This paper investigates a novel decision framework for efficient selection of interpolation curve based on distance minimization for 3D rendering applications. The point clouds obtained from low resolution 3D scanners...
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ISBN:
(纸本)9781479926084
This paper investigates a novel decision framework for efficient selection of interpolation curve based on distance minimization for 3D rendering applications. The point clouds obtained from low resolution 3D scanners like Microsoft's Kinect or from sparse reconstruction algorithms usually fail to provide accurate information about the surface, either due to occlusions during the scanning process or inability of the scanner to generate a dense model of the surface. The proposed decision framework selects the best interpolation technique on a local basis utilizing the voting parameters obtained from the original point cloud. This framework enables us to obtain the comparatively best fit interpolation curve for upsampling due to the decisive feature of the framework. Experimental results are carried out using two interpolation techniques viz., quadratic spline interpolation and cubic spline interpolation technique to demonstrate the usefulness of such a decision framework for 3D point cloud data. The proposed decision framework is generic and holds good for more than two interpolation techniques.
Recent technological advances in wireless body sensor networks (WBSN) have made it possible for the development of innovative medical applications to improve health care and the quality of life. Electroencephalography...
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ISBN:
(纸本)9781479914678;9781479914678
Recent technological advances in wireless body sensor networks (WBSN) have made it possible for the development of innovative medical applications to improve health care and the quality of life. Electroencephalography (EEG)-based applications lie at the heart of this promising technologies. However, excessive power consumption may render some of these applications inapplicable. Hence, intelligent energy efficient methods are needed to improve such applications. In this work, such improved efficiency can be obtained by utilizing smart compression techniques, which reduce airtime over energy-hungry wireless channels;In particular, discrete wavelet transform (DWT) and compressive sensing (CS) are used for EEG signals acquisition and compression. To achieve low-complexity energy-efficient system, the proposed technique makes use of the receiver feedback signals in order to switch between both algorithms based on the application needs. Experimental study has shown that the proposed algorithm effectively reconfigures the utilized compression algorithm parameters based on a channel feed back signal.
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