Aiming at the security analysis problem of KATAN48 algorithm， a conditional differential cryptanalysis method of KATAN48 algorithm based on neural distinguishers was proposed. First， the basic principle of multiple output differences neural distinguishers was studied and applied to KATAN48 algorithm. According to the data format of KATAN48 algorithm， the input format and hyperparameters of the deep residual neural network were adjusted. Then， the Mixed-Integer Linear Programming （MILP） model of KATAN48 algorithm was established to search the prepended differential paths and the corresponding constraint conditions. At last， using the multiple output differences neural distinguishers， at most 80-round of the practical key recovery attack results of KATAN48 algorithm were given. Experimental results show that in the single key setting， the number of practical attack rounds of KATAN48 algorithm is increased by 10 rounds， the number of recoverable key bits of KATAN48 algorithm is increased by 22 bit and the data complexity and time complexity of KATAN48 algorithm are reduced from 2³⁴ and 2³⁴ to 2^16.39 and 2^19.68 respectively. Compared to the previous practical attack at the single-key setting， the proposed method can effectively increase the number of attack rounds and recoverable key bits， and reduces the computational complexity of attack.

Aiming at the problem of high misdiagnosis rate, high false positive rate and low detection accuracy in pulmonary nodule computer-aided detection, a method of nodular detection based on three-dimensional shape index and Hessian matrix eigenvalue was proposed. Firstly, the parenchyma region was extracted and the eigenvalues and eigenvectors of the Hessian matrix were calculated. Secondly, the three-dimensional shape index formula was deduced by the two-dimensional shape index, and the improved three-dimensional spherical like filter was constructed. Finally, in the parenchyma volume, the suspected nodule region was detected, and more false-positive regions were removed. The nodules were detected by the three-dimensional volume data, and the detected coordinates were input as the seeds of belief connect, and the three-dimensional data was splited to pick out three-dimensional nodules. The experimental results show that the proposed algorithm can effectively detect different types of pulmonary nodules, and has better detection effect on the ground glass nodules which are more difficult to detect, reduces the false positive rate of nodules, and finally reaches 92.36% accuracy rate and 96.52% sensitivity.

To measure the pitch of twisted-pair wires, a kind of image detection framework was put forward. With image segmentation, image restoration, image thinning, curve fitting and scale setting, the pitch of twisted-pair wires was calculated in real time. In combination with this framework, to deal with the problem that the traditional two-dimensional maximum between-cluster variance algorithm (Otsu) runs too slow, a new fast algorithm based on regional diagonal points was proposed. With redefining two-dimensional histogram area, using the quick lookup table and recursion method, it reduced running time drastically. To solve the problem of image missing, an edge detection algorithm was adopted. After repairing, the image thinning operation was acted on the image. The least square method was used to fit the single pixel point of thinning image, then fitting curve was acquired. It could acquire the pitch of twisted-pair wires in the image by calculating the distance between the fitting curve intersections. Finally the distance in image was converted to an observed value by the scale. The experimental results show that the segmentation time of fast algorithm is about 0.22% of traditional algorithm. And two segmentation results of algorithms are identical. With the pitch from the image detection method comparing with its real value, results show that the absolute errors between both of them are 0.48%. Through the image detection method, the pitch is measured accurately and the efficiency of twisted-pair pitch measurement is improved.

Executing spatial association rule mining repeatedly is often necessary to get interesting and effective rules. Though incremental maintenance algorithms can be introduced to improve the efficiency of association rule mining, currently there exists no such algorithm that can use spatial datasets directly. To solve this problem, the update strategy of the discovered rules was discussed. Both threshold changes and spatial datasets updates were taken into consideration, and an incremental mining algorithm called Incremental Spatial Apriori (ISA) was suggested. ISA algorithm aimed to update frequent predicate sets and association rules after the minimum support threshold decreased or new spatial layers added. This algorithm did not rely on the creation and update of spatial transaction tables; it directly used spatial layers as input data. In experiments with real-world data, the mining result extracted by ISA and Apriori-like algorithms are identical, but ISA can save 20.0% to 71.0% time comparatively. Besides, 1372722 rules were successfully updated with the filtering method, costing less than 0.1 seconds. These results indicate the incremental update strategy and algorithm for spatial association rules suggested in this paper are correct, efficient and applicable.

Methods for manifold embedding have the following issues: on one hand, neighborhood graph is constructed in such high-dimensionality of original space that it tends to work poorly; on the other hand, appropriate values for the neighborhood size and heat kernel parameter involved in graph construction are generally difficult to be assigned. To address these problems, a new semi-supervised dimensionality reduction algorithm called SparsE Discriminant Analysis (SEDA) was proposed. Firstly, SEDA set up a sparse graph to preserve the global information and geometric structure of the data based on sparse representation. Secondly, it applied both sparse graph and Fisher criterion to seek the optimal projection. The experimental results on a broad range of data sets show that SEDA is superior to many popular dimensionality reduction methods.

Fuzzy C-Means (FCM) clustering algorithm is very sensitive to image noise when it is used to image segmentation. As an improvement of FCM, Possibility FCM (PFCM) clustering algorithm can reduce the influence of image noise on image segmentation to some extent. However, since no spatial information of the image is taken into consideration, PFCM can not perform well when the image contains much noise. In order to further improve the segmentation accuracy of PFCM when much noise is present in the image, a new Spatial PFCM (SPFCM) algorithm was proposed by incorporating the spatial information of each pixel into the traditional PFCM algorithm in this paper. Both synthetic and IKONOS images with different kinds of noise were applied, and the segmentation results show that the proposed SPFCM clustering prevails over the FCM, PFCM, FCM-S1 and FCM-S2 visually and quantitatively. When dealing with different image noise, its average segmentation rate is as high as 99.71%, which shows the effectiveness of the proposed algorithm.

With reference to the structure of the network control system in the UUV, the paper analyzed time delay of the network in detail. A math method for message transmission simulation was put forward. Then the simulation of message wait-time delay which is indeterminism was implemented. By the simulation figure, the states of the transmission message can be observed distinctly. Meanwhile the data of the time delay was recorded, which provided the theoretical basis for the design of general performance based on CAN.