This paper presents an approach for automatic classification of pulsed Terahertz (THz), or T-ray, signals highlighting their potential in biomedical, pharmaceutical and security applications. T-ray classification syst...
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This paper presents an approach for automatic classification of pulsed Terahertz (THz), or T-ray, signals highlighting their potential in biomedical, pharmaceutical and security applications. T-ray classification systems supply a wealth of information about test samples and make possible the discrimination of heterogeneous layers within an object. In this paper, a novel technique involving the use of Auto Regressive (AR) and Auto Regressive Moving Average (ARMA) models on the wavelet transforms of measured T-ray pulse data is presented. Two example applications are examined - the classi. cation of normal human bone (NHB) osteoblasts against human osteosarcoma (HOS) cells and the identification of six different powder samples. A variety of model types and orders are used to generate descriptive features for subsequent classification. Wavelet-based de-noising with soft threshold shrinkage is applied to the measured T-ray signals prior to modeling. For classi. cation, a simple Mahalanobis distance classi. er is used. After feature extraction, classi. cation accuracy for cancerous and normal cell types is 93%, whereas for powders, it is 98%.
Of all the paranasal sinuses, frontal sinus (FS) morphology, volumes, outlines, and cross-sectional areas vary most and so their statistical noise presents particular challenges. To assess and control this statistical...
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Of all the paranasal sinuses, frontal sinus (FS) morphology, volumes, outlines, and cross-sectional areas vary most and so their statistical noise presents particular challenges. To assess and control this statistical noise requires a suite of mathematical techniques that: model their volume and cross-sectional area ontogeny, determine the uniqueness and fractal dimensions of their outlines (useful in forensics), smooth their outlines via Singular Value Decomposition (SVD), and model their expansion via percolation cluster models (PCMs). Published data sets of FS outlines, cross-sectional areas and volumes of Neanderthal and modern crania (obtained via CT-imaging techniques) are utilized here for application of these novel mathematical methods, which necessitate a modeling approach. Results show that: FS noisiness can be explained as cluster growth, their fractal outlines have properties similar to closed random walks (Brownian bridges) about predefined curves, and the PCMs can simulate the emergence of lamellae. The statistical properties derived from the analysis techniques presented here suggest that an emergence of the lamellae via PCMs (with pinning and quenching-correlated noise) resolves the masticatory stress debate by showing that the lamellae are indeed responses to masticatory stresses, but these are of so low a level that they cannot be measured with strain gauges. PCMs and Brownian bridges, defined by local rules, lead to the emergence of macroscopically observable morphologies. The methodologies presented here contribute to research in emergence phenomena and are not confined to morphological analyses of frontal sinuses. Anat Rec, 291:1455-1478, 2008. (C) 2008 Wiley-Liss, Inc.
In this paper, we propose a method of pattern recognition of EMG signals of hand gesture using spectral estimation and neural network. Proposed system is composed of the yule-walker algorithm and the LVQ The use of th...
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ISBN:
(纸本)9784907764289
In this paper, we propose a method of pattern recognition of EMG signals of hand gesture using spectral estimation and neural network. Proposed system is composed of the yule-walker algorithm and the LVQ The use of the yule-walker algorithm is to estimates the power spectral density (PSD) of the signal. The spectral estimate returned is the magnitude squared frequency response of AR model. A fine tuning step will then be incorporated to improve the accuracy of the classification by way of the LVQ We describe in detail the experiment conducted to verify the usefulness of the proposed method for EMG pattern classification of hand gesture.
Online denoising is motivated by real-time applications in the industrial process, where the data must be utilizable soon after it is collected. Since the noise in practical process is usually colored, it is quite a c...
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Online denoising is motivated by real-time applications in the industrial process, where the data must be utilizable soon after it is collected. Since the noise in practical process is usually colored, it is quite a challenge for denoising techniques. In this paper, a novel online denoising method was proposed to achieve the processing of the practical measurement data with colored noise, and the characteristics of the colored noise were considered in the dynamic model via an adaptive parameter. The proposed method consists of two parts within a closed loop: the first one is to estimate the system state based on the second-order adaptive statistics model and the other is to update the adaptive parameter in the model using the yule-walker algorithm. Specifically, the state estimation process was implemented via the Kalman filter in a recursive way, and the online purpose was therefore attained. Experimental data in a reinforced concrete structure test was used to verify the effectiveness of the proposed method. Results show the proposed method not only dealt with the signals with colored noise, but also achieved a tradeoff between efficiency and accuracy.
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