is obtained by coherently accumulating the improved 3-D BP results of multi-circular SAR. The proposed method solves effectively the problem of low elevation resolution of single-pass CSAR, improves 3-D imaging details, and reduces greatly the time of CSAR 3-D imaging simultaneously. The simulated 3-D imaging results of the conical target and GOTCHA data set from the US Air Force Laboratory verify the effectiveness of the proposed method.

Due to lack of enough Independent Identically Distributed (IID) training samples, it seriously degrades the clutter suppression performance of the traditional Space-Time Adaptive Processing (STAP) algorithms in heterogeneous clutter and target rich environment. To solve the problem, a heterogeneous clutter suppression method for the airborne plane array radar is proposed, which is robust to the array error. Firstly, the clutter representation basis matrix is constructed by the radar system parameters priori knowledge. Next, with the consideration of array error, it estimates iteratively the clutter representation coefficient and array error by the least square criterion. Finally, the clutter cancellation is conducted by the obtained optimal clutter representation coefficient and array error in element-pulse domain. The proposed method does not need to estimate the statistical properties of cell under test and has no aperture loss. In addition, it does not need any training sample and can suppress effectively the heterogeneous clutter of airborne planar array radar echo data in rich target environment even if range ambiguity exists. Simulation results verify the validity of the proposed method.

Based on the governing equations satisfied by the electromagnetic method of the frequency domain, the finite element method is used to realize the forward simulation of 2.5-Dimensional a (2.5D) Ground Penetrating Radar (GPR) in the frequency domain. The law of the electromagnetic field spectrum in the wavenumber domain with the relative permittivity and the transmission and reception distance is analyzed in detail. The selection of the wave number in the 2.5D GPR forward modeling simulation is discussed. Based on the comparison of the computational efficiency of the Open MP parallel algorithm and the serial algorithm, the results show that the 2.5D GPR numerical simulation method in the frequency domain has the characteristics of high efficiency, high precision, and high parallelism. It provides important theoretical reference and technical support for radar forward modeling, and provides an important foundation for GPR full waveform inversion.

In recent years, convolutional neural networks are widely used in single image deblurring problems. The receptive field size and network depth of convolutional neural networks can affect the performance of image deblurring algorithms. In order to improve the performance of image deblurring algorithm by increasing the receptive field, an image blind deblurring algorithm based on deep multi-level wavelet transform is proposed. Embedding the wavelet transform into the encoder-decoder architecture enhances the sparsity of the image features while increasing the receptive field. In order to reconstruct high-quality images in the wavelet domain, the paper leverges to multi-scale dilated dense block to extract multi-scale information of images, and introduces feature fusion blocks to fuse adaptively features between encoder and decoder. In addition, due to the difference in representation of image information between the wavelet domain and the spatial domain, in order to fuse these different feature representations, the spatial domain reconstruction module is used to improve further the quality of the reconstructed image in the spatial domain. The experimental results show that the proposed method has better performance on Structural SIMilarity index (SSIM) and Peak Signal-to-Noise Ratio, and has better visual effects on real blurred images.

With the rapid development of deep learning technology, videos with changed faces generated by deep neural networks (i.e., Deepfake videos) become more and more indistinguishable. As a result, the threat raised by Deepfake videos becomes greater and greater. In literature, there are some convolutional neural networks-based detection algorithms for fake face videos. Although those algorithms perform well when the training set and the testing set are from the same dataset, their performance could deteriorate dramatically in cross-dataset scenario where the training and the testing sets are from different sources. Motivated by the fabrication course of fake face videos, this article attempts to solve the problem of fake faces detection with the way of image splicing detection. A neural network borrowed from image segmentation is adopted for predicting  the tampered face area from which a tampering mask is obtained through denoising and thresholding the probability map. Using the prior knowledge of face tampering that the changing of face mainly happens in face region, a new way is proposed to determine the Face-Intersection over Union (Face-IoU) and to further improve the ratio calculation method. The Face-Intersection over Union with Penalty (Face-IoUP) is used as the classification criterion for deepfake video detection. The proposed method is impletmented using three basic image segmentation neural networks separately and is tested them on datasets of TIMIT, FaceForensics++, Fake Face in the Wild(FFW). Compared with current methods in literature, the HTER (Half Total Error Rate) in cross-dataset test decreases significantly while the detection accuracy in intra-dataset test keeps high. For the Deep Fake Detection(DFD) dataset with higher synthesis quality, the proposed method still performs very well. Experimental results validate the proposed method and demonstrate its good generality.

Considering the problem of the low anti-noise performance when Fuzzy C-Means clustering (FCM) algorithm is applied to image segmentation, a FCM clustering algorithm with fast and adaptive non-local spatial constraint and membership linking is proposed in this paper. Firstly, in order to increase the computing speed of non-local spatial term, a fast method is proposed by modifying the loop based on all pixels in an image into a loop based on search window and by utilising spatial shift image and recursive Gaussian filter. Next, the squared difference between original image and non-local spatial term is calculated as adaptive weight of non-local information term. The squared difference is reciprocally transformed as adaptive weight of the original image. Finally, the membership linking is established to reduce the iteration steps before convergence by adding the square of the sum of all the membership degrees in every cluster in logarithmic form as the denominator of the objectvie function. Experiments on noisy artificial and natural images prove that this proposed algorithm has superior performance in terms of Segmentation accuracy, mean intersection over union, normalized mutual information, running time and iteration steps.

In recent years, the method of extracting depth features from siamese networks has become one of the hotspots in visual tracking because of its balanced in accuracy and speed. However, the traditional siamese network does not extract the deeper features of the target to maintain generalization performance, and most siamese architecture networks usually process one local neighborhood at a time, which makes the appearance model local and non-robust to appearance changes. In view of this problem, a densenet-siamese network with global context feature module for object tracking algorithm is proposed. This paper innovatively takes densenet network as the backbone of siamese network, adopts a new design scheme of dense feature reuse connection network, which reduces the parameters between layers while constructing deeper network, and enhances the generalization performance of the algorithm. In addition, in order to cope with the appearance changes in the process of object tracking, the Global Context feature Module (GC-Model) is embedded in the siamese network branches to improve the tracking accuracy. The experimental results on the VOT2017 and OTB50 datasets show that comparing with the current mainstream tracking algorithms, the Tracker has obvious advantages in tracking accuracy and robustness, and has good tracking effect in scale change, low resolution, occlusion and so on.

For the problem of flight delay propagation caused by flight delay, a flight delay wave prediction model based on CBAM-CondenseNet is presented. Firstly, by analyzing the delays propagation in the aviation network caused by flight delays, the flight chain affected by the pre-order delays is determined; Secondly, the selected flight chain data is cleaned and the flight information and airport information are fused; Finally, an improved CBAM-CondenseNet algorithm is proposed to extract the number of fused flights. According to feature extraction, a Softmax classifier is constructed to predict the delays of the first departure flights and the subsequent flights. The CBAM-CondenseNet algorithm proposed in this paper combines the advantages of CondenseNet and CBAM, and uses channel and spatial attention mechanism to enhance the transmission of deep information in network structure. The experimental results show that the improved algorithm can effectively improve the network performance, and the prediction accuracy can reach 97.55%.

Regarding as the measure of the electrical fields produced by the active brain, ElectroEncephaloGraphy (EEG) is a brain mapping and neuroimaging technique widely used inside and outside of the clinical domain, which is also widely used in Brain–Computer Interfaces (BCI). However, low spatial resolution is regarded as the deficiency of EEG signified from researches, which can fortunately be made up by synthetic analysis of data from different channels. In order to efficiently obtain subspace features with discriminant characteristics from EEG channel information, a Multi-Channel Convolutional Neural Networks (MC-CNN) model is proposed for MI-EEG decoding. Firstly input data is pre-processed form selected multi-channel signals, then the time-spatial features are extracted using a novel 2D Convolutional Neural Networks (CNN). Finally, these features are transformed to discriminant sub-space of information with Auto-Encoder (AE) to guide the identification network. The experimental results show that the proposed multi-channel spatial feature extraction method has certain advantages in recognition performance and efficiency.

Median filtering is the basic filtering method in classical image processing. However, the corresponding models are still rare in quantum image processing. To address the median filtering of quantum images, a new method based on quantum median calculation is proposed. The method uses an iterative comparison method to sort the target pixels to obtain a median value. Firstly, the quantum circuits of various basic modules needed to implement median filtering are introduced. Then the quantum implementation method of median calculation is presented in detail. Finally, the overall circuit frame of quantum image median filtering is given. The complexity analysis shows that the method has exponential acceleration for its classical counterpart. The simulation results on the classical computer verify the validity and feasibility of the proposed method.

To solve the problem that Quantum Secret Sharing (QSS) protocol is difficult to resist inner-cheating attack, by utilizing the method of secret message authentication to present a general model of verifiable quantum secret sharing protocols, a new verification algorithm is proposed based on the two-particle transform of Bell states, and then a new verifiable quantum secret sharing protocol is proposed. Compared with the verification algorithms of the existing verifiable quantum secret sharing protocol, the new verification algorithm can not only resist effectively the typical attack strategies such as the inner-cheating attack, but also improves greatly the efficiency of the protocol, and has good scalability which can be combined with the existing quantum secret sharing protocols.

To address the problems of the leakage of access structure, high computation of user side and lack of integrity verification in current Attribute-Based Keyword Search (ABKS) scheme, a verifiable attribute-based keyword search scheme with privacy preservation is proposed. The scheme adopts the ordered multi-valued attribute access structure and ordered multi-valued attribute set, and fixes the position of each attribute to reduce the parameters and related computation cost and to improve the efficiency of the scheme, while in key generation, the Hash values of specific attributes are calculated to distinguish the different values of multi-valued attributes. At the same time, Hash and pair operation are used to hide the access structure and prevent the disclosure of the access structure. The inverted index structure and Merkle tree are used to establish the data authentication tree, which can verify the correctness of the document returned by the cloud server provider and the result of outsourced decryption. In addition, outsourced decryption is used to reduce the computation cost on the user side. Finally, formal proofs and experimental results show that the scheme achieve verifiability of shared data in the cloud, keyword undistinguishable and keyword unlinkable, and is efficient.

With the development of network security technology, network security protocol emerges one by one, which requires functional support from network forwarding devices. Due to the independence of protocols, the programmable data plane enables rapid deployment of security protocols. However, the current programmable data plane has the problem that the header is parsed multiple times, the exclusive data plane and the cryptographic algorithm are difficult to implement. In view of the above problems, VCP4(Virtualization Cryptogram P4) as a virtualized programmable data plane for security protocols is proposed, which reduces the number of parsing times and improves the header parsing efficiency by introducing a description header. The control flow queue generator and the dynamic mapping table are used to achieve the virtualization of the programmable data plane, thereby realizing the isolation of the data plane under the multi-tenant and solving the problem of the exclusive data plane. A cryptographic algorithm primitive is added to the VCP4 language compiler to implement a cryptographic algorithm that can be reused. Finally, the VCP4 resource utilization, virtualization performance and security protocol performance are evaluated. The results show that the implementation of VCP4 brings less performance loss, and the code amount can be reduced by 50%.

Considering the problem of information entropy being low and easily disturbed by environmental factors in the traditional Physical Unclonable Function (PUF), a PUF scheme is designed to generate multiple stable information entropy. By analyzing the frequency data generated by the ring oscillator on the FPGA, the feature bits representing the characteristics of the ring are extracted from each ring as information entropy. By studying the temperature characteristics of the inverter, a new oscillating ring is formed by the current hungry inverter and the conventional inverter to reduce the influence of temperature on the reliability of the generated information entropy. Through Cadence IC simulation and experiments on zynq7000 series FPGA development platform, the results show that the improved PUF structure can generate more information entropy with the same number of oscillatory rings, and its reliability and uniqueness are improved.

An efficient time domain hybrid method is presented consisting of Finite-Difference Time-Domain (FDTD) method, Transmission Line (TL) equations, and Ngspice software to be well applied to the coupling analysis of transmission lines terminated with complex circuits excited by space electromagnetic fields. The significant features of this presented method are that it can realize the co-calculation of electromagnetic field radiation and transient responses on the lines and complex circuits, and avoid modeling the structures of transmission lines and circuits directly. Firstly, the complex circuits are replaced by the characteristic impedances of corresponding transmission lines, and then the FDTD method combined with TL equations is adopted to solve the incident currents on these impedances. Secondly, the incident currents are introduced into the complex circuits as excitation sources at each time step of FDTD simulation, which are combined with the circuits to form the netlist files. Finally, transient responses on the elements of circuits are obtained by using the Ngspice software. Numerical simulations are utilized to verify the correctness and efficiency of this hybrid method by comparing with the electromagnetic software CST in simulation results and consumptions of memories and computation time.