2011, Vol.16, No.11

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

pISSN : 1598-849X / eISSN : 2383-9945
https://journal.kci.go.kr/jksci

The requirement of a system-on-a-chip (SoC) design is increasing, which combines various and complex functional units on a single device. However, short time to market prohibits to release the device. To satisfy this shorter time-to-market, verification of both hardware and software at the same time is important. A virtual platform-based design method supports faster verification of these combined software and hardware by reusing pre-defined intellectual properties (IP). In this paper, we introduce the virtual platform-based design and redesign the existing ARM processor based S3C2440A system using the virtual platform-based method. In addtion, we implement and evaluate the performance of a JPEG decoder on the S3C2440A virtual platform. Furthermore, we introduce an optimized technique of the JPEG decoder using the ARM based inline assembly language, and then verify the performance improvement on the virtual platform. Such virtual platform-based design allows to verify both software and hardware at the same time and can meet the requirement of the shorter time-to-market.

In this paper, we design and implement of gate management system by face recognition using smart phone. We investigate various algorithms for face recognition on smart phones. First step in any face recognition system is face detection. We investigated algorithms like color segmentation, template matching etc. for face detection, and Eigen & Fisher face for face recognition. The algorithms have been first profiled in MATLAB and then implemented on the Android phone. While implementing the algorithms, we made a tradeoff between accuracy and computational complexity of the algorithm mainly because we are implementing the face recognition system on a smart phone with limited hardware capabilities.

Collaborative filtering system is the clustering about user is built and then based on that clustering results will recommend the preferred item to the user. However, building user clustering is time consuming and also once the users evaluate and give feedback about the film then rebuilding the system is not simple. In this paper, genre pattern of movie recommendation systems is being used and in order to simplify and reduce time of rebuilding user clustering. A Frequent pattern networks is used and then extracts user preference genre patterns and through that extracted patterns user clustering will be built. Through built the clustering for all neighboring users to collaborative filtering is applied and then recommends movies to the user. When receiving user information feedback, traditional collaborative filtering is to rebuild the clustering for all neighbouring users to research and do the clustering . However by using frequent pattern Networks, through user clustering based on genre pattern, collaborative filtering is applied and when rebuilding user clustering inquiry limited by search time can be reduced. After receiving user information feedback through proposed user clustering based on genre pattern , the time that need to spent on re-establishing user clustering can be reduced and also enable the possibility of traditional collaborative filtering systems and recommendation of a similar performance.