In Fourier transform infrared spectrometer(FTIR), the optical path difference of laser metrology is applied to increase spectrum measure precision. However, due to the characteristics of laser single frequency and ste...
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ISBN:
(纸本)9780819495587
In Fourier transform infrared spectrometer(FTIR), the optical path difference of laser metrology is applied to increase spectrum measure precision. However, due to the characteristics of laser single frequency and steady frequency, the uneven speed of moving mirror, circuit delay etc. cause the deviation of sampling point. In this paper, based on the interference theory of spectrometer, according to the Fourier contrary transform and methods of error analysis, the theory model between relative error of spectrum measure and the deviation of sampling point is established. Some simulation computations of the model have been done, the result indicates that, as a theory basis, the model can be applied in analyzing the sampling error of spectrometer and correction algorithm.
Projection data acquired from a positron emission tomography (PET) scanner consist of true, scattered and random events. Scattered events can cause severe artifacts and quantitation errors in reconstructed PET images ...
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ISBN:
(纸本)9781538622834
Projection data acquired from a positron emission tomography (PET) scanner consist of true, scattered and random events. Scattered events can cause severe artifacts and quantitation errors in reconstructed PET images unless corrected for properly. A scatter correction algorithm is required to predict scattered events from the measurement. Scatter correction requires estimation of both single scatter and multiple scatter profiles. Usually, single scatter profiles are calculated by model-based simulation and multiple scatter profiles are estimated by a kernel-based convolution method. However, design of the convolution kernels for multiple scatter estimation is sophisticated and requires fine parameter tuning. In this work, we adopt deep learning techniques for scatter estimation. We propose two convolutional neural networks. The first network estimates multiple scatter profiles from single scatter profiles, replacing the kernel-based convolution method. The second network is designed to predict the total scatter profiles (including single and multiple scatters) directly from the input of emission and attenuation sinograms. Initial results from both networks show a promise with the potential for more accurate and faster scatter correction for PET.
Doppler weather radar is a type of radar used to locate precipitation, calculate its motion, and estimate its type (rain, snow, hail, etc.). Modern weather radars are mostly pulse-Doppler radars, capable of detecting ...
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ISBN:
(纸本)9781467310017
Doppler weather radar is a type of radar used to locate precipitation, calculate its motion, and estimate its type (rain, snow, hail, etc.). Modern weather radars are mostly pulse-Doppler radars, capable of detecting the motion of rain droplets in addition to the intensity of the precipitation. Data obtained from weather radars can be analyzed to determine the structure of storms and their potential to cause severe weather s uch as local short-term rainfall. In recent years, the installation places of the Doppler weather radars are increased in order to predict more exactly the local short-term rainfall. However, Doppler weather radars which are installed closely cause interference to each other. As a result, the correct meteorological data are no longer obtained from the radars. From now on, the number of the radars will increase because of more localization of short-term rainfall. Therefore, the evaluation of conventional interference prevention techniques and improvement of the techniques are indispensable for stable and reliable operation of the radars. In this paper, clarifying the characteristics of the conventional interference detection and correction algorithm in Doppler weather radars, we propose a novel algorithm and show through computer simulation that it is more effective than the conventional one.
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