Identifying patient posture while they are lying in bed is an important task in medical applications such as monitoring a patient after a surgical intervention, sleep supervision to identify behavioral and physiological markers, or for bedsore prevention. An acceptable strategy to identify the patient’s position is the classification of images created from a grid of pressure sensors located in the bed. These samples can be arranged based on supervised learning methods. Usually, image conditioning is required before images are loaded into a learning method to increase classification accuracy. However, continuous monitoring of a person requires large amounts of time and computational resources if complex pre-processing algorithms are used. So, the problem is to classify the image posture of patients with different weights, heights, and positions by using minimal sample conditioning for a specific supervised learning method. In this work, it is proposed to identify the patient posture from pressure sensor images by using well-known and simple conditioning techniques and selecting the optimal texture descriptors for the Support Vector Machine (SVM) method. This is in order to obtain the best classification and to avoid image over-processing in the conditioning stage for the SVM. The experimental stages are performed with the color models Red, Green, and Blue (RGB) and Hue, Saturation, and Value (HSV). The results show an increase in accuracy from 86.9% to 92.9% and in kappa value from 0.825 to 0.904 using image conditioning with histogram equalization and a median filter, respectively.

Constant monitoring is necessary for powdery mildew prevention in field crops because, as a fungal disease, it modifies the green pigments of the leaves and is responsible for production losses. Therefore, there is a need for solutions that assure early disease detection to realize proactive control and management of the disease. The methodology currently used for the identification of powdery mildew disease uses RGB leaf images to detect damage levels. In the early stage of the disease, no symptoms are visible, but this is a point at which the disease can be controlled before the symptoms appear. This study proposes the implementation of a support vector machine to identify powdery mildew on cucurbit plants using RGB images and color transformations. First, we use an image dataset that provides photos covering five growing seasons in different locations and under natural light conditions. Twenty-two texture descriptors using the gray-level co-occurrence matrix result are calculated as the main features. The proposed damage levels are ’healthy leaves’, ’leaves in the fungal germination phase’, ’leaves with first symptoms’, and ’diseased leaves’. The implementation reveals that the accuracy in the L * a * b color space is higher than that when using the combined components, with an accuracy value of 94% and kappa Cohen of 0.7638.

In photovoltaic systems, the use of DC/DC converter is necessary to provide wide transformation ranges on voltages and currents, as well as, the capacity to supply currents higher or lower than the supply current provided by the photovoltaic modules. Another aspect that converters must satisfy is associated with load consumption requirements such as high current levels for applications such as LED lighting, electric cars and battery banks. However, high currents lead to higher conduction losses. Other requirement in these systems is to assure that power processing by the converter be performed efficiently. This paper proposes the use of switched converter based on the Reduced Redundant Power Processing (R2P2) concept to satisfying high current levels and low voltages in a photovoltaic system, where the input provided by the photovoltaic panels is maintained continuous. Here, the design procedure converter is developed and the description of the operation modes during the switching process, as well as, the average model. The results obtained by simulation and experimental measurements are presented.

The current energy requirements of the residential, industrial, and commercial sectors are met by direct current (DC) electronic loads. Distributed generation (DG) systems which use photovoltaic systems, domestic wind generators, and grid integration, require maximizing energy transfer efficiency in these systems. The DC/DC converters integrated as interfaces between different sources of DG systems have the function to optimize the energy transferred to the DC bus. This paper proposes integrating a Quadratic Buck converter with reduced power processing as an interface between DG system sources and the DC bus. The design and operation of the converter in continuous conduction mode, as well as its average and linear models, are presented. The analysis of the reduced redundant power processing in the converter is also provided, along with the design of the DC bus voltage regulator.

El análisis de firmas espectrales es uno de los métodos de diagnóstico más utilizados para identificar enfermedades en las plantas. Con este fin, se deben considerar diferentes técnicas de adquisición de información para detectar los diferentes niveles de una enfermedades o plaga en particular, como en el caso de las enfermedades fúngicas. En este estudio, se consideraron plantas cucúrbitas en las cuales se identificaron tres etapas de niveles de una enfermedad fúngica que son las hojas en la etapa de germinación del hongo, hojas con primeros síntomas y hojas enfermas. Se utilizó una base de datos con firmas espectrales de hojas de calabacita. A continuación, se propone el análisis de frecuencia de las características espectrales utilizando la transformada de Fourier para extraer características de los coeficientes obtenidos y partir de bloques de clasificación con máquinas de vectores de soporte para la estimación del nivel de daño. Se demostraron precisiones de clasificación del 98.3%. Por lo tanto, este método se puede utilizar para diagnosticar el grado de daño en diferentes cultivos.