Aiming at the problem of semi-focus images caused by improper focusing of far and near visual fields during digital image shooting， a multi-focus image fusion Network with Cascade fusion and enhanced reconstruction （CasNet） was proposed. Firstly， a cascade sampling module was constructed to calculate and merge the residuals of feature maps sampled at different depths for efficient utilization of focused features at different scales. Secondly， a lightweight multi-head self-attention mechanism was improved to perform dimensional residual calculation on feature maps for feature enhancement of the image and make the feature maps present better distribution in different dimensions. Thirdly， convolution channel attention stacking was used to complete feature reconstruction. Finally， interval convolution was used for up- and down-sampling during the sampling process， so as to retain more original image features. Experimental results demonstrate that CasNet achieves better results in metrics such as Average Gradient （AG） and Gray-Level Difference （GLD） on multi-focus image benchmark test sets Lytro， MFFW， grayscale， and MFI-WHU compared to popular methods such as SESF-Fuse （Spatially Enhanced Spatial Frequency-based Fusion） and U2Fusion （Unified Unsupervised Fusion network）.

The prediction quality of simple linear models for time series forecasting often surpasses that of deep models such as Transformers. However， on datasets with a large number of channels， deep models， particularly Multi-Layer Perceptron （MLP）， can outperform simple linear models. Aiming at the differences in error power spectrum between simple linear models and MLPs in time series forecasting， an High-frequency enhanced time series prediction model based on multi-layer perceptron — HiFNet （High-Frequency Network） was proposed. Firstly， the fitting capability of MLPs within low-frequency bands was utilized. Then， the Adaptive Series Decomposition （ASD） module and the grouped linear layer were adopted to address the overfitting issue of MLPs in high-frequency bands and the issue of channel independence strategy failing to handle the channel redundancy effectively， thereby enhancing the robustness of MLPs in high-frequency band. Finally， experiments were conducted to HiFNet on standard datasets in the fields of meteorology， power， and transportation. The results demonstrate that the Mean Squared Error （MSE） of HiFNet is reduced by up to 23.6%， 10.0%， 35.1%， and 6.5%， respectively， compared to those of NLinear， RLinear， SegRNN （Segment Recurrent Neural Network）， and PatchTST （Patch Time Series Transformer）. At the same time， the grouped linear layer alleviates the impact of the channel redundancy by learning low-rank representations related to channels.

In view of the problems such as the slow computation of large flow data, the high computation pressure on the server, a set of relay computation and dynamic diversion model of computing-intensive large flow data was proposed. Firstly, in the distributed environment, the in-memory data storage technology was used to determine the computation amounts and complexity levels of the computation tasks. At the same time, the nodes were sorted by the node resource capacity, and the tasks were dynamically allocated to different nodes for parallel computing. Meanwhile, the computation tasks were decomposed by a relay processing mode, so as to guarantee the performance and accuracy requirements of high flow complex computing tasks. Through analysis and comparison, it can be seen that the running time of multiple nodes is shorter than that of the single node, and the computation speed of multiple nodes is faster than that of the single node when dealing with data volume of more than 10 000 levels. At the same time, when the model is applied in practice, it can be seen that the model can not only reduce the running time in high concurrency scenarios but also save more computing resources.

In order to solve the problems of the traditional Hand-Foot-Mouth Disease (HFMD) incidence trend prediction algorithm, such as low prediction accuracy, lack of the combination of other influencing factors and short prediction time, a method of long-term prediction using meteorological factors and Long Short-Term Memory (LSTM) network was proposed. First, the sliding window was used to convert the incidence sequence into the input and output of the network. Then, the LSTM network was used for data modeling and prediction, and the iterative prediction was used to obtain the long-term prediction results. Finally, the temperature and humidity variables were added to the network to compare the impact of these variables on the prediction results. Experimental results show that adding meteorological factors can improve the prediction accuracy of the model. The proposed model has the Mean Absolute Error (MAE) on the Jinan dataset of 74.9, and the MAE on the Guangzhou dataset of 427.7. Compared with the commonly used Seasonal Autoregressive Integrated Moving Average (SARIMA) model and Support Vector Regression (SVR) model, the proposed model has the prediction accuracy higher, which proves that the model is an effective experimental method for the prediction of the incidence trend of HFMD.

Concerning the serious packet loss and high end-to-end delay in wireless sensor networks, a Routing Protocol Optimized for Data Transmission Delay (RPODTD) was proposed. Firstly, according to the data transmission result, the channel detection conditions were classified, and the effective detection ratio and transmission efficiency were introduced as the evaluation indexes of nodes. Then, the queuing delay of data packet was estimated by the difference between actual delay and theoretical delay. Finally, the maximum and minimum queuing delay thresholds were given for judging whether to change the transmission path according to the interval that the queuing delay belongs to. In the simulation experiment on OMNeT++, compared with link quality and delay based Composite Load Balancing routing protocol (ComLoB) and Congestion Avoidance multipath routing protocol based on Routing Protocol for Low-power and lossy network (CA-RPL), RPODTD has the average end-to-end delay of nodes reduced by 78.87% and 51.81% respectively, and the node loss rate reduced by 40.71% and 68.43% respectively, and the node mortality rate reduced by 25.42% and 44.62% respectively. The simulation results show that the proposed RPODTD can effectively reduce the end-to-end delay, decrease the packet loss rate and extend the network life cycle.

Methods of parallel computation are used in validating topology of polygons stored in simple feature model. This paper designed and implemented a parallel algorithm of validating topology of polygons stored in simple feature model. The algorithm changed the master-slave strategy based on characteristics of topology validation and generated threads in master processor to implement task parallelism. Running time of computing and writing topology errors was hidden in this way. MPI and PThread were used to achieve the combination of processes and threads. The land use data of 5 cities in Jiangsu, China, was used to check the performance of this algorithm. After testing, this parallel algorithm is able to validate topology of massive polygons stored in simple feature model correctly and efficiently. Compared with master-slave strategy, the speedup of this algorithm increases by 20%.

Many existing image classification algorithms cannot be used for big image data. A new approach was proposed to accelerate big image classification based on MapReduce. The whole image classification process was reconstructed to fit the MapReduce programming model. First, the Scale Invariant Feature Transform (SIFT) feature was extracted by MapReduce, then it was converted to sparse vector using sparse coding to get the sparse feature of the image. The MapReduce was also used to distributed training of random forest, and on the basis of it, the big image classification was achieved parallel. The MapReduce based algorithm was evaluated on a Hadoop cluster. The experimental results show that the proposed approach can classify images simultaneously on Hadoop cluster with a good speedup rate.

The traditional visibility meters are expensive, their sampling is limited, and some of the existing video measurement methods need artificial markers and are of poor stability. In order to solve these problems, a new algorithm for weather recognition and traffic visibility estimation through fixed camera was proposed based on lane detection and image inflection. Different from previous research, our traffic model added homogenous fog factor in traffic scenes. The algorithm consisted of three steps. Firstly, calculate the scene activity map. With the help of the Area Search Algorithm (ASA) combined with texture features, extract area for identifying. The current weather condition is foggy if the pixels from top to bottom in the extracted area change in hyperbolic fashion. At the same time calculate inflection point of image brightness curve in the extracted area. Secondly, detect traffic lane based on the retractable window algorithm, extract the lane’s endpoint and calibrate the fixed camera. Finally, according to the visibility definition, calculate traffic scene visibility by International Meteological Organization based on monocular camera model and light propagation model in fog weather condition. Through experiments of visibility estimation for three different scenes, the experimental results show that the algorithm is consistent with human eye’s observation and the accuracy rate is up to 86% while the inspection error is within 20m.

With reference to network coding theory, a reliable data transmission policy,MGrowth Codes was proposed, for wireless sensor network environment. Through a gradientbased routing design, all data can converge to sink node (Sink). In addition, the data transmission policy can also use encoded packet to decode other encoded packets, which can further enhance the data recoverability. After the network simulation, MGrowth Codes can effectively increase the throughput of the wireless sensor network and improve the reliability of data transmission.

Based on the intrinsic features of spatial distribution, temporal distribution and high dynamic of parking activities, a negotiation approach was introduced to the design of an intelligent parking guidance system. The IEEE FIPA compliant multi-Agent system called active negotiation-based intelligent parking guidance system (AIPGIS) was proposed. The architecture, operation mechanism, negotiation algorithms and characteristics were analyzed and presented. The AIPGIS can implement effective sharing of urban traffic state information and strengthen the coordination and decision-making capacities of the active Agents.