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This article presents a dataset of thermographic images of terrain with antipersonnel mines to identify the presence or absence of these artifacts using machine learning and artificial vision techniques. The dataset has 2700 thermographic images acquired at different heights, using a Zenmuse XT infrared camera (7-13 µm), embedded in the DJI Matrice 100 drone. The data acquisition experiment consists of capturing aerial infrared images of a terrain where elements with characteristics similar to antipersonnel mines type legbreaker were buried. The mines were planted in the ground between 0 cm and 10 cm deep and were spread over an area of 10 m x 10 m. The drone used a flight protocol that set the trajectory, the time of the flight, the acquisition height, and the image sampling frequency. This dataset was used in "Detection of "legbreaker" antipersonnel landmines by analysis of aerial thermographic images of the soil" [7].
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Visual tracking of objects is a fundamental technology for industry 4.0, allowing the integration of digital content and real-world objects. The industrial operation known as manual cargo packing can benefit from the visual tracking of objects. No dataset exists to evaluate the visual tracking algorithms on manual packing scenarios. To close this gap, this article presents 6D-ViCuT, a dataset of images, and 6D pose ground truth of cuboids in a manual packing operation in intralogistics. The initial release of the dataset comprehends 28 sessions acquired in a space that rebuilds a manual packing zone: indoors, area of (6 × 4 × 2) m3, and warehouse illumination. The data acquisition experiment involves capturing images from fixed and mobile RGBD devices and a motion capture system while an operator performs a manual packing operation. Each session contains between 6 and 18 boxes from an available set of 10 types, with each type varying in height, width, depth, and texture. Each session had a duration in the range of 1 to 5 minutes. Each session exhibits operator speed and box type differences (box texture, size heterogeneity, occlusion).
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This article presents the capture protocol to acquire hyperspectral images, which can be used to quantify the concentration of total phosphorus in soil samples. 152 soil samples were prepared, and a hyperspectral cube made up of 145 images in the VIS-NIR bands, between 420 and 1000 nm, was obtained from each of them. The images obtained were taken with the Bayspec OCIF Series hyperspectral camera, in push-broom function, using a platform that includes an illumination system that offers a continuous spectrum in the range of interest. The samples were prepared with a soil from the Santander de Quilichao region, Cauca, Colombia, and mixed with known concentrations of P2O5 fertilizer, so that a total mass of 50 g was obtained. Each sample was deposited in a round black plastic container, 6 cm in diameter and a depth of 1 cm. The soil samples were analyzed in the laboratory to establish the concentration of total phosphorus. Therefore, the database is made up of the images associated with the hyperspectral cube of each sample, and four tables: the first describes the properties of the soil used to obtain the mixtures, the second the composition of the fertilizer used, the third describes the soil-fertilizer ratio to make up the samples, and the fourth was the laboratory analysis of the total phosphorus content of the analyzed samples.
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This article presents a dataset for thermal characterization of photovoltaic systems to identify snail trails and hot spot failures. This dataset has 277 thermographic aerial images that were acquired by a Zenmuse XT IR camera (7-13 µ m wavelength) from a DJI Matrice 100 1drone (quadcopter). Additionally, our dataset includes the next environmental measurements: temperature, wind speed, and irradiance. The experimental set up consisted in a photovoltaic array of 4 serial monocrystalline Si panels (string) and an electronic equipment emulating a real load. The conditions for images acquisition were stablished in a flight protocol in which we defined altitude, attitude, and weather conditions.
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Resumen En este artículo se presenta un sistema portátil para el monitoreo ambulatorio del ritmo cardiaco y la detección temprana de las arritmias cardiacas de mayor riesgo. El sistema consta de un sensor con tres electrodos superficiales para la captura de la señal ECG, la cual se transmite vía Bluetooth a un dispositivo móvil con Android, en donde se realiza el análisis de la señal capturada durante lapsos de 5 s. El sistema propuesto distingue entre Ritmo Normal [Ritmo Sinusal - RS), Taquicardia Ventricular [TV), Fibrilación Ventricular [FV) y Asistolia, con una precisión del 100%, 55%, 75% y 90% respectivamente. Sin embargo, el sistema puede recuperarse de los errores rápidamente en el análisis de la trama subsecuente. Este trabajo se centra en el uso de dispositivos móviles de uso cotidiano, multitarea y de fácil acceso, implementando algoritmos en el dominio del tiempo para la extracción de parámetros, los cuales son idóneos para ser usados en aplicaciones móviles principalmente por su baja carga computacional y posibilidad de ejecución en tiempo real, permitiendo la detección de anomalías cardiacas de forma automática y rápida sin la necesidad de una supervisión constante por parte de un especialista para el análisis preliminar.
Abstract This paper presents a portable system for ambulatory heart rate monitoring and early detection of cardiac arrhythmias at high risk. The system consists of a sensor with three surface electrodes to capture the ECG signal, which is transmitted via bluetooth to a mobile device with Android, where the analysis is performed of the acquired signal during a time of 5 s. The proposed system distinguishes between Normal Rhythm [Rhythm Sinus - RS), Ventricular Tachycardia [VT), Ventricular Fibrillation [VF) and Asystole with an accuracy of 100%, 55%, 75% and 90% respectively. However, the system can quickly recover from errors in the subsequent analysis frame. This work focuses on using regular mobile devices which have multitasking and easy access characteristics, implementing algorithms in time domain for extracting parameters that are suitable to use in mobile applications, mainly because of their low computational load and possibility of execution in real time, allowing the detection of cardiac abnormalities automatically and quickly without the need of constant supervision by a specialist for preliminary analysis.
Resumo Neste artigo se apresenta um sistema portátil para o monitoramento da freqüência cardíaca ambulatorial e detecção precoce das arritmias cardíacas de mais risco. O sistema possui um sensor com três eletrodos superficiais para pegar o sinal ECG, o qual é transmitido via Bluetooth para um dispositivo móvel com Android, onde se faz a análise do sinal capturado durante um período de 5 s. O sistema proposto distingue entre Normal Ritmo [Ritmo Sinusal - RS), Taquicardia Ventricular [TV), Fibrilação Ventricular [FV) e Assistolia, com uma precisão do 100%, 55%, 75% e 90%, respectivamente. Porém, o sistema pode - se recuperar rapidamente dos erros na análise do quadro subsequente. Este trabalho centra-se no uso de dispositivos móveis de utilização diária, multitarefa e utilização acessível, implementação de algoritmos no domínio do tempo para a extração de parâmetros que são adequados para utilização em aplicações móveis, principalmente pela baixa carga computacional e possibilidade de execução em tempo real, permitindo a detecção de anormalidades cardíacas numa forma automática e rápida sem a necessidade de um controlo constante por um especialista para análise preliminar.