It the rate control algorithms of the new generation video coding standard H.266/VVC （Versatile Video Coding）， the rate-distortion optimization technique with independent coding parameters is adopted. However， the Coding Tree Units （CTUs） within the same frame affect others in the spatial domain， and there are global coding parameters. At the same time， in the CTU-level bit allocation formulas， approximated coding parameters for bit allocation are used， resulting in the reduction of rate control accuracy and coding performance. To address these issues， a spatial-domain global optimization algorithm for CTU-level bit allocation called RTE_RC （Rate Control with Recursive Taylor Expansion） was proposed， and the global coding parameters were approximated by using a recursive algorithm. Firstly， a globally optimized bit allocation model in spatial-domain was established. Secondly， a recursive algorithm was used to calculate the global Lagrange multiplier in the CTU-level bit allocation formula. Finally， the bit allocation of coding units was optimized and the coding units were coded. Experimental results show that under the Low-Delay Prediction frame （LDP） configuration， compared with the rate control algorithm VTM_RC （Rate Control algorithm Versatile Test Model）， the proposed algorithm has the rate control error decreased from 0.46% to 0.02%， the bit-rate saved by 2.48 percentage points， and the coding time reduced by 3.52%. Therefore， the rate control accuracy and rate distortion performance are significantly improved by the proposed algorithm.

In the Remaining Useful Life （RUL） prediction methods of aero-engine， the data at different time steps are not weighted simultaneously， including the original data and the extracted features， which leads to the problem of low accuracy of RUL prediction.Therefore， an RUL prediction method based on optimized hybrid model was proposed. Firstly， three different paths were chosen to extract features. 1） The mean value and trend coefficient of the original data were input into the fully connected network. 2） The original data were input into Bidirectional Long Short-Term Memory （Bi-LSTM） network， and the attention mechanism was used to process the obtained features. 3） The attention mechanism was used to process the original data， and the weighted features were input into Convolutional Neural Network （CNN） and Bi-LSTM network. Then， the idea of fusing multi-path features for prediction was adopted， the above-mentioned extracted features were fused and input into the fully connected network to obtain the RUL prediction result. Finally， the Company-Modular Aero-Propulsion System Simulation （C-MAPSS） datasets were used to verify the effectiveness of the method. Experimental results show that the proposed method performs well on all the four datasets. Taking FD001 dataset as an example， the Root Mean Square Error （RMSE） of the proposed method is reduced by 9.01% compared to that of Bi-LSTM network.

Rate-Distortion （R-D） optimization is a crucial technique in video encoders. However， the widely used independent R-D optimization is far from being global optimal. In order to further improve the compression performance of High Efficiency Video Coding （HEVC）， a two-pass encoding algorithm combined with both R-D dependency and R-D characteristic was proposed. Firstly， the current frame was encoded with the original method in HEVC， and the number of bits consumed by the current frame and the R-D model parameters of each Coding Tree Unit （CTU） were obtained. Then， combined with time domain dependent rate distortion optimization， the optimal Lagrange multiplier and quantization parameter for each CTU were determined according to the information including current frame bit budget and R-D model parameters. Finally， the current frame was re-encoded， where each CTU had different optimization goal according to its Lagrange multiplier. Experimental results show that the proposed algorithm achieves significant rate-distortion performance improvement. Specifically， the proposed algorithm saves 3.5% and 3.8% bitrate at the same coding quality， compared with the original HEVC encoder， under the coding configurations of low-delay B and P frames.

Forged and tampered data frames should be identified and filtered out to ensure network security and efficiency. However， the existing schemes usually fail to work when verification devices are attacked or maliciously controlled in the Software Defined Network （SDN）. To solve the above problem， a blockchain-based data frame security verification mechanism was proposed. Firstly， a Proof of Frame Forwarding （PoFF） consensus algorithm was designed and used to build a lightweight blockchain system. Then， an efficient data frame security verifying scheme for SDN data frame was proposed on the basis of this blockchain system. Finally， a flexible semi-random verifying scheme was presented to balance the verification efficiency and the resource cost. Simulation results show that compared with the hash chain based verifying scheme， the proposed scheme decreases the missed detection rate significantly when an equal proportion of switches are maliciously controlled. Specifically， when the proportion is 40%， the decrease effect is very obvious， the missed detection rate can still be kept no more than 32% in the basic verification mode， and can be further reduced to 7% with the assistance of the semi-random verifying scheme. Both are much lower than the missed detection rate of 72% in the hash chain based verifying scheme， and the resource overhead and communication cost introduced by the proposed mechanism are within a reasonable range. Additionally， the proposed scheme can still maintain good verification performance and efficiency even when the SDN controller is completely unable to work.

Object-oriented analysis of polarimetric Synthetic Aperture Radar (SAR) has been used commonly, while the polarimetric decomposition is still based on pixel, which is inefficient to extract polarimetric information. A object-based method was proposed for polarimetric decomposition. The coherent matrix of object was constructed by weighted iteration of scattering coefficient of similarity, and the convergence of coherent matrix was analyzed, therefore polarimetric information could be obtained through the coherent matrix of object instead of pixel， which can improve the efficiency of obtaining polarimetric features. To more fully reflect the terrain target, spatial features of object were extracted. After feature selection, polarimetric SAR image classification experiments using Support Vector Machine (SVM) demonstrate the effectiveness of the proposed method.

Traditional Deterministic Finite Automata (DFA) based regular expression matching can only process one character per cycle, which is the speed bottleneck. A new algorithm named Multi-Character DFA (MC-DFA) was proposed for high throughput matching and precise positioning. It combined the one character transition in traditional DFA together to handle multi-character processing per cycle. A new transition matrix compress algorithm was also proposed to reduce the redundancy introduced by MC-DFA. The result demonstrates that MC-DFA can improve the throughput efficiently while requiring acceptable memory. For a set of 300 regexes, 8C-DFA obtains a throughput of 7.88Gb/s, memory usage less than 6MB and 19.24s preprocessing time, better than traditional methods.

Embedded system is vulnerable to buffer overflow attack. In order to solve this problem, a block based protection scheme was proposed after analyzing the memory management of μC/OS-Ⅱ. By making a combination of all the memory blocks which belong to one task and managing it through the established block_table, the introduced scheme protected the safety through creating isolation between task memories, checking and controlling the access of memory blocks. Then, an effective analysis about this scheme was given. In addition, a buffer overflow attack experiment was operated on Nios Ⅱ with the improved uC/OS-Ⅱ, and the results show that the proposed scheme is feasible.

A moving object tracking algorithm with related multi-regions based on Kalman filter was proposed to solve the occlusion and tracking excursion. Through locating multiple regions on the target and constructing undirected graphs, the algorithm calculated the predicted position of the center of each region by using Kalman filter firstly. Then by combining gray histogram match with positional relations of adjacent regions, it calculated the observation center of each region. At last, it realized tracking by revising the observation centers using Kalman filter. The experimental results of human tracking of two regions show that the proposed algorithm has better robustness and real-time performance than the result of tracking each region using Mean Shift algorithm under the occlusion or similar targets and backgrounds.

Though PUF is a new concept in recent years, since it has application prospect for the security of system authentication and key generation, it has become a hot research topic in the field of hardware security. In order to get the whole picture of physical unclonable function for better application in the future research work, first, based on the the different PUF implementations available, this paper classified the detailed design, and summed up the main problems. Then based on these categories, this paper proposed a nonformal property description. Next, from the point of view of cryptographic applications, this paper summarized the PUF application. Finally, this paper pointed out a few meaningful PUF future research directions.

The checking of bullet's primer is the most important step in controlling the quality of bullet products. In order to segment the image of bullet's primer surface defect accurately, a new method of image segmentation was proposed. According to the checking requirement and the properties of cartridge's bottom, firstly, the image of bullet's primer was ascertained approximately to be detected, and Log operator was applied to extract the circle edge of primer. After analyzing both advantages and disadvantages of the Hough transform and the least square method, a new algorithm of circle detection combined improved Hough transform and the least square method was proposed, by which the center of circle and radius were acquired accurately. Finally, the image of primer circle was extracted by the parameters of circle, the primer surface defect was segmented by threshold, and the results of segmentation were optimized by mathematical morphology. The experimental results show that the proposed method is of accuracy and robustness in the application of bullet's primer surface defect segmentation. The average wrong segmentation rate is below 10%, and the average deviation is less than 17 pixels.

Focusing on the problems of poor locating performance and ignoring anchor nodes' inaccuracy in traditional DV-Hop algorithm which is one of the range-free algorithms in Wireless Sensor Network (WSN), an intelligent algorithm for locating nodes based on Processing Strategy of Optimal Hopping Distances (PSOHD) was proposed. Fully considering the effect of dynamic topology and anchor nodes' difference, this algorithm firstly introduced two communication radii with anchor nodes to calculate the nodes of communication range respectively. Secondly, the average one-hop distance among anchor nodes was refined by means of weighted least squares estimation. Finally, the average one-hop distance used by each locating node for estimating its location was optimized through weighting the N received average one-hop distances from anchor nodes. In addition, Particle Swarm Optimization (PSO) algorithm was presented to locate the unknown nodes. The simulation results show that the improved algorithm has obviously better location precision at the cost of increasing appropriate energy. Thus, it is a practical scheme for WSN with both inaccuracy of anchor nodes and random dynamic topology.

In this paper, the problem of emergency resources scheduling on Generalized Continuous Consumption System with Uncertain Rescue Time is discussed. In order to describe the continuous request and design effective and practical algorithm, the continuous consumption In this paper, the problem of emergency resources scheduling on generalized continuous consumption system with uncertain rescue time was discussed. In order to describe the continuous request and design effective and practical algorithm, the continuous consumption satisfied degree model was established. Corresponding scheduling scheme, the probable duration of supply disruption was listed for reference. In light of emergency system given permitted duration of supply disruption, the scheduling model of permitted discontinuity duration was formulated. The relation between consumption continuity constraint and the earliest emergency start-time objective was harmonized. Finally, simulation examples illustrate the rationality of the model and the efficiency of the algorithm proposed in this paper.

To remove hidden risks and solve neglected Denial-of-Service (DoS) attacks in the back-end database in current low-cost Radio Frequency Identification (RFID) authentication protocols, a new anonymous bidirectional RFID authentication protocol based on low-cost tags was proposed, and the performance analysis was conducted. According to the simple operative logic and shielded operation of reader, this scheme was designed by utilizing two cascading 16 bit Cyclic Redundancy Check (CRC) messages as mutual authentication factor between the label and the reader. The analytical results show the proposed protocol possesses untraceability, authenticity and usability and resists replaying attack and synchronization attack. Overall, it is a RFID low-power security authentication scheme of security, efficiency and practicality.

Since uC/os-II can not provide the restriction on same priority tasks,a strategy was proposed base on round-robin algorithm scheduling .It borrowed two priority tasks in the kernel as the clock source and the polling engine, so that tasks with the same priority work under the lowest priority in turns. The method allows the kernel to support up to 192 tasks in the same priority without losing the real-time. The experiment and comparison indicated that this project was more simple and practical than the one we already have.

In the particle filter framework, estimation accuracy strongly depends on the choice of proposal distribution. The traditional particle filter uses system transition probability as the proposal distribution without considering the new observing information; therefore, they cannot give accurate estimation. A new tracking framework applied with particle filter algorithm was proposed, which used Galerkin's method to construct proposal distribution. This proposal distribution enhanced the estimation accuracy compared to traditional filters. In the proposed framework, color model and shape model were adaptively fused, and a new model update scheme was also proposed to improve the stability of the object tracking. The experimental results demonstrate the availability of the proposed algorithm.