Análisis de la condición del campo de fútbol basado en el agrupamiento de k-means

Football, a highly popular sport all over the world, requires that professional footballers practice it on a field of play in ideal conditions, which, among other things, includes the usage and maintenance of healthy natural grass. In this study, we present an unsupervised allocator strategy for image analysis of football pitches that uses k-means clustering and color comparison to assess whether a playing field is in good or bad condition. Our approach considers proportions of dominant RGB colors for automatized decision-making. We developed a prototype and tested it with a series of images; this paper offers a comparison between the findings of this test and our expected results.El fútbol, un deporte muy popular en todo el mundo, requiere que los futbolistas profesionales lo practiquen en un campo de juego en condiciones ideales, lo que, entre otras cosas, incluye el uso y mantenimiento de un césped natural saludable. En este estudio, presentamos una estrategia de asignación sin supervisión para el análisis de imágenes de campos de fútbol que utiliza agrupamiento k-means y comparación de colores para evaluar si un campo de juego está en buenas o malas condiciones. Nuestro enfoque considera las proporciones de los colores RGB dominantes para automatizar la toma de decisiones. Para tal fin, se desarrolló un prototipo que se probó con una serie de imágenes; los resultados obtenidos se compararon con los esperados

Development of biodegradable hybrid polymer film for detection of formaldehyde in seafood products

Despite the enormous accomplishments of current sensing methods, portable and sensitive sensing materials remains a challenging issue. Herein, a novel of a biodegradable hybrid polymer film was developed for quantitative analysis of formaldehyde seafood, including Lutjanus erythropterus, Euthynnus affinis, Caranx indicus, and Penaeus monodon at Sabah, Malaysia. In this research, starch and chitosan were introduced as the substrate to entrap Nash colorimetric reagents for the fabrication of biodegradable films for detection of formaldehyde. Under optimal conditions, excellent linearity (R2 = 0.9918) of colorimetric response was obtained in formaldehyde concentration ranges of 100 to 0 ppm, with a limit of detection and quantification calculated to be 5 and 16.8 ppm, respectively. The developed film was successfully applied to the identification and quantification of formaldehyde in four different seafood samples with satisfactory recoveries, and RSD values obtained range between 98.80%–104.65% and 0.12%–1.21%, respectively. The present research demonstrated short response time (within 5 min) that provides reliable methods for application in biosensing, which exhibited the advantage of this well-performing platform for application in the food, environmental, and medical disciplines sensing

Multi-sensor data fusion and parallel factor analysis reveals kinetics of wood weathering

Understanding mechanisms of materials deterioration during service life is fundamental for their confident use in the building sector. This work presents analysis of time series of data related to wood weathering acquired at three scales (molecular, microscopic, macroscopic) with different sensors. By using several complementary techniques, the material description is precise and complete; however, the data provided by multiple equipment are often not directly comparable due to different resolution, sensitivity and/or data format. This paper presents an alternative approach for multi-sensor data fusion and modelling of the deterioration processes by means of PARAFAC model. Time series data generated within this research were arranged in a data cube of dimensions samples × sensors × measuring time. The original protocol for data fusion as well as novel meta parameters, such as cumulative nested biplot, was proposed and tested. It was possible to successfully differentiate weathering trends of diverse materials on the basis of the NIR spectra and selected surface appearance indicators. A unique advantage for such visualization of the PARAFAC model output is the possibility of straightforward comparison of the degradation kinetics and deterioration trends simultaneously for all tested materials

A New Approach for Detection of Retinal Haemorrhages in Colour Fundus Images

We propose a new approach for retinal haemorrhages detection in a retinal image. Our approach is divided into three steps. Pre-processing step which consists of green and V band extraction, histogram matching, contrast enhancement and morphological opening operation is needed to improve the image quality. In the most important step, retinal haemorrhage which is a benchmark of diabetic retinopathy is extracted from colour retinal images using a two-dimensional matched filter. Finally, a post-processing step is performed to reduce the false positive of detected haemorrhages. Our approach is validated on 89 public retinal images from DIARETDB1 database. Three validation parameters, namely sensitivity, specificity and accuracy are calculated by comparing our result to its corresponding hand labelled haemorrhages image. The obtained average sensitivity, specificity and accuracy are 0.91, 0.98 and 0.98, respectively. This achievement is much better than that of other published methods

Assessing primate’s pelage colour using RGB method in Malayan Pale-thighed Surili (Presbytis siamensis siamensis)

The Red, Green and Blue (RGB) colour model has been used to investigate relationships between primates' physiological and colour data. This study uses the RGB method to determine various pelage hues in white-thighed surili at different latitudes in Peninsular Malaysia. Universiti Kebangsaan Malaysia (UKM) represents the lowland while Genting Highlands and Fraser’s Hill represent the highland area. Results indicated that no significant values were found based on the sample t-test on every section of the samples except on the nose (Green). Our findings can be utilised further for systematic and population genetic studies of Presbytis siamensis siamensis in Peninsular Malaysia

Spatiotemporal Data Augmentation of MODIS-LANDSAT Water Bodies Using Generative Adversarial Networks

The monitoring of the shape and area of a water body is an essential component for many Earth science and Hydrological applications. For this purpose, these applications require remote sensing data which provides accurate analysis of the water bodies. In this thesis the same is being attempted, first, a model is created that can map the information from one kind of satellite that captures the data from a distance of 500m to another data that is captured by a different satellite at a distance of 30m. To achieve this, we first collected the data from both of the satellites and translated the data from one satellite to another using our proposed Hydro-GAN model. This translation gives us the accurate shape, boundary, and area of the water body. We evaluated the method by using several different similarity metrics for the area and the shape of the water body. The second part of this thesis involves augmenting the data that we obtained from the Hydro-GAN model with the original data and using this enriched data to predict the area of a water body in the future. We used the case study of Great Salt lake for this purpose. The results indicated that our proposed model was creating accurate area and shape of the water bodies. When we used our proposed model to generate data at a resolution of 30m it gave us better areal and shape accuracy. If we get more data at this resolution, we can use that data to better predict coastal lines, boundaries, as well as erosion monitoring

Influence of the illumination spectrum and observation angle on temperature measurements using thermochromic liquid crystals

As measurements of velocity and temperature fields are of paramount importance for analyzing heat transfer problems, the development and characterization of measuring techniques is an ongoing challenge. In this respect, optical measurements have become a powerful tool, as both quantities can be measured noninvasively. For instance, combining particle image velocimetry (PIV) and particle image thermometry (PIT) using thermochromic liquid crystals (TLCs) as tracer particles allows for a simultaneous measurement of velocity and temperature fields with low uncertainty. However, the temperature dependency of the color appearance of TLCs, which is used for the temperature measurements, is affected by several experimental parameters. In particular, the spectrum of the white light source, necessary for the illumination of TLCs, shows a greater influence on the range of color play with temperature of TLCs. Therefore, two different spectral distributions of the white light illumination have been tested. The results clearly indicate that a spectrum with reduced intensities in the blue range and increased intensities in the red range leads to a higher sensitivity for temperature measurements, which decreases the measurement uncertainty. Furthermore, the influence of the angle between illumination and observation of TLCs has been studied in detail. It is shown that the temperature measurement range of TLCs drastically decreases with an increasing angle between illumination and observation. A high sensitivity is obtained for angles in between and , promising temperature measurements with a very low uncertainty within this range. Finally, a new calibration approach for temperature measurements via the color of TLCs is presented. Based on linear interpolation of the temperature dependent value of hue, uncertainties in the range of 0.1 K are possible, offering the possibility to measure very small temperature differences. The potential of the developed approach is shown at the example of simultaneous measurements of velocity and temperature fields in Rayleigh–Bénard convection

On the application of neural networks for temperature field measurements using thermochromic liquid crystals

This study presents an investigation regarding the applicability of neural networks for temperature measurements using thermochromic liquid crystals (TLCs) and discusses advantages as well as disadvantages of common calibration approaches. For the characterization of the measurement technique, the dependency of the color of the TLCs on the temperature as well as on the observation angle and, therefore, on the position within the field of view of a color camera is analyzed in detail. In order to consider the influence of the position within the field of view on the color, neural networks are applied for the calibration of the temperature measurements. In particular, the focus of this study is on analysis of the error of temperature measurement for different network configurations as well as training methods, yielding a mean absolute deviation and a mean standard deviation in the range of 0.1 K for instantaneous measurements. On the basis of a comparison of this standard deviation to that of two further calibration approaches, it is shown that neural networks are suited for temperature measurements via the color of TLCs. Finally, the applicability of this measurement technique is illustrated at an exemplary temperature measurement in a horizontal plane of a Rayleigh-Bénard cell with large aspect ratio, which clearly shows the emergence of convective flow patterns by means of the temperature field