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Atmospheric longwave downward radiation (Ld) is one of the significant components of net radiation (Rn), and it drives several essential ecosystem processes. Ld can be estimated with simple empirical methods using atmospheric emissivity (εa) submodels. In this study, eight εa global models were evaluated, and the one with the best performance was calibrated on a global scale using a parametric instability analysis approach. Climatic data were obtained from a dynamically consistent scale resolution of basic atmospheric quantities and computed parameters known as NCEP/NCAR reanalysis (NNR) data. The model's goodness of fit was evaluated with monthly average values of the NNR data. The εa Brutsaert model resulted in the best performance, and then it was calibrated. The seasonal global trend of Brutsaert’s εa equation calibrated coefficient ranged between 1.2 and 1.4, and five homogeneous zones with similar behavior (clusters) were found with the K-means analysis. Finally, the calibrated Brutsaert’s εa equation improved the Rn estimation, with an error reduction, at the worldwide scale, of 64%. Meanwhile, the error reduction for every cluster ranged from 18 to 77%. Hence, Brutsaert’s equation coefficient should not be considered a constant value for use in εa estimation, nor in time nor space.

The nature of the earth observation satellite systems attitude and orbit control systems, makes them inherently susceptible to microvibrations. The largest sources of microvibrations in the current satellite platforms market are: reaction wheels, mechanical movements inside the thermal control subsystem, the changing thermal environment, and the vibration of the satellite structure due to its flexibility. Microvibrations can be categorized according to its frequency into two classes: low-frequency and high-frequency. The first group affects the positioning accuracy of the image capture system, while the second decreases the image spatial resolution. This work address the second group. The spatial resolution of satellite on-board imaging systems has continuously increased in the last few years. This increasehas positioned microvibrations as an important factor in the payload performance budget, many times now becoming a driving limiting factor in the on-space spatial resolution of the earth-observation payloads. This effect is generally complicated to test and calibrate on-ground, due to the difficulties of simulating the real environment at integrated satellite level, and presents more variance on satellite platforms on which the quality control processes are less stringent (like NewSpace). This work proposes a lightweight multi-image super-resolution algorithm that can help coping with microvibration effects on hyperspectral payloads, resulting in an enhanced spatial and spectral image quality for the same on-board sensor and optics.

La uva de mesa (Vitis vinífera) es uno de los cultivos de mayor relevancia económica y social en Sonora, México. Debido a la precipitación escasa y la demanda evaporativa elevada de la zona, es un cultivo con un requerimiento alto de agua. Por ello su producción depende de la aplicación de riego y es importante estimar con precisión y de manera extensiva la evapotranspiración de este cultivo (ETC) para mejorar la eficiencia del riego a corto plazo. Esta investigación se realizó durante 2018 y 2019 con el objetivo de desarrollar y evaluar un modelo entre el índice de vegetación de diferencias normalizadas (NDVI) calculado con imágenes Sentinel-2, y el coeficiente de cultivo (KC) determinado con el sistema de covarianza de vórtices (Eddy Covariance, EC) como opción para estimar la ETC de un viñedo de uva de mesa en la Costa de Hermosillo, Sonora. Con los datos de NDVI y de KC del año 2018 se construyó un modelo de regresión simple con inicio forzado al origen en las coordenadas nulas (KCNDVI = 0.9467 NDVI; R2 = 0.74) como base para estimar la ETC. Al validarlo con datos diarios del 2019 se obtuvo una R2 de 0.76 y un CME de 0.11 al relacionar KC vs. KCNDVI, mientras que al relacionar ETC vs. ETC estimada se encontró una R2 de 0.92 y un CME de 0.67 mm d-1. Los resultados indicaron que la ETC puede estimarse con precisión adecuada y de manera oportuna con el modelo propuesto. Sin embargo, se encontró que el modelo puede subestimar la ETC durante la temporada de desarrollo máximo del cultivo debido a la saturación del NDVI. Mientras que en invierno cuando los valores de NDVI dependen de las características estructurales del suelo y de los restos de la poda invernal, la ETC puede sobreestimarse.

The increasing prevalence of chronic non-communicable diseases makes it a priority to develop tools for enhancing their management. On this matter, Artificial Intelligence algorithms have proven to be successful in early diagnosis, prediction and analysis in the medical field. Nonetheless, two main issues arise when dealing with medical data: lack of high-fidelity datasets and maintenance of patient's privacy. To face these problems, different techniques of synthetic data generation have emerged as a possible solution. In this work, a framework based on synthetic data generation algorithms was developed. Eight medical datasets containing tabular data were used to test this framework. Three different statistical metrics were used to analyze the preservation of synthetic data integrity and six different synthetic data generation sizes were tested. Besides, the generated synthetic datasets were used to train four different supervised Machine Learning classifiers alone, and also combined with the real data. F1-score was used to evaluate classification performance. The main goal of this work is to assess the feasibility of the use of synthetic data generation in medical data in two ways: preservation of data integrity and maintenance of classification performance.

Hyperspectral (HS) imaging (HSI) expands the number of channels captured within the electromagnetic spectrum with respect to regular imaging. Thus, microscopic HSI can improve cancer diagnosis by automatic classification of cells. However, homogeneous focus is difficult to achieve in such images, being the aim of this work to automatically quantify their focus for further image correction. A HS image database for focus assessment was captured. Subjective scores of image focus were obtained from 24 subjects and then correlated to state-of-the-art methods. Maximum Local Variation, Fast Image Sharpness block-based Method and Local Phase Coherence algorithms provided the best correlation results. With respect to execution time, LPC was the fastest.

Background: Sociodemographic data indicate the progressive increase in life expectancy and the prevalence of Alzheimer’s disease (AD). AD is raised as one of the greatest public health problems. Its etiology is twofold: on the one hand, non-modifiable factors and on the other, modifiable. Objective: This study aims to develop a processing framework based on machine learning (ML) and optimization algorithms to study sociodemographic, clinical, and analytical variables, selecting the best combination among them for an accurate discrimination between controls and subjects with major neurocognitive disorder (MNCD). Methods: This research is based on an observational-analytical design. Two research groups were established: MNCD group (n = 46) and control group (n = 38). ML and optimization algorithms were employed to automatically diagnose MNCD. Results: Twelve out of 37 variables were identified in the validation set as the most relevant for MNCD diagnosis. Sensitivity of 100%and specificity of 71%were achieved using a Random Forest classifier. Conclusion: ML is a potential tool for automatic prediction of MNCD which can be applied to relatively small preclinical and clinical data sets. These results can be interpreted to support the influence of the environment on the development of AD.

A study was performed to evaluate the clumped model in estimating olive orchard evapotranspiration (ETa) using meteorological data and high-resolution thermal infrared (TIR) imagery obtained from a camera onboard an unmanned aerial vehicle (UAV). An experimental site was established within a superintensive drip-irrigated olive (cv. Arbequina) orchard located in the Pencahue Valley (35.49° S, 71.73°W, and 85 m above sea level), Maule Region, Chile. UAV-based TIR images were collected to obtain the land surface temperature at a very high resolution on 12 clear-sky days during the 2015–2016 growing season. Measurements of the latent heat flux (LE) obtained from an eddy covariance (EC) system were analyzed to assess the clumped model. In addition, submodels to calculate the net radiation (Rn) and soil heat flux (G) were evaluated using a four-way net radiometer and soil heat flux plates with soil thermocouples, respectively. Comparisons indicated that the root mean square error (RMSE) and mean absolute error (MAE) values for LE were 37 and 27 W m−2, respectively, while those for ETa were 0.44 and 0.35 mm day−1, respectively. Both UAV-based values for Rn and G were estimated with RMSE

Made available in DSpace on 2021-06-25T12:31:15Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-01-03 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) The acai palm (Euterpe oleracea Mart.) is a fruit from the Amazon that is originally found in flooded areas. Over recent years, its marketability has gained significant interest in Brazil and abroad because of its agronomic, nutritional and economic potential. However, there is a lack of technical-scientific information about crop water requirements for irrigation management during the reproductive phase of acai palm. Therefore, the aim of this research was to estimate the water requirements (crop evapotranspiration (ETc), single (Kc) and dual (Kcb + Ke) crop coefficients) of acai palm in the Eastern Amazon (Brazil) using the Bowen ratio system. A micrometeorological tower was installed in the center of an experimental area to monitor ETc and meteorological variables. Phenological development was monitored during two growing seasons. Soil water evaporation was determined on a daily scale using weighing microlysimeters. Ke and Kcb values were calculated by the ratio of soil surface evaporation and transpiration to reference evapotranspiration (ETo), respectively. Total water requirements of the acai palms were 1165 mm, with a daily average of 3.49 mm day(-1) for the growing season. The average values of Kc, Ke and Kcb for the acai palm were 1.08; 0.21 and 0.84, respectively. The findings will assist the design of irrigation management protocols for acai trees that are better tailored to satisfy crop water requirements. This will allow improved water use efficiency, ensuring tree crop sustainability. Fed Rural Univ Amazonia, Grad Program Agron, Belem, Para, Brazil Univ Estadual Paulista, Grad Program Agron Crop Prod, Jaboticabal, SP, Brazil Fed Rural Univ Amazonia, Forest Engn, Belem, Para, Brazil Fed Univ Para, Dept Agron, Altamira, Para, Brazil Fed Rural Univ Amazonia, Dept Agron, Capitao Poco, Para, Brazil Museu Paraense Emilio Goeldi, Inst Training Program, MPEG, Belem, Para, Brazil Univ Talca, Res & Extens Ctr Irrigat & Agroclimatol CITRA, Casilla 747, Talca 3460000, Chile Univ Talca, Res Program Adaptat Agr Climate Change A2C2, Fac Agr Sci, Casilla 747, Talca 3460000, Chile Fed Rural Univ Amazonia, Belem, Para, Brazil Univ Estadual Paulista, Grad Program Agron Crop Prod, Jaboticabal, SP, Brazil

We evaluated the effects of different post-harvest irrigation strategies on yield, fruit quality and quantity, and water productivity (WP) of sweet cherry trees, over a two-growing season experimen...

Mushy Halibut Syndrome (MHS) is a condition that appears in Greenland halibut and manifests itself as abnormally opaque, flaccid and jelly-like flesh. Fish affected by this syndrome show poor meat quality, which results in negative consequences for the fish industry. The research community has not carefully investigated this condition, nor novel technologies for MHS detection have been proposed. In this research work, we propose using hyperspectral imaging to detect MHS. After collecting a dataset of hyperspectral images of halibut affected by MHS, two different goals were targeted. Firstly, the estimation of the chemical composition of the samples (specifically fat and water content) from their spectral data by using constrained spectral unmixing. Secondly, supervised classification using partial least squares discriminant analysis (PLS-DA) was evaluated to identify specimens affected by MHS. The outcomes of our study suggest that the prediction of fat from the spectral data is possible, but the prediction of the water content was not found to be accurate. However, the detection of MHS using PLS-DA was precise for hyperspectral images from both fillets and whole fish, with lower bounds of 75% and 83% for precision and recall, respectively. Our findings suggest hyperspectral imaging as a suitable technology for the early screening of MHS.

Currently, intraoperative guidance tools used for brain tumor resection assistance during surgery have several limitations. Hyperspectral (HS) imaging is arising as a novel imaging technique that could offer new capabilities to delineate brain tumor tissue in surgical-time. However, the HS acquisition systems have some limitations regarding spatial and spectral resolution depending on the spectral range to be captured. Image fusion techniques combine information from different sensors to obtain an HS cube with improved spatial and spectral resolution. This paper describes the contributions to HS image fusion using two push-broom HS cameras, covering the visual and near-infrared (VNIR) [400–1000 nm] and near-infrared (NIR) [900–1700 nm] spectral ranges, which are integrated into an intraoperative HS acquisition system developed to delineate brain tumor tissue during neurosurgical procedures. Both HS images were registered using intensity-based and feature-based techniques with different geometric transformations to perform the HS image fusion, obtaining an HS cube with wide spectral range [435–1638 nm]. Four HS datasets were captured to verify the image registration and the fusion process. Moreover, segmentation and classification methods were evaluated to compare the performance results between the use of the VNIR and NIR data, independently, with respect to the fused data. The results reveal that the proposed methodology for fusing VNIR–NIR data improves the classification results up to 21% of accuracy with respect to the use of each data modality independently, depending on the targeted classification problem.

The increase of vineyard's water consumption due to the Global Warming Phenomenon (GWP) has forced the winegrowers to strengthen their irrigation and water stewardship efforts, intended for maintaining this resource's long-term sustainable use. Due to water being a limited resource, implementing the Water Footprint (WF) concept in winegrapes production provides helpful information for sustainable water stewardship. Currently, an automated version of the satellite-based METRIC (Mapping Evapotranspiration with Internalized Calibration) model, the Google Earth Engine Evapotranspiration Flux (EEFlux) platform, has been suggested as an alternative to analyzing the spatial variability of an entire field's water consumption throughout the growing season. This work aimed to evaluate the potential application of the EEFlux satellite's actual evapotranspiration (ET

Oxygen saturation (StO 2 ) measurement allows to detect different clinical conditions related with the low oxygenation of tissues or is used to monitor the quality and safety of organ transplantation. This study is focused on the visualization and measurement of StO 2 using hyperspectral imaging (HSI) through non-contact skin captures, targeting a potential real-time monitoring application. A customized acquisition system composed by a hyperspectral camera (covering the 470-900 nm spectral range) and a thermal camera was developed to capture images of hands in a non-contact fashion. An experimental procedure was established to measure the evolution of StO 2 in healthy hands where a compression of the index finger or brachial artery were performed. StO 2 measurements were performed in normal, compression, and reperfusion states. Two mathematical models with different sets of wavelengths were evaluated. The results show the proposed models, which employed two wavelengths (660 and 880 nm), obtain reliable StO 2 values, providing a potential non-contact imaging tool for StO 2 measurement.

Este artículo se encuentra disponible en la siguiente URL: https://www.mdpi.com/2071-1050/13/15/8388 Este artículo pertenece al número especial "The Application of Robotics in Sustainability Education". New technologies have offered great alternatives for education. In this context, we place robotics and programming as innovative and versatile tools that adapt to active methodologies. With the arrival of COVID-19 and lockdowns, physical resources were kept out of use, and the virtual lectures did not propose to incorporate these elements in a meaningful way. This recent situation raises as an objective of study the need to evaluate if robotics and programming are content that can be taught virtually in these circumstances, without physical resources and without face-to-face lectures. To do this, a mixed methodology consisting of questionnaires and interviews has been incorporated, aimed at primary education teachers, families, and primary education grade students. The results suggest that the virtualization of robotics and programming is a feasible and beneficial alternative for students, which allows the development of digital skills, while it is enhanced with the use of audiovisual materials and online resources. Even though face-to-face classes have other benefits not offered by virtualization, and teacher training needs to be up to the task to face this situation, it is a matter of time to respond to these situations and to guarantee a high-quality distance education.

Hyperspectral imaging (HSI) is a technology able to measure information about the spectral reflectance or transmission of light from the surface. The spectral data, usually within the ultraviolet and infrared regions of the electromagnetic spectrum, provide information about the interaction between light and different materials within the image. This fact enables the identification of different materials based on such spectral information. In recent years, this technology is being actively explored for clinical applications. One of the most relevant challenges in medical HSI is the information extraction, where image processing methods are used to extract useful information for disease detection and diagnosis. In this chapter, we provide an overview of the information extraction techniques for HSI. First, we introduce the background of HSI, and the main motivations of its usage for medical applications. Second, we present information extraction techniques based on both light propagation models within tissue and machine learning approaches. Then, we survey the usage of such information extraction techniques in HSI biomedical research applications. Finally, we discuss the main advantages and disadvantages of the most commonly used image processing approaches and the current challenges in HSI information extraction techniques in clinical applications.

A study was carried out to develop and validate models that simulate grapevine phenology of the cultivars Cabernet Sauvignon, Merlot, Chardonnay and Sauvignon Blanc growing under Mediterranean climate conditions. In this study, phenology models were developed using a monomolecular equation, where the dependent and independent variables were the Eichhorn and Lorenz (1977) phenological (ELP) scale modified by Coombe (1995) and growing degree days (GDD), respectively. From the beginning of budburst to harvest, measurements of ELP and GDD were collected weekly from 49 commercial vineyards located in the Maule Region, Chile (between 34° 40′ and 36° 33′ south latitude, 72° 38′ and 70° 18′ west longitude). The results showed significant nonlinear correlations between the GDD and ELP scale, with values of R2 ranging between 0.95 and 0.98. Moreover, the validation indicated that the phenological models were able to predict the ELP scale with values of the root mean square error (RMSE), mean absolute error (MAE) and agreement index (dr) raging between 1.6–3.0, 1.3–2.5 and 88–89%, respectively. Major disagreements were observed near the harvest stage (ELP = 40) which mainly depends on farm management.

The aim of this research was to evaluate the clumped model for estimating latent heat flux (LE) and actual evapotranspiration (ETa) over a non-water-stressed olive orchard. Additionally, submodels to compute the net radiation Rn, soil heat flux G, and canopy resistance were also included. For this objective, a database was used from an experimental unit inside a commercial superintensive drip-irrigated olive orchard located in the Pencahue Valley, Maule Region, Chile (35°23′S, 71°44′W; 96 m above sea level) during the 2009/10 and 2010/11 growing seasons. The evaluation was carried out using measurements of LE obtained from an eddy covariance (EC) system. In addition, estimated values of Rn, G, and were compared with ground-truth measurements from a four-way net radiometer, soil heat flux plates with soil thermocouples, and a portable porometer, respectively. Results indicated that the clumped model underestimated LE and ETa with errors of 11.0% and 3.0%, respectively. Values of the root-mean-square error (RMSE), mean bias error (MBE), and index of agreement dr for LE were 35 W m−2, −1.0 W m−2, and 0.96, while those for ETa were 0.48 mm day−1, 0.04 mm day−1, and 0.64, respectively. The submodels computed Rn and G with errors less than 6% and RMSE values less than 65 W m−2, while the Jarvis-type model predicted with RMSE = 41 s m−1 and MBE = 7.0 s m−1. Finally, a sensitivity analysis indicated that the ETa estimated by the clumped model was significantly affected by variations of ±30% in the values of the LAI and the minimum stomatal resistance rstmin.

Many irrigation strategies have been proposed in olive orchards to overcome both increasing water scarcity and competition for water with other sectors of society. However, threshold values of stomatal conductance (gs) and stem water potential (Ψstem) for use in designing deficit irrigation strategies have not yet been adequately defined. Thus, an experiment was conducted to determine gs and Ψstem thresholds for water stress in a super-intensive olive orchard (cv. Arbequina) located in Pencahue Valley (Maule Region, Chile) over three consecutive growing seasons. The experimental design was completely randomized with four irrigation treatments. The stem water potential (Ψstem) of the T1 treatment was maintained between − 1.4 and − 2.2 MPa, while the T2, T3, and T4 treatments did not receive irrigation from fruit set until they reached a Ψstem threshold of approximately − 3.5, − 5.0, and − 6.0 MPa, respectively. Stomatal conductance (gs), transpiration (Tl), net CO2 assimilation (An), and stem water potential (Ψstem) were measured fortnightly at midday. A significant nonlinear correlation between An and gs was used to establish different levels of water stress. Water stress was considered to be mild or absent when the gs values were greater than 0.18 mol m−2 s−1, whereas water stress was estimated to increase from moderate to severe as gs decreased significantly below 0.18 mol m−2 s−1. Similarly, water stress using Ψstem was determined to be mild or absent above − 2.0 MPa. Such categorizations should provide valuable information for maintaining trees well-watered in critical phenological phases. Fil: Ahumada Orellana, L.. Universidad de Talca; Chile Fil: Ortega Farías, S.. Universidad de Talca; Chile Fil: Poblete Echeverría, C.. Stellenbosch University; Sudáfrica Fil: Searles, Peter Stoughton. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de Catamarca. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Secretaría de Industria y Minería. Servicio Geológico Minero Argentino. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Provincia de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja; Argentina

In some applications of biomedical imaging, a linear mixture model can represent the constitutive elements (end-members) and their contributions (abundances) per pixel of the image. In this work, the extended blind end-member and abundance extraction (EBEAE) methodology is mathematically formulated to address the blind linear unmixing (BLU) problem subject to positivity constraints in optical measurements. The EBEAE algorithm is based on a constrained quadratic optimization and an alternated least-squares strategy to jointly estimate end-members and their abundances. In our proposal, a local approach is used to estimate the abundances of each end-member by maximizing their entropy, and a global technique is adopted to iteratively identify the end-members by reducing the similarity among them. All the cost functions are normalized, and four initialization approaches are suggested for the end-members matrix. Synthetic datasets are used first for the EBEAE validation at different noise types and levels, and its performance is compared to state-of-the-art algorithms in BLU. In a second stage, three experimental biomedical imaging applications are addressed with EBEAE: m-FLIM for chemometric analysis in oral cavity samples, OCT for macrophages identification in post-mortem artery samples, and hyper-spectral images for in-vivo brain tissue classification and tumor identification. In our evaluations, EBEAE was able to provide a quantitative analysis of the samples with none or minimal a priori information.

Currently, the use of hyperspectral imaging (HSI) for the inspection of microscopic samples is an emerging trend in different fields. The use of push-broom hyperspectral (HS) cameras against other HSI technologies is motivated by their high spectral resolution and their capabilities to exploit spectral ranges beyond 1000 nm. Nevertheless, using push-broom cameras in miscroscopes imposes to perform an accurate spatial scanning of the sample to collect the HS data. In this manuscript, we present a methodology to correctly set-up a push-broom HS microscope to acquire high-quality HS images. Firstly, we describe a custom 3D printed mechanical system developed to perform the spatial scanning by producing a precise linear movement of the microscope stage. Then, we discuss how the dynamic range maximisation, the focusing, the alignment and the adequate speed determination affect the overall quality of the images. Finally, we present some examples of HS data showing the most common defects that usually appear when capturing HS images using a push-broom camera, and also a set of images acquired from real microscopic samples.

The use of hyperspectral imaging for medical applications is becoming more common in recent years. One of the main obstacles that researchers find when developing hyperspectral algorithms for medical applications is the lack of specific, publicly available, and hyperspectral medical data. The work described in this paper was developed within the framework of the European project HELICoiD (HypErspectraL Imaging Cancer Detection), which had as a main goal the application of hyperspectral imaging to the delineation of brain tumors in real-time during neurosurgical operations. In this paper, the methodology followed to generate the first hyperspectral database of in-vivo human brain tissues is presented. Data was acquired employing a customized hyperspectral acquisition system capable of capturing information in the Visual and Near InfraRed (VNIR) range from 400 to 1000 nm. Repeatability was assessed for the cases where two images of the same scene were captured consecutively. The analysis reveals that the system works more efficiently in the spectral range between 450 and 900 nm. A total of 36 hyperspectral images from 22 different patients were obtained. From these data, more than 300 000 spectral signatures were labeled employing a semi-automatic methodology based on the spectral angle mapper algorithm. Four different classes were defined: normal tissue, tumor tissue, blood vessel, and background elements. All the hyperspectral data has been made available in a public repository.

Hyperspectral (HS) imaging presents itself as a non-contact, non-ionizing and non-invasive technique, proven to be suitable for medical diagnosis. However, the volume of information contained in these images makes difficult providing the surgeon with information about the boundaries in real-time. To that end, High-Performance-Computing (HPC) platforms become necessary. This paper presents a comparison between the performances provided by five different HPC platforms while processing a spatial-spectral approach to classify HS images, assessing their main benefits and drawbacks. To provide a complete study, two different medical applications, with two different requirements, have been analyzed. The first application consists of HS images taken from neurosurgical operations; the second one presents HS images taken from dermatological interventions. While the main constraint for neurosurgical applications is the processing time, in other environments, as the dermatological one, other requirements can be considered. In that sense, energy efficiency is becoming a major challenge, since this kind of applications are usually developed as hand-held devices, thus depending on the battery capacity. These requirements have been considered to choose the target platforms: on the one hand, three of the most powerful Graphic Processing Units (GPUs) available in the market; and, on the other hand, a low-power GPU and a manycore architecture, both specifically thought for being used in battery-dependent environments.

Hyperspectral (HS) imaging is a novel technique that allows for better understanding of materials, being an improvement in multiple applications. However, one of its main drawbacks is the focus assessment. This issue has already been covered for RGB images. Thus, in this study, it is going to be revised several no reference RGB image quality assessment algorithms (NR-IQA). To this aim, a HS image database was created by capturing different images of the same specimen at different working distances. NR-IQA algorithms were tested over monochromatic images extracted from the HS images. Additionally, a study through each independent wavelength was carried out. Results showed that some algorithms perform better for calibration samples and another ones for biological samples. Furthermore, focus differences were found in the initial and final wavelengths. In conclusion, HS image results are similar to the one obtained for RGB images but, there is still room for improvement.

Actual and maximum rates of evaporation (E) and evapotranspiration (ET) are important to the operation of atmospheric process models and for hydrologic and agricultural modeling. Because rates of evapotranspiration are limited by both the available energy at the surface and the availability of water, a variety of techniques can be used for estimation. The near-maximum ET under nonlimiting water availability can be closely approximated by the reference ET concept using near-surface observations of air temperature, humidity, wind speed, and solar radiation via the Penman–Monteith or a similar method. The determination of actual rates of ET when water is limiting demands a more complex approach, and often requires daily (or even more frequent) water balance data for the upper soil layers. An alternative is to measure the actual ET using micrometeorological techniques such as the eddy-covariance and Bowen ratio methods. The application of standardized calculations for the reference ET is discussed, as are iterative surface energy balance–aerodynamic combinations, which are useful in conditions where water is limiting.

A study was carried out to evaluate the METRIC (Mapping EvapoTranspiration at high Resolution with Internalized Calibration) model to estimate water requirements or actual evapotranspiration (ETa) for a drip-irrigated apple orchard located in the Maule Region, Chile (lat. 35°25'S; long. 71°23'W; 189 m above mean sea level). For estimating ETa using the METRIC model, seven satellite images (Landsat 7 ETM+) acquired during clear sky days were used from the 2012-2013 growing season. The performance of METRIC was evaluated using measurements of ETa from an Eddy Covariance system (EC) at the time of satellite overpass (11:30 h). The statistical analysis indicated that the METRIC model overestimated ETa values by about 7% with a root mean square error (RMSE), mean absolute error (MAE) and index of agreement (d) of 0.42 mm d-1, 0.29 mm d-1 and 0.88, respectively. Main errors of the METRIC model were associated with the selection of the hot pixels which were difficult to obtain for some satellite images.

Neurocognitive disorders (NCD) affect over 50 million people globally. The detection biomarkers using brain imaging or cerebrospinal fluid are expensive procedures. Blood-based biomarkers such as plasma or serum present a cost-effective alternative. The work presented in this paper is focused on the use of hyperspectral (HS) imaging (HSI) to classify plasma samples in order to discriminate between patients with major NCD and healthy control subjects. HS images of plasma samples were obtained using a SWIR (Short-Wave Infrared) camera able to capture 273 bands within the 900-2,500 nm spectral range. A preliminary HSI database was obtained with 20 major NCD samples and 20 control samples. This data was segmented and classified using pixel-wise supervised classification algorithms, achieving 75% sensitivity and 100% specificity results with the best classifier in the test set.

Hyperspectral Imaging (HSI) for dermatology applications lacks a physical model to differentiate between cancerous or non-cancerous pigmented skin lesions. In this paper the statistical properties of a set of HSI data are exploited as an alternative to this limitation. The hyperspectral dermatologic database employed in the experiments is composed by 40 noncancerous and 36 cancerous pigmented skin lesions (PSLs) obtained from 61 patients. The preliminary experiments suggest the potential of a simple statistics metrics, such as the coefficient of variation, to distinguish between cancerous and non-cancerous PSLs using hyperspectral data. A sensitivity result of 100% was achieved in the test set providing an overall accuracy classification of 80%.

The early detection of skin cancer is of crucial importance to plan an effective therapy to treat the lesion. In routine medical practice, the diagnosis is based on the visual inspection of the lesion and it relies on the dermatologists&rsquo, expertise. After a first examination, the dermatologist may require a biopsy to confirm if the lesion is malignant or not. This methodology suffers from false positives and negatives issues, leading to unnecessary surgical procedures. Hyperspectral imaging is gaining relevance in this medical field since it is a non-invasive and non-ionizing technique, capable of providing higher accuracy than traditional imaging methods. Therefore, the development of an automatic classification system based on hyperspectral images could improve the medical practice to distinguish pigmented skin lesions from malignant, benign, and atypical lesions. Additionally, the system can assist general practitioners in first aid care to prevent noncritical lesions from reaching dermatologists, thereby alleviating the workload of medical specialists. In this paper is presented a parallel pipeline for skin cancer detection that exploits hyperspectral imaging. The computational times of the serial processing have been reduced by adopting multicore and many-core technologies, such as OpenMP and CUDA paradigms. Different parallel approaches have been combined, leading to the development of fifteen classification pipeline versions. Experimental results using in-vivo hyperspectral images show that a hybrid parallel approach is capable of classifying an image of 50 &times, 50 pixels with 125 bands in less than 1 s.

Hyperspectral imaging is a multidimensional optical technique with the potential of providing fast and accurate tissue classification. The main challenge is the adequate processing of the multidimensional information usually linked to long processing times and significant computational costs, which require expensive hardware. In this study, we address the problem of tissue classification for intraoperative hyperspectral images of in vivo brain tissue. For this goal, two methodologies are introduced that rely on a blind linear unmixing (BLU) scheme for practical tissue classification. Both methodologies identify the characteristic end-members related to the studied tissue classes by BLU from a training dataset and classify the pixels by a minimum distance approach. The proposed methodologies are compared with a machine learning method based on a supervised support vector machine (SVM) classifier. The methodologies based on BLU achieve speedup factors of ~459&times, and ~429&times, compared to the SVM scheme, while keeping constant and even slightly improving the classification performance.

Current engineering bachelor degrees usually suffer from a lack of finalist content, and also a suitable orientation to the real practical professional activities. This work addresses the implementation of a project-based methodology using a fully functional modular battery management system (BMS) for “Power Electronics” practical laboratory lessons, combining the knowledge and specifications of three related subjects taught in the last course of “Automatic and Electronic Industrial Engineering” bachelor degree.

In Bachelor and Master's Thesis, it is highly advisable to involve students in research projects. Additionally, project-based approaches where several final degree theses are related could encourage the motivation of students. Especially in STEM (Science, Technology, Engineering and Mathematics) subjects, this divide-and-conquer strategy could lead in skills improvements of students in different, but related, fields. In this manuscript, we present the development of a controlled and highly stable power supply to be used as excitation of the light source of a hyperspectral imaging system.

The combination of hyperspectral imaging (HSI) and digital pathology may yield more accurate diagnosis. In this work, we propose the use of superpixels in HS images for combining regions of pixels that can be classified according to their spectral information to classify glioblastoma (GB) brain tumors in histologic slides. The superpixels are generated by a modified simple linear iterative clustering (SLIC) method to accommodate HS images. This work employs a dataset of H&amp, E (Hematoxylin and Eosin) stained histology slides from 13 patients with GB and over 426,000 superpixels. A linear support vector machine (SVM) classifier was performed on independent training, validation, and testing datasets. The results of this investigation show that the proposed method can detect GB brain tumors from non-tumor samples with average sensitivity and specificity of 87% and 81%, respectively. The overall accuracy of this method is 83%. The study demonstrates that hyperspectral digital pathology can be useful for detecting GB brain tumors by exploiting spectral information alone on a superpixel level.

Hyperspectral imaging (HSI), which acquires up to hundreds of bands, has been proposed as a promising imaging modality for digitized histology beyond RGB imaging to provide more quantitative information to assist pathologists with disease detection in samples. While digitized RGB histology is quite standardized and easy to acquire, histological HSI often requires custom-made equipment and longer imaging times compared to RGB. In this work, we present a dataset of corresponding RGB digitized histology and histological HSI of breast cancer, and we develop a conditional generative adversarial network (GAN) to artificially synthesize HSI from standard RGB images of normal and cancer cells. The results of the GAN synthesized HSI are promising, showing structural similarity (SSIM) of approximately 80% and mean absolute error (MAE) of 6 to 11%. Further work is needed to establish the ability of generating HSI from RGB images on larger datasets.

In recent years, hyperspectral imaging (HSI) has been shown as a promising imaging modality to assist pathologists in the diagnosis of histological samples. In this work, we present the use of HSI for discriminating between normal and tumor breast cancer cells. Our customized HSI system includes a hyperspectral (HS) push-broom camera, which is attached to a standard microscope, and home-made software system for the control of image acquisition. Our HS microscopic system works in the visible and near-infrared (VNIR) spectral range (400 - 1000 nm). Using this system, 112 HS images were captured from histologic samples of human patients using 20× magnification. Cell-level annotations were made by an expert pathologist in digitized slides and were then registered with the HS images. A deep learning neural network was developed for the HS image classification, which consists of nine 2D convolutional layers. Different experiments were designed to split the data into training, validation and testing sets. In all experiments, the training and the testing set correspond to independent patients. The results show an area under the curve (AUCs) of more than 0.89 for all the experiments. The combination of HSI and deep learning techniques can provide a useful tool to aid pathologists in the automatic detection of cancer cells on digitized pathologic images.

The midday stem water potential (&Psi, s) and stomatal conductance (gs) have been traditionally used to monitor the water status of cherry trees (Prunus avium L.). Due to the complexity of direct measurement, the use of infrared thermography has been proposed as an alternative. This study compares &Psi, s and gs against crop water stress indexes (CWSI) calculated from thermal infrared (TIR) data from high-resolution (HR) and low-resolution (LR) cameras for two cherry tree cultivars: &lsquo, Regina&rsquo, and &lsquo, Sweetheart&rsquo, For this purpose, a water stress&ndash, recovery cycle experiment was carried out at the post-harvest period in a commercial drip-irrigated cherry tree orchard under three irrigation treatments based on &Psi, s levels. The water status of trees was measured weekly using &Psi, s, gs, and compared to CWSIs, computed from both thermal cameras. Results showed that the accuracy in the estimation of CWSIs was not statistically significant when comparing both cameras for the representation of &Psi, s and gs in both cultivars. The performance of all evaluated physiological indicators presented similar trends for both cultivars, and the averaged differences between CWSI&rsquo, s from both cameras were 11 &plusmn, 0.27%. However, these CWSI&rsquo, s were not able to detect differences among irrigation treatments as compared to &Psi, s and gs.