Concerning the shortcomings of unbalanced search， easy to fall into local optimum and weak comprehensive solution performance of Teaching-Learning-Based Optimization （TLBO） algorithm in dealing with optimization problems， an improved TLBO based on equilibrium optimization and Lévy flight strategy， namely ELMTLBO （Equilibrium-Lévy-Mutation TLBO）， was proposed. Firstly， an elite equilibrium guidance strategy was designed to improve the global optimization ability of the algorithm through the equilibrium guidance of multiple elite individuals in the population. Secondly， a strategy combining Lévy flight with adaptive weight was added after the learner phase of TLBO algorithm， and adaptive scaling was performed by the weight to the step size generated by Lévy flight， which improved the population's local optimization ability and enhanced the self-adaptability of individuals to complex environments. Finally， a mutation operator pool escape strategy was designed to improve the population diversity of the algorithm by the cooperative guidance of multiple mutation operators. To verify the effectiveness of the algorithm improvement， the comprehensive convergence performance of the ELMTLBO algorithm was compared with 7 state-of-the-art intelligent optimization algorithms such as the Dwarf Mongoose Optimization Algorithm （DMOA）， as well as the same type of algorithms such as Balanced TLBO （BTLBO） and standard TLBO on 15 international test functions. The statistical experiment results show that compared with advanced intelligent optimization algorithms and TLBO algorithm variants， ELMTLBO algorithm can effectively balance its search ability， not only solving both unimodal and multimodal problems， but also having significant optimization ability in complex multimodal problems. It can be seen that with the combined effect of different strategies， ELMTLBO algorithm has outstanding comprehensive optimization performance and stable global convergence performance. In addition， ELMTLBO algorithm was successfully applied to the Multiple Sequence Alignment （MSA） problem based on Hidden Markov Model （HMM）， and the high-quality aligned sequences obtained by this algorithm can be used in disease diagnosis， gene tracing and some other fields， which can provide good algorithmic support for the development of bioinformatics.

Aiming at the problems of the slow model convergence and low diagnosis accuracy due to the time-series fault diagnosis data with strong noise in the industrial field， an improved one-Dimensional Convolutional and Bidirectional Long Short-Term Memory（1DCNN-BiLSTM） Neural Network fault diagnosis method was proposed. The method includes preprocessing of fault vibration signals， automatic feature extraction and vibration signal classification. Firstly， Complete Ensemble Empirical Mode Decomposition with Adaptive Noise （CEEMDAN） technology was used to preprocess the original vibration signal. Secondly， the 1DCNN-BiLSTM dual channel model was constructed， and the processed signal was input into the Bidirectional Long Short-Term Memory （BiLSTM） model channel and the One-dimensional Convolution Neural Network （1DCNN） model channel to fully extract the timing correlation characteristics， the non-correlation characteristics of the local space and the weak periodic laws of the signal. Thirdly， in response to the problem of strong noise in the signal， the Squeeze and Excitation Network （SENet） module was improved and applied to the two different channels. Finally， the features extracted from the two channels were fused by putting them into the fully connected layer， and the accurate identification of equipment faults was realized by the help of the Softmax classifier. The bearing dataset of Case Western Reserve University was used for experimental comparison and verification. The results show that after applying the improved SENet module to the 1DCNN channel and the stacked BiLSTM channel at the same time， the 1DCNN-BiLSTM dual channel model performs the highest diagnosis accuracy 96.87% with fast convergence， which is better than traditional one-channel models， thereby effectively improving the efficiency of equipment fault diagnosis.

The Iterative Closest Point (ICP) algorithm requires two point clouds to have a good initialization to start, otherwise the algorithm may easily get trapped into local optimum. In order to solve the problem, a novel translation domain estimating based global point cloud registration algorithm was proposed. The translation domain was estimated according to axis-aligned bounding box of calculating the defuzzification principal point clouds of data and model point clouds. With the estimated translation domain and [-π, π] ³ rotation domain, an improved globally optimal ICP was used to register for global searching. The proposed algorithm could estimate translation domain adaptively and register globally according to the point clouds for registration. The process of registration did not need to calculate the feature information of point clouds and was efficient for any initialization with less setting parameters. The experimental results show that the proposed algorithm can get accurate registration results of global optimization automatically, and also improve the efficiency of global registration.

To improve the convergence rate of digital repeater echo cancellation, firstly, the echo cancellation technique based on adaptive filter was studyed; secondly, the recursion algorithm of adaptive filter was improved by the technical schemes that two adaptive filters compute in parallel and update the weights jointly and recursively. Since the error signal to adjust weights of the two adaptive filters was generated in different ways, the schemes were divided into two categories: scheme one is that the weights of two filters were adjusted by error signal of echo cancellation (simultaneously); scheme two is that the weights of the first filter were adjusted by error signal as the difference value of received signal from antenna and output signal of the first filter; and the weights of the second filter were adjusted by error signal as the difference value of the above-mentioned error signal and the output signal of the second filter (separately). The simulation results show that the convergence rate of the echo cancellation is increased by 11.11%~17.78% in the improved technique scheme, so as to improve the condition effectively with the slow convergence rate of digital repeater echo cancellation.

A new approach for automatic stitching of aerial image sequence was proposed, based on the SIFT (Scale Invariant Feature Transform) features. It consisted of two core steps: image registration and mosaic. The variations between two consecutive frames in aerial sequence are always very notable, thus the present commonlyused featurebased methods are not very suitable for their registration. Concerning this, an iterative registration method using the SIFT features was put forward. Moreover, for image mosaic, a new color fusion method was proposed to achieve smooth stitching results based on human visual features. Finally, experiments on real aerial sequences prove the effectiveness of the proposed stitching approach.