RESUMEN
The Sunrise missions consist of observing the magnetic field of the sun continuously for a few days from the stratosphere. In these missions, a balloon supporting a telescope and associated instrumentation, including a Tunable Magnetograph (TuMag), is lifted into the stratosphere. In the camera of this instrument, the image sensor sends its data to a Field Programmable Gate Array (FPGA) using eight transmission channels. These channels must be previously calibrated for a correct delivery of the image. For this mission, the FPGA has been exchanged for a newer and larger one, so the firmware has been adapted to the new device. In addition, the calibration algorithm has been parallelized as the main innovation of this work, taking advantage of the increase in logic resources of the new FPGA, in order to minimize the calibration time of the channels. The algorithm has been implemented specifically for this instrument without using the Input Serial Deserializer (ISERDES) Intellectual Property (IP), since this IP does not support the deserialization of the data sent by the image sensor to the FPGA.
Asunto(s)
Algoritmos , Campos Magnéticos , CalibraciónRESUMEN
Road traffic is responsible for the majority of air pollutant emissions in the cities, often presenting high concentrations that exceed the limits set by the EU. This poses a serious threat to human health. In this sense, modelling methods have been developed to estimate emission factors in the transport sector. Countries consider emission inventories to be important for assessing emission levels in order to identify air quality and to further contribute in this field to reduce hazardous emissions that affect human health and the environment. The main goal of this work is to design and implement an artificial intelligence-based (AI) system to estimate pollution and consumption of real-world traffic roads. The system is a pipeline structure that is comprised of three fundamental blocks: classification and localisation, screen coordinates to world coordinates transform and emission estimation. The authors propose a novel system that combines existing technologies, such as convolutional neural networks and emission models, to enable a camera to be an emission detector. Compared with other real-world emission measurement methods (LIDAR, speed and acceleration sensors, weather sensors and cameras), our system integrates all measurements into a single sensor: the camera combined with a processing unit. The system was tested on a ground truth dataset. The speed estimation obtained from our AI algorithm is compared with real data measurements resulting in a 5.59% average error. Then these estimations are fed to a model to understand how the errors propagate. This yielded an average error of 12.67% for emitted particle matter, 19.57% for emitted gases and 5.48% for consumed fuel and energy.
Asunto(s)
Contaminantes Atmosféricos , Contaminación del Aire , Contaminación por Tráfico Vehicular , Humanos , Emisiones de Vehículos/análisis , Material Particulado/análisis , Inteligencia Artificial , Monitoreo del Ambiente/métodos , Contaminantes Atmosféricos/análisis , Contaminación del Aire/análisis , GasesRESUMEN
Organ baths have been successfully used for over a century to study the contractile or relaxation effects of drugs. Indeed, most of our understanding of vascular pharmacology is based on such in vitro studies. However, multiple parallel organ baths that require mechanical transduction consume relatively large amounts of drugs, gases, and buffers, and they take up a considerable bench space. In addition, such experiments have a high demand in terms of cost and animals, and the tissue preparation is labor intensive and slow. For these reasons, organ baths are no longer in the front line of industrial pharmacological research and they have almost disappeared from most academic laboratories. We have developed a very simple system, which can be implemented virtually in any laboratory, for the automatic analyses of rat aorta ring contraction based on optical methods and using multi-well plates. Rat aorta rings (≈0.5 mm wide) were situated in 96-multi-well plates, and the luminal vessel areas were continuously monitored using a USB camera driven by newly developed algorithms. Liquids were handled using multichannel pipettes, although these procedures can be automated for drug screening. The concentration-response curves obtained were similar to those reported in the literature using traditional force transduction techniques on isolated tissues. This system can also be used with other tissue preparations and for simultaneous fluorescence measurements. The new system described here offers a simple, cheap, and reliable alternative to the classic organ bath system.
Asunto(s)
Aorta/fisiología , Técnicas In Vitro , Vasoconstricción , Animales , Aorta/efectos de los fármacos , Broncoconstrictores/farmacología , Masculino , Cloruro de Metacolina/farmacología , Agonistas Muscarínicos/farmacología , Nitroprusiato/farmacología , Fenilefrina/farmacología , Cloruro de Potasio/farmacología , Ratas Sprague-Dawley , Tráquea/efectos de los fármacos , Tráquea/fisiología , Vasoconstricción/efectos de los fármacos , Vasoconstrictores/farmacología , Vasodilatadores/farmacologíaRESUMEN
Isometric or isotonic transducers have traditionally been used to study the contractile/relaxation effects of drugs on isolated tissues. However, these mechanical sensors are expensive and delicate, and they are associated with certain disadvantages when performing experiments in the laboratory. In this paper, a method that uses an image sensor to measure the contractile effect of drugs on blood vessel rings and other luminal organs is presented. The new method is based on an image-processing algorithm, and it provides a fast, easy and non-expensive way to analyze the effects of such drugs. In our tests, we have obtained dose-response curves from rat aorta rings that are equivalent to those achieved with classical mechanic sensors.