ASTF: Visual Abstractions of Time-Varying Patterns in Radio Signals

A time-frequency diagram is a commonly used visualization for observing the time-frequency distribution of radio signals and analyzing their time-varying patterns of communication states in radio monitoring and management. While it excels when performing short-term signal analyses, it becomes inadaptable for long-term signal analyses because it cannot adequately depict signal time-varying patterns in a large time span on a space-limited screen. This research thus presents an abstract signal time-frequency (ASTF) diagram to address this problem. In the diagram design, a visual abstraction method is proposed to visually encode signal communication state changes in time slices. A time segmentation algorithm is proposed to divide a large time span into time slices.Three new quantified metrics and a loss function are defined to ensure the preservation of important time-varying information in the time segmentation. An algorithm performance experiment and a user study are conducted to evaluate the effectiveness of the diagram for long-term signal analyses.Comment: 11 pages, 9 figure

Impact of Correlated Color Temperature on Visitors’ Perception and Preference in Virtual Reality Museum Exhibitions

From the perspective of psychophysiological evaluation, this paper provides a theoretical reference for the lighting settings of museums. In order to study the impact of correlated color temperature (CCT) on visitors’ perception and preference in museum exhibitions, an experiment was conducted in the ergonomics laboratory of Nanjing Forestry University. We invited 50 participants to visit the virtual reality museum exhibitions with different CCTs, built by Autodesk 3D’s Max 2017. Specific psychophysiology variables—eye movement, electrodermal activity (EDA), and heart rate variability (HRV)—and the perception and preference of participants were collected. The results indicated that the association of CCT with eye movement, HRV, and some perceptual dimensions was significant. Under high illumination conditions with different CCTs, the pupil diameter and warmth decreased with the increase in CCT, but the comfort and pleasure scores increased first and then decreased. The CCT scenes sorted by LF/HF ratio from high to low were 4500 K, 6000 K, and 3000 K, which was consistent with the results of preference ranking. The LF/HF ratio showed significant sex differences and major discrepancies

Impact of Correlated Color Temperature on Visitors’ Perception and Preference in Virtual Reality Museum Exhibitions

From the perspective of psychophysiological evaluation, this paper provides a theoretical reference for the lighting settings of museums. In order to study the impact of correlated color temperature (CCT) on visitors’ perception and preference in museum exhibitions, an experiment was conducted in the ergonomics laboratory of Nanjing Forestry University. We invited 50 participants to visit the virtual reality museum exhibitions with different CCTs, built by Autodesk 3D’s Max 2017. Specific psychophysiology variables—eye movement, electrodermal activity (EDA), and heart rate variability (HRV)—and the perception and preference of participants were collected. The results indicated that the association of CCT with eye movement, HRV, and some perceptual dimensions was significant. Under high illumination conditions with different CCTs, the pupil diameter and warmth decreased with the increase in CCT, but the comfort and pleasure scores increased first and then decreased. The CCT scenes sorted by LF/HF ratio from high to low were 4500 K, 6000 K, and 3000 K, which was consistent with the results of preference ranking. The LF/HF ratio showed significant sex differences and major discrepancies

Remote Sensing Extraction of Agricultural Land in Shandong Province, China, from 2016 to 2020 Based on Google Earth Engine

Timely and effective access to agricultural land-change information is of great significance for the government when formulating agricultural policies. Due to the vast area of Shandong Province, the current research on agricultural land use in Shandong Province is very limited. The classification accuracy of the current classification methods also needs to be improved. In this paper, with the support of the Google Earth Engine (GEE) platform and based on Landsat 8 time series image data, a multiple machine learning algorithm was used to obtain the spatial variation distribution information of agricultural land in Shandong Province from 2016 to 2020. Firstly, a high-quality cloud-free synthetic Landsat 8 image dataset for Shandong Province from 2016 to 2020 was obtained using GEE. Secondly, the thematic index series was calculated to obtain the phenological characteristics of agricultural land, and the time periods with significant differences in terms of water, agricultural land, artificial surface, woodland and bare land were selected for classification. Feature information, such as texture features, spectral features and terrain features, was constructed, and the random forest method was used to select and optimize the features. Thirdly, the random forest, gradient boosting tree, decision tree and ensemble learning algorithms were used for classification, and the accuracy of the four classifiers was compared. The information on agricultural land changes was extracted and the causes were analyzed. The results show the following: (1) the multi-spatial index time series method is more accurate than the single thematic index time series when obtaining phenological characteristics; (2) the ensemble learning method is more accurate than the single classifier. The overall classification accuracy of the five agricultural land-extraction results in Shandong Province obtained by the ensemble learning method was above 0.9; (3) the annual decrease in agricultural land in Shandong Province from 2016 to 2020 was related to the increase in artificial land-surface area and urbanization rate

Insights into Triterpene Acids in Fermented Mycelia of Edible Fungus <i>Poria cocos</i> by a Comparative Study

As an edible sclerotia-forming fungus, Poria cocos is widely used as a food supplement and as a tonic in China. High-performance liquid chromatography-quadrupole/time-of-flight mass spectrometry (HPLC-QTOF-MS/MS) was applied to identify triterpene acids in fermented mycelia of P. cocos, as well as the epidermis and inner part of natural sclerotia. A total of 19 triterpene acids were identified in fermented mycelia, whereas 31 were identified in the epidermis and 24 in the inner part. Nine triterpene acids were quantitatively determined, and the concentrations of two valuable triterpenes, dehydropachymic acid and pachymic acid, reached 1.07 mg/g and 0.61 mg/g in the fermented mycelia part, respectively, and were both significantly higher than the concentration in the two natural parts. The fermented mycelia could be a good choice for producing some target triterpene compounds and functional foods through fermentation thanks to the high concentration of some triterpene acids