A new self-organizing neural gas model based on Bregman divergences

In this paper, a new self-organizing neural gas model that we call Growing Hierarchical Bregman Neural Gas (GHBNG) has been proposed. Our proposal is based on the Growing Hierarchical Neural Gas (GHNG) in which Bregman divergences are incorporated in order to compute the winning neuron. This model has been applied to anomaly detection in video sequences together with a Faster R-CNN as an object detector module. Experimental results not only confirm the effectiveness of the GHBNG for the detection of anomalous object in video sequences but also its selforganization capabilities.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Vehicle Type Detection by Convolutional Neural Networks

In this work a new vehicle type detection procedure for traffic surveillance videos is proposed. A Convolutional Neural Network is integrated into a vehicle tracking system in order to accomplish this task. Solutions for vehicle overlapping, differing vehicle sizes and poor spatial resolution are presented. The system is tested on well known benchmarks, and multiclass recognition performance results are reported. Our proposal is shown to attain good results over a wide range of difficult situations.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Pixel Features for Self-organizing Map Based Detection of Foreground Objects in Dynamic Environments

Among current foreground detection algorithms for video sequences, methods based on self-organizing maps are obtaining a greater relevance. In this work we propose a probabilistic self-organising map based model, which uses a uniform distribution to represent the foreground. A suitable set of characteristic pixel features is chosen to train the probabilistic model. Our approach has been compared to some competing methods on a test set of benchmark videos, with favorable results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Road pollution estimation using static cameras and neural networks

Este artículo presenta una metodología para estimar la contaminación en carreteras mediante el análisis de secuencias de video de tráfico. El objetivo es aprovechar la gran red de cámaras IP existente en el sistema de carreteras de cualquier estado o país para estimar la contaminación en cada área. Esta propuesta utiliza redes neuronales de aprendizaje profundo para la detección de objetos, y un modelo de estimación de contaminación basado en la frecuencia de vehículos y su velocidad. Los experimentos muestran prometedores resultados que sugieren que el sistema se puede usar en solitario o combinado con los sistemas existentes para medir la contaminación en carreteras.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

The effect of verbal instructions in contingency learning depends on the time available to process the cue: evidence in favor of associative models

Much of the research in human contingency learning has tried to dissociate associative and inferential processes. One feature that has been regarded as relevant for these dissociations is the time available to process the cue. A brief presentation of the cue may facilitate the activation of associative processes whereas a long one may favor the activation of inferential processes. The two experiments reported here used a two-phase task. In Phase 1, four different cue-outcome relationships were programmed. In Phase 2, two of these relationships were changed. Participants knew about some of these changes through verbal instructions. The effect of these instructions was measured during Phase 2 in two groups that differed in the time available to process the cue, either 250 or 1500 ms. The results showed that the control of performance produced by verbal instructions differed depending on the time available to process the cue. Only in the 1500 ms group, the verbal instructions were able to affect what it had been learnt during Phase 1. Thus, the results are consistent with the hypothesis that a brief presentation of the cue during Phase 2 facilitates the activation of associative processesUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Proyecto MICIN Proyecto PSI 2011-2466

Improved detection of small objects in road network sequences using CNN and super resolution

The detection of small objects is one of the problems present in deep learning due to the context of the scene or the low number of pixels of the objects to be detected. According to these problems, current pre-trained models based on convolutional neural networks usually give a poor average precision, highlighting some as CenterNet HourGlass104 with a mean average precision of 25.6%, or SSD-512 with 9%. This work focuses on the detection of small objects. In particular, our proposal aims to vehicle detection from images captured by video surveillance cameras with pretrained models without modifying their structures, so it does not require retraining the network to improve the detection rate of the elements. For better performance, a technique has been developed which, starting from certain initial regions, detects a higher number of objects and improves their class inference without modifying or retraining the network. The neural network is integrated with processes that are in charge of increasing the resolution of the images to improve the object detection performance. This solution has been tested for a set of traffic images containing elements of different scales to check the efficiency depending on the detections obtained by the model. Our proposal achieves good results in a wide range of situations, obtaining, for example, an average score of 45.1% with the EfficientDet-D4 model for the first video sequence, compared to the 24.3% accuracy initially provided by the pre-trained model.This work is partially supported by the Ministry of Science, Innovation and Universities of Spain [grant number RTI2018-094645-B-I00], project name Automated detection with low-cost hardware of unusual activities in video sequences. It is also partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084, project name Detection of anomalous behaviour agents by deep learning in low-cost video surveillance intelligent systems. All of them include funds from the European Regional Development Fund (ERDF). It is also partially supported by the University of Málaga (Spain) under grants B1-2019_01, project name Anomaly detection on roads by moving cameras, and B1-2019_02, project name Self-Organizing Neural Systems for Non-Stationary Environments. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga. The authors acknowledge the funding from the Universidad de Málaga. I.G.-A. is funded by a scholarship from the Autonomous Government of Andalusia (Spain) under the Young Employment operative program [grant number SNGJ5Y6-15]. They also gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs. Funding for open access charge: Universidad de Málaga / CBUA

Blood Cell Classification Using the Hough Transform and Convolutional Neural Networks

https://doi.org/10.1007/978-3-319-77712-2_62The detection of red blood cells in blood samples can be crucial for the disease detection in its early stages. The use of image processing techniques can accelerate and improve the effectiveness and efficiency of this detection. In this work, the use of the Circle Hough transform for cell detection and artificial neural networks for their identification as a red blood cell is proposed. Specifically, the application of neural networks (MLP) as a standard classification technique with (MLP) is compared with new proposals related to deep learning such as convolutional neural networks (CNNs). The different experiments carried out reveal the high classification ratio and show promising results after the application of the CNNs.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Influence of the electric energy non-regulated market in the intensive aquaculture plants associated to cooling effluents

En este trabajo se analiza el efecto que la liberalización del mercado eléctrico tiene sobre la variación de los regímenes de temperatura del agua en plantas de acuicultura intensiva que aprovechan los efluentes de refrigeración de centrales generadoras de electricidad. Para ello se han utilizado datos de una instalación dedicada al engorde de anguilas europeas, la cual toma el agua caliente del efluente de refrigeración de la Central Térmica de Puente Nuevo (Córdoba). Los resultados indican que la liberalización del mercado del sector eléctrico tiene una influencia significativa sobre la forma y cantidad de energía generada por la Central Térmica, y por consiguiente sobre el régimen termal del efluente de refrigeración. Los niveles de temperatura en el interior de la instalación son dependientes asimismo de la temperatura del agua en el efluente de refrigeración, estimándose la disminución de los índices de crecimiento debidos a este factor en un 5%.In this paper, the effect of the electric energy non-regulated market in the water thermal regimes variation of intensive fishfarms that use the heated water for cooling of power plants is analysed. This way, data of aneel intensive rearing system was used. In this fishfarm the heated water is drawn from the cooling effluent of the Puente Nuevo power plant (Córdoba). The results show that the non-regulated market has a significant effect on the form and amount of generated energy and the thermal regime of the cooling effluent. The temperature levels in the fishfarm depend of the water temperature of cooling effluent, being estimated the decrease of the growth index in 5%

Color Space Selection for Self-Organizing Map Based Foreground Detection in Video Sequences

The selection of the best color space is a fundamental task in detecting foreground objects on scenes. In many situations, especially on dynamic backgrounds, neither grayscale nor RGB color spaces represent the best solution to detect foreground objects. Other standard color spaces, such as YCbCr or HSV, have been proposed for background modeling in the literature; although the best results have been achieved using diverse color spaces according to the application, scene, algorithm, etc. In this work, a color space and color component weighting selection process is proposed to detect foreground objects in video sequences using self-organizing maps. Experimental results are also provided using well known benchmark videos.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Methodological Advances in the Design of Photovoltaic Irrigation

In this study, an algorithm has been developed that manages photovoltaic solar energy in such a manner that all generated power is delivered to the system formed by a pump and irrigation network with compensated emitters. The algorithm is based on the daily work matrix that is updated daily by considering water and energy balances. The algorithm determines an irrigation priority for the sectors of irrigation of the farm based on programmed irrigation time and water deficits in the soil and synchronises the energy produced with the energy requirement of the hydraulic system according to the priority set for each day, obtaining the combinations of irrigation sectors appropriate to the photovoltaic power available. It takes into account the increment/decrease in the pressure of the water distribution network in response to increases/decreases in photovoltaic energy by increasing/decreasing the rotational speed of the pump, thus increasing/decreasing the power transferred to the system. The application to a real case of a 10-hectare farm divided into four sectors implies an efficient use of the energy of 26.15% per year and savings in CO2 emissions of 6.29 tonnes per year