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10.3390/s150614241 http://scihub22266oqcxt.onion/10.3390/s150614241 C4507697!4507697!26091392     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sensors+(Basel) 2015 ; 15 (6): 14241-60 Nephropedia Template TP {{tp|p=26091392|t=2015. Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines |pdf=|usr=}}{{26091392}} gab.com Text Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines JO: Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=26091392 #FOAvip Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation?based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking?to reduce the dimensions of images?and binarization?to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements. Twit Text FOAVip Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines ????Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=26091392 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26091392 #FOAvip English Wikipedia <ref>{{Cite pmid|26091392}}</ref> Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines #MMPMID26091392 del Val L; Izquierdo-Fuente A; Villacorta JJ; Raboso M Sensors (Basel) 2015[Jun]; 15 (6): 14241-60 PMID26091392show ga Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation?based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking?to reduce the dimensions of images?and binarization?to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements. äAcoustic Biometric System Based on Preprocessing Techniques and Linear(epmc).pdf DeepDyve Pubget Overpricing