The Multi-Object Tracking （MOT） task needs to track multiple objects at the same time and ensures the continuity of object identities. To solve the problems in the current MOT process， such as object occlusion， object ID Switch （IDSW） and object loss， the Transformer-based MOT model was improved， and a multi-object tracking method based on dual-decoder Transformer was proposed. Firstly， a set of trajectories was generated by model initialization in the first frame， and in each frame after the first one， attention was used to establish the association between frames. Secondly， the dual-decoder was used to correct the tracked object information. One decoder was used to detect the objects， and the other one was used to track the objects. Thirdly， the histogram template matching was applied to find the lost objects after completing the tracking. Finally， the Kalman filter was utilized to track and predict the occluded objects， and the occluded results were associated with the newly detected objects to ensure the continuity of the tracking results. In addition， on the basis of TrackFormer， the modeling of apparent statistical characteristics and motion features was added to realize the fusion between different structures. Experimental results on MOT17 dataset show that compared with TrackFormer， the proposed algorithm has the IDentity F1 Score （IDF1） increased by 0.87 percentage points， the Multiple Object Tracking Accuracy （MOTA） increased by 0.41 percentage points， and the IDSW number reduced by 16.3%. The proposed method also achieves good results on MOT16 and MOT20 datasets. Consequently， the proposed method can effectively deal with the object occlusion problem， maintain object identity information， and reduce object identity loss.

Aiming at the existing problems in user trajectory data modeling such as the sparsity of check-in points, long-term dependencies and complex moving patterns, a social network user trajectory prediction model based on self-supervised learning, called SeNext, was proposed to model and train the user trajectory to predict the next Point Of Interest (POI) of the user. First, data augmentation was utilized to expand the training trajectory samples, which solved the problem of the deficiency of model generalization capability caused by insufficient data and too few footprints of some users. Second, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN) and attention mechanism were adopted into the modeling of current and historical trajectories respectively, so as to extract effective representations from high-dimensional sparse data to match the most similar moving patterns of users in the past. Finally, SeNext learned the implicit representations in the latent space by combining self-supervised learning and introducing contrastive loss Noise Contrastive Estimation (InfoNCE) to predict the next POI of the user. Experimental results show that compared to the state-of-the-artVariational Attention based Next (VANext)model, SeNext improves the prediction accuracy about 11% on Top@1.

In the field of object detection, it is hoped that the model trained in the domain with a lot of labels can be applied to other domains without labels, but different domain distributions are always different to each other, such difference will result in a sharp decline of model performance in domain transfer. To improve the model performance of object detection in domain transfer, the domain transfer was addressed on two levels, including the global-level transfer and the local-level transfer, which were corresponding to different feature alignment methods, that is, the global-level adopted selective alignment and the local-level adopted full alignment. The proposed domain transfer framework was constructed based on Single Shot MultiBox Detector (SSD) model and was disposed of two domain adaptors corresponding to global and local level respectively for the purpose of alleviating the domain difference. The specific training was implemented by the adversarial network algorithm, and the consistency regularization was used to further improve the domain transfer performance of the model. The effectiveness of the proposed domain transfer model was verified by many experiments. Experimental results show that on various datasets, the proposed model outperforms the existing common domain transfer models such as Domain Adaptation-Faster Region-based Convolutional Neural Network(DA-FRCNN), Adversarial Discriminative Domain Adaptation (ADDA), Dynamic Adversarial Adaptation Network (DAAN) by 5%-10% in term of mean Average Precision (mAP).

To deal with the problem that the number of antennas increases when Quadrature Spatial Modulation (QSM) improves the transmission rate, which requires a lot of resources and makes it difficult to achieve, a two-dimensional Space Code Orthogonal Index Modulation (SCOIM) was proposed. The transmitter information bits were mapped to the Pseudo Noise (PN) code index, the antenna index and the modulation symbol respectively. The in-phase part and orthogonal part of the modulation symbol spreaded spectrum through selecting activated PN code respectively, and were transmitted by activated antennas respectively. Analysis and simulation results show that in the comparison experiments with QSM, the index resource of SCOIM can be saved at least half at the same transmission rate and the saving increases exponentially with the increase of transmission rate. What's more, SCOIM has a performance advantage of about 5 dB when the bit error rate is 10 ^-4.

Aiming at the problems of Artificial Bee Colony (ABC) algorithm, which are the single search mechanism and the high coupling between global search and local search, a Multi-Population ABC (MPABC) algorithm based on hybrid search was proposed. Firstly, the population was sorted according to the fitness value to get an ordered queue, which was divided into three sorted subgroups including random subgroup, core subgroup and balanced subgroup. Secondly, different difference vectors were constructed according to the corresponding individual selection mechanism and search strategy to different subgroups. Finally, in the process of group search, the effective control of individuals with different fitness functions was realized through three subgroups, thus improving the balance ability of global search and local search. The simulation results based on 16 benchmark functions show that compared with ABC algorithm with Variable Search Strategy (ABCVSS), Modified ABC algorithm based on selection probability (MABC), Particle Swarm-inspired Multi-Elitist ABC (PS-MEABC) algorithm, Multi-Search Strategy of the ABC (MSSABC) and Improved ABC algorithm for optimizing high-dimensional complex functions (IABC), MPABC achieves a better optimization effect; and on the solution of high dimensional (100 dimensions) problems, compared with ABC, MPABC has higher convergence speed which is increased by about 23% and better search accuracy.

Maximizing customer satisfaction is directly related to the enterprise profit and market competitiveness for the supermarket as a service enterprise, so it is important to optimize the retail checkout operation. Firstly, the retail checkout scheduling problem was described by a triplet of α/β/γ, maximizing customer satisfaction was taken as the first goal and minimizing operating cost was taken as the second goal with machine usage restriction and the rule of First In First Out (FIFO). The corresponding mathematical model was established, and then an algorithm was designed using plant growth simulation algorithm. 〖BP(〗Finally, the actual data was used to simulate, and the results prove that the study has effectiveness and feasibility. 〖BP)〗Finally, a numerical simulation of actual cases was used to verify the effectiveness and feasibility of the method.

To improve the performance of mobile database system, a cache model was proposed for mobile database. A kind of synchronization algorithm based on message digest was used in this model. By comparing the value of message digest in mobile client and server, the algorithm completed the cache synchronization, and maintained the consistency of mobile client cache and the data in server. The timeliness of the data and the priority of the transaction were considered in this model. A cache replacement algorithm based on cost function was designed. The experimental results show that the cache hit rate of the proposed algorithm is higher than Least Recently Used (LRU) and Least Access-to-Update Ratio (LA2U) algorithm along with the increase of the number of cache data. At the same time, the restart rate of transaction is lower than LRU and LA2U while the frequency of access increases. The performance of the cache of mobile database is improved.

With the development of Web technology, accessing OLAP services through Web is a strong trend. The characteristics of accessing OLAP services based on Web were analyzed. In order to solve the problems of poor performance and license limitation, the concept of object pool was introduced into OLAP service systems. Due to the support of OLAP Object pool, web users can access the OLAP services easily and efficiently.