Pub Date : 2018-07-01DOI: 10.4018/IJSSCI.2018070102
Yushi Li, G. Baciu, Yu Han, Chenhui Li
This article describes a novel 3D image-based indoor localization system integrated with an improved SfM (structure from motion) approach and an obstacle removal component. In contrast with existing state-of-the-art localization techniques focusing on static outdoor or indoor environments, the adverse effects, generated by moving obstacles in busy indoor spaces, are considered in this work. In particular, the problem of occlusion removal is converted into a separation problem of moving foreground and static background. A low-rank and sparse matrix decomposition approach is used to solve this problem efficiently. Moreover, a SfM with RT (re-triangulation) is adopted in order to handle the drifting problem of incremental SfM method in indoor scene reconstruction. To evaluate the performance of the system, three data sets and the corresponding query sets are established to simulate different states of the indoor environment. Quantitative experimental results demonstrate that both query registration rate and localization accuracy increase significantly after integrating the authors' improvements.
{"title":"Improved SfM-Based Indoor Localization with Occlusion Removal","authors":"Yushi Li, G. Baciu, Yu Han, Chenhui Li","doi":"10.4018/IJSSCI.2018070102","DOIUrl":"https://doi.org/10.4018/IJSSCI.2018070102","url":null,"abstract":"This article describes a novel 3D image-based indoor localization system integrated with an improved SfM (structure from motion) approach and an obstacle removal component. In contrast with existing state-of-the-art localization techniques focusing on static outdoor or indoor environments, the adverse effects, generated by moving obstacles in busy indoor spaces, are considered in this work. In particular, the problem of occlusion removal is converted into a separation problem of moving foreground and static background. A low-rank and sparse matrix decomposition approach is used to solve this problem efficiently. Moreover, a SfM with RT (re-triangulation) is adopted in order to handle the drifting problem of incremental SfM method in indoor scene reconstruction. To evaluate the performance of the system, three data sets and the corresponding query sets are established to simulate different states of the indoor environment. Quantitative experimental results demonstrate that both query registration rate and localization accuracy increase significantly after integrating the authors' improvements.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125015197","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 : 2018-07-01DOI: 10.4018/IJSSCI.2018070103
Wei Zhou, Haimin Guo, Yaoting Lin
This article describes how under the influence of traps and trap ranges in size, final moisture content in oil production and changes in the reservoir is very large. Due to this, thin and dispersed oil concentration, facies changes and oil complexes, and strong segmentation, results in poor comparability between wells and conventional methods of seismic reservoir prediction has been more difficult to meet the development needs of the block. Therefore, the introduction of methods of seismic data structure characteristics, with a method based on sequence structure of underground rock formations, rock and petroleum allows the prediction for oil and gas purposes. Through the application of seismic structural properties, the result has been verified in practice and achieved a good application effect.
{"title":"Application of Structural Properties of Seismic Data to Prediction of Hydrocarbon Distribution","authors":"Wei Zhou, Haimin Guo, Yaoting Lin","doi":"10.4018/IJSSCI.2018070103","DOIUrl":"https://doi.org/10.4018/IJSSCI.2018070103","url":null,"abstract":"This article describes how under the influence of traps and trap ranges in size, final moisture content in oil production and changes in the reservoir is very large. Due to this, thin and dispersed oil concentration, facies changes and oil complexes, and strong segmentation, results in poor comparability between wells and conventional methods of seismic reservoir prediction has been more difficult to meet the development needs of the block. Therefore, the introduction of methods of seismic data structure characteristics, with a method based on sequence structure of underground rock formations, rock and petroleum allows the prediction for oil and gas purposes. Through the application of seismic structural properties, the result has been verified in practice and achieved a good application effect.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129929310","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 : 2018-07-01DOI: 10.4018/IJSSCI.2018070101
Mohamed A. Aref, S. Jayaweera
This article presents a design of a wideband autonomous cognitive radio (WACR) for anti-jamming and interference-avoidance. The proposed system model allows multiple WACRs to simultaneously operate over the same spectrum range producing a multi-agent environment. The objective of each radio is to predict and evade a dynamic jammer signal as well as avoiding transmissions of other WACRs. The proposed cognitive framework is made of two operations: sensing and transmission. Each operation is helped by its own learning algorithm based on Q-learning, but both will be experiencing the same RF environment. The simulation results indicate that the proposed cognitive anti-jamming technique has low computational complexity and significantly outperforms non-cognitive sub-band selection policy while being sufficiently robust against the impact of sensing errors.
{"title":"Jamming-Resilient Wideband Cognitive Radios with Multi-Agent Reinforcement Learning","authors":"Mohamed A. Aref, S. Jayaweera","doi":"10.4018/IJSSCI.2018070101","DOIUrl":"https://doi.org/10.4018/IJSSCI.2018070101","url":null,"abstract":"This article presents a design of a wideband autonomous cognitive radio (WACR) for anti-jamming and interference-avoidance. The proposed system model allows multiple WACRs to simultaneously operate over the same spectrum range producing a multi-agent environment. The objective of each radio is to predict and evade a dynamic jammer signal as well as avoiding transmissions of other WACRs. The proposed cognitive framework is made of two operations: sensing and transmission. Each operation is helped by its own learning algorithm based on Q-learning, but both will be experiencing the same RF environment. The simulation results indicate that the proposed cognitive anti-jamming technique has low computational complexity and significantly outperforms non-cognitive sub-band selection policy while being sufficiently robust against the impact of sensing errors.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130168272","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 : 2018-07-01DOI: 10.4018/IJSSCI.2018070104
Xu Jie, Dawei Qi
In this article, in order to improve tree volume calculation method, a measurement method based on tree information point feature extraction is proposed, the method based on image processing and binocular vision, according to the measurement result of information point change and tree growth model, achieve through the distance change of information point to study the tree volume change. The visual measurement method is compared with the traditional method, the feasibility and accuracy of the method are proven. From the results, tree volume changes through the information point feature extraction and the traditional breast diameter measurement is very similar, the maximal percentage increase is 2.570% and 2.546%, the minimum percentage increase is 0.092% and 0.068%, which shows that volume change is consistent with the results, confirmed the tree volume change scheme of visual measurement is feasible and the result is reliable, which can reduce the impact of environmental change in the manual measurement.
{"title":"Dynamic Monitoring of Forest Volumes by a Feature Extraction Method","authors":"Xu Jie, Dawei Qi","doi":"10.4018/IJSSCI.2018070104","DOIUrl":"https://doi.org/10.4018/IJSSCI.2018070104","url":null,"abstract":"In this article, in order to improve tree volume calculation method, a measurement method based on tree information point feature extraction is proposed, the method based on image processing and binocular vision, according to the measurement result of information point change and tree growth model, achieve through the distance change of information point to study the tree volume change. The visual measurement method is compared with the traditional method, the feasibility and accuracy of the method are proven. From the results, tree volume changes through the information point feature extraction and the traditional breast diameter measurement is very similar, the maximal percentage increase is 2.570% and 2.546%, the minimum percentage increase is 0.092% and 0.068%, which shows that volume change is consistent with the results, confirmed the tree volume change scheme of visual measurement is feasible and the result is reliable, which can reduce the impact of environmental change in the manual measurement.","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114983432","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 : 2018-04-01DOI: 10.4018/IJSSCI.2018040103
P. Guo, A. Evans, P. Bhattacharya
{"title":"Nuclei Segmentation for Quantification of Brain Tumors in Digital Pathology Images","authors":"P. Guo, A. Evans, P. Bhattacharya","doi":"10.4018/IJSSCI.2018040103","DOIUrl":"https://doi.org/10.4018/IJSSCI.2018040103","url":null,"abstract":"","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":" 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120829443","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 : 2018-04-01DOI: 10.4018/IJSSCI.2018040104
K. Priya, A. Kavitha
{"title":"Investigations on the Brain Connectivity Parameters for Co-Morbidities of Autism Using EEG","authors":"K. Priya, A. Kavitha","doi":"10.4018/IJSSCI.2018040104","DOIUrl":"https://doi.org/10.4018/IJSSCI.2018040104","url":null,"abstract":"","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125420000","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":"Discovering Attribute-Specific Features From Online Reviews: What Is the Gap Between Automated Tools and Human Cognition?","authors":"X. Jing, Penghao Wang, Julia M. Rayz","doi":"10.4018/IJSSCI.2018040101","DOIUrl":"https://doi.org/10.4018/IJSSCI.2018040101","url":null,"abstract":"Thisarticledescribeshowonlinereviewsplayanimportantroleindatadrivendecisionmaking. Manyeffortshavebeeninvestedindeterminingtheoverallsentimentcarriedbythereviews.However, oftentimes,theoverallratingsofthereviewsdonotrepresentopinionstowardspecificattributes ofaproduct.Anidealopinionminingtoolshouldaimatfindingboththeproductattributesand theircorrespondingopinions.Theauthorsproposeanapproachforextractingtheattributespecific featuresfromonlinereviewsusingaWord2Vecmodelcombinedwithclustering.Twoexperiments aredescribed in thispaper: thefirst focuseson testing theperformanceof theWord2Vecmodel onextractingproductaspectwords,thesecondaddresseshowwelltheextractedfeaturesobtained arerecognizablebyhumancognition.Anewmetriccalledthe“splitvalue”thatisbasedoncluster similarityanddiversityisintroducedtoexaminetheconsistencyofclusteringalgorithm.Theauthors’ experimentssuggestthatmeaningfulclusters,whichprovideinsightstotheproductattributesand sentiments,couldbeextractedfromthereviews. KeyWORDS Artificial Intelligence, Clustering, Cognition, Feature Extraction, Opinion Mining, Text Understand, Word2Vec","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"35 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134450398","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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