RESUMEN
In order to remove the sawtoothed noise in the spectrum of hyperspectral remote sensing and improve the accuracy of information extraction using spectrum in the present research, the spectrum of vegetation in the USGS (United States Geological Survey) spectrum library was used to simulate the performance of wavelet denoising. These spectra were measured by a custom-modified and computer-controlled Beckman spectrometer at the USGS Denver Spectroscopy Lab. The wavelength accuracy is about 5 nm in the NIR and 2 nm in the visible. In the experiment, noise with signal to noise ratio (SNR) 30 was first added to the spectrum, and then removed by the wavelet denoising approach. For the purpose of finding the optimal parameters combinations, the SNR, mean squared error (MSE), spectral angle (SA) and integrated evaluation coefficient eta were used to evaluate the approach's denoising effects. Denoising effect is directly proportional to SNR, and inversely proportional to MSE, SA and the integrated evaluation coefficient eta. Denoising results show that the sawtoothed noise in noisy spectrum was basically eliminated, and the denoised spectrum basically coincides with the original spectrum, maintaining a good spectral characteristic of the curve. Evaluation results show that the optimal denoising can be achieved by firstly decomposing the noisy spectrum into 3-7 levels using db12, db10, sym9 and sym6 wavelets, then processing the wavelet transform coefficients by soft-threshold functions, and finally estimating the thresholds by heursure threshold selection rule and rescaling using a single estimation of level noise based on first-level coefficients. However, this approach depends on the noise level, which means that for different noise level the optimal parameters combination is also diverse.
RESUMEN
OBJECTIVE: Epidemiological studies suggested that poor sleep is a potentially novel risk factor for several health outcomes currently; however, there are no validated questionnaires that can systematically measure sleep parameters within these studies. We evaluated the reliability and validity of 17-item sleep factors questionnaire (SFQ), which was developed to comprehensively assess long-term sleep habits for the Jiujiang Breast Cancer Study (JBCS), Jiujiang, China. METHODS: The participants included 100 women aged 18-74years, who were randomly selected from the JBCS project, and completed a SFQ at baseline and again 1year later, and 4 quarterly 30 consecutive days (a total of 120days) sleep diaries over this same year. Reliability was tested by comparing the 2 SFQs; validity by comparing the average measures between the SFQ and the 4 sleep diaries. RESULTS: Validity analysis showed moderate correlation (γ=0.41) for sleep duration with the adjusted concordance correlation coefficient (CCCadj) of 0.54; the weighted κ statistics indicated an excellent agreement for night/shift work and sleep medication use; fair-to-moderate for sleep quality, light at night (LAN), nighttime sleeping with light on, sleep noise and nap time; slight-to-fair for sleep quality and nighttime wakings frequency. Reliability analysis showed excellent correlation for night/shift work and sleep medication use; fair-to-moderate for LAN, nighttime wakings frequency, insomnia frequency, sleep noise and nap time; but slight-to-fair for insomnia frequency and nighttime sleeping with light on; the CCCadj for sleep duration was 0.61. CONCLUSIONS: The SFQ showed reasonable reliability and validity for sleep assessments in most domains.