Pub Date : 2011-10-01DOI: 10.1109/OSSC.2011.6184695
Ping Chen, Peipei Wang, Jianfeng Sun
In recent years, areas such as 3G and their applications are expanding. As a result, it makes requirements for high-speed wireless communication increasingly urgent. LTE (Long Term Evolution) project uses OFDM (Orthogonal Frequency Division Multiplexing) and MIMO (Multiple-Input Multiple-Out-put) technology as its sole criterion for the evolution of wireless networks to improve and enhance the 3G (UMTS) air-access technology. The main purpose of this paper is to use SCILAB platform to design, implement and analyze the OFDM communication system.
{"title":"Design and implement of the OFDM communication system","authors":"Ping Chen, Peipei Wang, Jianfeng Sun","doi":"10.1109/OSSC.2011.6184695","DOIUrl":"https://doi.org/10.1109/OSSC.2011.6184695","url":null,"abstract":"In recent years, areas such as 3G and their applications are expanding. As a result, it makes requirements for high-speed wireless communication increasingly urgent. LTE (Long Term Evolution) project uses OFDM (Orthogonal Frequency Division Multiplexing) and MIMO (Multiple-Input Multiple-Out-put) technology as its sole criterion for the evolution of wireless networks to improve and enhance the 3G (UMTS) air-access technology. The main purpose of this paper is to use SCILAB platform to design, implement and analyze the OFDM communication system.","PeriodicalId":197116,"journal":{"name":"2011 IEEE International Workshop on Open-source Software for Scientific Computation","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121636985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-01DOI: 10.1109/OSSC.2011.6184705
Baoyong Zhao, Yingjian Qi, Xingzhen Tao
Radial basis function (RBF) network is one of the significant neural networks. It has been used successfully in various fields. But in RBF network approximation algorithm, the initial value of the network weights, Gauss function center vector and broad-based vector is not easy to determine, and when these parameter choice is undeserved, RBF network approximation precision will decline and even the serious consequences of network spread will be produced. By using genetic algorithm in this paper, which can better realize RBF network parameter optimization, thereby increasing the accuracy of approximation. Scilab is open source software and has good simulation capabilities. Experiments using Scilab shows that the optimization method of genetic neural network is feasible and results are satisfied.
{"title":"Optimization research of genetic neural network based on Scilab","authors":"Baoyong Zhao, Yingjian Qi, Xingzhen Tao","doi":"10.1109/OSSC.2011.6184705","DOIUrl":"https://doi.org/10.1109/OSSC.2011.6184705","url":null,"abstract":"Radial basis function (RBF) network is one of the significant neural networks. It has been used successfully in various fields. But in RBF network approximation algorithm, the initial value of the network weights, Gauss function center vector and broad-based vector is not easy to determine, and when these parameter choice is undeserved, RBF network approximation precision will decline and even the serious consequences of network spread will be produced. By using genetic algorithm in this paper, which can better realize RBF network parameter optimization, thereby increasing the accuracy of approximation. Scilab is open source software and has good simulation capabilities. Experiments using Scilab shows that the optimization method of genetic neural network is feasible and results are satisfied.","PeriodicalId":197116,"journal":{"name":"2011 IEEE International Workshop on Open-source Software for Scientific Computation","volume":"35 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113992115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-01DOI: 10.1109/OSSC.2011.6184687
Xingbo Wang
The paper puts forward a new direct algorithm for computing the inverse of a square matrix. The algorithm adopts a skill to compute the inverse of a regular matrix via computing the inverse of another lower-ranked matrix and contains neither iterations nor divisions in its computations—it is division-free. Compared with other direct algorithms, the new algorithm is easier to implement with either a recursive procedure or a recurrent procedure and has a preferable time complexity for denser matrices. Mathematical deductions of the algorithm are presented in detail and analytic formulas are exhibited for time complexity and spatial complexity. Also, the recursive procedure and the recurrent procedure are demonstrated for the implementation, and applications are introduced with comparative studies to apply the algorithm to tridiagonal matrices and bordered tridiagonal matrices.
{"title":"A rank-reducing and division-free algorithm for inverse of square matrices","authors":"Xingbo Wang","doi":"10.1109/OSSC.2011.6184687","DOIUrl":"https://doi.org/10.1109/OSSC.2011.6184687","url":null,"abstract":"The paper puts forward a new direct algorithm for computing the inverse of a square matrix. The algorithm adopts a skill to compute the inverse of a regular matrix via computing the inverse of another lower-ranked matrix and contains neither iterations nor divisions in its computations—it is division-free. Compared with other direct algorithms, the new algorithm is easier to implement with either a recursive procedure or a recurrent procedure and has a preferable time complexity for denser matrices. Mathematical deductions of the algorithm are presented in detail and analytic formulas are exhibited for time complexity and spatial complexity. Also, the recursive procedure and the recurrent procedure are demonstrated for the implementation, and applications are introduced with comparative studies to apply the algorithm to tridiagonal matrices and bordered tridiagonal matrices.","PeriodicalId":197116,"journal":{"name":"2011 IEEE International Workshop on Open-source Software for Scientific Computation","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125843214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-01DOI: 10.1109/OSSC.2011.6184703
Xiaodong Zhang, Xiaoli Li
As a small and open-source Real Time Operation System (RTOS), μC/OS-II shows unique advantage suitable for the embedded system education. A practicable platform for embedded system education is presented based on the μC/OS-II and 8051 derivatives as the core. Rested on my experience, this paper sets forth the features, mode and advice of embedded system education. The practice shows the platform of education is simple and easy to understand, and is able to prompt the learning of μC/OS-II.
{"title":"Research of μC/OS-II education based on the 8051 derivatives","authors":"Xiaodong Zhang, Xiaoli Li","doi":"10.1109/OSSC.2011.6184703","DOIUrl":"https://doi.org/10.1109/OSSC.2011.6184703","url":null,"abstract":"As a small and open-source Real Time Operation System (RTOS), μC/OS-II shows unique advantage suitable for the embedded system education. A practicable platform for embedded system education is presented based on the μC/OS-II and 8051 derivatives as the core. Rested on my experience, this paper sets forth the features, mode and advice of embedded system education. The practice shows the platform of education is simple and easy to understand, and is able to prompt the learning of μC/OS-II.","PeriodicalId":197116,"journal":{"name":"2011 IEEE International Workshop on Open-source Software for Scientific Computation","volume":"579 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132744828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-01DOI: 10.1109/OSSC.2011.6184707
Hongying Gu, Shunguo Qiao, Cheng Yang
There has been a rapid increase in the need of accurate and reliable personal identification technologies in recent years. Among all the biometric techniques known, iris recognition is taken as one of the most promising methods, due to its low error rates without being invasive. Usually an iris recognition system consists of four steps: image acquisition, preprocessing, feature extraction and identification or verification. Among these steps, iris localization is a necessary and important step in iris preprocessing. In order to be more feasible in real world application environment, the performance is a key factor. In this paper, we propose an efficient localization algorithm using standard deviation which is optimized for performance. Overall it achieves a promising result on various iris datasets compared to previous work. Besides, our method gets 52% execution time deduction compared to a traditional implementation reference for the localization.
{"title":"An efficient iris localization algorithm based on standard deviations","authors":"Hongying Gu, Shunguo Qiao, Cheng Yang","doi":"10.1109/OSSC.2011.6184707","DOIUrl":"https://doi.org/10.1109/OSSC.2011.6184707","url":null,"abstract":"There has been a rapid increase in the need of accurate and reliable personal identification technologies in recent years. Among all the biometric techniques known, iris recognition is taken as one of the most promising methods, due to its low error rates without being invasive. Usually an iris recognition system consists of four steps: image acquisition, preprocessing, feature extraction and identification or verification. Among these steps, iris localization is a necessary and important step in iris preprocessing. In order to be more feasible in real world application environment, the performance is a key factor. In this paper, we propose an efficient localization algorithm using standard deviation which is optimized for performance. Overall it achieves a promising result on various iris datasets compared to previous work. Besides, our method gets 52% execution time deduction compared to a traditional implementation reference for the localization.","PeriodicalId":197116,"journal":{"name":"2011 IEEE International Workshop on Open-source Software for Scientific Computation","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123269885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-01DOI: 10.1109/OSSC.2011.6184710
Ya-Jun Qu, Bao-Gang Hu
This work describes a toolbox of nonlinear regression models developed on an open-source platform of Scilab. The models are formed from radial basis function (RBF) neural network structures. For a fast calculation of the models, we adopt a linear solver in implementations. A specific effort is made on applications of linear priors, which presents a unique feature different from other existing regression toolboxes. In this work, we define linear priors to be a class of prior information that exhibits a linear relation to the attributes of interests, such as variables, free parameters, or their functions of the models. Two approaches of incorporating linear priors are implemented in the models, namely, Lagrange Multiplier (LM) and Direct Elimination (DE). Several numerical examples are demonstrated in the toolbox for the educational purpose on learning nonlinear regression models. From the numerical examples, users can understand the importance of utilizing linear priors in models. The linear priors include the hard constraints on interpolation points and soft constraints on ranking list.
{"title":"A Scilab toolbox of nonlinear regression models using a linear solver","authors":"Ya-Jun Qu, Bao-Gang Hu","doi":"10.1109/OSSC.2011.6184710","DOIUrl":"https://doi.org/10.1109/OSSC.2011.6184710","url":null,"abstract":"This work describes a toolbox of nonlinear regression models developed on an open-source platform of Scilab. The models are formed from radial basis function (RBF) neural network structures. For a fast calculation of the models, we adopt a linear solver in implementations. A specific effort is made on applications of linear priors, which presents a unique feature different from other existing regression toolboxes. In this work, we define linear priors to be a class of prior information that exhibits a linear relation to the attributes of interests, such as variables, free parameters, or their functions of the models. Two approaches of incorporating linear priors are implemented in the models, namely, Lagrange Multiplier (LM) and Direct Elimination (DE). Several numerical examples are demonstrated in the toolbox for the educational purpose on learning nonlinear regression models. From the numerical examples, users can understand the importance of utilizing linear priors in models. The linear priors include the hard constraints on interpolation points and soft constraints on ranking list.","PeriodicalId":197116,"journal":{"name":"2011 IEEE International Workshop on Open-source Software for Scientific Computation","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116673264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-01DOI: 10.1109/OSSC.2011.6184711
Xiong Luo, Hao Li
Nowadays, subjects of cloud computing, especially cloud storage, are still thought to be new fields with few teaching opportunities. This paper discusses how to realize GET/PUT requests between REST client and servers, utilize CDMI (Cloud Data Management Interface) standard to carry on teaching experiment of cloud storage communication under Linux environment. Practice proves that it is useful to help students understand basic concepts of cloud environment and mechanism of cloud storage, and offer students access to the cloud standard.
{"title":"Experiment design for cloud storage application based on CDMI","authors":"Xiong Luo, Hao Li","doi":"10.1109/OSSC.2011.6184711","DOIUrl":"https://doi.org/10.1109/OSSC.2011.6184711","url":null,"abstract":"Nowadays, subjects of cloud computing, especially cloud storage, are still thought to be new fields with few teaching opportunities. This paper discusses how to realize GET/PUT requests between REST client and servers, utilize CDMI (Cloud Data Management Interface) standard to carry on teaching experiment of cloud storage communication under Linux environment. Practice proves that it is useful to help students understand basic concepts of cloud environment and mechanism of cloud storage, and offer students access to the cloud standard.","PeriodicalId":197116,"journal":{"name":"2011 IEEE International Workshop on Open-source Software for Scientific Computation","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125001915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-01DOI: 10.1109/OSSC.2011.6184715
Cheng Soon Ong
Machine Learning (ML) is a scientific field comprised of both theoretical and empirical results. For methodological advances, one key aspect of reproducible research is the ability to compare a proposed approach with the current state of the art. Such a comparison can be theoretical in nature, but often a detailed theoretical analysis is not possible or may not tell the whole story. In such cases, an empirical comparison is necessary. To produce reproducible machine learning research, there are three main required components that need to be easily available: - The paper describing the method clearly and comprehensively. - The data on which the results are computed. - Software (possibly source code) that implements the method and produces the figures and tables of results in the paper. We share our experiences about mloss.org and mldata.org, community efforts towards encouraging open source software and open data in machine learning.
{"title":"Towards open machine learning: Mloss.org and mldata.org","authors":"Cheng Soon Ong","doi":"10.1109/OSSC.2011.6184715","DOIUrl":"https://doi.org/10.1109/OSSC.2011.6184715","url":null,"abstract":"Machine Learning (ML) is a scientific field comprised of both theoretical and empirical results. For methodological advances, one key aspect of reproducible research is the ability to compare a proposed approach with the current state of the art. Such a comparison can be theoretical in nature, but often a detailed theoretical analysis is not possible or may not tell the whole story. In such cases, an empirical comparison is necessary. To produce reproducible machine learning research, there are three main required components that need to be easily available: - The paper describing the method clearly and comprehensively. - The data on which the results are computed. - Software (possibly source code) that implements the method and produces the figures and tables of results in the paper. We share our experiences about mloss.org and mldata.org, community efforts towards encouraging open source software and open data in machine learning.","PeriodicalId":197116,"journal":{"name":"2011 IEEE International Workshop on Open-source Software for Scientific Computation","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115903024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-01DOI: 10.1109/OSSC.2011.6184702
Ping Chen, Lin-yan Li
As the Key technology of Fourth generation Mobile, Orthogonal frequency division multiplexing (OFDM) has become a mainstream in the current high-speed data transmission system with the higher spectral efficiency and the ability to resist multi-path make the technology. In OFDM simulation system, clock program for the signal stream processing is one of the sticking point of parsing the entire system correctly. In this paper, with OFDM transmission system simulation in SCICOS graphical simulation platform, we focus on analyzing the Key issues of clock synchronization and data stream processing, and ultimately give the solutions.
{"title":"Analysis and resolution of key issues in OFDM system simulation","authors":"Ping Chen, Lin-yan Li","doi":"10.1109/OSSC.2011.6184702","DOIUrl":"https://doi.org/10.1109/OSSC.2011.6184702","url":null,"abstract":"As the Key technology of Fourth generation Mobile, Orthogonal frequency division multiplexing (OFDM) has become a mainstream in the current high-speed data transmission system with the higher spectral efficiency and the ability to resist multi-path make the technology. In OFDM simulation system, clock program for the signal stream processing is one of the sticking point of parsing the entire system correctly. In this paper, with OFDM transmission system simulation in SCICOS graphical simulation platform, we focus on analyzing the Key issues of clock synchronization and data stream processing, and ultimately give the solutions.","PeriodicalId":197116,"journal":{"name":"2011 IEEE International Workshop on Open-source Software for Scientific Computation","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130282938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-01DOI: 10.1109/OSSC.2011.6184701
Yong Wang, Hao Wang, Zhicai Gu
Data mining is a key in knowledge discovery process. In recent years, its application is becoming a fast growing field, and more and more software products are developed based on different application background. In this paper, we make a survey of data mining tools used for real projects, evaluate their impact factors with a new definition and reveal that open source softwares are becoming more widely used and there is seldom single software is used for solving real problems.
{"title":"A survey of data mining softwares used for real projects","authors":"Yong Wang, Hao Wang, Zhicai Gu","doi":"10.1109/OSSC.2011.6184701","DOIUrl":"https://doi.org/10.1109/OSSC.2011.6184701","url":null,"abstract":"Data mining is a key in knowledge discovery process. In recent years, its application is becoming a fast growing field, and more and more software products are developed based on different application background. In this paper, we make a survey of data mining tools used for real projects, evaluate their impact factors with a new definition and reveal that open source softwares are becoming more widely used and there is seldom single software is used for solving real problems.","PeriodicalId":197116,"journal":{"name":"2011 IEEE International Workshop on Open-source Software for Scientific Computation","volume":"437 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122883508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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