Miguel Macías

An Internet of Things Example: Classrooms Access Control over Near Field Communication

The Internet of Things is one of the ideas that has become increasingly relevant in recent years. It involves connecting things to the Internet in order to retrieve information from them at any time and from anywhere. In the Internet of Things, sensor networks that exchange information wirelessly via Wi-Fi, Bluetooth, Zigbee or RF are common. In this sense, our paper presents a way in which each classroom control is accessed through Near Field Communication (NFC) and the information is shared via radio frequency. These data are published on the Web and could easily be used for building applications from the data collected. As a result, our application collects information from the classroom to create a control classroom tool that displays access to and the status of all the classrooms graphically and also connects this data with social networks.

Acetic acid detection threshold in synthetic wine samples of a portable electronic nose.

Wine quality is related to its intrinsic visual, taste, or aroma characteristics and is reflected in the price paid for that wine. One of the most important wine faults is the excessive concentration of acetic acid which can cause a wine to take on vinegar aromas and reduce its varietal character. Thereby it is very important for the wine industry to have methods, like electronic noses, for real-time monitoring the excessive concentration of acetic acid in wines. However, aroma characterization of alcoholic beverages with sensor array electronic noses is a difficult challenge due to the masking effect of ethanol. In this work, in order to detect the presence of acetic acid in synthetic wine samples (aqueous ethanol solution at 10% v/v) we use a detection unit which consists of a commercial electronic nose and a HSS32 auto sampler, in combination with a neural network classifier (MLP). To find the characteristic vector representative of the sample that we want to classify, first we select the sensors, and the section of the sensors response curves, where the probability of detecting the presence of acetic acid will be higher, and then we apply Principal Component Analysis (PCA) such that each sensor response curve is represented by the coefficients of its first principal components. Results show that the PEN3 electronic nose is able to detect and discriminate wine samples doped with acetic acid in concentrations equal or greater than 2 g/L.

SEGMENTATION OF BOVINE LIVESTOCK IMAGES USING GA AND ASM IN A TWO-STEP APPROACH

In this work a system based on genetic algorithms (GA) and active shape models (ASM) is presented that, in a two-step approach, is able to accurately find a particular object within the image. The aim of the first stage is to approximately locate the object in order to serve as an initialization for the second. Following a method similar to that used by other authors, a model of the desired shape (cows in lateral position) is constructed using PDM, and later the search within the image is carried out based on instances of that model, and using genetic algorithm techniques, for which several objective functions are suggested. In the second stage we attempt to make a fine adjustment of our instance to the object in the image, starting from the result of the previous process. Consequently, we propose two different techniques compared afterwards: deformable contours (specifically active shape models) and a restricted genetic search. Finally, the system is tested over a database of 309 animal images, and results are presented.

A Comparative Study of Two Neural Models for Cloud Screening of Iberian Peninsula Meteosat Images

In this work we make a comparative study of the results obtained in the automatic interpretation of the Iberian Peninsula Meteosat images by means of neural networks techniques, in particular, multi-layer perceptrons and self organizing maps. The interpretation of these images implies their segmentation in the classes SEA (S), LAND (L), LOW CLOUDS (CL), MIDDLE CLOUDS (CM), HIGH CLOUDS (CH) and CLOUDS WITH VERTICAL GROWTH (CV).

Consistent performance measurement of a system to detect masses in mammograms based on blind feature extraction

BackgroundBreast cancer continues to be a leading cause of cancer deaths among women, especially in Western countries. In the last two decades, many methods have been proposed to achieve a robust mammography‐based computer aided detection (CAD) system. A CAD system should provide high performance over time and in different clinical situations. I.e., the system should be adaptable to different clinical situations and should provide consistent performance.MethodsWe tested our system seeking a measure of the guarantee of its consistent performance. The method is based on blind feature extraction by independent component analysis (ICA) and classification by neural networks (NN) or SVM classifiers. The test mammograms were from the Digital Database for Screening Mammography (DDSM). This database was constructed collaboratively by four institutions over more than 10 years. We took advantage of this to train our system using the mammograms from each institution separately, and then testing it on the remaining mammograms. We performed another experiment to compare the results and thus obtain the measure sought. This experiment consists in to form the learning sets with all available prototypes regardless of the institution in which them were generated, obtaining in that way the overall results.ResultsThe smallest variation from comparing the results of the testing set in each experiment (performed by training the system using the mammograms from one institution and testing with the remaining) with those of the overall result, considering the success rate for an intermediate decision maker threshold, was roughly 5%, and the largest variation was roughly 17%. But, if we considere the area under ROC curve, the smallest variation was close to 4%, and the largest variation was about a 6%.ConclusionsConsidering the heterogeneity in the datasets used to train and test our system in each case, we think that the variation of performance obtained when the results are compared with the overall results is acceptable in both cases, for NN and SVM classifiers. The present method is therefore very general in that it is able to adapt to different clinical situations and provide consistent performance.

GA Techniques Applied to Contour Search in Images of Bovine Livestock

In this work a system based on genetic algorithms is presented that generates valid initializations for deformable models methods. Following a systematics similar to that used by other authors, a model of the shape we are looking for (cows in lateral position) is constructed using PDM, and later the search within the image is made based on instances of that model, and using genetic algorithms techniques. Since we have color images, several objective functions are suggested that take advantage of this information, which are tested later over a database of 309 animal images taken directly in the field.

Application of ANN techniques to automated identification of bovine livestock

In this work a classification system is presented that, taking lateral images of cattle as inputs, is able to identify the animals and classify them by breed into previously learnt classes. The system consists of two fundamental parts. In the first one, a deformable-model-based preprocessing of the image is made, in which the contour of the animal in the photograph is sought, extracted, and normalized. Next, a neural classifier is presented that, supplemented with a decision-maker at its output, makes the distribution into classes. In the last part, the results obtained in a real application of this methodology are presented.

On the capability of GAs to obtain families of codes with good correlation properties

In the last years, some works have appeared that propose the use of Genetic Algorithms (GAs) to obtain families of binary sequences with good correlation properties. In this paper, one of those approaches is analyzed with a double objective. First, the results obtained by GAs are compared with a set of optimal Kasami sequences, one of the best solutions known until now. Second, the convenience of considering, in the GA objective function the filtering effect of the transducers, a process that notably increases the total computing, is investigated. Finally, the performance of one of the families obtained with the GA is shown applied to a particular sensory system based on ultrasonic technology.

Application of Repeated GA to Deformable Template Matching in Cattle Images

In this work, a system that enables accurate location of contours within outdoors–taken digital images is presented. This system is based on the optimization of an objective function involving a selective edge detector and a distance map extractor, applied sequentially to the original image. In order to guarantee a certain probability of success, the repeated execution of GA is applied. The performance of the system is tested against a database of 138 images of cows in lateral position, obtaining a rate of successes up to 92 % in the best of the cases.

Large neural net simulation under Beowulf-like systems

In this work we broach the problem of large neural network simulation using low-cost distributed systems. We have developed for the purpose high-performance client-server simulation software, where the server runs on a multiprocessor Beowulf system. On the basis of a performance analysis, we propose an estimator for the simulation time.