Pub Date : 2018-11-29DOI: 10.1007/978-981-13-3582-2_9
R. Vanaja, S. Mukherjee
{"title":"Novel Wrapper-Based Feature Selection for Efficient Clinical Decision Support System","authors":"R. Vanaja, S. Mukherjee","doi":"10.1007/978-981-13-3582-2_9","DOIUrl":"https://doi.org/10.1007/978-981-13-3582-2_9","url":null,"abstract":"","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"35 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2018-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77081020","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 : 2018-11-29DOI: 10.1007/978-981-13-3582-2_10
Ulka Shirole, Manjusha S. Joshi, Pritish K. Bagul
{"title":"Linear and Nonlinear Analysis of Cardiac and Diabetic Subjects","authors":"Ulka Shirole, Manjusha S. Joshi, Pritish K. Bagul","doi":"10.1007/978-981-13-3582-2_10","DOIUrl":"https://doi.org/10.1007/978-981-13-3582-2_10","url":null,"abstract":"","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"1 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2018-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79945047","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 : 2018-10-01DOI: 10.1142/S2424922X18500109
Kimberly Leung, A. Subramanian, S. Shen
This paper studies the statistical characteristics of a unique long-term high-resolution precipitable water vapor (PWV) data set at Darwin, Australia, from 12 March 2002 to 28 February 2011. To understand the convective precipitation processes for climate model development, the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) program made high-frequency radar observations of PWV at the Darwin ARM site and released the best estimates from the radar data retrievals for this time period. Based on the best estimates, we produced a PWV data set on a uniform 20-s time grid. The gridded data were sufficient to show the fractal behavior of precipitable water with Hausdorff dimension equal to 1.9. Fourier power spectral analysis revealed modulation instability due to two sideband frequencies near the diurnal cycle, which manifests as nonlinearity of an atmospheric system. The statistics of PWV extreme values and daily rainfall data show that Darwin’s PWV has El Nino Southern Oscillation (ENSO) signatures and has potential to be a predictor for weather forecasting. The right skewness of the PWV data was identified, which implies an important property of tropical atmosphere: ample capacity to hold water vapor. The statistical characteristics of this long-term high-resolution PWV data will facilitate the development and validation of climate models, particularly stochastic models.
{"title":"Statistical Characteristics of Long-Term High-Resolution Precipitable Water Vapor Data at Darwin","authors":"Kimberly Leung, A. Subramanian, S. Shen","doi":"10.1142/S2424922X18500109","DOIUrl":"https://doi.org/10.1142/S2424922X18500109","url":null,"abstract":"This paper studies the statistical characteristics of a unique long-term high-resolution precipitable water vapor (PWV) data set at Darwin, Australia, from 12 March 2002 to 28 February 2011. To understand the convective precipitation processes for climate model development, the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) program made high-frequency radar observations of PWV at the Darwin ARM site and released the best estimates from the radar data retrievals for this time period. Based on the best estimates, we produced a PWV data set on a uniform 20-s time grid. The gridded data were sufficient to show the fractal behavior of precipitable water with Hausdorff dimension equal to 1.9. Fourier power spectral analysis revealed modulation instability due to two sideband frequencies near the diurnal cycle, which manifests as nonlinearity of an atmospheric system. The statistics of PWV extreme values and daily rainfall data show that Darwin’s PWV has El Nino Southern Oscillation (ENSO) signatures and has potential to be a predictor for weather forecasting. The right skewness of the PWV data was identified, which implies an important property of tropical atmosphere: ample capacity to hold water vapor. The statistical characteristics of this long-term high-resolution PWV data will facilitate the development and validation of climate models, particularly stochastic models.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"34 1","pages":"1850010:1-1850010:11"},"PeriodicalIF":0.6,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72940298","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 : 2018-10-01DOI: 10.1142/S2424922X18500080
L. Lämmlein, S. Shen
This paper presents a multivariate linear regression reconstruction for the near-global annual precipitation anomalies with 1-deg latitude–longitude resolution from 1900 to 2015. The regression’s explanatory variables are the empirical orthogonal functions (EOFs), computed from the Global Precipitation Climatology Project (GPCP) dataset. The data for the regression’s dependent variable are from the station dataset of the Global Historical Climatology Network (GHCN). The data for the explanatory variables are the EOF data at the GHCN data locations. Compared to the earlier work of reconstruction at [Formula: see text] latitude–longitude resolution, our current reconstruction has two contributions. First, the spatial resolution is reduced to [Formula: see text] latitude–longitude. The finer resolution allows the data to be more useful in applications, such as historical drought assessment for a given region. Second, the multivariate regression is directly computed from linear regression models and hence includes the intercept term, which is not a coefficient of an EOF. The intercept enables a more realistic detection of the long-term trend of the spatial average. The trend of the global average annual precipitation from 1900 to 2015 is 0.133 (mm/day)/100a for the reconstruction with an intercept, and is 0.022 (mm/day)/100a without an intercept. The latter agrees with the trends of other models. The reconstruction error is assessed by a time-varying standard deviation.
{"title":"A Multivariate Regression Reconstruction of the Quasi-Global Annual Precipitation on 1-Deg Grid From 1900 To 2015","authors":"L. Lämmlein, S. Shen","doi":"10.1142/S2424922X18500080","DOIUrl":"https://doi.org/10.1142/S2424922X18500080","url":null,"abstract":"This paper presents a multivariate linear regression reconstruction for the near-global annual precipitation anomalies with 1-deg latitude–longitude resolution from 1900 to 2015. The regression’s explanatory variables are the empirical orthogonal functions (EOFs), computed from the Global Precipitation Climatology Project (GPCP) dataset. The data for the regression’s dependent variable are from the station dataset of the Global Historical Climatology Network (GHCN). The data for the explanatory variables are the EOF data at the GHCN data locations. Compared to the earlier work of reconstruction at [Formula: see text] latitude–longitude resolution, our current reconstruction has two contributions. First, the spatial resolution is reduced to [Formula: see text] latitude–longitude. The finer resolution allows the data to be more useful in applications, such as historical drought assessment for a given region. Second, the multivariate regression is directly computed from linear regression models and hence includes the intercept term, which is not a coefficient of an EOF. The intercept enables a more realistic detection of the long-term trend of the spatial average. The trend of the global average annual precipitation from 1900 to 2015 is 0.133 (mm/day)/100a for the reconstruction with an intercept, and is 0.022 (mm/day)/100a without an intercept. The latter agrees with the trends of other models. The reconstruction error is assessed by a time-varying standard deviation.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"25 1","pages":"1850008:1-1850008:17"},"PeriodicalIF":0.6,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82573116","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 : 2018-10-01DOI: 10.1142/S2424922X18500092
G. Özel, E. Altun, M. Alizadeh, Mahdieh Mozafari
In this paper, a new heavy-tailed distribution is used to model data with a strong right tail, as often occuring in practical situations. The proposed distribution is derived from the log-normal distribution, by using odd log-logistic distribution. Statistical properties of this distribution, including hazard function, moments, quantile function, and asymptotics, are derived. The unknown parameters are estimated by the maximum likelihood estimation procedure. For different parameter settings and sample sizes, a simulation study is performed and the performance of the new distribution is compared to beta log-normal. The new lifetime model can be very useful and its superiority is illustrated by means of two real data sets.
{"title":"The Odd Log-Logistic Log-Normal Distribution with Theory and Applications","authors":"G. Özel, E. Altun, M. Alizadeh, Mahdieh Mozafari","doi":"10.1142/S2424922X18500092","DOIUrl":"https://doi.org/10.1142/S2424922X18500092","url":null,"abstract":"In this paper, a new heavy-tailed distribution is used to model data with a strong right tail, as often occuring in practical situations. The proposed distribution is derived from the log-normal distribution, by using odd log-logistic distribution. Statistical properties of this distribution, including hazard function, moments, quantile function, and asymptotics, are derived. The unknown parameters are estimated by the maximum likelihood estimation procedure. For different parameter settings and sample sizes, a simulation study is performed and the performance of the new distribution is compared to beta log-normal. The new lifetime model can be very useful and its superiority is illustrated by means of two real data sets.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"27 1","pages":"1850009:1-1850009:20"},"PeriodicalIF":0.6,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79675450","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 : 2018-08-15DOI: 10.1142/S2424922X18400089
A. Delis, C. Miosso, J. Carvalho, A. Rocha, G. Borges
Information extracted from the surface electromyographic (sEMG) signals can allow for the detection of movement intention in transfemoral prostheses. The sEMG can help estimate the angle between th...
从表面肌电图(sEMG)信号中提取的信息可以检测经股假体的运动意图。肌电图可以帮助估计…
{"title":"Continuous Estimation Prediction of Knee Joint Angles Using Fusion of Electromyographic and Inertial Sensors for Active Transfemoral Leg Prostheses","authors":"A. Delis, C. Miosso, J. Carvalho, A. Rocha, G. Borges","doi":"10.1142/S2424922X18400089","DOIUrl":"https://doi.org/10.1142/S2424922X18400089","url":null,"abstract":"Information extracted from the surface electromyographic (sEMG) signals can allow for the detection of movement intention in transfemoral prostheses. The sEMG can help estimate the angle between th...","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"66 1","pages":"1840008:1-1840008:30"},"PeriodicalIF":0.6,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80895310","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 : 2018-08-15DOI: 10.1142/S2424922X18400053
Javier Castillo-Garcia, S. Muller, E. C. Bravo, T. Filho, A. D. Souza
This work describes the development of a nonfatigating Brain–Computer Interface (BCI) based on Steady State Evoked Potentials (SSVEP) and Event-Related Desynchronization (ERD) to control an autonomous car. Through a graphical interface presented to the user in the autonomous car, destination places are shown. The selection of commands is performed through visual stimuli and brain signals. The signals are captured on the occipital region of the scalp, and are processed in order to obtain the necessary data for the planning system of the autonomous car. Test performed obtained success rate of 90% for a synchronous BCI and 83% for an asynchronous BCI. The proposed system is a hybrid-BCI, which includes the ability to enable and disable the visual stimuli, reducing fatigue associated with the use of SSVEP-based BCIs. The video showing this development can be accessed on: cbeb2020.org/AutonomousCarVideo.mp4.
{"title":"Nonfatigating Brain-Computer Interface Based on SSVEP and ERD to Command an Autonomous Car","authors":"Javier Castillo-Garcia, S. Muller, E. C. Bravo, T. Filho, A. D. Souza","doi":"10.1142/S2424922X18400053","DOIUrl":"https://doi.org/10.1142/S2424922X18400053","url":null,"abstract":"This work describes the development of a nonfatigating Brain–Computer Interface (BCI) based on Steady State Evoked Potentials (SSVEP) and Event-Related Desynchronization (ERD) to control an autonomous car. Through a graphical interface presented to the user in the autonomous car, destination places are shown. The selection of commands is performed through visual stimuli and brain signals. The signals are captured on the occipital region of the scalp, and are processed in order to obtain the necessary data for the planning system of the autonomous car. Test performed obtained success rate of 90% for a synchronous BCI and 83% for an asynchronous BCI. The proposed system is a hybrid-BCI, which includes the ability to enable and disable the visual stimuli, reducing fatigue associated with the use of SSVEP-based BCIs. The video showing this development can be accessed on: cbeb2020.org/AutonomousCarVideo.mp4.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"10 1","pages":"1840005:1-1840005:22"},"PeriodicalIF":0.6,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78452976","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 : 2018-08-15DOI: 10.1142/S2424922X1840003X
B. Machado, André Fonseca, E. Morya, E. A. Júnior
In this work, we developed two novel approaches to characterize dynamical properties of brain electrical activity, based on cross-prediction errors analysis. The first, a test called γ-sets, provid...
{"title":"Chaotic Dynamics in Brain Activity: An Approach Based on Cross-Prediction Errors for Nonstationary Signals","authors":"B. Machado, André Fonseca, E. Morya, E. A. Júnior","doi":"10.1142/S2424922X1840003X","DOIUrl":"https://doi.org/10.1142/S2424922X1840003X","url":null,"abstract":"In this work, we developed two novel approaches to characterize dynamical properties of brain electrical activity, based on cross-prediction errors analysis. The first, a test called γ-sets, provid...","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"8 1","pages":"1840003:1-1840003:16"},"PeriodicalIF":0.6,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87366241","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 : 2018-08-15DOI: 10.1142/S2424922X18400041
Javier Castillo-Garcia, E. C. Bravo, T. Filho
Background: An adaptive Brain–Computer Interface (aBCI) is an extension of a traditional Brain–Computer Interface (BCI). In this work, trial rejection, median filter and software agent are included...
{"title":"Adaptive Spontaneous Brain-Computer Interfaces Based on Software Agents","authors":"Javier Castillo-Garcia, E. C. Bravo, T. Filho","doi":"10.1142/S2424922X18400041","DOIUrl":"https://doi.org/10.1142/S2424922X18400041","url":null,"abstract":"Background: An adaptive Brain–Computer Interface (aBCI) is an extension of a traditional Brain–Computer Interface (BCI). In this work, trial rejection, median filter and software agent are included...","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"403 1","pages":"1840004:1-1840004:13"},"PeriodicalIF":0.6,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80958735","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 : 2018-08-15DOI: 10.1142/S2424922X18400065
Dharmendra Gurve, S. Krishnan
A new Convolutional Neural Network (CNN) architecture to classify nonstationary biomedical signals using their time–frequency representations is proposed. The present method uses the spectrogram of...
{"title":"Deep Learning of EEG Time-Frequency Representations for Identifying Eye States","authors":"Dharmendra Gurve, S. Krishnan","doi":"10.1142/S2424922X18400065","DOIUrl":"https://doi.org/10.1142/S2424922X18400065","url":null,"abstract":"A new Convolutional Neural Network (CNN) architecture to classify nonstationary biomedical signals using their time–frequency representations is proposed. The present method uses the spectrogram of...","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"36 1","pages":"1840006:1-1840006:13"},"PeriodicalIF":0.6,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74447497","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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