There are problems of miscellaneous computer congifuration,inflexible hardware and software upgrades,chip monopoly and stopping production,and increasingly evident bottleneck of volume and power consumption in bus interface implemented by ASIC chip.To solve these problems,this paper introduces the design of partial reconfiguration intelligent I/O interface based on the ZYNQ-7000 series Field Programmable Gate Array(FPGA) from Xilinx.By using programmable System-on-Chip(SoC) technology,based on PetaLinux development environment and Vivado2014.4 development tool,taking RS232,RS422 and CAN bus interfaces as example,the user can switch the bus interface configuration instructions via TCP/IP network data packet,and dynamically switch the corresponding local bit stream file,thus achieving the actual configuration of each interface and on-demand communication.Simulation results show that the combination of partial-reconfiguration technology and SoC technology makes the product design process more flexible and reduces the product’s dependence on hardware and update cost as well as the consumption of resources and power.In a certain extent,it also enhances the product safety and reliability.
The existing public verifiable multi-secret sharing schemes without trusted centers can not make sub-secrets self-selected and periodical renewable simultaneously.In order to solve the problem,a public verifiable and renewable multi-secret sharing scheme without trusted centers is proposed.Every participant selects sub-secret and then generates shadow secret.Shadow secrets can be transmitted on public channel based on signcryption algorithm,and the validity of the distribution shadow secrets can be checked by anyone.The one-way hash chains are used to make the shadow sub-secret renewable.The correctness and security of the scheme are analyzed,and the cheatings of the scheme are checked.Compared with existing schemes,the proposed scheme does not need trusted center.Participants can select the sub-secret themselves.And multi-secret can be verified publicly,renewable and shared.
New words or compound words are not included in the dictionary of text segmentation system,however these words have strong theme performances.To address this problem,the key words extraction algorithm based on chi-square value of co-concurrence words is proposed.Co-concurrence words are extracted by the associations among words,which are established according to the dependency parsing from the Language Technology Platform (LTP).The chi-square is used to test whether obvious differences exist among the distributions of co-concurrence words.Co-concurrence words with higher obvious differences have greater probability of being key words.The algorithm is also valid for the single word.Taken the single word and co-concurrence words as candidate key words,the algorithm extracts full text key words with the consideration of the chi-square value,word frequency and number of the candidate key words.Experimental result shows that the key words extraction algorithm based on chi-square value of co-concurrence words is better than the TextRank algorithm as the precision of key words extraction reaches 38.07% and the accuracy of the co-concurrence words reaches 80.15%.
Classic descriptors such as Scale Invariant Feature Transform(SIFT) and Speeded up Robust Feature(SURF) have some drawbacks in storage capacity and parameter adaptive learning,so a binary descriptor for images based on Adaboost is proposed,which can obtain image descriptor from optimal learning.A general framework using the learning method to obtain the image descriptor is developed,and a modified similarity function is presented on the basis of similarity function based on threshold response,by which the image descriptors and binary descriptors can be quickly learned.Weak learners are constructed by using the gradient features of the image,and the optimal weights and non-linear characteristic response of weak learners are computed by using the Adaboost method.The resulting local feature descriptor is discriminative and robust.Experimental results on image matching show that the proposed binary descriptor occupies less storage space and has good matching performance.