Thoracic Aortic Aneurysm and Dissection (TAAD) is one of the life-threatening cardiovascular diseases, and the histological changes of Medial Degeneration (MD) have important clinical significance for the diagnosis and early intervention of TAAD. Focusing on the issue that the diagnosis of MD is time-consuming and prone to poor consistency because of the great complexity in histological images, a deep learning based classification method of histological images was proposed, and it was applied to four types of MD pathological changes to verify its performance. In the method, an improved Convolutional Neural Network (CNN) model was employed based on the GoogLeNet. Firstly, transfer learning was adopted for applying the prior knowledge to the expression of TAAD histopathological images. Then, Focal loss and L2 regularization were utilized to solve the data imbalance problem, so as to optimize the model performance. Experimental results show that the proposed model is able to achieve the average accuracy of four-class classification of 98.78%, showing a good generalizability. It can be seen that the proposed method can effectively improve the diagnostic efficiency of pathologists.

Aiming at the deep structure and high computational complexity of current network models based on deep learning for super-resolution image reconstruction, as well as the problem that the networks can not operate effectively on resource-constrained devices caused by the high storage space requirement for the network models, a super-resolution convolutional neural network compression method based on knowledge distillation was proposed. This method utilizes a teacher network with large parameters and good reconstruction effect as well as a student network with few parameters and poor reconstruction effect. Firstly the teacher network was trained; then knowledge distillation method was used to transfer knowledge from teacher network to student network; finally the reconstruction effect of the student network was improved without changing the network structure and the parameters of the student network. The Peak Signal-to-Noise Ratio (PSNR) was used to evaluate the quality of reconstruction in the experiments. Compared to the student network without knowledge distillation method, the student network using the knowledge distillation method has the PSNR increased by 0.53 dB, 0.37 dB, 0.24 dB and 0.45 dB respectively on four public test sets when the magnification times is 3. Without changing the structure of student network, the proposed method significantly improves the super-resolution reconstruction effect of the student network.

Aiming at the premature convergence problem of standard Particle Swarm Optimization (PSO) in solving complex optimization problem, a new search PSO algorithm based on gradient descent method was proposed. Firstly, when the global extremum exceeds the preset maximum number of unchanged iterations, the global extremum was judged to be in the extreme trap. Then, the gradient descent method was used to proceed twice search, a tabu area was constituted with the center of optimal extremum point and the radius of specific length to prevent particles repeatedly search the same area. Finally, new particles were generated based on the population diversity criteria to replace the particles that would be eliminated. The twice search algorithm and other four improved algorithms were applied to the optimization of four typical test functions. The simulation results show that the convergence accuracy of the twice search particle swarm algorithm is higher up to 10 orders of magnitude, the convergence speed is faster and it is easier to find the global optimal solution.

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.

Patent contains lots of information about background, technology, function and so on, which plays an important role in innovation field. Patent is something created by innovation knowledge, at the same time, it promotes us to make more use of innovation knowledge and break the inherent thinking and the limitation of knowledge, which inspires designers in the process of product design. From the term of innovation design, a new method for extracting innovation knowledge was proposed based on combination feature and maximum entropy classifier. The natural language processing was used, patent terms recognition algorithm was given, and word feature and syntactic feature of the closed package tree in the shortest path were jointed to compute the middle result. After that, the maximum entropy algorithm was applied to extract innovation knowledge based on semantic analysis and mark the attributes of knowledge. The results show that the combination feature can effectively deal with patent issues which need to be solved, and the relationships among the semantic role of knowledge innovation about target function, function principle and position feature in the technical scheme.

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 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.

According to the problem of the traditional Gravitational Search Algorithm (GSA) such as falling into the local minimum point easily, a hybrid algorithm based on Estimation of Distribution (ED) and gravitational search (GSEDA) was proposed. By characterizing the distribution of current solutions found by GSA, ED was used to generate promising solutions based on the constructed probability matrix, thus guiding the search to new solution areas. The proposed GSEDA was able to balance the exploration and exploitation of the search, therefore possessing a better local optima jumping capacity. The experimental results based on the traveling salesman problem indicate that GSEDA performs better than traditional algorithms in terms of solution quality and robustness.

In order to enhance the performance of the synchronization and demodulation, a Very high frequency (VHF) Data Broadcast (VDB) signal demodulation algorithm based on the solution of differential equation was proposed. This algorithm eliminated the synchronization performance deterioration caused by the frequency offset. And frame synchronization, bit synchronization, frequency offset estimation and correction could be completed within a single set of synchronization symbols. The simulation results show that the method is effective to enhance the VDB signal demodulation performance.

To remove the stripe noise from the line scan images, a transform domain destriping method which combined Fourier transform and wavelet decomposition was proposed. Firstly, the image was decomposed using multi-resolution wavelet decomposition to separate the subband which contained the stripe noise from other subbands. Then the subband that contained stripe noise was transformed into Fourier coefficients. The Fourier coefficients were processed by a band-stop filter to remove the stripe noise. The live collected cotton foreign fiber images with stripe noise were used in the simulation experiment. The experimental results indicate that the proposed approach which combined Fourier transform with wavelet decomposition can effectively remove the stripe noise from the image while preserving the characteristics of the original image. It gets better destriping effect than just using Fourier transform or wavelet decomposition separately.

For the typical steganographic algorithms in JPEG images, this paper firstly analyzed the correlation between neighboring coefficients of intra- and inter-block in Discrete Cosiine Transform (DCT) domain, and then extracted the conditional distribution probability matrix of the bilateral coefficients as the sensitive steganalytic features by taking the middle coefficient of three neighboring coefficients as the condition. At last, this paper proposed a JPEG image steganalytic algorithm on a basis of bilateral transition probability distribution of DCT coefficients. The experimental results show that, for different embedding ratios, the algorithm proposed in this paper outperforms the existing algorithms.

In this paper, the author put forward a Least Significant Bit (LSB) matching steganalytic algorithm. Through calculating the second-order differential of the image pixels on horizontal and vertical directions, the differential matrix was obtained and it was used as sensitive feature extracting source and the second-order Markov transformation matrix of the differential matrix was extracted as the features, according to the LSB matching steganography with high security. According to the experimental result shows that the algorithm proposed in the paper speedups the detection process in a large extent and enhances the performance and practicability of the steganalytic algorithm while maintaining high detection accuracy, compared with the algorithms based on first-order differential Markov transition probability matrix.

The evolutionary mode of Differential Evolution (DE) was analyzed, and modified differentiation operator and selection operator with self-accelerated characteristic were proposed. Then the Self-Accelerated and Variable Neighbourhood searching of Differential Evolution (SAVNDE) algorithm was advanced using these new operators and variable neighbourhood search which improved the local search ability of algorithm. On the basis of the three evolution models, the simulation results on five classical benchmark functions show that SAVNDE has the same convergence rate of DE, and can achieve more optimization results in shorter time.

T tree, combining the advantages of AVL tree and B tree, can organize index data efficiently, thus providing good storage efficiency and search performance for main memory database. The design and implementation of T tree index was presented in the main memory database system named SwiftMMDB which was developed by the authors' research group recently. The insert and delete operations of traditional T tree were improved through node splitting and node populating method. The rotation number for balance in T tree was reduced. As a result, the retrieve efficiency and performance of main memory database system are improved.

To meet the diverse service requirements in heterogeneous network and adapt to the dynamic changes in the network environment, choose the most suitable network for every session and realize network load balance, a new algorithm based on Q-learning for the selection of wireless access network in the heterogeneous network composed of HSDPA and WiMax was proposed. By this algorithm, the most suitable network for each session can be selected according to network load conditions, service attribute, mobility and different locations of terminal. At last, the simulation results show that the algorithm can lower system blocking probability, increase spectrum utility and realize the autonomy of network selection.

The research of text similarity analysis and text clustering is mostly based on feature words. Because Chinese text does not have a natural delimiter between words, it must solve two problems such as Chinese word segmentation and higher-level dimensions feature vector spaces. In order to reduce the higher complexity, a novel investigation method of text similarity analysis using the association of Chinese characters was probed without using feature words. The notation of Chinese Character Association Measurement was defined， and the Chinese Character Association Measurement matrix to represent the Chinese text documents was constructed. Then a Chinese text similarity algorithm based on Chinese Character Association Measurement Matrix is proposed. The experiment result shows the Chinese Character Association Measurement is better than the mutual information and the T test and the bi-gram frequency. Without Chinese word segmentation, so this algorithm is useful in massive Chinese data corpus.

It proposed an algorithm for mining frequent patterns by finding the frequent extensions and merging sub-trees in a conversely constructed FP-tree. The performance of the algorithm is superior to the one of FP-Growth both in time and space consuming. It runs over two times faster than the FP-Growth and has a good scalability.

According to the development of Web field, the construction of Web application based on J2EE was introduced. The popular approach of deploying the static and dynamic content on to Application Server was discussed, and the original approach of dividing files between the Web server and Application Server was researched, furthermore a new approch of performance improvement of Web application by separating static and dynamic content is advanced. Finally, using IBM HTTP Server and WebSphere Application Server, the efficiency comparison between the two approach was presented.