RESUMO
Potatoes are popular among consumers due to their high yield and delicious taste. However, due to the numerous varieties of potatoes, different varieties are suitable for different processing methods. Therefore, it is necessary to distinguish varieties after harvest to meet the needs of processing enterprises and consumers. In this study, a new visible-near-infrared spectroscopic analysis method was proposed, which can achieve detection of five potato varieties. The method measures the transmission and reflection spectra of potatoes using a spectral acquisition system, encodes one-dimensional spectra into two-dimensional images using Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), Markov Transition Field (MTF) and Recurrence Plot (RP), and improves the coordinated attention mechanism module and embeds the improved module into the ConvNeXt V2 model to build the ConvNeXt V2-CAP model for potato variety classification. The results show that compared with directly using one-dimensional classification models, image encoding of spectral data for classification greatly improves the accuracy. Among them, the best accuracy of 99.54% is achieved by using GADF image encoding of transmission spectra combined with the ConvNeXt V2-CAP model for classification, which is 16.28% higher than the highest accuracy of the one-dimensional classification model. The CAP attention mechanism module improves the performance of the model, especially when the dataset is small. When the training set is reduced to 150 images, the accuracy of the model is improved by 2.33% compared to the original model. Therefore, it is feasible to classify potato varieties using visible-near infrared spectroscopy and image encoding technology.
RESUMO
Sleep apnea is probably the most common respiratory disorder; respiration and blood oxygen saturation (SpO2) are major concerns in sleep apnea and are also the two main parameters checked by polysomnography (PSG, the gold standard for diagnosing sleep apnea). In this study, we used a simple, non-invasive monitoring system based on photoplethysmography (PPG) to continuously monitor SpO2 and heart rate (HR) for individuals at home. Various breathing experiments were conducted to investigate the relationship between SpO2, HR, and apnea under different conditions, where two techniques (empirical formula and customized formula) for calculating SpO2 and two methods (resting HR and instantaneous HR) for assessing HR were compared. Various adaptive filters were implemented to compare the effectiveness in removing motion artifacts (MAs) during the tests. This study fills the gap in the literature by comparing the performance of different adaptive filters on estimating SpO2 and HR during apnea. The results showed that up-down finger motion introduced more MA than left-right motion, and the errors in SpO2 estimation were increased as the frequency of movement was increased; due to the low sampling frequency features of these tests, the insertion of adaptive filter increased the noise in the data instead of eliminating the MA for SpO2 estimation; the normal least mean squares (NLMS) filter is more effective in removing MA in HR estimation than other filters.