Concerning at the high energy consumption of heterogeneous multi-core platforms, an algorithm for solving the optimal energy allocation scheme of periodic tasks by using optimization theory was proposed. The optimal energy consumption problem of periodic tasks was modeled and added constraints to the model. According to the optimization theory, the binary integer programming problem was relaxed to obtain the convex optimization problem. The interior point method was used to solve the optimization problem and the relaxed distribution matrix was obtained. The allocation scheme for partial tasks was obtained after the judgement processing of the decision matrix. On this basis, the iterative method was used to find the allocation scheme for the remaining tasks. Experimental results show that the energy consumption of this distribution scheme is reduced by about 1.4% compared with the similar optimization theory algorithm, and compared with the optimization theory algorithm with the similar energy consumption, the execution time of this scheme is reduced by 86%. At the same time, the energy consumption of the scheme is only 2.6% higher than the theoretically optimal energy consumption.

Focusing on the low accuracy and long detection time of arc detection in engineering drawing vectorization, a fast arc detection algorithm based on tangent lines matching was proposed. Firstly, tangent lines on the circle outer boundary were detected from eight directions (0, π/4, π/2, …, 7π/4) and were added in tangent lines set. Secondly, the tangent lines in the set were paired up, and the center and radius of circles were estimated to obtain circle candidate set. Finally, tracing detection was performed for every candidate circle after merging data of circle candidate set, and every candidate circle was ascertained as a circle or an arc. The paring process was executed during the tangent lines searching, and the number of pairing was effectively reduced by removing the relative tangent lines of the identified candidate circle. In the contrast experiments with RANdom SAmple Consensus (RANSAC) algorithm and Effective Voting Method (EVM), the proposed method reached average detection accuracy of 97.250%, and the average detection time was 12.290 s, which were better than those of the comparison methods. The experimental results illustrate that the proposed method can effectively detect the arc which length is greater than 1/8 circumference in low noise image, improve the accuracy of detection and shorten the detection time.