An X-ray tube spectra measuring method is presented in this paper. The measurement is accomplished by reconstruction from attenuation data based on a nine-parameter tungsten anode X-ray spectral model. The proposed mo...
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ISBN:
(纸本)0819461857
An X-ray tube spectra measuring method is presented in this paper. The measurement is accomplished by reconstruction from attenuation data based on a nine-parameter tungsten anode X-ray spectral model. The proposed model is derived from a physical basis and composed of three parts: bremsstrahlung spectra, photoemission attenuation by X-ray tube inherent and added filter, characteristic radiations denoted by four Dirac delta functions. Firstly, for simplicity, the four characteristic radiations of the spectra model are merged into two according to a reasonable hypothesis. Secondly, the spectra reconstruction based on the modified model is carried out by calculating the model parameters from measured attenuation data. To further improve stability, two kinds of materials are used as the attenuators. Experiments show that this method can reach high precision and is insensitive to the noise in measured attenuation data. From the 10 measured attenuation data (7 from Al, and 3 from Copper) with 5% Poisson noise added, the precision of the reconstructed spectra can reach 98.56% for the 70kVp X-ray tube with tiny characteristic radiation, and 98.24% for the 120kVp X-ray tube with characteristic radiation. Spectrum is the characteristic of X-ray tube and widely used for many purposes. In engineering, the spectrum is mostly reconstructed from attenuation data, which is an ill-posed problem in mathematics. The method we presented with the features of including characteristic radiation, insensitive to noise and demanding fewer attenuation data will help to solve this problem perfectly.
Faults of sensor data will always present in sensor networks because of unreliable communication links, measurement interference and harsh environment. Developing fusion algorithms that can tolerate faults is necessar...
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ISBN:
(纸本)9781849191388
Faults of sensor data will always present in sensor networks because of unreliable communication links, measurement interference and harsh environment. Developing fusion algorithms that can tolerate faults is necessary for reliable sensor network applications. In this paper, we study the fault tolerant fusion for moving vehicle classification based on Marzullo's interval fusion algorithm. The unreliable sensor data are represented using interval estimations. To reduce communication cost, quantized interval representation is adopted. Simulation results demonstrate the validity of the interval fusion algorithm. By using quantized representation, the communication cost is reduced.
Parallel coordinate descent algorithms emerge with the growing demand of large-scale optimization. In general, previous algorithms are usually limited by their divergence under high degree of parallelism (DOP), or nee...
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image segmentation is an important research topic in imageprocessing and computer vision community. In this paper, a new unsupervised method for MR brain image segmentation is proposed based on fuzzy c-means (FCM) an...
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Gait is thought to be the most effective feature for human recognition in the distance. For optimal performance, the feature should include as many different types of information as possible, so in this paper, we pres...
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Rectification for airborne linear images is an indispensable preprocessing step. This paper presents in detail a two-step rectification algorithm. The first step is to establish the model of direct georeference positi...
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Rectification for airborne linear images is an indispensable preprocessing step. This paper presents in detail a two-step rectification algorithm. The first step is to establish the model of direct georeference position using the data provided by the Po- sitioning and Orientation System (POS) and obtain the mathematical relationships between the image points and ground reference points. The second step is to apply polynomial distortion model and Bilinear Interpolation to get the final precise rectified images. In this step, a reference image is required and some ground control points (GCPs) are selected. Experiments showed that the final rectified images are satisfactory, and that our two-step rectification algorithm is very effective.
A full domain optimum neural network (FDONN) and its application to imagerecognition are proposed in this paper. In general, we cann't ensure the devised neural network to converge to a global minimum. In this pa...
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In this article, we present the projective equation of a circle in a perspective view, which naturally encodes such important geometric entities as the projected circle center, the vanishing point of the normal direct...
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To over come the drawbacks existing in current measurement methods for detecting and controlling colors in printing process, a new medal for color separation and dot recognition is proposed from a view of digital imag...
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To over come the drawbacks existing in current measurement methods for detecting and controlling colors in printing process, a new medal for color separation and dot recognition is proposed from a view of digital imageprocessing and patter recognition. In this model, firstly data samples are collected from some color patches by the Fuzzy C-Means （FCM） method; then a classifier based on the Cerebellar Model Articulation Controller （CMAC） is constructed which is used to recognize color pattern of each pixel in a microscopic halftone image. The principle of color separation and the algorithm model are introduced and the experiments show the effectiveness of the CMAC-based classifier as opposed to the BP network.
Kernel principal component analysis (KPCA) as a powerful nonlinear feature extraction method has proven as a preprocessing step for classification algorithm. A face recognition approach based on KPCA and genetic algor...
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