Due to the high complexity， weak individual differences， and short inter-class distances of fundus image features， pure Convolutional Neural Networks （CNNs） and attention based networks cannot achieve satisfactory accuracy in fundus disease image classification tasks. To this end， Attention Bilinear Involution Neural Network （ABINN） model was implemented for fundus disease image classification by using the involution operator. The parameter amount of ABINN model was only 11% of that of the traditional Bilinear Convolutional Neural Network （BCNN） model. In ABINN model， the underlying semantic information and spatial structure information of the fundus image were extracted and the second-order features of them were fused. It is an effective parallel connection between CNN and attention method. In addition， two instantiation methods for attention calculation based on involution operator， Attention Subnetwork based on PaTch （AST） and Attention Subnetwork based on PiXel （ASX）， were proposed. These two methods were able to calculate attention within the CNN basic structure， thereby enabling bilinear sub-networks to be trained and fused in the same architecture. Experimental results on public fundus image dataset OIA-ODIR show that ABINN model has the accuracy of 85%， which is 15.8 percentage points higher than that of the common BCNN model and 0.9 percentage points higher than that of TransEye （Transformer Eye） model.

Aircraft brake pads play a very important role in the process of aircraft braking. It is of great significance to accurately predict the Remaining Useful Life (RUL) of aircraft brake pads for reducing braking faults and saving human and material resources. Aiming at the non-stationary and nonlinear characteristics of the aircraft brake pads wear sequence, a model for predicting the RUL of the aircraft brake pads based on Bidirectional Long Short-Term Memory (BiLSTM) network was proposed, namely VMD-BiLSTM model. Firstly, the method of Variational Mode Decomposition (VMD) was used to decompose the original wear sequence into several sub-sequences with different frequencies and bandwidths to reduce the non-stationarity of the sequence. Then, the BiLSTM neural network prediction models were constructed for the decomposed subsequences. Finally, the prediction values of the sub-sequences were superimposed to obtain the final prediction result of brake pads wear value, so as to realize the life prediction of the brake pads. The simulation results show that the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) of VMD-BiLSTM model are 0.466 and 0.898% respectively, both of which are better than those of the comparison models, verifying the superiority of VMD-BiLSTM model.

The performance of some current area-based image registration algorithms declines when image transformation parameters are of both wide range and high precision. Concerning this problem, a parameter-adaptive registration algorithm was proposed based on the natures of image transformation in frequency/space domain, and the estimation steps and fusion method for rotation parameter and shift parameter were designed. A set of simulation experiments were implemented to compare the performance of the proposed algorithm with the Vandewalle's and improved Keren's. Mean square error and standard deviation of square error were used as evaluation indicators for registration precision and parameters adaptation. The two indicators of the proposed algorithm are lower than those of the other two methods, which means the proposed algorithm has adaptive ability in wide range parameters estimation and high accuracy of registration.

In view of the low delivery ratio of conventional probabilistic routing in opportunistic networks, an improved routing algorithm based on History Meeting Predictability Routing (HMPR) was put forward. The algorithm was primarily based on the contact duration and the meeting frequency of history information of nodes, and predicted the utility of packets successfully delivered to the destination. Through comparing the utility value, nodes could determine packets whether to be forwarded from them to next hop nodes. The simulation results show that, compared with traditional epidemic routing and probabilistic routing, the proposed routing scheme has better performance in the delivery ratio of packets, the average delay time and the average buffer time.

A novel detection mechanism, called Cooperative RSS-based Sybil Detection (CRSD) used in static and resource-limited Wireless Sensor Networks (WSN) was proposed. CRSD took use of the Received Signal Strength (RSS) to induce the distance between two nodes and further determined the positions of the identities of interests by use of the RSS information from multiple neighbor nodes. A Sybil attack was detected when two or more different identities with almost the same position were got. The simulations and results show that, first, Sybil attack can certainly deteriorate the system performance significantly and furthermore, CRSD can detect such attack in most cases and thus facilitate the avoidance of using Sybil node to protect the overall performance effectively.