Adrião Duarte Dória Neto

Relating De Morgan triples with Atanassov's intuitionistic De Morgan triples via automorphisms

In this paper the relation between De Morgan triples on the unit interval and Atanassovs intuitionistic De Morgan triples is presented, showing how to obtain, in a canonical way, Atanassovs intuitionistic De Morgan triples from De Morgan triples. Moreover, we also show that the automorphisms on the unit interval and on L∗ (the intuitionistic value lattice) are in one-to-one correspondence and how automorphisms on L∗ act on Atanassovs intuitionistic De Morgan triples. It is also proved that the action of automorphisms and the canonical construction of De Morgan triples on L∗ commutes.

Synaptic Homeostasis and Restructuring across the Sleep-Wake Cycle

Sleep is critical for hippocampus-dependent memory consolidation. However, the underlying mechanisms of synaptic plasticity are poorly understood. The central controversy is on whether long-term potentiation (LTP) takes a role during sleep and which would be its specific effect on memory. To address this question, we used immunohistochemistry to measure phosphorylation of Ca2+/calmodulin-dependent protein kinase II (pCaMKIIα) in the rat hippocampus immediately after specific sleep-wake states were interrupted. Control animals not exposed to novel objects during waking (WK) showed stable pCaMKIIα levels across the sleep-wake cycle, but animals exposed to novel objects showed a decrease during subsequent slow-wave sleep (SWS) followed by a rebound during rapid-eye-movement sleep (REM). The levels of pCaMKIIα during REM were proportional to cortical spindles near SWS/REM transitions. Based on these results, we modeled sleep-dependent LTP on a network of fully connected excitatory neurons fed with spikes recorded from the rat hippocampus across WK, SWS and REM. Sleep without LTP orderly rescaled synaptic weights to a narrow range of intermediate values. In contrast, LTP triggered near the SWS/REM transition led to marked swaps in synaptic weight ranking. To better understand the interaction between rescaling and restructuring during sleep, we implemented synaptic homeostasis and embossing in a detailed hippocampal-cortical model with both excitatory and inhibitory neurons. Synaptic homeostasis was implemented by weakening potentiation and strengthening depression, while synaptic embossing was simulated by evoking LTP on selected synapses. We observed that synaptic homeostasis facilitates controlled synaptic restructuring. The results imply a mechanism for a cognitive synergy between SWS and REM, and suggest that LTP at the SWS/REM transition critically influences the effect of sleep: Its lack determines synaptic homeostasis, its presence causes synaptic restructuring.

New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM)

This study proposes new developmental stages for Callithrix jacchus, using K-Means algorithm and an artificial neural network-self-organising maps (SOM) as computational tools, based on weight and age. Eight developmental stages are proposed: Infant I, II and III, Juvenile I and II, Sub adult, Young adult and Older adult. This classification is consistent with the first appearance of several behavioural and physiological characteristics and thus may have generality in defining critical developmental periods. It also reveals differences in male and female development and establishes a stage for the onset of the final adult life cycle. This classification is also important to understanding the biology of the ontogenetic development of common marmosets, providing new insights for the management and care of captive animals and improving age estimate indicators when specimens are captured in long term monitoring of free ranging groups.

Creating an ensemble of diverse support vector machines using Adaboost

Support vector machines are one of the most employed methods of pattern classification, and the Adaboost algorithm is an effective way of improving the performance of the weak learners that compose the ensemble. In this article, we propose to create an Adaboost-based ensemble of SVM, by altering the Gaussian width parameter of the RBF-SVM. Using data sets from the UCI repository, we made tests to evaluate the algorithm.

Classification System of Pathological Voices Using Correntropy

This paper proposes the use of a similarity measure based on information theory called correntropy for the automatic classification of pathological voices. By using correntropy, it is possible to obtain descriptors that aggregate distinct spectral characteristics for healthy and pathological voices. Experiments using computational simulation demonstrate that such descriptors are very efficient in the characterization of vocal dysfunctions, leading to a success rate of 97% in the classification. With this new architecture, the classification process of vocal pathologies becomes much more simple and efficient.

Real time leak detection system applied to oil pipelines using sonic technology and neural networks

This work proposes a leak detection system using sonic technology, wavelet transform and neural networks to decompose and analyze pressure signals from oil pipelines in real time. The similarity between pressure and sound signals makes it possible to treat the first through digital filtering and wavelet decomposition together with a neural network to characterize and classify leak profiles. The leak detection system logic is embedded on 32 bit/150 MHz floating point DSPs. This system uses piezoresistive sensors, converters to the communication interface (Ethernet) and GPS devices, which are responsible for synchronizing reports and leak alarms. The DSPs code was written using ANSI C language.

A multi-level approach using genetic algorithms in an ensemble of Least Squares Support Vector Machines

GA is employed to guide the ensemble design in all aspects.The entire structure of the ensemble is optimized simultaneously.Multiple ways to generate diversity are explored.Results show the effectiveness of feature selection and ensemble pruning. Despite the ensemble systems have been shown to be an efficient method to increase the accuracy and stability of learning algorithms in recent decades, its construction has a question to be elucidated: diversity. The disagreement among the models that compose the ensemble can be generated when they are built under different circumstances, such as training dataset, parameter setting and selection of learning algorithms. The ensemble may be viewed as a structure with three levels: input space, the base components and the combining block of the components responses. We propose a multi-level approach using genetic algorithms to build the ensemble of Least Squares Support Vector Machines (LS-SVM), performing a feature selection in the input space, the parameterization and the choice of which models will compose the ensemble at the component level and finding a weight vector which best represents the importance of each classifier in the final response of the ensemble. The combination of feature selection and parameterization should help create even more diversity. In order to evaluate the performance of the proposed approach, we use some benchmarks to compare with other classification algorithms, including some change in the fitness function of our approach.

Image processing for eye detection and classification of the gaze direction

The vision has many sensors responsible for capturing information that is sent to the brain. The gaze reflects its attention, intention and interest of the brain towards the outside world. Therefore, the detection of the gaze direction is a promising alternative for the simulation programs, virtual reality applications and human-machine special communication. Cheaper devices to capture images and increase the power processing of personal computers motivate studies that allow human-machine interactivity. The application of techniques to detect the gaze direction has the possibility of improving significantly the interaction between people with motor deficiency and personal computers. The objective of this work is to provide a system that uses techniques of digital image processing to classify the gaze direction. The results show the complexity and efficiency of a system that performs not only the acquisition of images but also their classification by using artificial neural networks.

Atanassov's intuitionistic fuzzy probability and Markov chains

Fuzzy probabilities are an extension of the concept of probabilities with application in several practical problems. The former are probabilities represented through fuzzy numbers, to indicate the uncertainty in the value assigned to a probability. Moreover, Krassimir Atanassov in 1983 added an extra degree of uncertainty to classic fuzzy sets for modeling the hesitation and uncertainty about the degree of membership. This new theory of fuzzy sets is nowadays known as Atanassov intuitionistic fuzzy set theory. This work will extend the notion of fuzzy probabilities by representing probabilities through the Atanassov intuitionistic fuzzy numbers instead of fuzzy numbers.

Classification of cardiac arrhythmias using competitive networks

Information generated by sensors that collect a patients vital signals are continuous and unlimited data sequences. Traditionally, this information requires special equipment and programs to monitor them. These programs process and react to the continuous entry of data from different origins. Thus, the purpose of this study is to analyze the data produced by these biomedical devices, in this case the electrocardiogram (ECG). Processing uses a neural classifier, Kohonen competitive neural networks, detecting if the ECG shows any cardiac arrhythmia. In fact, it is possible to classify an ECG signal and thereby detect if it is exhibiting or not any alteration, according to normality.