Abstract: X-ray Fluorescence Molecular Imaging (XFMI) holds great promise as a low-cost molecular imaging modality for biomedical applications with high chemical sensitivity. However, for in vivo biomedical applications, a key technical bottleneck is the relatively low chemical sensitivity of XFMI, especially at a reasonably low radiation dose. In laboratory x-ray source based XFMI, one of the main factors that limits the chemical sensitivity of XFMI is the scattered x-rays. We will present our latest findings on improving the chemical sensitivity of XFMI using excitation beam spectrum optimization. XFMI imaging experiments on two mouse-sized phantoms were conducted at three different excitation beam spectra. Our results show that the minimum detectable concentration (MDC) of iodine can be readily increased by five times via excitation spectrum optimization. Findings from this investigation could find use for in vivo pre-clinical small-animal XFMI in the future.
Abstract: This paper presents an intelligent tuning method of
microwave filter based on complex neural network and improved
space mapping. The tuning process consists of two stages: the initial
tuning and the fine tuning. At the beginning of the tuning, the return
loss of the filter is transferred to the passband via the error of phase.
During the fine tuning, the phase shift caused by the transmission line
and the higher order mode is removed by the curve fitting. Then, an
Cauchy method based on the admittance parameter (Y-parameter) is
used to extract the coupling matrix. The influence of the resonant
cavity loss is eliminated during the parameter extraction process. By
using processed data pairs (the amount of screw variation and the
variation of the coupling matrix), a tuning model is established by
the complex neural network. In view of the improved space mapping
algorithm, the mapping relationship between the actual model and
the ideal model is established, and the amplitude and direction of the
tuning is constantly updated. Finally, the tuning experiment of the
eight order coaxial cavity filter shows that the proposed method has
a good effect in tuning time and tuning precision.
Abstract: Multiple Sclerosis (MS) is a disease which affects the
central nervous system and causes balance problem. In clinical, this
disorder is usually evaluated using static posturography. Some linear
or nonlinear measures, extracted from the posturographic data (i.e.
center of pressure, COP) recorded during a balance test, has been
used to analyze postural control of MS patients. In this study, the
trend (TREND) and the sample entropy (SampEn), two nonlinear
parameters were chosen to investigate their relationships with the
expanded disability status scale (EDSS) score. 40 volunteers with
different EDSS scores participated in our experiments with eyes open
(EO) and closed (EC). TREND and 2 types of SampEn (SampEn1
and SampEn2) were calculated for each combined COP’s position
signal. The results have shown that TREND had a weak negative
correlation to EDSS while SampEn2 had a strong positive correlation
to EDSS. Compared to TREND and SampEn1, SampEn2 showed a
better significant correlation to EDSS and an ability to discriminate
the MS patients in the EC case. In addition, the outcome of the study
suggests that the multi-dimensional nonlinear analysis could provide
some information about the impact of disability progression in MS on
dynamics of the COP data.