Nonlinear analysis of heart rate variability （HRV） has become important as heart behaves as a complex system. In this work, the approximate entropy （ApEn） has been used as a nonlinear measure. A new concept of est...Nonlinear analysis of heart rate variability （HRV） has become important as heart behaves as a complex system. In this work, the approximate entropy （ApEn） has been used as a nonlinear measure. A new concept of estimating the ApEn in different segments of long length of the recorded data called modified multiple scale （segment） entropy （MMPE） is introduced. The idea of estimating the approximate entropy in different segments is useful to detect the nonlinear dynamics of the heart present in the entire length of data. The present work has been carried out for three cases namely the normal healthy heart （NHH） data, congestive heart failure （CHF） data and Atrial fibrillation （AF） data and the data are analyzed using MMPE techniques. It is observed that the mean value of ApEn for NHH data is much higher than the mean values for CHF data and AF data. The ApEn profiles of CHF, AF and NHH data for different segments obtained using MPE profiles measures the heart dynamism for the three different cases. Also the power spectral density is obtained using fast fourier transform （FFT） analysis and the ratio of LF/HF （low frequency/high frequency） power are computed on multiple scales/segments namely MPLH （multiple scale low frequency to high frequency） for the NHH data, CHF data and AF data and analyzed using MPLH techniques. The results are presented and discussed in the paper.展开更多
Large-scale unsupervised semantic segmentation(LUSS)is a sophisticated process that aims to segment similar areas within an image without relying on labeled training data.While existing methodologies have made substan...Large-scale unsupervised semantic segmentation(LUSS)is a sophisticated process that aims to segment similar areas within an image without relying on labeled training data.While existing methodologies have made substantial progress in this area,there is ample scope for enhancement.We thus introduce the PASS-SAM model,a comprehensive solution that amalgamates the benefits of various models to improve segmentation performance.展开更多
The object-oriented information extraction technique was used to improve classification accuracy,and addressed the problem that HJ-1 CCD remote sensing images have only four spectral bands with moderate spatial resolu...The object-oriented information extraction technique was used to improve classification accuracy,and addressed the problem that HJ-1 CCD remote sensing images have only four spectral bands with moderate spatial resolution.We used two key techniques:the selection of optimum image segmentation scale and the development of an appropriate object-oriented information extraction strategy.With the principle of minimizing merge cost of merging neighboring pixels/objects,we used spatial autocorrelation index Moran's I and the variance index to select the optimum segmentation scale.The Nearest Neighborhood(NN)classifier based on sampling and a knowledge-based fuzzy classifier were used in the object-oriented information extraction strategy.In this classification step,feature optimization was used to improve information extraction accuracy using reduced data dimension.These two techniques were applied to land cover information extraction for Shanghai city using a HJ-1 CCD image.Results indicate that the information extraction accuracy of the object-oriented method was much higher than that of the pixel-based method.展开更多
文摘Nonlinear analysis of heart rate variability （HRV） has become important as heart behaves as a complex system. In this work, the approximate entropy （ApEn） has been used as a nonlinear measure. A new concept of estimating the ApEn in different segments of long length of the recorded data called modified multiple scale （segment） entropy （MMPE） is introduced. The idea of estimating the approximate entropy in different segments is useful to detect the nonlinear dynamics of the heart present in the entire length of data. The present work has been carried out for three cases namely the normal healthy heart （NHH） data, congestive heart failure （CHF） data and Atrial fibrillation （AF） data and the data are analyzed using MMPE techniques. It is observed that the mean value of ApEn for NHH data is much higher than the mean values for CHF data and AF data. The ApEn profiles of CHF, AF and NHH data for different segments obtained using MPE profiles measures the heart dynamism for the three different cases. Also the power spectral density is obtained using fast fourier transform （FFT） analysis and the ratio of LF/HF （low frequency/high frequency） power are computed on multiple scales/segments namely MPLH （multiple scale low frequency to high frequency） for the NHH data, CHF data and AF data and analyzed using MPLH techniques. The results are presented and discussed in the paper.
文摘Large-scale unsupervised semantic segmentation(LUSS)is a sophisticated process that aims to segment similar areas within an image without relying on labeled training data.While existing methodologies have made substantial progress in this area,there is ample scope for enhancement.We thus introduce the PASS-SAM model,a comprehensive solution that amalgamates the benefits of various models to improve segmentation performance.
基金supported by National Key Technology Research and Development Program of China(Grant Nos.2008BAC34B02 and 2008BAC3403)
文摘The object-oriented information extraction technique was used to improve classification accuracy,and addressed the problem that HJ-1 CCD remote sensing images have only four spectral bands with moderate spatial resolution.We used two key techniques:the selection of optimum image segmentation scale and the development of an appropriate object-oriented information extraction strategy.With the principle of minimizing merge cost of merging neighboring pixels/objects,we used spatial autocorrelation index Moran's I and the variance index to select the optimum segmentation scale.The Nearest Neighborhood(NN)classifier based on sampling and a knowledge-based fuzzy classifier were used in the object-oriented information extraction strategy.In this classification step,feature optimization was used to improve information extraction accuracy using reduced data dimension.These two techniques were applied to land cover information extraction for Shanghai city using a HJ-1 CCD image.Results indicate that the information extraction accuracy of the object-oriented method was much higher than that of the pixel-based method.
