{"title":"Content Based Image Retrieval using Multimodal Data based on CCA","authors":"I. Sayad","doi":"10.17781/P002447","DOIUrl":"https://doi.org/10.17781/P002447","url":null,"abstract":"","PeriodicalId":211757,"journal":{"name":"International journal of new computer architectures and their applications","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133280206","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}
Existing video communication systems, used in business or private life, provides file data transfer function. However, these systems need many manipulation steps by using mouse and keyboard. These steps are not easy to transfer files for the PC beginners. Furthermore, it is not intuitive action from the point of view of handing of things. In order to solve these issues, we have proposed 3D video communication system by using Kinect and Head Mounted Display (HMD). This system provides users communications with realistic sensation and intuitive manipulation. In this system, users can see the other user’s body part through HMD and communicate in AR (Augmented Reality) space. In this paper, we provide a intuitive file data transfer application by handing of AR objects using this system.
{"title":"DEVELOPMENT A FILE TRANSFER APPLICATION BY HANDOVER FOR 3D VIDEO COMMUNICATION SYSTEM IN SYNCHRONIZED AR SPACE","authors":"Y. Fujibayashi, H. Imamura","doi":"10.17781/p001980","DOIUrl":"https://doi.org/10.17781/p001980","url":null,"abstract":"Existing video communication systems, used in business or private life, provides file data transfer function. However, these systems need many manipulation steps by using mouse and keyboard. These steps are not easy to transfer files for the PC beginners. Furthermore, it is not intuitive action from the point of view of handing of things. In order to solve these issues, we have proposed 3D video communication system by using Kinect and Head Mounted Display (HMD). This system provides users communications with realistic sensation and intuitive manipulation. In this system, users can see the other user’s body part through HMD and communicate in AR (Augmented Reality) space. In this paper, we provide a intuitive file data transfer application by handing of AR objects using this system.","PeriodicalId":211757,"journal":{"name":"International journal of new computer architectures and their applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129546632","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}
{"title":"Temperature and Humidity Management of the Storage Houses of Food Using Data Logger","authors":"A. Alkandari","doi":"10.17781/P002366","DOIUrl":"https://doi.org/10.17781/P002366","url":null,"abstract":"","PeriodicalId":211757,"journal":{"name":"International journal of new computer architectures and their applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121315323","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}
Plant factories with artificial light (PFAL) are attracting worldwide attention as a technology for stably producing crops. One of the problems of PFAL is tipburn which is a physiological disorder of crops. Tipburn is a phenomenon in which plant growth point cells are necrotized. Lettuce cultivated in PFAL in particular has a high frequency of tipburn. When tipburn occurs, its identification is done by human eye observation, and tipburn leaves are trimmed by hand or tipburn lettuce is removed from products. These operations require much labor and cost. If tipburn identification can automatically be done using machine learning, the economic effect will be great and it will be a driving force for spreading PFAL. In this study, we aim to perform binary discrimination of tipburn occurrence and its non-occurrence about lettuce cultivated in PFAL using machine learning with convolutional neural networks. In particular, we aim to recognize the symptom of tipburn which means the early stages of tipburn immediately before leaf tips discolor blackly and the commercial value as the vegetables is damaged. The results of the experiments indicate that the recognition of the symptom of tipburn can be performed with high accuracy.
{"title":"Automatic Identification of Plant Physiological Disorders in Plant Factories\u0000with Artificial Light Using Convolutional Neural Networks","authors":"S. Shimamura, Seiichi Koakutsu Kenta Uehara","doi":"10.17781/p002611","DOIUrl":"https://doi.org/10.17781/p002611","url":null,"abstract":"Plant factories with artificial light (PFAL) are attracting worldwide attention as a technology for stably producing crops. One of the problems of PFAL is tipburn which is a physiological disorder of crops. Tipburn is a phenomenon in which plant growth point cells are necrotized. Lettuce cultivated in PFAL in particular has a high frequency of tipburn. When tipburn occurs, its identification is done by human eye observation, and tipburn leaves are trimmed by hand or tipburn lettuce is removed from products. These operations require much labor and cost. If tipburn identification can automatically be done using machine learning, the economic effect will be great and it will be a driving force for spreading PFAL. In this study, we aim to perform binary discrimination of tipburn occurrence and its non-occurrence about lettuce cultivated in PFAL using machine learning with convolutional neural networks. In particular, we aim to recognize the symptom of tipburn which means the early stages of tipburn immediately before leaf tips discolor blackly and the commercial value as the vegetables is damaged. The results of the experiments indicate that the recognition of the symptom of tipburn can be performed with high accuracy.","PeriodicalId":211757,"journal":{"name":"International journal of new computer architectures and their applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128278009","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}