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1
Vandrol, Jan, Janis Perren, and Adrian Koller. "Effect of Depth Band Replacement on Red, Green and Blue Image for Deep Learning Weed Detection." Sensors 25, no. 1 (2024): 161. https://doi.org/10.3390/s25010161.
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Automated agricultural robots are becoming more common with the decreased cost of sensor devices and increased computational capabilities of single-board computers. Weeding is one of the mundane and repetitive tasks that robots could be used to perform. The detection of weeds in crops is now common, and commercial solutions are entering the market rapidly. However, less work is carried out on combatting weeds in pastures. Weeds decrease the grazing yield of pastures and spread over time. Mowing the remaining weeds after grazing is not guaranteed to remove entrenched weeds. Periodic but selective cutting of weeds can be a solution to this problem. However, many weeds share similar textures and structures with grazing plants, making their detection difficult using the classic RGB (Red, Green, Blue) approach. Pixel depth estimation is considered a viable source of data for weed detection. However, systems utilizing RGBD (RGB plus Depth) are computationally expensive, making them nonviable for small, lightweight robots. Substituting one of the RGB bands with depth data could be a solution to this problem. In this study, we examined the effect of band substitution on the performance of lightweight YOLOv8 models using precision, recall and mAP50 metrics. Overall, the RDB band combination proved to be the best option for YOLOv8 small and medium detection models, with 0.621 and 0.634 mAP50 (for a mean average precision at 50% intersection over union) scores, respectively. In both instances, the classic RGB approach yielded lower accuracies of 0.574 and 0.613.
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Hawley, Dave, Thomas Graham, Michael Stasiak, and Mike Dixon. "Improving Cannabis Bud Quality and Yield with Subcanopy Lighting." HortScience 53, no. 11 (2018): 1593–99. http://dx.doi.org/10.21273/hortsci13173-18.
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The influence of light spectral quality on cannabis (Cannabis sativa L.) development is not well defined. It stands to reason that tailoring light quality to the specific needs of cannabis may increase bud quality, consistency, and yield. In this study, C. sativa L. ‘WP:Med (Wappa)’ plants were grown with either no supplemental subcanopy lighting (SCL) (control), or with red/blue (“Red-Blue”) or red-green-blue (“RGB”) supplemental SCL. Both Red-Blue and RGB SCL significantly increased yield and concentration of total Δ9-tetrahydrocannabinol (Δ9-THC) in bud tissue from the lower plant canopy. In the lower canopy, RGB SCL significantly increased concentrations of α-pinine and borneol, whereas both Red-Blue and RGB SCL increased concentrations of cis-nerolidol compared with the control treatment. In the upper canopy, concentrations of α-pinine, limonene, myrcene, and linalool were significantly greater with RGB SCL than the control, and cis-nerolidol concentration was significantly greater in both Red-Blue and RGB SCL treated plants relative to the control. Red-Blue SCL yielded a consistently more stable metabolome profile between the upper and lower canopy than RGB or control treated plants, which had significant variation in cannabigerolic acid (CBGA) concentrations between the upper and lower canopies. Overall, both Red-Blue and RGB SCL treatments significantly increased yield more than the control treatment, RGB SCL had the greatest impact on modifying terpene content, and Red-Blue produced a more homogenous bud cannabinoid and terpene profile throughout the canopy. These findings will help to inform growers in selecting a production light quality to best help them meet their specific production goals.
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Ji, Xin, Cheng-Yuan Li, and Li-Cai Deng. "Evaluating Helium Variations By Modeling Red Giant Branch Bump of Large Magellanic Cluster NGC 1978." Research in Astronomy and Astrophysics 22, no. 3 (2022): 035008. http://dx.doi.org/10.1088/1674-4527/ac46a3.
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Abstract Many evidences show that the Multiple Population (MP) features exist not only in old clusters but also in intermediate-age clusters in the Megallanic Clouds (MCs), which are characterized by star-to-star abundance scatter of several elements, including helium (He). The red giant branch bump (RGBB)'s photometric properties are proved to be related to the variation in helium abundances of the member stars in star clusters. We use the “Modules for Experiments in Stellar Astrophysics” (MESA) stellar evolution code to calculate the evolution sequences of stars along the red giant branch (RGB) with changing helium content. Following the RGB sequences, we then generate a luminosity function of the RGB stars within the grid of input helium abundances, which are compared with the observational data of an intermediate-age MC cluster NGC 1978.
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Piratelo, Paulo Henrique Martinez, Rodrigo Negri de Azeredo, Eduardo Massashi Yamao, et al. "Blending Colored and Depth CNN Pipelines in an Ensemble Learning Classification Approach for Warehouse Application Using Synthetic and Real Data." Machines 10, no. 1 (2021): 28. http://dx.doi.org/10.3390/machines10010028.
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Electric companies face flow control and inventory obstacles such as reliability, outlays, and time-consuming tasks. Convolutional Neural Networks (CNNs) combined with computational vision approaches can process image classification in warehouse management applications to tackle this problem. This study uses synthetic and real images applied to CNNs to deal with classification of inventory items. The results are compared to seek the neural networks that better suit this application. The methodology consists of fine-tuning several CNNs on Red–Green–Blue (RBG) and Red–Green–Blue-Depth (RGB-D) synthetic and real datasets, using the best architecture of each domain in a blended ensemble approach. The proposed blended ensemble approach was not yet explored in such an application, using RGB and RGB-D data, from synthetic and real domains. The use of a synthetic dataset improved accuracy, precision, recall and f1-score in comparison with models trained only on the real domain. Moreover, the use of a blend of DenseNet and Resnet pipelines for colored and depth images proved to outperform accuracy, precision and f1-score performance indicators over single CNNs, achieving an accuracy measurement of 95.23%. The classification task is a real logistics engineering problem handled by computer vision and artificial intelligence, making full use of RGB and RGB-D images of synthetic and real domains, applied in an approach of blended CNN pipelines.
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Kim, Yang Suk, Ha Yun Kim, Hee Soon Cheon, Sung Jin Cho, and Bumsik Kim. "Quality Characteristics of Nutriton Bar Added with Ginseng Powder1)." Table and Food Coordinate Society of Korea 17, no. 3 (2022): 95–107. http://dx.doi.org/10.26433/tfck.2022.17.3.95.
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This study was planed to develop a functional nutritional bar product tailored to the recent trend toward consumer health and convenience. We analyzed the sensory characteristics and consumer preferences of nutritional bars containing ginseng, and studied the quality characteristics of the final product. There were thre types of nutrition bars containing ginseng: RGA(aded by 10% of red ginseng powder), RGB(5% of red ginseng powder), and WG(5% of ginseng and soybean powder). Quality characteristics include color, pH and swetnes. Sensory tests were conducted on 23 people who had experience in consuming nutrition bars. Apearance, flavor, swetnes, sournes, biternes, texture (chewablity and strength), and overal taste were measured using 15 interval scale. RGB showed excelent palatability in the sensory test as a result while it showed lightnes (L) 35.7, rednes (a) 12.25, and yelownes (b) 15.58 in surface chromaticity with 56.2, 5.69 and 6.48 for crumbs. The pH and sugar content of RGB was 6.01 and 4.21, respectively. In conclusion, ginseng nutrient bar would be highly prefered if it has nutritious and delicious loking dark brown color, not to swet taste, crispy texture, nuts other than peanuts inside and some red ginseng scent. Further evaluation is neded for sensory characteristics of ginseng nutrient bar.
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Li, Yali, Ping Huang, Xia Qiu, et al. "Integration of Green and Far-Red Light with Red-Blue Light Enhances Shoot Multiplication in Micropropagated Strawberry." Horticulturae 11, no. 6 (2025): 701. https://doi.org/10.3390/horticulturae11060701.
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Light spectral composition critically regulates plant morphogenesis and molecular adaptation in controlled environments. This study investigated the synergistic effects of three light spectra, red-blue (RB, 7:3), red-blue-green (RGB, 7:3:1), and red-blue-far-red (RBFR, 7:3:1), on multiplication, morphogenesis, physiological traits, and transcriptomic dynamics in tissue-cultured strawberry (Fragaria × ananassa cv. ‘Benihoppe’). After 28 days of cultivation under controlled conditions (25 °C/22 °C day/night, 50 μmol·m−2·s−1 PPFD), RBFR and RGB treatments significantly enhanced shoot multiplication (38.8% and 24.2%, respectively), plant height, and callus biomass compared to RB light. RGB elevated chlorophyll a and b by 1.8- and 1.6-fold, respectively, while RBFR increased soluble protein content by 16%. Transcriptome analysis identified 144 and 376 differentially expressed genes (DEGs) under RGB and RBFR, respectively, enriched in pathways linked to circadian rhythm, auxin transport, and photosynthesis. Far-red light upregulated light signaling and photomorphogenesis genes, whereas green light enhanced chlorophyll biosynthesis while suppressing stress-responsive genes. These findings elucidate the spectral-specific regulatory mechanisms underlying strawberry micropropagation and provide a framework for optimizing multispectral LED systems in controlled-environment horticulture.
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Ashapure, Akash, Jinha Jung, Anjin Chang, Sungchan Oh, Murilo Maeda, and Juan Landivar. "A Comparative Study of RGB and Multispectral Sensor-Based Cotton Canopy Cover Modelling Using Multi-Temporal UAS Data." Remote Sensing 11, no. 23 (2019): 2757. http://dx.doi.org/10.3390/rs11232757.
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This study presents a comparative study of multispectral and RGB (red, green, and blue) sensor-based cotton canopy cover modelling using multi-temporal unmanned aircraft systems (UAS) imagery. Additionally, a canopy cover model using an RGB sensor is proposed that combines an RGB-based vegetation index with morphological closing. The field experiment was established in 2017 and 2018, where the whole study area was divided into approximately 1 x 1 m size grids. Grid-wise percentage canopy cover was computed using both RGB and multispectral sensors over multiple flights during the growing season of the cotton crop. Initially, the normalized difference vegetation index (NDVI)-based canopy cover was estimated, and this was used as a reference for the comparison with RGB-based canopy cover estimations. To test the maximum achievable performance of RGB-based canopy cover estimation, a pixel-wise classification method was implemented. Later, four RGB-based canopy cover estimation methods were implemented using RGB images, namely Canopeo, the excessive greenness index, the modified red green vegetation index and the red green blue vegetation index. The performance of RGB-based canopy cover estimation was evaluated using NDVI-based canopy cover estimation. The multispectral sensor-based canopy cover model was considered to be a more stable and accurately estimating canopy cover model, whereas the RGB-based canopy cover model was very unstable and failed to identify canopy when cotton leaves changed color after canopy maturation. The application of a morphological closing operation after the thresholding significantly improved the RGB-based canopy cover modeling. The red green blue vegetation index turned out to be the most efficient vegetation index to extract canopy cover with very low average root mean square error (2.94% for the 2017 dataset and 2.82% for the 2018 dataset), with respect to multispectral sensor-based canopy cover estimation. The proposed canopy cover model provides an affordable alternate of the multispectral sensors which are more sensitive and expensive.
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Tailo, M., A. P. Milone, E. P. Lagioia, et al. "Mass-loss law for red giant stars in simple population globular clusters." Monthly Notices of the Royal Astronomical Society 503, no. 1 (2021): 694–703. http://dx.doi.org/10.1093/mnras/stab568.
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ABSTRACT The amount of mass lost by stars during the red-giant branch (RGB) phase is one of the main parameters to understand and correctly model the late stages of stellar evolution. Nevertheless, a fully comprehensive knowledge of the RGB mass-loss is still missing. Galactic Globular Clusters (GCs) are ideal targets to derive empirical formulations of mass-loss, but the presence of multiple populations with different chemical compositions has been a major challenge to constrain stellar masses and RGB mass-losses. Recent work has disentangled the distinct stellar populations along the RGB and the horizontal branch (HB) of 46 GCs, thus providing the possibility to estimate the RGB mass-loss of each stellar population. The mass-losses inferred for the stellar populations with pristine chemical composition (called first-generation or 1G stars) tightly correlate with cluster metallicity. This finding allows us to derive an empirical RGB mass-loss law for 1G stars. In this paper, we investigate seven GCs with no evidence of multiple populations and derive the RGB mass-loss by means of high-precision Hubble-Space Telescope photometry and accurate synthetic photometry. We find a cluster-to-cluster variation in the mass-loss ranging from ∼0.1 to ∼0.3 M⊙. The RGB mass-loss of simple-population GCs correlates with the metallicity of the host cluster. The discovery that simple-population GCs and 1G stars of multiple population GCs follow similar mass-loss versus metallicity relations suggests that the resulting mass-loss law is a standard outcome of stellar evolution.
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Leščinskaitė, Alina, Rima Stonkutė, and Vladas Vansevičius. "AGB and RGB stars in the dwarf irregular galaxy Leo A." Astronomy & Astrophysics 647 (March 2021): A170. http://dx.doi.org/10.1051/0004-6361/202037967.
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Context. Leo A is a gas-rich dwarf irregular galaxy of low stellar mass located in the outskirts of the Local Group. It has an extended star formation history with stellar populations spanning a wide age range (∼0.01−10 Gyr). As Leo A is a well-isolated dwarf galaxy, it is a perfect target to study a galactic structure formed entirely by processes of self-induced star formation. Aims. Our aim is to study populations of the brightest asymptotic giant branch (AGB) stars and red giant branch (RGB) stars over the entire extent of the Leo A galaxy. Methods. We analysed populations of AGB and RGB stars in the Leo A galaxy using multicolour photometry data obtained with the Subaru Suprime-Cam (B, V, R, I, Hα) and HST ACS (F475W, F814W) cameras. In order to separate the Milky Way and Leo A populations of red stars, we developed a photometric method that enabled us to study the spatial distribution of AGB and RGB stars within the Leo A galaxy. Results. We found a previously unknown sequence of 26 peculiar RGB stars which probably have a strong CN band in their spectra (∼380−390 nm). This conclusion is supported by the infrared CN spectral features observed in four of these stars with available spectra from the literature. Additionally, we present a catalogue of 32 luminous AGB stars and 3 candidate AGB stars. Twelve AGB stars (three of them might have dusty envelopes) from this sample are newly identified; the remaining 20 AGB stars were already presented in the literature based on near-infrared observations. By splitting the RGB sequence into blue and red parts, we revealed different spatial distributions of the two subsets, with the former being more centrally concentrated than the latter. Cross-identification with spectroscopic data available in the literature suggests that the bulk of blue and red RGB stars are, on average, similar in metallicity; however, the red RGB stars might have an excess of metal-deficient stars of [Fe/H] < −1.8. We also found that the distributions of luminous AGB and blue RGB stars have nearly equal scale lengths (0.′87 ± 0.′06 and 0.′89 ± 0.′09, respectively), indicating that they could belong to the same generation. This conclusion is strengthened by the similarities of the cumulative distributions of AGB and blue RGB stars, both showing more centrally concentrated populations compared to red RGB stars. There is also a prominent decline in the ratio of AGB to RGB stars with an increasing radius. These results suggest that the star-forming disk of Leo A is shrinking, which is in agreement with the outside-in star formation scenario of dwarf galaxy evolution.
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Oh, Hyeon Eui, Ara Yoon, and Yoo Gyeong Park. "Red Light Enhances the Antioxidant Properties and Growth of Rubus hongnoensis." Plants 10, no. 12 (2021): 2589. http://dx.doi.org/10.3390/plants10122589.
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The purpose of this study was to determine the effect of light quality on R.hongnoensis growth, physiology, and antioxidant properties. Five light conditions were employed, including white (control), red (R), blue (B), combined LED of R, green (G), and B at 7:1:2 (RGB), as well as combined LED of R, G, B, and far-red (Fr) at 7:1:2:1 (RGBFr). R light had the greatest growth-promoting effect based on plant height, leaf length, leaf width, stem diameter, and leaf area. However, leaf width and root length exhibited the greatest growth under RGB. The fresh and dry weight of shoots and roots were highest under R and RGB light. Photosynthesis was highest under RGB and lowest under B. Transpiration was highest in RGBFr. Stomatal conductance and photosynthetic water use efficiency were greatest under RGBFr. Total phenol content and radical scavenging activity were highest under R, while total flavonoid content was highest under RGB. Superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX) activities were upregulated under W, whereas guaiacol peroxidase (GPX) activity was highest under RGB. The present results suggest that, among the tested light treatments, R light was most conductive for vegetative growth and antioxidant capacity in R. hongnoensis.
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Sahabi, Hossein, and Jalal Baradaran-Motie. "Detection of mite infested saffron plants using aerial imaging and machine learning classifier." Spanish Journal of Agricultural Research 22, no. 4 (2025): 20452. https://doi.org/10.5424/sjar/2024224-20452.
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Aim of study: To evaluate and develop a machine learning code that uses aerial images in visible and near infrared (NIR) spectra to detect mite-infested Saffron (Crocus sativus L.) plants through processing the spectral indices to classify healthy and diseased plants. This leads to the identification of the concentration points of the bulb mites and the estimation of the percentage of infestation in the field. Area of study: Khorasan-Razavi province, Torbat-Heydarieh, Iran. Material and methods: Five fields were randomly selected and their red-green-blue (RGB), as a typical visible spectral image, and NIR images were taken in two consecutive years. Seven spectral vegetation indices for NIR images including NIR-band, Red-band, normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI), difference red-nir ratio (DRN) and infrared percentage vegetation index (IPVI); and twelve indices for RGB images inlcuding red-band, green-band, blue-band, visible-band difference vegetation index (VDVI), visible atmospheric resistant index (VARI), triangular greenness index (TGI), normalized difference greenness index (NDGI), normalized green blue difference index (NGBDI), modified green red vegetation index (MGRVI), red green blue vegetation index (RGBVI), vegetative index (VEG) and excess of green index (EXG), were extracted and analysed. In order to detect affected plants, two support vector machine (SVM) classifiers with radial basis function (RBF) kernels were used separately for NIR and RGB images. Main results: The average accuracy of the SVM classifier models were estimated to be 82.3% for NIR images and 91.4% for RGB images during the test phase. Also, the accuracy of the developed models when evaluated in the field with respect to the confusion matrix method was 75.6% and 80.3% for the classification models for NIR and RGB images, respectively. Research highlights: RGB images were able to distinguish infested plants with better accuracy. Processing aerial images of lightweight drones could speed up the inspection of vast saffron fields.
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Slamet, B., D. Adinda, O. P. J. Nduru, and Samsuri. "Utilization of unmanned aerial vehicle (UAV) in identifying the characteristics of the riparian ecosystem of the Percut River, North Sumatra Province." IOP Conference Series: Earth and Environmental Science 1115, no. 1 (2022): 012083. http://dx.doi.org/10.1088/1755-1315/1115/1/012083.
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Abstract The Percut River border area has changed its function. Changes in land use result in a decrease in the area and types of vegetation that grow on river borders. Considering that the area is quite long and inaccessible in some locations, it can identify riparian vegetation by utilizing remote sensing technology. This study aims to identify the characteristics of the Percut river riparian vegetation using a UAV. Eight vegetation indices were used to analyse land cover types in this riparian ecosystem, namely Green-red ratio (GR), Green-red vegetation index (GRVI), RGB-based vegetation index (RGBVI), Visible atmospherically resistant index (VARI), Simple blue-green ratio (BGI2), Excess green index (ExG), Normalized green-blue difference index (NGBDI) and Modified green-red vegetation index (MGRVI). The analysis of the vegetation index based on RGB images shows that not all indices used are good in separating vegetation from other land covers. Analysis of riparian vegetation characteristics based on the RGB index is recommended using the RGBVI index (RGB-based vegetation index).
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Kim, J. W., H. I. Kim, and Y. J. Sohn. "Red-giant branch morphology of metal-poor globular clusters in the Galactic bulge." Proceedings of the International Astronomical Union 3, S245 (2007): 363–64. http://dx.doi.org/10.1017/s1743921308018139.
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AbstractUsing the (J − K, K) color-magnitude diagrams (CMDs) of 16 metal-poor globular clusters in the Galactic bulge, we investigate the morphological properties of their red-giant branch (RGB), comparing with those of metal-rich clusters in the Galactic bulge and metal-poor clusters in the Galactic halo. The RGB morphological parameters, such as colors at fixed magnitudes, magnitudes at a fixed color, the RGB slope, and a difference of color indices at two fixed magnitudes have been derived from the near-IR CMDs for each cluster. Metal-poor Galactic bulge clusters follow the previous empirical relations between colors at fixed magnitudes and magnitudes at a fixed color of the RGB and the cluster's metallicity. However, the RGB slope and the color difference parameters of some bulge clusters deviate slightly from the previous empirical linear relations for the other globular clusters, implying that the metal-poor bulge clusters may have different formation origin from the other globular clusters in the Galaxy.
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Li, Guang, Wenting Han, Shenjin Huang, Weitong Ma, Qian Ma, and Xin Cui. "Extraction of Sunflower Lodging Information Based on UAV Multi-Spectral Remote Sensing and Deep Learning." Remote Sensing 13, no. 14 (2021): 2721. http://dx.doi.org/10.3390/rs13142721.
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The rapid and accurate identification of sunflower lodging is important for the assessment of damage to sunflower crops. To develop a fast and accurate method of extraction of information on sunflower lodging, this study improves the inputs to SegNet and U-Net to render them suitable for multi-band image processing. Random forest and two improved deep learning methods are combined with RGB, RGB + NIR, RGB + red-edge, and RGB + NIR + red-edge bands of multi-spectral images captured by a UAV (unmanned aerial vehicle) to construct 12 models to extract information on sunflower lodging. These models are then combined with the method used to ignore edge-related information to predict sunflower lodging. The results of experiments show that the deep learning methods were superior to the random forest method in terms of the obtained lodging information and accuracy. The predictive accuracy of the model constructed by using a combination of SegNet and RGB + NIR had the highest overall accuracy of 88.23%. Adding NIR to RGB improved the accuracy of extraction of the lodging information whereas adding red-edge reduced it. An overlay analysis of the results for the lodging area shows that the extraction error was mainly caused by the failure of the model to recognize lodging in mixed areas and low-coverage areas. The predictive accuracy of information on sunflower lodging when edge-related information was ignored was about 2% higher than that obtained by using the direct splicing method.
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Kim, Hee Jin, Jae Hyun Ryou, Kang Ta Choi, Sun Mi Kim, Jee Taek Kim, and Doug Hyun Han. "Deficits in color detection in patients with Alzheimer disease." PLOS ONE 17, no. 1 (2022): e0262226. http://dx.doi.org/10.1371/journal.pone.0262226.
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Deficits in color vision and related retinal changes hold promise as early screening biomarkers in patients with Alzheimer’s disease. This study aimed to determine a cut-off score that can screen for Alzheimer’s dementia using a novel color vision threshold test named the red, green, and blue (RGB) modified color vision plate test (RGB-vision plate). We developed the RGB-vision plate consisting of 30 plates in which the red and green hues of Ishihara Plate No.22 were sequentially adjusted. A total of 108 older people participated in the mini-mental state examination (MMSE), Ishihara plate, and RGB-vision plate. For the analyses, the participants were divided into two groups: Alzheimer’s dementia (n = 42) and healthy controls (n = 38). K-means cluster analysis and ROC curve analysis were performed to identify the most appropriate cut-off score. As a result, the cut-off screening score for Alzheimer’s dementia on the RGB-vision plate was set at 25, with an area under the curve of 0.773 (p<0.001). Moreover, there was a negative correlation between the RGB-vision plate thresholds and MMSE scores (r = -0.36, p = 0.02). In conclusion, patients with Alzheimer’s dementia had a deficit in color vision. The RGB-vision plate is a potential early biomarker that may adequately detect Alzheimer’s dementia.
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Lee, Kyu Seung, Jaeho Shim, Hyunbok Lee, et al. "Unveiling the composite structures of emissive consolidated p–i–n junction nanocells for white light emission." Nanoscale 10, no. 29 (2018): 13867–74. http://dx.doi.org/10.1039/c8nr01842a.
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Hybrid organic-Red-Green-Blue (RGB) color quantum dots were incorporated into consolidated p(polymer)–i(RGB quantum dots)–n(small molecules) junction structures to fabricate a single active layer for a light emitting diode device for white electroluminescence.
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Kumar K, Kavin, Amirthavarshini S A, and Dhivya Dharshini T. "Optimization Of Memory Usage in High-Speed Cameras Using FPGA." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42409.
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High-speed cameras generate large amounts of data, making memory optimization difficult for real-time processing. This project minimizes data size by converting RGBA (Red, Green, Blue, Alpha) images to RGB, eliminating the Alpha channel to reduce memory usage. The captured images are provided as input to Verilog code in hexadecimal format, with the conversion done by MATLAB. Bilinear Interpolation is applied to reduce the potential quantization errors during the conversion, using the values of four surrounding pixels to smooth the image and maintain quality. After processing, the image is reconstructed using MATLAB, ensuring the integrity of the output. By utilizing the parallel processing capabilities of FPGAs, this method ensures real-time performance while optimizing memory usage. It is ideal for high-speed imaging applications where both efficiency and image quality are crucial. Keywords— Bilinear interpolation, RGBA, High-Speed Cameras, RGB, FPGA.
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Leščinskaitė, Alina, Rima Stonkutė, and Vladas Vansevičius. "Bright-red stars in the dwarf irregular galaxy Leo A." Proceedings of the International Astronomical Union 14, S344 (2018): 99–102. http://dx.doi.org/10.1017/s1743921318006427.
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AbstractWe analysed a population of bright-red (BR) stars in the dwarf irregular galaxy Leo A by using multicolour photometry data obtained with the Subaru/Suprime-Cam (B, V, R, I,Hα) and HST/ACS (F475W & F814W) instruments. In order to separate the Milky Way (MW) and Leo A populations of red stars, we developed a photometric method, which enabled us to study the spatial distribution of BR stars within the Leo A galaxy.We found a significant difference in the scale-length (S-L) of radial distributions of the “young” and “old” red giant branch (RGB) stars – 0′.82 ± 0′.04 and 1′53 ± 0′.03, respectively. Also, we determined the S-L of BR stars of 0′.85 ± 0′.05, which closely matches that of the “young” RGB stars. Additionally, we found a sequence of peculiar RGB stars and 8 dust-enshrouded stars in the Leo A galaxy.
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Dali, Mohd Rumaizan Maidan, Aiman Shahmi Azam, Mohamad Faizal Abd Rahman, et al. "Optical absorbance of RGB LEDs in pH measurement of colorimetric solution with phenol red reagent." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 3 (2022): 1330–37. https://doi.org/10.11591/ijeecs.v27.i3.pp1330-1337.
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The use of red, green, and blue (RGB) light-emitting-diode (LED) is a new trend in monochromatic colorimetric sensing due to cost-effective implementation. However, the application of RGB LED in pH measurement depends on the performance of the LED colour towards the colorimetric solution of interest, hence, needs to be evaluated. This work evaluated the performance of RGB LED for pH measurement system based on colorimetric approach using phenol red as a reagent. The main objective was to identify the LED colour with the best performance in terms of signal response and absorbance behavior. In this work, LED and photodiode were used as optical components and NI USB DAQ with LabView as the processing software. Four samples with known pH values were prepared and tested to obtain the voltage and absorbance behavior of each LED colour. Among all, the blue LED with wavelength ranged between 450-495 nm showed the best sensing behavior based on its linearity and error. Both voltage response and absorbance produced linear correlation with R 2=0.883 and R 2=0.9803, respectively. The significant finding from this study is useful in selecting the best RGB LED that is suitable for colorimetric pH measurement with phenol red as its colorimetric reagent.
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Purwanto, Anang Dwi, and Wikanti Asriningrum. "IDENTIFICATION OF MANGROVE FORESTS USING MULTISPECTRAL SATELLITE IMAGERIES." International Journal of Remote Sensing and Earth Sciences (IJReSES) 16, no. 1 (2019): 63. http://dx.doi.org/10.30536/j.ijreses.2019.v16.a3097.
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The visual identification of mangrove forests is greatly constrained by combinations of RGB composite. This research aims to determine the best combination of RGB composite for identifying mangrove forest in Segara Anakan, Cilacap using the Optimum Index Factor (OIF) method. The OIF method uses the standard deviation value and correlation coefficient from a combination of three image bands. The image data comprise Landsat 8 imagery acquired on 30 May 2013, Sentinel 2A imagery acquired on 18 March 2018 and images from SPOT 6 acquired on 10 January 2015. The results show that the band composites of 564 (NIR+SWIR+Red) from Landsat 8 and 8a114 (Vegetation Red Edge+SWIR+Red) from Sentinel 2A are the best RGB composites for identifying mangrove forest, in addition to those of 341 (Red+NIR+Blue) from SPOT 6. The near-infrared (NIR) and short-wave infrared (SWIR) bands play an important role in determining mangrove forests. The properties of vegetation are reflected strongly at the NIR wavelength and the SWIR band is very sensitive to evaporation and the identification of wetlands.
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Jørgensen, Andreas Christ Sølvsten, Josefina Montalbán, Andrea Miglio, et al. "Investigating surface correction relations for RGB stars." Monthly Notices of the Royal Astronomical Society 495, no. 4 (2020): 4965–80. http://dx.doi.org/10.1093/mnras/staa1480.
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ABSTRACT State-of-the-art stellar structure and evolution codes fail to adequately describe turbulent convection. For stars with convective envelopes such as red giants, this leads to an incomplete depiction of the surface layers. As a result, the predicted stellar oscillation frequencies are haunted by systematic errors, the so-called surface effect. Different empirically and theoretically motivated correction relations have been proposed to deal with this issue. In this paper, we compare the performance of these surface correction relations for red giant branch stars. For this purpose, we apply the different surface correction relations in asteroseismic analyses of eclipsing binaries and open clusters. In accordance with previous studies of main-sequence stars, we find that the use of different surface correction relations biases the derived global stellar properties, including stellar age, mass, and distance estimates. We, furthermore, demonstrate that the different relations lead to the same systematic errors for two different open clusters. Our results overall discourage from the use of surface correction relations that rely on reference stars to calibrate free parameters. Due to the demonstrated systematic biasing of the results, the use of appropriate surface correction relations is imperative to any asteroseismic analysis of red giants. Accurate mass, age, and distance estimates for red giants are fundamental when addressing questions that deal with the chemo-dynamical evolution of the Milky Way galaxy. In this way, our results also have implications for fields such as galactic archaeology that draw on findings from stellar physics.
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Feng, Shijie, Li Zhao, Jie Hu, Xiaolong Zhou, and Sixian Chan. "Depth-Quality Purification Feature Processing for Red Green Blue-Depth Salient Object Detection." Electronics 13, no. 1 (2023): 93. http://dx.doi.org/10.3390/electronics13010093.
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With the advances in deep learning technology, Red Green Blue-Depth (RGB-D) Salient Object Detection (SOD) based on convolutional neural networks (CNNs) is gaining more and more attention. However, the accuracy of current models is challenging. It has been found that the quality of the depth features profoundly affects the accuracy. Several current RGB-D SOD techniques do not consider the quality of the depth features and directly fuse the original depth features and Red Green Blue (RGB) features for training, resulting in enhanced precision of the model. To address this issue, we propose a depth-quality purification feature processing network for RGB-D SOD, named DQPFPNet. First, we design a depth-quality purification feature processing (DQPFP) module to filter the depth features in a multi-scale manner and fuse them with RGB features in a multi-scale manner. This module can control and enhance the depth features explicitly in the process of cross-modal fusion, avoiding injecting noise or misleading depth features. Second, to prevent overfitting and avoid neuron inactivation, we utilize the RReLU activation function in the training process. In addition, we introduce the pixel position adaptive importance (PPAI) loss, which integrates local structure information to assign different weights to each pixel, thus better guiding the network’s learning process and producing clearer details. Finally, a dual-stage decoder is designed to utilize contextual information to improve the modeling ability of the model and enhance the efficiency of the network. Extensive experiments on six RGB-D datasets demonstrate that DQPFPNet outperforms recent efficient models and delivers cutting-edge accuracy.
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Xie, Mingfeng, Jianping Liu, Pingsheng Zhou, et al. "Development of a digital imaging analysis system to evaluate the treatment response in superficial infantile hemangiomas." PLOS ONE 18, no. 3 (2023): e0282274. http://dx.doi.org/10.1371/journal.pone.0282274.
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Superficial infantile hemangiomas (IH) are benign vascular tumors common in children characterized by bright red "strawberry" lesions on the skin. In order to optimize the treatment for this disease, there is a need to develop objective tools to assess treatment response. Since a color change in the lesion is a good indicator of treatment response, we have developed a digital imaging system to quantify the values of red, green, and blue (RGB) difference and RGB ratio between the tumor and normal tissue to take into account the variations in color between different skin types. The efficacy of the proposed system in assessing treatment response in superficial IH was evaluated in relation to established visual and biochemical tools used to grade hemangiomas. As the treatment progressed, the RGB ratio was almost 1, while the RGB difference was close to 0, which indicates a good response to treatment. There was a strong correlation between the RGB score and the other visual grading systems. However, the correlation between the RGB scoring system and the biochemical method was weak. These findings suggest that the system can be used clinically to objectively and accurately evaluate disease progression and treatment response in patients diagnosed with superficial IH.
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Lee, Myung Gyoon. "Stellar Populations in the Pegasus Dwarf Galaxy." Symposium - International Astronomical Union 164 (1995): 413. http://dx.doi.org/10.1017/s0074180900109301.
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The Pegasus dwarf galaxy (DDO 216) is a resolved irregular galaxy. We present a study of stars in this galaxy based on BV RI CCD photometry obtained using the Palomar 1.5m telescope. The color-magnitude diagrams show (a) a dominant red giant branch (RGB) population, (b) a small number of asymptotic giant branch (AGB) population above the tip of the RGB (TRGB), and (c) a sparse popultion of massive young stars including the brightest red supergiants. The mean metallicity of the RGB has been estimated from the color of the RGB at MI = −3.5 mag: [Fe/H] = −1.5 ± 0.2 dex. The distance to this galaxy has been measured using the I-magnitude of the TRGB: (m – M)0 = 25.13 ± 0.11 mag (d = 1060 ± 50 kpc) (see Lee et al. 1993). This value is significantly smaller than the Cepheid distance estimate by Hoessel et al. (1990), (m – M)0 = 26.22±0.20 (d = 1750±160 kpc).
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Cao, Xiaofeng, Yulin Liu, Rui Yu, Dejun Han, and Baofeng Su. "A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population." Remote Sensing 13, no. 24 (2021): 5173. http://dx.doi.org/10.3390/rs13245173.
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High throughput phenotyping (HTP) for wheat (Triticum aestivum L.) stay green (SG) is expected in field breeding as SG is a beneficial phenotype for wheat high yield and environment adaptability. The RGB and multispectral imaging based on the unmanned aerial vehicle (UAV) are widely popular multi-purpose HTP platforms for crops in the field. The purpose of this study was to compare the potential of UAV RGB and multispectral images (MSI) in SG phenotyping of diversified wheat germplasm. The multi-temporal images of 450 samples (406 wheat genotypes) were obtained and the color indices (CIs) from RGB and MSI and spectral indices (SIs) from MSI were extracted, respectively. The four indices (CIs in RGB, CIs in MSI, SIs in MSI, and CIs + SIs in MSI) were used to detect four SG stages, respectively, by machine learning classifiers. Then, all indices’ dynamics were analyzed and the indices that varied monotonously and significantly were chosen to calculate wheat temporal stay green rates (SGR) to quantify the SG in diverse genotypes. The correlations between indices’ SGR and wheat yield were assessed and the dynamics of some indices’ SGR with different yield correlations were tracked in three visual observed SG grades samples. In SG stage detection, classifiers best average accuracy reached 93.20–98.60% and 93.80–98.80% in train and test set, respectively, and the SIs containing red edge or near-infrared band were more effective than the CIs calculated only by visible bands. Indices’ temporal SGR could quantify SG changes on a population level, but showed some differences in the correlation with yield and in tracking visual SG grades samples. In SIs, the SGR of Normalized Difference Red-edge Index (NDRE), Red-edge Chlorophyll Index (CIRE), and Normalized Difference Vegetation Index (NDVI) in MSI showed high correlations with yield and could track visual SG grades at an earlier stage of grain filling. In CIs, the SGR of Normalized Green Red Difference Index (NGRDI), the Green Leaf Index (GLI) in RGB and MSI showed low correlations with yield and could only track visual SG grades at late grain filling stage and that of Norm Red (NormR) in RGB images failed to track visual SG grades. This study preliminarily confirms the MSI is more available and reliable than RGB in phenotyping for wheat SG. The index-based SGR in this study could act as HTP reference solutions for SG in diversified wheat genotypes.
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Kwak, Jeonghoon, and Yunsick Sung. "Automatic 3D Landmark Extraction System Based on an Encoder–Decoder Using Fusion of Vision and LiDAR." Remote Sensing 12, no. 7 (2020): 1142. http://dx.doi.org/10.3390/rs12071142.
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To provide a realistic environment for remote sensing applications, point clouds are used to realize a three-dimensional (3D) digital world for the user. Motion recognition of objects, e.g., humans, is required to provide realistic experiences in the 3D digital world. To recognize a user’s motions, 3D landmarks are provided by analyzing a 3D point cloud collected through a light detection and ranging (LiDAR) system or a red green blue (RGB) image collected visually. However, manual supervision is required to extract 3D landmarks as to whether they originate from the RGB image or the 3D point cloud. Thus, there is a need for a method for extracting 3D landmarks without manual supervision. Herein, an RGB image and a 3D point cloud are used to extract 3D landmarks. The 3D point cloud is utilized as the relative distance between a LiDAR and a user. Because it cannot contain all information the user’s entire body due to disparities, it cannot generate a dense depth image that provides the boundary of user’s body. Therefore, up-sampling is performed to increase the density of the depth image generated based on the 3D point cloud; the density depends on the 3D point cloud. This paper proposes a system for extracting 3D landmarks using 3D point clouds and RGB images without manual supervision. A depth image provides the boundary of a user’s motion and is generated by using 3D point cloud and RGB image collected by a LiDAR and an RGB camera, respectively. To extract 3D landmarks automatically, an encoder–decoder model is trained with the generated depth images, and the RGB images and 3D landmarks are extracted from these images with the trained encoder model. The method of extracting 3D landmarks using RGB depth (RGBD) images was verified experimentally, and 3D landmarks were extracted to evaluate the user’s motions with RGBD images. In this manner, landmarks could be extracted according to the user’s motions, rather than by extracting them using the RGB images. The depth images generated by the proposed method were 1.832 times denser than the up-sampling-based depth images generated with bilateral filtering.
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Mohd Ali, Nursabillilah, Nahrul Khair Alang Md Rashid, and Yasir Mohd Mustafah. "Performance Comparison between RGB and HSV Color Segmentations for Road Signs Detection." Applied Mechanics and Materials 393 (September 2013): 550–55. http://dx.doi.org/10.4028/www.scientific.net/amm.393.550.
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This paper compares the performance of RGB and HSV color segmentations method in road signs detection. The road signs images are taken under various illumination changes, partial occlusion and rotational changes. The proposed algorithms using both RGB and HSV color space are able to detect the 3 standard types of colored images namely Red, Yellow and Blue. The experiment shows that the HSV color algorithm achieved better detection accuracy compared to RGB color space.
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Benitez-Palacios, Diego, Murat Uzundag, Maja Vučković, et al. "Volume-limited sample of low-mass red giant stars, the progenitors of hot subdwarf stars." Astronomy & Astrophysics 697 (May 2025): A98. https://doi.org/10.1051/0004-6361/202452782.
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Context. Binary hot subdwarf B (sdB) stars are typically produced from low-mass red giant branch (RGB) stars that have lost almost all their envelopes through binary mass transfer while still fusing helium in their cores. Particularly, when a low-mass red giant enters stable Roche lobe overflow (RLOF) mass transfer near the tip of the RGB, a long-period sdB binary may be formed. Aims. We aim to extend our previous volume-limited sample of 211 stars within 200 pc, a homogeneous sample of low-mass red giants, predicted progenitors of wide sdB binaries, to 500 pc and validate it. Additionally, our goal is to provide the distribution of stellar parameters for these stars. Methods. We refined our original 500 pc sample by incorporating Gaia DR3 parallax values and interstellar extinction measurements. Next, we collected multi-epoch high-resolution spectra for 230 stars in the volume-limited sample using the CORALIE échelle spectrograph from 2019 to 2023. To confirm or discard binarity, we combined astrometric parameters from Gaia with the resulting radial velocity variations. We derived the distribution of stellar parameters using evolutionary models and employed the equivalent evolutionary phase to verify the evolutionary stage of the stars in our sample. Finally, we compared our stellar parameters with the literature. Results. The derived stellar parameters confirmed that 82% of stars in our sample are indeed in the RGB phase, while 18% are red clump (RC) contaminants. This was expected due to the overlapping of RGB and RC stars in the colour-magnitude diagram. Additionally, 75% of the confirmed RGB stars have a high probability of being part of a binary system. Comparison with the literature shows good overall agreement with a scatter ≲15% in stellar parameters, while the masses show somewhat higher dispersion (∼20%). Conclusions. We have obtained the most complete volume-limited sample of binary RGB star candidates within 500 pc. These systems are likely progenitors of hot subdwarfs and other classes of stripped helium stars.
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Dali, Mohd Rumaizan Maidan, Aiman Shahmi Azam, Mohamad Faizal Abd Rahma, et al. "Optical absorbance of RGB LEDs in pH measurement of colorimetric solution with phenol red reagent." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 3 (2022): 1330. http://dx.doi.org/10.11591/ijeecs.v27.i3.pp1330-1337.
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The use of red, green, and blue ( RGB ) light-emitting-diode ( LED ) is a new trend in monochromatic colorimetric sensing due to cost-effective implementation. However, the application of RGB LED in pH measurement depends on the performance of the LED colour towards the colorimetric solution of interest, hence, needs to be evaluated. This work evaluated the performance of RGB LED for pH measurement system based on colorimetric approach using phenol red as a reagent. The main objective was to identify the LED colour with the best performance in terms of signal response and a bsorbance behavior. In this work, LED and photodiode were used as optical components and NI USB DAQ with LabView as the processing software. Four samples with known pH values were prepared and tested to obtain the voltage and absorbance behavior of each LED colour. Among all, the blue LED with wavelength ranged between 450 - 495 nm showed the best sensing behavior based on its linearity and error. Both voltage response and absorbance produced linear correlation with R 2=0.883 and R 2=0.9803, respectively. The significant finding from this study is useful in selecting the best RGB LED that is suitable for colorimetric pH measurement with phenol red as its colorimetric reagent.
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Carillo, Petronia, Christophe El-Nakhel, Veronica De Micco, et al. "Morpho-Metric and Specialized Metabolites Modulation of Parsley Microgreens through Selective LED Wavebands." Agronomy 12, no. 7 (2022): 1502. http://dx.doi.org/10.3390/agronomy12071502.
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Plant factories and high-tech greenhouses offer the opportunity to modulate plant growth, morphology and qualitative content through the management of artificial light (intensity, photoperiod and spectrum). In this study, three Light Emitting Diode (LED) lighting systems, with blue (B, 460 nm), red (R, 650 nm) and mixed red + green-yellow + blue (RGB) light were used to grow parsley microgreens to understand how light quality could change the phenotype and the profile of secondary metabolites. Plants showed altered morphological characteristics and higher amounts of secondary metabolites under RGB LEDs treatment. The results demonstrated that microgreens under red light showed the highest fresh yield, petiole length, coumaric acid content but also the highest nitrate content. Plants under RGB light showed the highest dry matter percentage and highest content of total and single polyphenols content, while blue light showed the highest ascorbic acid and ABTS antioxidant activity. Moreover, microgreens under red light showed more compact leaves with less intercellular spaces, while under blue and RGB light, the leaves displayed ticker spongy mesophyll with higher percentage of intercellular spaces. Therefore, the specific spectral band was able to modify not only the metabolic profile, but also it could modulate the differentiation of mesophyll cells. Light quality as a preharvest factor helps to shape the final parsley microgreens product as a whole, not only in terms of yield and quality, but also from a morpho-anatomical point of view.
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Zhou, Yutao, Chun Wang, Hongliang Yan, et al. "Li-rich Giants in LAMOST Survey. III. The Statistical Analysis of Li-rich Giants." Astrophysical Journal 931, no. 2 (2022): 136. http://dx.doi.org/10.3847/1538-4357/ac6b3a.
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Abstract The puzzle of the Li-rich giant is still unsolved, contradicting the prediction of the standard stellar models. Although the exact evolutionary stages play a key role in the knowledge of Li-rich giants, a limited number of Li-rich giants have been observed with high-quality asteroseismic parameters to clearly distinguish the stellar evolutionary stages. Based on the LAMOST Data Release 7 (DR7), we applied a data-driven neural network method to derive the parameters for giant stars, which contain the largest number of Li-rich giants. The red giant stars are classified into three stages of Red Giant Branch (RGB), Primary Red Clump (PRC), and Secondary Red Clump (SRC) relying on the estimated asteroseismic parameters. In the statistical analysis of the properties (i.e., stellar mass, carbon, nitrogen, Li-rich distribution, and frequency) of Li-rich giants, we found that (1) most of the Li-rich RGB stars are suggested to be the descendants of Li-rich pre-RGB stars and/or the result of engulfment of planet or substellar companions; (2) the massive Li-rich SRC stars could be the natural consequence of Li depletion from the high-mass Li-rich RGB stars; and (3) internal mixing processes near the helium flash can account for the phenomenon of Li richness on PRC that dominated the Li-rich giants. Based on the comparison of [C/N] distributions between Li-rich and normal PRC stars, the Li-enriched processes probably depend on the stellar mass.
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Skala, Vaclav. "Multispectral Image Generation from RGB Based on WSL Color Representation: Wavelength, Saturation, and Lightness." Computers 12, no. 9 (2023): 182. http://dx.doi.org/10.3390/computers12090182.
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Image processing techniques are based nearly exclusively on RGB (red–green–blue) representation, which is significantly influenced by technological issues. The RGB triplet represents a mixture of the wavelength, saturation, and lightness values of light. It leads to unexpected chromaticity artifacts in processing. Therefore, processing based on the wavelength, saturation, and lightness should be more resistant to the introduction of color artifacts. The proposed process of converting RGB values to corresponding wavelengths is not straightforward. In this contribution, a novel simple and accurate method for extracting the wavelength, saturation, and lightness of a color represented by an RGB triplet is described. The conversion relies on the known RGB values of the rainbow spectrum and accommodates variations in color saturation.
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Lopes, José, and Ilídio Lopes. "Dark matter capture and annihilation in stars: Impact on the red giant branch tip." Astronomy & Astrophysics 651 (July 2021): A101. http://dx.doi.org/10.1051/0004-6361/202140750.
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Context. While stars have often been used as laboratories to study dark matter (DM), red giant branch (RGB) stars and all the rich phenomenology they encompass have frequently been overlooked by such endeavors. Aims. We study the capture, evaporation, and annihilation of weakly interacting massive particle (WIMP) DM in low-mass RGB stars (M = 0.8−1.4 M⊙). Methods. We used a modified stellar evolution code to study the effects of DM self-annihilation on the structure and evolution of low-mass RGB stars. Results. We find that the number of DM particles that accumulate inside low-mass RGB stars is not only constant during this phase of evolution, but also mostly independent of the stellar mass and to some extent stellar metallicity. Moreover, we find that the energy injected into the stellar core due to DM annihilation can promote the conditions necessary for helium burning and thus trigger an early end of the RGB phase. The premature end of the RGB, which is most pronounced for DM particles with mχ ≃ 100 GeV, is thus achieved at a lower helium core mass, which results in a lower luminosity at the tip of the red giant branch (TRGB). Although in the current WIMP paradigm, these effects are only relevant if the number of DM particles inside the star is extremely large, we find that for light WIMPs (mχ ≃ 4 GeV), relevant deviations from the standard TRGB luminosity (∼8%) can be achieved with conditions that can be realistic in the inner parsec of the Milky Way.
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James, Dizna, Smitha Subramanian, Abinaya O. Omkumar, et al. "Presence of red giant population in the foreground stellar substructure of the Small Magellanic Cloud." Monthly Notices of the Royal Astronomical Society 508, no. 4 (2021): 5854–63. http://dx.doi.org/10.1093/mnras/stab2873.
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ABSTRACT The eastern region of the Small Magellanic Cloud (SMC) is found to have a foreground stellar substructure, which is identified as a distance bimodality (∼12 kpc apart) in the previous studies using red clump (RC) stars. Interestingly, studies of red giant branch (RGB) stars in the eastern SMC indicate a bimodal radial velocity (RV) distribution. In this study, we investigate the connection between these two bimodal distributions to better understand the nature and origin of the foreground stellar substructure in the eastern SMC. We use the Gaia Early Data Release 3 astrometric data and archival RV data of RGB stars for this study. We find a bimodal RV distribution of RGB stars (separated by ∼35–45 km s−1) in the eastern and south-western (SW) outer regions. The observed proper motion values of the lower and higher RV RGB components in the eastern regions are similar to those of the foreground and main-body RC stars, respectively. This suggests that the two RGB populations in the eastern region are separated by a similar distance to those of the RC stars, and the RGB stars in the lower RV component are part of the foreground substructure. Based on the differences in the distance and RV of the two components, we estimate an approximate time of formation of this substructure as 307 ± 65 Myr ago. This is comparable with the values predicted by simulations for the recent epoch of tidal interaction between the Magellanic Clouds. Comparison of the observed properties of RGB stars, in the outer SW region, with N-body simulations shows that the higher RV component in the SW region is at a farther distance than the main body, indicating the presence of a stellar counter-bridge in the SW region of the SMC.
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Blouin, Simon, Huaqing Mao, Falk Herwig, Pavel Denissenkov, Paul R. Woodward, and William R. Thompson. "3D hydrodynamics simulations of internal gravity waves in red giant branch stars." Monthly Notices of the Royal Astronomical Society 522, no. 2 (2023): 1706–25. http://dx.doi.org/10.1093/mnras/stad1115.
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ABSTRACT We present the first 3D hydrodynamics simulations of the excitation and propagation of internal gravity waves (IGWs) in the radiative interiors of low-mass stars on the red giant branch (RGB). We use the ppmstar explicit gas dynamics code to simulate a portion of the convective envelope and all the radiative zone down to the hydrogen-burning shell of a $1.2\, {\rm M}_{\odot }$ upper RGB star. We perform simulations for different grid resolutions (7683, 15363, and 28803), a range of driving luminosities, and two different stratifications (corresponding to the bump luminosity and the tip of the RGB). Our RGB tip simulations can be directly performed at the nominal luminosity, circumventing the need for extrapolations to lower luminosities. A rich, continuous spectrum of IGWs is observed, with a significant amount of total power contained at high wavenumbers. By following the time evolution of a passive dye in the stable layers, we find that IGW mixing in our simulations is weaker than predicted by a simple analytical prescription based on shear mixing and not efficient enough to explain the missing RGB extra mixing. However, we may be underestimating the efficiency of IGW mixing given that our simulations include a limited portion of the convective envelope. Quadrupling its radial extent compared to our fiducial set-up increases convective velocities by up to a factor 2 and IGW velocities by up to a factor 4. We also report the formation of a $\sim 0.2\, H_P$ penetration zone and evidence that IGWs are excited by plumes that overshoot into the stable layers.
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Wang, Hai-Feng, Giovanni Carraro, Xin Li, et al. "Age Determination of LAMOST Red Giant Branch Stars Based on the Gradient Boosting Decision Tree Method." Astrophysical Journal 967, no. 1 (2024): 37. http://dx.doi.org/10.3847/1538-4357/ad3b90.
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Abstract In this study, we estimate the stellar ages of LAMOST DR8 red giant branch (RGB) stars based on the gradient boosting decision tree (GBDT) algorithm. We used 2643 RGB stars extracted from the APOKASC-2 asteroseismological catalog as the training data set. After selecting the parameters ([α/Fe], [C/Fe], T eff, [N/Fe], [C/H], log g) highly correlated with age using GBDT, we apply the same GBDT method to the new catalog of more than 590,000 stars classified as RGB stars. The test data set shows that the median relative error is around 11.6% for the method. We also compare the predicted ages of RGB stars with other studies (e.g., based on APOGEE) and find some systematic differences. The final uncertainty is about 15%–30% compared to the ages of open clusters. Then, we present the spatial distribution of the RGB sample with an age determination, which could recreate the expected result, and discuss systematic biases. All these diagnostics show that one can apply the GBDT method to other stellar samples to estimate atmospheric parameters and age.
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Feng, Haikuan, Huilin Tao, Zhenhai Li, Guijun Yang, and Chunjiang Zhao. "Comparison of UAV RGB Imagery and Hyperspectral Remote-Sensing Data for Monitoring Winter Wheat Growth." Remote Sensing 14, no. 15 (2022): 3811. http://dx.doi.org/10.3390/rs14153811.
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Although crop-growth monitoring is important for agricultural managers, it has always been a difficult research topic. However, unmanned aerial vehicles (UAVs) equipped with RGB and hyperspectral cameras can now acquire high-resolution remote-sensing images, which facilitates and accelerates such monitoring. To explore the effect of monitoring a single crop-growth indicator and multiple indicators, this study combines six growth indicators (plant nitrogen content, above-ground biomass, plant water content, chlorophyll, leaf area index, and plant height) into the new comprehensive growth index (CGI). We investigate the performance of RGB imagery and hyperspectral data for monitoring crop growth based on multi-time estimation of the CGI. The CGI is estimated from the vegetation indices based on UAV hyperspectral data treated by linear, nonlinear, and multiple linear regression (MLR), partial least squares (PLSR), and random forest (RF). The results are as follows: (1) The RGB-imagery indices red reflectance (r), the excess-red index (EXR), the vegetation atmospherically resistant index (VARI), and the modified green-red vegetation index (MGRVI), as well as the spectral indices consisting of the linear combination index (LCI), the modified simple ratio index (MSR), the simple ratio vegetation index (SR), and the normalized difference vegetation index (NDVI), are more strongly correlated with the CGI than a single growth-monitoring indicator. (2) The CGI estimation model is constructed by comparing a single RGB-imagery index and a spectral index, and the optimal RGB-imagery index corresponding to each of the four growth stages in order is r, r, r, EXR; the optimal spectral index is LCI for all four growth stages. (3) The MLR, PLSR, and RF methods are used to estimate the CGI. The MLR method produces the best estimates. (4) Finally, the CGI is more accurately estimated using the UAV hyperspectral indices than using the RGB-image indices.
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Sun, Xue, and Li Zhao. "RGB Pixel Brightness Characteristics of Linked Color Imaging in Early Gastric Cancer: A Pilot Study." Gastroenterology Research and Practice 2020 (March 31, 2020): 1–7. http://dx.doi.org/10.1155/2020/2105874.
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Background and Aims. Linked color imaging (LCI) helps screen and diagnose for early gastric cancer by color contrast in different mucosa. RGB (red, green, and blue) pixel brightness quantifies colors, which is relatively objective. Limited studies have combined LCI images with RGB to help screen for early gastric cancer (EGC). We aimed to evaluate the RGB pixel brightness characteristics of EGC and noncancer areas in LCI images. Methods. We retrospectively reviewed early gastric cancer (EGC) patients and LCI images. All pictures were evaluated by at least two endoscopic physicians. RGB pixel brightness analysis of LCI images was performed in MATLAB software to compare the cancer with noncancer areas. Receiver operating characteristic (ROC) curve was analyzed for sensitivity, specificity, cut-off, and area under the curve (AUC). Results. Overall, 38 early gastric cancer patients were enrolled with 38 LCI images. Pixel brightness of red, green, and blue in cancer was remarkably higher than those in noncancer areas (190.24±37.10 vs. 160.00±40.35, p<0.001; 117.96±33.91 vs. 105.33±30.01, p=0.039; 114.36±34.88 vs. 90.93±30.14, p<0.001, respectively). Helicobacter plyori (Hp) infection was not relevant to RGB distribution of EGC. Whether the score of Kyoto Classification of Gastritis (KCG) is ≥4 or <4, the pixel brightness of red, green, and blue was not disturbed in both cancer and noncancer (p>0.05). Receiver operating characteristic (ROC) curve for differentiating cancer from noncancer was calculated. The maximum area under the curve (AUC) was 0.767 in B/G, with a sensitivity of 0.605, a specificity of 0.921, and a cut-off of 0.97. Conclusions. RGB pixel brightness was useful and more objective in distinguishing early gastric cancer for LCI images.
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McCormick, Caroline, Steven R. Majewski, Verne V. Smith, et al. "An investigation of non-canonical mixing in red giant stars using APOGEE 12C/13C ratios observed in open cluster stars." Monthly Notices of the Royal Astronomical Society 524, no. 3 (2023): 4418–30. http://dx.doi.org/10.1093/mnras/stad2156.
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ABSTRACT Standard stellar evolution theory poorly predicts the surface abundances of chemical species in low-mass, red giant branch (RGB) stars. Observations show an enhancement of p–p chain and CNO cycle products in red giant envelopes, which suggests the existence of non-canonical mixing that brings interior burning products to the surface of these stars. The 12C/13C ratio is a highly sensitive abundance metric used to probe this mixing. We investigate extra RGB mixing by examining: (1) how 12C/13C is altered along the RGB, and (2) how 12C/13C changes for stars of varying age and mass. Our sample consists of 43 red giants, spread over 15 open clusters from the Sloan Digital Sky Survey’s APOGEE DR17, that have reliable 12C/13C ratios derived from their APOGEE spectra. We vetted these 12C/13C ratios and compared them as a function of evolution and age/mass to the standard mixing model of stellar evolution, and to a model that includes prescriptions for RGB thermohaline mixing and stellar rotation. We find that the observations deviate from standard mixing models, implying the need for extra mixing. Additionally, some of the abundance patterns depart from the thermohaline model, and it is unclear whether these differences are due to incomplete observations, issues inherent to the model, our assumption of the cause of extra mixing, or any combination of these factors. Nevertheless, the surface abundances across our age/mass range clearly deviate from the standard model, agreeing with the notion of a universal mechanism for RGB extra mixing in low-mass stars.
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Sermyagin, Alexander A., Arsen V. Dotsev, Alexandra S. Abdelmanova, Johann Sölkner, and Natalia A. Zinovieva. "PSXIV-26 Selection footprints in Russian red cattle identified by linkage disequilibrium blocks based on SNP data." Journal of Animal Science 99, Supplement_3 (2021): 255–56. http://dx.doi.org/10.1093/jas/skab235.467.
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Abstract Among the variety of cattle breeds in Russia, the Russian red dual-purpose cattle breeds have great importance because of their ability to produce high milk yields as well as to provide excellent milk quality. The low census size of the Russian red cattle breeds requires development of programs for conservation of their biodiversity. Our study aimed to investigate selection footprints in Russian red cattle breeds, using high values of linkage disequilibrium (LD) in SNP haplotype blocks as indicators. For finding such LD blocks, we used the genotypes (≈35K SNPs) of Red Gorbatov (RGB, n = 26), Bestuzhev (BST,n = 27), and Suksun (SKS,n = 17) breeds, as well as Red Holstein (RH,n = 16) as an outgroup. Quality control and LD calculations for different distances were performed in Plink 1.90. Top 0.01% SNP pairs by LD value (0.9≤r2&lt; 1.0) were selected for further analysis. The effective population size derived from LD patterns was estimated using SNeP tool. Comparison of LD values for 70 kb interval between breeds and chromosomes by MANOVA pairwise testing significantly distinguished RH/RGB and BST/SKS breeds (P &lt; 0.05-0.001). LD values among chromosomes were 0.195–0.287 for RH, 0.194–0.272 for RGB, 0.172–0.237 for BST, and 0.157–0.217 for SKS. The SKS and BST breeds had higher Ne values (84 and 113, respectively) compared to RH (63) and RGB (79). Selection footprints by LD blocks in Russian red cattle genome covered several relevant genes on BTA1 (EPHA6,DGKG), BTA2 (LRP1B,THSD7B,STAT1), BTA5 (CPM,BAIAP2L2), BTA9 (TRDN,UTRN), BTA10 (KCNN2,CAPN3), BTA11 (SH3RF3,RABGAP1,RALGPS1), BTA14 (ZNF16,ARHGAP39,TOX,DGAT1), and BTA19 (MYH10), BTA22 (FHIT). Detected genes were found to be responsible for milk fat and protein contents, fatty acid composition, somatic cells score, fertility, feet and legs, and udder conformation traits. Our results can be useful for developing the breeding and conservation programs of the Russian red cattle genetic resources. The study was funded by RFBR within project No. 20-516-00020
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Kumar, K. Arun, Shanmukha N. T, Lokeshappa B, and Vinayaka M. "Kinetic Studies On Reactive Dye Removal From Aqueous Solution By Using Arecanut Peel." International Journal of Membrane Science and Technology 9, no. 2 (2022): 155–64. http://dx.doi.org/10.15379/ijmst.v9i2.3679.
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This research aims at finding the effectiveness of Remazole Red RGB dye removal using arecanut peel, an agricultural waste, as an activated carbon. The arecanut peel-activated carbon was prepared in the laboratory by carbonization followed by activation. Adsorption studies were carried out to look for the effect of different experimental scenarios, like different pH values, varying contact times, the initial concentration of dye, and changing arecanut peel carbon dosage, on the removal efficiency of Remazole Red RGB dye from the experimental solution. The equilibrium experimental results were checked for the applicability of the Langmuir and Freundlich isotherm models and the kinetic models. The batch test result was a maximum dye removal of 83% with an initial dye concentration of 5 mg/L at an adsorbent dose of 0.625 g/L at dye pH 4 in a 50-minute time span. For Remazole Red RGB dye removal, the test result is unfavorable for the Langmuir isotherm model but suits well for he Freundlich i isotherm model. The maximum adsorption capacity of arecanut peel carbon on Remazole Red RGB dye was 3.89 mg/g. It was evident that the adsorption process is favorable for the pseudo-second-order rate kinetics. It was seen that intra-particle diffusion is not the only rate-limiting step in this adsorption experimental system; also, regression results show that the linear regression model gives the best outcome. The end result of this study confirms that powder arecanut peel activated carbon was the right option for removing reactive dye from an aqueous solution.
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Zhang, Yuanxi, Ye Tian, Change Zheng, Dong Zhao, Po Gao, and Ke Duan. "SEGMENTATION OF APPLE POINT CLOUDS BASED ON ROI IN RGB IMAGES." INMATEH Agricultural Engineering 59, no. 3 (2019): 209–18. http://dx.doi.org/10.35633/inmateh-59-23.
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Autonomous harvesting and evaluation of apples reduce the labour cost. Segmentation of apple point clouds from consumer-grade RGB-D camera is the most important and challenging step in the harvesting process due to the complex structure of apple trees. This paper put forward a segmentation method of apple point clouds based on regions of interest (ROI) in RGB images. Firstly, an annotated RGB dataset of apple trees was built and applied to train the optimized Faster R-CNN to locate ROI containing apples in RGB images. Secondly, the relationship between RGB images and depth images was built to roughly segment the apple point clouds by ROI. Finally, the quality control procedure (QCP) was proposed to improve the quality of segmented apple point clouds. Images for training mainly included two lighting condition, two colours and three apple varieties in orchard, making this method more suitable for practical applications. QCP performed well in filtering noise points and achieved Purity as 96.7% and 96.2% for red and green apples, respectively. Through the comparison method, experimental results indicated that the segmentation method based on ROI is more effective and accurate for red and green apples in orchard. The segmentation method of point clouds based on ROI has great potential for segmentation of point clouds in unstructured scenes.
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Anderson, Connor J., Daniel Heins, Keith C. Pelletier, and Joseph F. Knight. "Using Voting-Based Ensemble Classifiers to Map Invasive Phragmites australis." Remote Sensing 15, no. 14 (2023): 3511. http://dx.doi.org/10.3390/rs15143511.
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Machine learning is frequently combined with imagery acquired from uncrewed aircraft systems (UASs) to detect invasive plants. Having prior knowledge of which machine learning algorithm will produce the most accurate results is difficult. This study examines the efficacy of a voting-based ensemble classifier to identify invasive Phragmites australis from three-band (red, green, blue; RGB) and five-band (red, green, blue, red edge, near-infrared; multispectral; MS) UAS imagery acquired over multiple Minnesota wetlands. A Random Forest, histogram-based gradient-boosting classification tree, and two artificial neural networks were used within the voting-based ensemble classifier. Classifications from the RGB and multispectral imagery were compared across validation sites both with and without post-processing from an object-based image analysis (OBIA) workflow (post-machine learning OBIA rule set; post-ML OBIA rule set). Results from this study suggest that a voting-based ensemble classifier can accurately identify invasive Phragmites australis from RGB and multispectral imagery. Accuracies greater than 80% were attained by the voting-based ensemble classifier for both the RGB and multispectral imagery. The highest accuracy, 91%, was achieved when using the multispectral imagery, a canopy height model, and a post-ML OBIA rule set. The study emphasizes the need for further research regarding the accurate identification of Phragmites australis at low stem densities.
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Nurfawaidi, Arjun, Bambang Kuswandi, and Lestyo Wulandari. "Pengembangan Label Pintar untuk Indikator Kesegaran Daging Sapi pada Kemasan." Pustaka Kesehatan 6, no. 2 (2018): 199. http://dx.doi.org/10.19184/pk.v6i2.7560.
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 Meat is a high nutritional food that rich of protein, fat, and sugar. Smart label has been applied as a beef freshness detector. Two types of pH indicator have been used i.e bromocresol purple (BCP) and methyl red (MR) as dual indicator freshness. The objective of this research was to determine the beef freshness using smart label at room temperature. The color change of the smart label was examined by imageJ software to determine the freshness degree using the mean RGB value. The beef was examined every 2 hours for pH and total volatile base (TVB-N) analysis during the 24 hours storage at room temperature. The result showed that color indicator will change according to the beef freshness, bromocresol purple turned from yellow to purple (mean RGB 171.465 ± 1.122) and methyl red turned from red to yellow (mean RGB 162.082 ± 1.315). The beef freshness at room temperature decreased as the pH increase from 5.61 to 6.23 along with the color change of smart label. Furthermore, the color would change when 0.022 %N of TVB-N has been reached. Therefore, the beef freshness can be determined by using smart label based on dual indicator of bromocresol purple and methyl red in room temperature. 
 
 Keywords: beef freshness, smart label, pH, TVB
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Liu, Yanli, Heng Zhang, and Chao Huang. "A Novel RGB-D SLAM Algorithm Based on Cloud Robotics." Sensors 19, no. 23 (2019): 5288. http://dx.doi.org/10.3390/s19235288.
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In this paper, we present a novel red-green-blue-depth simultaneous localization and mapping (RGB-D SLAM) algorithm based on cloud robotics, which combines RGB-D SLAM with the cloud robot and offloads the back-end process of the RGB-D SLAM algorithm to the cloud. This paper analyzes the front and back parts of the original RGB-D SLAM algorithm and improves the algorithm from three aspects: feature extraction, point cloud registration, and pose optimization. Experiments show the superiority of the improved algorithm. In addition, taking advantage of the cloud robotics, the RGB-D SLAM algorithm is combined with the cloud robot and the back-end part of the computationally intensive algorithm is offloaded to the cloud. Experimental validation is provided, which compares the cloud robotic-based RGB-D SLAM algorithm with the local RGB-D SLAM algorithm. The results of the experiments demonstrate the superiority of our framework. The combination of cloud robotics and RGB-D SLAM can not only improve the efficiency of SLAM but also reduce the robot’s price and size.
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Mallick, Anohita, Bacham E. Reddy, and C. Muthumariappan. "Probing infrared excess connection with Li enhancement among red clump giants." Monthly Notices of the Royal Astronomical Society 511, no. 3 (2022): 3741–50. http://dx.doi.org/10.1093/mnras/stac224.
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ABSTRACT We have performed a search among low-mass red giants for finding evidence for merger scenario for triggering He-flash and subsequent Li enhancement. We chose a sample of red giants from GALAH survey with well-measured Li abundances, and near- and mid-IR fluxes from 2MASS and WISE surveys, respectively. The sample contains 418 cool red clump giants and 359 upper red giant branch (RGB) giants. Most of the giants and majority of super Li-rich giants show no IR excess. Only five red clump giants and one RGB giant show IR excess. Notably, of the five red clump giants with IR excess, three are super Li-rich (A(Li) ≥ 3.2 dex) and two are Li-rich (A(Li) ≥ 1.0 dex). Results suggest that Li enhancement among red clump giants may be due to two channels: one resulting from in situ He-flash in single-star evolution and the other due to He-flash triggered by events like merger of He-white dwarfs with giants’ He-inert core on RGB. In the latter case, IR excess, as a result of mass-loss, is expected from merger events. We have modelled IR excess in all six giants using dusty code and derived dust parameters. The estimated kinematic ages and time-scales of dust envelopes of the super Li-rich phase suggest that Li enhancement took place very recently. Further, the analysis shows a significantly higher proportion (four out of five red clump giants) of rapid rotators (vsini ≥ 8 km s−1) among Li-rich giants with IR excess compared to Li-normal and Li-rich giants with no IR excess.
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Madore, Barry F., Wendy L. Freedman, Kayla A. Owens, and In Sung Jang. "Quantifying Uncertainties on the Tip of the Red Giant Branch Method." Astronomical Journal 166, no. 1 (2023): 2. http://dx.doi.org/10.3847/1538-3881/acd3f3.
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Abstract We present an extensive grid of numerical simulations quantifying the uncertainties in measurements of the tip of the red giant branch (TRGB). These simulations incorporate a luminosity function composed of 2 mag of red giant branch (RGB) stars leading up to the tip, with asymptotic giant branch (AGB) stars contributing exclusively to the luminosity function for at least a magnitude above the RGB tip. We quantify the sensitivity of the TRGB detection and measurement to three important error sources: (1) the sample size of stars near the tip, (2) the photometric measurement uncertainties at the tip, and (3) the degree of self-crowding of the RGB population. The self-crowding creates a population of supra-TRGB stars due to the blending of one or more RGB stars just below the tip. This last population is ultimately difficult, although still possible, to disentangle from true AGB stars. In the analysis given here, the precepts and general methodology as used in the Chicago-Carnegie Hubble Program (CCHP) have been followed. However, in the appendix, we introduce and test a set of new tip detection kernels, which internally incorporate self-consistent smoothing. These are generalizations of the two-step model used by the CCHP (smoothing followed by Sobel-filter tip detection), where the new kernels are based on successive binomial-coefficient approximations to the derivative-of-a-Gaussian edge-detector, as is commonly used in modern digital image processing.
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Alongi, M., G. Bertelli, A. Bressan, and C. Chiosi. "Effects of envelope overshoot on the bump of the Red Giant Branch luminosity function." Symposium - International Astronomical Union 148 (1991): 325–26. http://dx.doi.org/10.1017/s0074180900200685.
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We show that a certain amount of non-local overshoot at the base of the outer convective envelop of low mass stars, while climbing along the RGB toward central He-ignition, explains the shift of about 0.4V mag required to bring the luminosity of the bump predicted in the theoretical RGB luminosity functions into agreement with the observational one.
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Ogasawara, Kouki, Kazuki Nakamura, and Norihisa Kobayashi. "Thermally controlled dual-mode display media with red-green-blue coloration and fluorescence via energy transfer between emission materials and leuco dyes." Journal of Materials Chemistry C 4, no. 21 (2016): 4805–13. http://dx.doi.org/10.1039/c6tc01027j.
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Avetikov, D. S., O. P. Bukhanchenko, S. O. Stavitsky, O. S. Ivanytska та V. M. Scrypnyk. "Удосконалення діагностики післяопераційних атрофічних та нормотрофічних рубців шкіри шляхом застосування колірної системи RGB". Klinicheskaia khirurgiia 85, № 8 (2018): 38–40. http://dx.doi.org/10.26779/2522-1396.2018.08.38.
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Мета. Удосконалення методів диференціальної діагностики атрофічних та нормотрофічних рубців шкіри голови та шиї шляхом використання комп’ютерної візуалізації цифрових світлин із застосуванням колірної системи RGB (Red, Green, Blue).
 Матеріали і методи. Ми спостерігали 60 пацієнтів із післяопераційними рубцями шкіри голови та шиї. Рубцевозмінену тканину додатково обстежували, аналізуючи цифрові світлини у програмі Adobe Photoshop CC із використанням системи кольорів RGB.
 Результати. Застосовуючи колірну систему RGB у діагностиці післяопераційних рубців шкіри щелепно-лицевої ділянки, достовірно визначали тип рубця. Залежно від типу рубця обирали оптимальний метод хірургічного втручання або консервативної терапії.
 Висновки. Застосування системи кольорів RGB у програмі Adobe Photoshop СС дає можливість диференціювати атрофічні та нормотрофічні рубці шкіри за показниками інтенсивності забарвлення кольоровим спектром.