Deep learning based algorithms such as YOLO （You Only Look Once） and Faster Region-Convolutional Neural Network （Faster R-CNN） require a huge amount of training data to ensure the precision of the model， and it is difficult to obtain data and the cost of labeling data is high in many scenarios. And due to the lack of massive training data， the detection range is limited. Aiming at the above problems， a few-shot object Detection algorithm based on Siamese Network was proposed， namely SiamDet， with the purpose of training an object detection model with certain generalization ability by using a few annotated images. Firstly， a Siamese network based on depthwise separable convolution was proposed， and a feature extraction network ResNet-DW was designed to solve the overfitting problem caused by insufficient samples. Secondly， an object detection algorithm SiamDet was proposed based on Siamese network， and based on ResNet-DW， Region Proposal Network （RPN） was introduced to locate the interested objects. Thirdly， binary cross entropy loss was introduced for training， and contrast training strategy was used to increase the distinction among categories. Experimental results show that SiamDet has good object detection ability for few-shot objects， and SiamDet improves AP₅₀ by 4.1% on MS-COCO 20-way 2-shot and 2.6% on PASCAL VOC 5-way 5-shot compared with the suboptimal algorithm DeFRCN （Decoupled Faster R-CNN）.

A low power branch encoding method was presented for decreasing the SoC bus power dissipation. This method's basic principle is: for the address bus, when the address bus is sequential, the address bus is frozen, and when the address bus is non-sequential, the window size is adjusted dynamically to apply the Bus-Invert (BI) method on the address bus. For the data bus, two threshold values are figured out for different data size respectively. If the Hamming distance locates between these two threshold values, the valid-data-channel switching dense area is found and inverted, otherwise applies the BI encoding. This method's encoding and decoding circuits are realized in the Advanced High Performance Bus (AHB) system. The experimental result demonstrates that compared with uncoded situation, this method decreases the address/data bus toggle rate by 51.2%/22.4%, and the system power is reduced by 28.9%. Compared with T0,BI and other encoding methods realized in the same system, the branch encoding is more superior in the toggle rate and power dissipation.

Using economic model to research grid resource management is a new hot topic in the field of grid computing. Based on current grid resources management methods, an online reverse auction technologybased approach to allocating computational grid resources was proposed concerning oversupply environment. The corresponding QoS function is defined to analyze the application range of this approach as well as its advantages. Finally, the results of simulation experiments indicate that this method is effective for computational grid resource allocation.