The quality of low-light images is poor and Low-Light Image Enhancement （LLIE） aims to improve the visual quality. Most of LLIE algorithms focus on enhancing luminance and contrast， while neglecting details. To solve this issue， a Progressive Enhancement algorithm for low-light images based on Layer Guidance （PELG） was proposed， which enhanced algorithm images to a suitable illumination level and reconstructed clear details. First， to reduce the task complexity and improve the efficiency， the image was decomposed into several frequency components by Laplace Pyramid （LP） decomposition. Secondly， since different frequency components exhibit correlation， a Transformer-based fusion model and a lightweight fusion model were respectively proposed for layer guidance. The Transformer-based model was applied between the low-frequency and the lowest high-frequency components. The lightweight model was applied between two neighbouring high-frequency components. By doing so， components were enhanced in a coarse-to-fine manner. Finally， the LP was used to reconstruct the image with uniform brightness and clear details. The experimental results show that， the proposed algorithm achieves the Peak Signal-to-Noise Ratio （PSNR） 2.3 dB higher than DSLR （Deep Stacked Laplacian Restorer） on LOL（LOw-Light dataset）-v1 and 0.55 dB higher than UNIE （Unsupervised Night Image Enhancement） on LOL-v2. Compared with other state-of-the-art LLIE algorithms， the proposed algorithm has shorter runtime and achieves significant improvement in objective and subjective quality， which is more suitable for real scenes.

Concerning the large number of computing needs of vehicle task offloading and the limited computing capacity of local edge servers in the Internet of Vehicles （IoV）， a Hierarchical Resource Allocation Mechanism of cooperative mobile edge computing （HRAM） was proposed. In this algorithm， the computing resources of Mobile Edge Computing （MEC） servers were reasonably allocated and effectively utilized with a multi-layer architecture，so that the data multi-hop forwarding delay between different MEC servers was reduced， and the delay of task offloading requests was optimized. Firstly， the system model， communication model， decision model， and calculation model of the IoV edge computing were built. Next， the Analytic Hierarchy Process （AHP） was used to comprehensively consider multiple factors to determine the target server the offloaded task transferred to. Finally， a task routing strategy with dynamic weights was proposed to make use of communication capabilities of the overall network to shorten the request delay of task offloading. Simulation results show that compared with Resource Allocation of Task Offloading in Single-hop （RATOS） algorithm and Resource Allocation of Task Offloading in Multi-hop （RATOM） algorithm， HRAM algorithm reduces the request delay of task offloading by 40.16% and 19.01% respectively， and this algorithm can satisfy the computing needs of more offloaded tasks under the premise of meeting the maximum tolerable delay.

In commercial digital cameras， due to the limitation of Complementary Metal Oxide Semiconductor （CMOS） sensors， there is only one color channel information for each pixel in the sampled image. Therefore， the Color image DeMosaicking （CDM） algorithm is required to restore the full-color images. However， most of the existing Convolutional Neural Network （CNN）-based CDM algorithms cannot achieve satisfactory performance with relatively low computational complexity and small network parameter number. To solve this problem， a CDM network based on Inter-channel Correlation and Enhanced Information Distillation （ICEID） was proposed. Firstly， to fully utilize the inter-channel correlation of the color image， an inter-channel guided reconstruction structure was designed to obtain the initial CDM result. Secondly， an Enhanced Information Distillation Module （EIDM）， which can effectively extract and refine features from image with relatively small parameter number， was presented to enhance the reconstructed full-color image in high efficiency. Experimental results demonstrate that compared with many state-of-the-art CDM methods， the proposed algorithm achieves significant improvement in both objective quality and subjective quality， and has relatively low computational complexity and small network parameter number.

As an important way to reduce energy consumption of the data center in cloud computing, virtual machine migration is widely used. By combing the trisecting-acting-outcome model of three-way decision, a Virtual Machine Migration scheduling strategy based on Three-Way Decision(TWD-VMM) was proposed. First, a hierarchical threshold tree was built to search all the possible threshold values to obtain the pair of thresholds with the lowest total energy consumption with the data center energy consumption as the optimization target. Thus, three regions were created:high-load region, medium-load region and low-load region. Second, different migration strategies were used for hosts with different loads. Specifically, for high-load hosts, the multidimensional resource balance and host load reduction after the pre-migration of hosts were adopted as targets; for low-load hosts, the host multidimensional resource balance after pre-placing hosts was mainly considered; for medium-load hosts, the virtual machines migrated from other regions would be accepted if they still met the medium-load characteristics. The experiments were conducted on CloudSim simulator, and TWD-VMM was compare with Threshold-based energy-efficient VM Scheduling in cloud datacenters(TVMS), Virtual machine migration Scheduling method optimising Energy-Efficiency of data center(EEVS) and Virtual Machine migration Scheduling to Reduce Energy consumption in datacenter(REVMS) algorithms respectively in the aspects including host load, balance of host multidimensional resource utilization and total data center energy consumption. The results show that TWD-VMM algorithm effectively improves host resource utilization and balances host load with an average energy consumption reduction of 27%.

Face liveness detection is an important part of the face recognition process, and is particularly important for the security of identity verification. In view of the cheating methods such as photo, video, mask, hood and head model in the face recognition process, the RGB map and depth map information of the face was collected by the Intel Realsense camera, and a lightweight liveness detection of feature fusion was proposed based on MobileNetV3 to fuse the features of the depth map and the RGB map together and perform the end-to-end training. To solve the problem of large parameter quantity in deep learning and the distinction of the weight areas by the network tail, the method of using Streaming Module at the network tail was proposed to reduce the quantity of network parameters and distinguish weight regions. Simulation experiments were performed on CASIA-SURF dataset and the constructed CQNU-LN dataset. The results show that, on both datasets, the proposed method achieves an accuracy of 95% with TPR@FPR=10E-4, which is increased by 0.1% and 0.05% respectively compared to ShuffleNet with the highest accuracy in the comparison methods. The accuracy of the proposed method reaches an accuracy of 95.2% with TPR@FPR=10E-4 on the constructed CQNU-3Dmask dataset, which is improved by 0.9% and 6.5% respectively compared to those of the method training RGB maps only and the method training depth maps only. In addition, the proposed model has the parameter quantity of only 1.8 MB and FLoating-point Operations Per second (FLOPs) of only 1.5×10 ⁶. The proposed method can perform accurate and real-time liveness detection on the extracted face target in practical applications.

The combinational modulated radar signal by linear frequency modulation and pseudo random code can increase signal complexity and reduce the probability of signal interception. However, this combination keeps the disadvantage of high sidelobe of linear frequency modulation and cannot overcome the increase of main lobe width caused by adding window function. Using the advantages of nonlinear frequency modulation signal generated by traditional stationary phase principle in suppressing sidelobe, a method which combined piecewise fitting nonlinear frequency modulation with Barker code modulation was proposed, and by using their respective advantages, the proposed method can not only reduce the ambiguity between distance and velocity, but also suppress the sidelobe. Finally, the simulation curve of explicit function model of this signal and ambiguity function were analyzed. The simulation results show that this method not only further reduces the ambiguity between distance and velocity and improves the performance of low probability signal interception, but also produces lower sidelobe than that of traditional nonlinear frequency modulation, which illustrates the feasibility and efficiency of the method used for radar signal.

How to use the limited transmission opportunity to transmit the information of the vehicle service perception reliably is the "bottleneck" problem in the development of intelligent transportation. By utilizing the motion law of vehicles in public transport, the hop by hop message forwarding mechanism based on opportunistic contact between nodes was put forward. And in combination with the characteristics of the public transport system, the cooperative Delay and Tolerant Network (DTN) routing strategy (TF) based on urban public transport mobility model was designed. Firstly, according to the characteristics of public transportation mobile model itself, such as bus, intercity bus nodes were grouped based on their motion paths, and a packet DTN routing algorithm based on fixed moving path was proposed. Then the taxi, human nodes were defined as free nodes, and a kind of DTN routing strategy based on forward factor control was designed as a supplement to the packet routing mechanism. The simulation results show that compared with the Epidemic, Prophet and Spray And Wait (SAW) routing algorithms, TF routing algorithm has higher message delivery ratio and lower average delay.

Focusing on the issue that the Signal-to-Clutter-and-Noise Ratio (SCNR) of echo signal is low when cognitive radar detects extended target, a waveform design method based on SCNR was proposed. Firstly, the relation between the SCNR of cognitive radar echo signal and the Energy Spectral Density (ESD) of transmitted signal, was gotten by establishing extended target detection model other than previous point target model; secondly, according to the maximum SCNR criterion, the global optimal solution of the transmitted signal ESD was deduced; finally, in order to get a meaningful time-domain signal, ESD was synthesized to be a constant amplitude based on phase-modulation after combining with the Minimum Mean-Square Error (MMSE) and iterative algorithm, which met the emission requirements of radar. In the simulation, the amplitude of time-domain synthesized signal is uniform, and its SCNR at the output of the matched filter is 19.133 dB, only 0.005 dB less than the ideal value. The results show that not only can the time-domain waveform meet the requirement of constant amplitude, but also the SCNR obtained at receiver output can achieve the best approximation to the ideal value, and it improves the performance of the extended target detection.

Concerning the problem that the weak target might be covered by the range side-lobes of the strong one and the range side-lobes could only be suppressed to a certain value, an improved Kalman-Minimum Mean-Square Error (K-MMSE) algorithm was proposed in this paper. This algorithm combined the Kalman filter with the Minimum Mean-Square Error (MMSE), and it was an effective method for suppressing range side-lobes of adaptive pulse compression. In the simulation, the proposed algorithm was compared with the traditional matched filter and other improved matched filters such as MMSE in a single target or multiple targets environments, and then found that the side-lobe levels, the Peak-SideLobe Ratio (PSLR) and Integrated SideLobe Ratio (ISLR) of the Point Spread Function (PSF) were all decreased obviously in comparison with the previous two methods. The simulation results show that the method can suppress range side-lobes well and detect the weak targets well either under both the condition of a single target and the condition of multiple targets.

Traditional learning-based super-resolution algorithms generally adopt training images for learning dictionary pairs, they are time-consuming, and the results strongly depend on the training images. To address these problems, a new super-resolution approach from a single color image was proposed based on sparse coding model. According to image self-similarity and redundancy features, this algorithm utilized low-resolution image itself for training dictionary pairs, combined with image pyramid structure. Meanwhile, in view of color images, the sparse representation based color image storage technology was used, which concatenated the values of three channels to a single vector and directly represented them sparsely. The experimental results illustrate that the proposed method not only can generate high-resolution images with better visual effects and higher Peak Signal-to-Noise Ratio (PSNR) but also has less computation time.

Hausdorff distance is sensitive to acnodes when using it as the measure to describe the similarity of two point sets. Therefore, a new image matching method based on Hausdorff distance of vector length was proposed. Considering the mutual correlation of pixels in the image, one pixel was connected to the others in one image, a set of vector lengths was composed, and then each pixel corresponded to one vector length set. Then, the modified Hausdorff distance between the vector length set corresponding with each pixel in template image and matching image was computed out. At last, quantified image matching results were obtained. The experiment shows that, the efficiency of the new method to deal with image matching problems in random noisy situations is so remarkable.

Van Genuchten equation is the most commonly used soil water characteristic curve equation, and its parameter value precision is the key to the use of the equation. In order to solve these parameters accurately, the Harmony Search (HS) algorithm was introduced, and a new HS algorithm based on the current global information named IGHS was proposed. IGHS algorithm has the following characteristics: firstly, IGHS employs a new method for generating new solution vectors, which uses the current global optimum in the harmony memory. Secondly, in order to avoid premature and enhance global search ability, IGHS disturbs the current global optimum at a certain probability. Lastly, the algorithm is simple, and easy to implement. The experimental results show that the solution accuracy of IGHS is similar to the random Particle Swarm Optimization (PSO) algorithm, but the convergence of IGHS is faster than PSO and the calculated amount is smaller, so IGHS can be used as a new method to calculate Van Genuchten equation parameters.

The existing technology of color separation, especially the spot color separation, can no longer meet the requirements of prepress processing efficiency or printing quality.Aimed at this situation, the fuzzy C-means clustering algorithm(FCM) was put forward. The algorithm, based on the classification of pixels, carried fuzzy clustering on the grayscale of images in order to get image clustering center at first, and then put each pixel to the corresponding category according to the grayscale of each pixel and the maximum membership degree. Experimental result shows that image segmentation based on fuzzy rules is intuitive and easy to realize and has achieved a good segmentation effect.

A new illumination robust Mean Shift tracking method was proposed. This method combined color feature and local binary pattern feature to describe the target. The illumination invariant characteristic of local binary pattern made the model more robust. To avoid the unstable bin-to-bin similarity applied in the original Mean Shift tracking method, a cross-bin similarity was used to make the feature matching more stable. Experiments show that the proposed method is robust than the original Mean Shift tracking method under illumination changes.