Vignetting is the phenomenon that the intensity of the pixel in the image decreases along the radial direction. In order to solve the problem that it affects the accuracy of computer vision task and image processing, a method of single image vignetting correction based on constrained log-intensity entropy under low-pass filtering was proposed. Firstly, the vignetting model was established by using a sixth order polynomial function of even term. Secondly, the minimum log-intensity entropy of the target image was calculated by low-pass filtering. Under the constraint of the target value, the optimal parameter solution of the vignetting model was obtained, which can satisfy the change rule of the vignetting function and reduce the log-intensity entropy of the image. Finally, vignetting was eliminated by using inverse compensation of vignetting model. Vignetting correction results were evaluated by Structural SIMilarity index (SSIM) and Root Mean Square Error (RMSE). Experimental results show that the proposed method can not only effectively recover the brightness information of the vignetting area to get real and natural non-vignetting image, but also effectively correct the different degrees of vignetting with a good visual consistency.

In order to more effectively combine the detail information of high resolution PANchromatic (PAN) image and the spectral information of low resolution MultiSpectral (MS) image, an improved panchromatic sharpening algorithm based on sparse representation was proposed. Firstly, the intensity channel of an MS image was down-sampled and then up-sampled to get its low-frequency components. Secondly, the MS image intensity channel minus low-frequency components to obtain its high-frequency components. Random sampling was performed in the acquired high and low frequency components to construct a dictionary. Thirdly, the PAN image was decomposed to get the high-frequency components by using the constructed overcomplete dictionary. Finally, the high-frequency components of the PAN image were injected into the MS image to obtain the desired high-resolution MS image. After a number of experiments, it was found that the proposed algorithm subjectively retains the spectral information and injects a large amount of spatial details. Compared with component substitution method, multiresolution analysis method and sparse representation method, the reconstructed high resolution MS image by the proposed algorithm is more clear, and the correlation coefficient and other objective evaluation indicators of the proposed algorithm are also better.

In order to save the energy consumption in cloud data center, some greedy algorithms-based Virtual Machine (VM) migration strategies were proposed. In these strategies, the virtual migration process was divided into physical host status detection, virtual machine selection and virtual machine placement, and the greedy algorithm was adopted in the process of virtual selection and virtual placement respectively. The three proposed migration strategies were:Minimum Host Utilization selection, Maximum Host Utilization placement (MinMax_Host_Utilization); Maximum Host Power Usage selection, Minimum Host Power Usage placement (MaxMin_Host_Power_Usage); Minimum Host MIPS selection, Maximum Host MIPS placement (MinMax_Host_MIPS). The maximum or minimum thresholds were set for the processor utilization efficency, the energy consumption and the processor computing power of physical host. According to the principle of greedy algorithm, the virtual machines with indicators higher or lower than the thresholds should be migrated. With CloudSim as the simulated cloud data center, the test results show that compared with the static threshold and median absolute deviation migration strategies existing in CloudSim, the proposed strategies have the total energy consumption reduced by 15%, the virtual machine migration number decreased by 60%, and the average SLA violation rate lowered about 5%.

To resolve the problems of feature homogeneity in unsupervised training of Restricted Boltzmann Machine (RBM) and non-adaptiveness of Sparse Restricted Boltzmann Machine (SRBM), a new sparse mechanism method of RBM based on competitive learning was designed. Firstly, a distance measurement was designed based on the cosine value between the neuron weight vector and the input vector to evaluate the similarity. Secondly, the optimal matching implicit unit based on distance measurement was selected for different samples during training. Thirdly, the sparse penalty for other hidden units was calculated according to the activation state of the optimal matching hidden unit. Finally, the parameters were updated and the competitive sparseness was applied to the construction of Deep Boltzmann Machine (DBM) based on the deep model training process. The handwritten number recognition results show that, compared with the mechanism using the sum of squared errors as the regularization factor, the classification accuracy of DBM based on new sparse mechanism is improved by 0.74%, and the average sparsity measurement is increased by 5.6%, without the need to set sparse parameters. Therefore, the proposed sparse mechanism can improve the training efficiency of unsupervised training model, such as RBM, and can be applied into the construction of deep models.

Concerning the ranging inaccuracy problem based on radio signal phase information under multi-path environments, a two-step ranging approach based on double tags was proposed. Each target was attached with double tags. Through single frequency subcarrier amplitude modulation, firstly, the wrapped phase information of carrier signal was extracted, the distance between reader and tag within half wavelength of carrier signal was calculated and fine ranging estimation value was achieved. Secondly, the unwrapped phase information of subcarrier signal was extracted, and the integral multiple of half wavelength within the distance of reader and tag was calculated. Thirdly, the average multiple was calculated between double tags, the distance of average multiple of half wavelength was used as coarse ranging value. Finally, the final ranging result was estimated by the sum of the fine ranging value and coarse ranging value. Additionally, single reader and double-tag based geometric localization method was introduced to reduce the cost of hardware facilities. The simulation results show that, under multi-path environments, compared with the directly ranging with subcarrier phase, the average ranging error of double tags based two-step ranging approach is reduced by 35%, and the final average localization error is about 0.43 m, and the maximum error is about 1 m. The proposed approach can effectively improve the accuracy of phase based localization technology and also reduce the hardware cost.

Conventional Direction-Of-Arrival (DOA) estimation approaches suffer from low angular resolution or relying on a large number of snapshots. The sparsity-based SPICE can work with few snapshots and has high resolution and low sidelobe level, but it only applies to narrowband signals. To solve the above problems, a new FrFT-SPICE method was proposed to estimate the DOA of wideband chirp signals with high resolution based on a few snapshots. First, the wideband chirp signal was taken on the Fractional Fourier Transform (FrFT) under a specific order so that the chirp wave in time domain could be converted into sine wave with single frequency in FrFT domain. Then, the steering vector of the received signal was obtained in FrFT domain. Finally, SPICE algorithm was utilized with the obtained steering vector to estimate the DOA of the wideband chirp. In the simulation with the same scanning grid and same snapshots, the DOA resolution level of the proposed FrFT-SPICE method was better than that of the FrFT-MUSIC method which combines MUltiple SIgnal Classification (MUSIC) algorithm and FrFT algorithm; and compared to the SR-IAA which utilizes Spatial Resampling (SR) and IAA (Iterative Adaptive Approach), the proposed method had a better accuracy. The simulation results show that the proposed method can estimate the DOA of wideband chirp signals with high accuracy and resolution based on only a few snapshots.

Aiming at the problems of current aviation card readers, include poor portability, slow speed and tags' little capacity, a design method of large capacity Radio Frequency Identification (RFID) system based on STM32 was proposed. Using STM32 microprocessor as a core and adopting CR95HF radio chip, a new handled RFID card reader which worked in High Frequency (HF) and supported ISO 15693, ISO 18092 protocols was designed. The design of power, antenna and optimization of software speed, error rate was discussed in detail. A new large compiled capacity passive tag was also designed whose capacity is up to 32KB to form a large capacity RFID system with card reader. The experimental results show that, compared with the traditional card reader, the reading and writing speed of the card reader increases by 2.2 times, error rate reduces by 91.7% and tag capacity increases 255 times. It provides a better choice for fast, accurate and high data requirements of aviation logistics.

To solve the rank-deficient problem in the underdetermined Multiple-Input Multiple-Output (underdetermined MIMO) systems, this paper proposed the ordering λ-Generalized Sphere Decoding (λ-GSD) algorithm based on reliability measurement. The proposed algorithm transformed the rank-deficient channel matrix into the full-column-ranked one, and adopted a new ordering strategy based on reliability measurement, and then sorted the sub-optimal values of the Minimum Mean Square Error （MMSE） algorithm in a descending order and made the first point as the initial value of the λ-GSD algorithm to reduce the initial search radius. Meanwhile, the decreasing rate of the radius was accelerated with an exponential converging in the algorithm. The simulation results indicate that the proposed algorithm can approach the optimum maximum-likelihood decoding performance and has a lower average operation time than the original λ-GSD algorithm.