Concerning the problem that the transmission and storage of massive redundant image data in Internet of Things （IoT） lead to high resource consumption and potential privacy leakage， a Deep Compressive Sensing （DCS） network for IoT images and its chaotic encryption protection method were proposed. Firstly， an improved DCS network was proposed to achieve high-quality image compression and reconstruction. In this network， residual blocks in conventional deep reconstruction network were modified using a channel attention mechanism， and a parallel fusion design integrating multi-scale branches and a fusion module was adopted， thereby improving the reconstruction performance of traditional deep reconstruction network based on residual stacking in convolutional layers. Secondly， a multi-cavity chaotic system was proposed to realize spherical cavity expansion in the X， Y， or Z direction — either in one direction or both directions — through spherical coordinate transformation and two sets of parity-controlled step functions. This system has chaotic properties and randomness， making it suitable for image encryption. Finally， based on the proposed DCS network and the multi-cavity chaotic system， encryption and decryption for DCS measured images was designed using chaotic index scrambling and diffusion， and detailed security analysis was conducted， thereby guaranteeing the security of image transmission. Experimental results show that compared with the classical DCS method CSNet+， the proposed network achieves an average increase of 0.606 dB （0.25-1.42 dB） in Peak Signal-to-Noise Ratio （PSNR） and an average 1.11 percentage point （0.69-2.17 percentage point） improvement in Structural Similarity Index Measure （SSIM）.

In order to strengthen anti-interference and anti-interception performance of chaotic system in communication link， and improve complexity of chaotic system behavior， based on typical Chua’s circuit and step function， a new type of grid multi-scroll chaotic system family with controllable quantity was constructed. First， two sets of step functions were used as nonlinear controllers of the system， which respectively controlled the numbers of the odd and even columns and the arranging rows for the grid multi-scroll chaotic attractors， and kept the scrolls and bonds in chaotic attractors being interleaved with each other. As a result， the arbitrary number of odd and even columns for the grid multi-scroll were realized. Then， the dynamic properties of system such as equilibrium point， Lyapunov exponent and attractor were theoretically analyzed and numerically simulated. Finally， the hardware experiment results of up to 4 rows and 12 columns of grid multi-scroll were given by Field Programmable Gate Array （FPGA）. Hardware and software experimental results are in full agreement with theoretical analysis results， which furtherly proves the proposed system’s physical realizability.

Focused on the large inter-user interference in Multiple Input Multiple Output-Non-Orthogonal Multiple Access (MIMO-NOMA) technology, an algorithm merging user scheduling and BeamForming (BF) was proposed. Firstly, during the course of user scheduling, in order to simultaneously take intra-cluster user interference and inter-cluster user interference into account, all user groupings were initially sparsely processed by the L1-norm regularization method according to the channel difference among users. In the respect of user channel correlation, two users with large channel correlation were divided into a cluster. Secondly, Fractional Transmit Power Control (FTPC) was used to implement the power allocation of the intra-cluster users. Finally, an objective optimization function based on sum rate maximization criterion was constructed, which was solved by Successive Convex Approximation (SCA) method to obtain the BF matrix. Compared with OMA (Orthogonal Multiple Access), the proposed scheme achieves 84.3% improvement in system capacity, and compared with the traditional correlation user clustering method, it achieves 20.2% improvement in fairness. The theoretical analysis and simulation results show that the proposed scheme not only suppresses the intra-cluster interference and inter-cluster user interference effectively, but also ensures the fairness among users.

Concerning the problem that the existing synthesis-analysis dictionary learning method can not effectively eliminate the differences between the samples of the same class and ignore the different effects of different features on the classification, an image classification method based on Multi-view Feature Projection and Synthesis-analysis Dictionary Learning (MFPSDL) was put forward. Firstly, different feature projection matrices were learned for different features in the process of synthesis-analysis dictionary learning, so the influence of the within-class differences on recognition was reduced. Secondly, discriminant constraint was added to the synthesis-analysis dictionary, so that similar sparse representation coefficients were obtained for samples of the same class. Finally, by learning different weights for different features, multiple features could be fully integrated. Several experiments were carried out on the Labeled Faces in the Wild (LFW) and Modified National Institute of Standards and Technology (MNIST) database, the training time of MFPSDL method on LFW and MNIST databases were 61.236 s and 52.281 s. Compared with Fisher Discrimination Dictionary Learning (FDDL), Lable Consistent K Singular Value Decomposition (LC- KSVD) and Dictionary Pair Learning (DPL), the recognition rate of MFPSDL method on LFW and MNIST was increased by at least 2.15 and 2.08 percentage points. The experimental results show that MFPSDL method can obtain higher recognition rate while keeping low time complexity, and it is suitable for image classification.

Geometric transforms of the object in the imaging process can be represented by affine transform in most situations. Therefore, a method for shape matching using corners was proposed. Firstly, the corner of contour using Multi-scale Product Laplacian of Gaussian (MPLoG) operator was detected, and the feature points based on corner interval were adaptively extracted to obtain the key feature of shape. In order to cope with affine transform, the similarity of two shapes on Grassmann manifold Gr(2,n) were represented and measured. Finally, the iterative sequence shift matching was adopted for overcoming the dependency of Grassmann manifold on the starting point, and achieving shape matching. The proposed algorithm was tested on the database of shapes. The simulation results show that the proposed method can achieve shape recognition and retrieval effectively, and it has strong robustness against noise.

This paper investigated the limited feedback technology based on the Multiple Input Multiple Output Orthogonal Frequency Division Multiple (MIMO-OFDM) system in LTE-Advanced applications. A new limited feedback method based on recursive feedback was proposed. The method utilized the correlation between the subcarrier channels. The codewords for the current subcarrier could be determined by ordering the result of the inner product with respect to the codewords of the previous subcarrier. Then the variable length encoding method was used to code the index of the new codewords. The experimental results show that the recursive feedback algorithm based on variable length coding can reduce the feedback bits effectively in LTE-Advanced systems.

Under the condition of coherent multipath environment, the algorithms of temporal smoothing and reconstruction of Toeplitz matrix were adopted respectively to estimate the DoAs (Direction of Arrivals) of complex coherent signals, furthermore these two algorithms were compared through mathematical analysis and computer simulation, and the following conclusion could be obtained: Both of these two algorithms for DoA estimation of complex coherent signals have the effectivity, the performance of algorithm based on reconstruction of Toeplitz matrix is relatively better but it will lose the array aperture, the algorithm based on temporal smoothing will not lose array aperture and can estimate M-1 coherent multi-paths (M is the number of array elements), however, it has a slightly larger calculation load.

Directed towards the test sequencing problem in the sequential fault diagnosis for large scale systems, an algorithm based on information entropy for design the fault diagnosis strategy with least test cost was presented. The algorithm requires less computation than the traditional methods, and uses the test results, test cost, and fault probabilities efficiently. This algorithm is suitable for on-line or off-line diagnosis and maintenance process. The design process of the algorithm was presented, and an example was used to illustrate the validity of the method.