Pub Date : 2011-06-01DOI: 10.1109/IWECMS.2011.5952383
N. Nasreddine, J. Boizard, J. Fourniols, C. Escriba
The design, manufacture and deployment of embedded systems become increasingly complex and multidisciplinary process. Before the steps of manufacturing and deployment, and during the conception phase, we should have a tool to localize defects and to choose which modifications we can do to eliminate them. This procedure could be done by a simulation and validation phase. Given the increasing complexity of systems such as telecommunications systems, control systems and others, a specific simulation and validation process must take place. This simulation ideally made in a single development environment, should cover different areas and all components of the system. In this paper, we briefly describe the behavioral models of the elements of a large scale wireless sensors network (WSN) used to create our simulator, focusing specifically on the model of the transmission channel, and how we can detect the defects from the behavioral simulator. In side to legacy network simulator, for the testing and modeling of communication protocols, this simulator shouldn't omit WSN specific aspect, in accuracy it covers the modeling of node platforms, protocols, and real world phenomena.
{"title":"Defects localisation tool for wireless sensors network conception","authors":"N. Nasreddine, J. Boizard, J. Fourniols, C. Escriba","doi":"10.1109/IWECMS.2011.5952383","DOIUrl":"https://doi.org/10.1109/IWECMS.2011.5952383","url":null,"abstract":"The design, manufacture and deployment of embedded systems become increasingly complex and multidisciplinary process. Before the steps of manufacturing and deployment, and during the conception phase, we should have a tool to localize defects and to choose which modifications we can do to eliminate them. This procedure could be done by a simulation and validation phase. Given the increasing complexity of systems such as telecommunications systems, control systems and others, a specific simulation and validation process must take place. This simulation ideally made in a single development environment, should cover different areas and all components of the system. In this paper, we briefly describe the behavioral models of the elements of a large scale wireless sensors network (WSN) used to create our simulator, focusing specifically on the model of the transmission channel, and how we can detect the defects from the behavioral simulator. In side to legacy network simulator, for the testing and modeling of communication protocols, this simulator shouldn't omit WSN specific aspect, in accuracy it covers the modeling of node platforms, protocols, and real world phenomena.","PeriodicalId":211450,"journal":{"name":"2011 10th International Workshop on Electronics, Control, Measurement and Signals","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116966617","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 : 2011-06-01DOI: 10.1109/IWECMS.2011.5952378
Ikram Ben Kouas, P. Joly
We propose a set of features to characterize faces in images. The goal is to use these features to automatically select the most relevant images to train an identification tool. Those features are derived from a set of constraints usually required to allow the recognition process. A filtering tool based on the Adaboost algorithm is used as a basic process to test the relevance of these features for such a task. In these experiments we obtained a rate of 87% of good selection. In other words, among all the faces kept after the filtering process, 87% are compliant with the predefined constraints, and can be used to train an identification tool.
{"title":"Automatic keyface selection for known people identification in images","authors":"Ikram Ben Kouas, P. Joly","doi":"10.1109/IWECMS.2011.5952378","DOIUrl":"https://doi.org/10.1109/IWECMS.2011.5952378","url":null,"abstract":"We propose a set of features to characterize faces in images. The goal is to use these features to automatically select the most relevant images to train an identification tool. Those features are derived from a set of constraints usually required to allow the recognition process. A filtering tool based on the Adaboost algorithm is used as a basic process to test the relevance of these features for such a task. In these experiments we obtained a rate of 87% of good selection. In other words, among all the faces kept after the filtering process, 87% are compliant with the predefined constraints, and can be used to train an identification tool.","PeriodicalId":211450,"journal":{"name":"2011 10th International Workshop on Electronics, Control, Measurement and Signals","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132132222","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：