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The EEG has showed that contains relevant information about recognition of emotional states. It is important to analyze the EEG signals to understand the emotional states not only from a time series approach but also determining the importance of the generating process of these signals, the location of electrodes and the relationship between the EEG signals. From the EEG signals of each emotional state, a functional connectivity measurement was used to construct adjacency matrices: lagged phase synchronization (LPS), averaging adjacency matrices we built a prototype network for each emotion. Based on these networks, we extracted a set node features seeking to understand their behavior and the relationship between them. We found through the strength and degree, the group of representative electrodes for each emotional state, finding differences from intensity of measurement and the spatial location of these electrodes. In addition, analyzing the cluster coefficient, degree, and strength, we find differences between the networks from the spatial patterns associated with the electrodes with the highest coefficient. This analysis can also gain evidence from the connectivity elements shared between emotional states, allowing to cluster emotions and concluding about the relationship of emotions from EEG perspective.

This paper addresses the cerebral cortex maps construction from EEG signals getting an information simplification method for an emotional state phenomenon description. Bi-dimensional density distribution of main signal features are identified and a comparison to a previous approach is presented. Feature extraction scheme is performed via windowed EEG signals Stationary Wavelet Transform with the Daubechies Family (1–10); nine temporal and spectral descriptors are computed from the decomposed signal. Recursive feature selection method based on training a Random forest classifier using a one-vs-all scheme with the full features space, then a ranking procedure via gini importance, eliminating the bottom features and restarting the entire process over the new subset. Stopping criteria is the maximum accuracy. The main contribution is the analysis of the resulting subset features as a proxy for cerebral cortex maps looking for the cognitive processes understanding from surface signals. Identifying the common location of different emotional states in the central and frontal lobes, allowing to be strong parietal and temporal lobes differentiators for different emotions.

Unsupervised learning methods are commonly used to perform the non-trivial task of uncovering structure in biological data. However, conventional approaches rely on methods that make assumptions about data distribution and reduce the dimensionality of the input data. Here we propose the incorporation of entropy related measures into the process of constructing graph-based representations for biological datasets in order to uncover their inner structure. Experimental results demonstrated the potential of the proposed entropy-based graph data representation to cope with biological applications related to unsupervised learning problems, such as metagenomic binning and neuronal spike sorting, in which it is necessary to organize data into unknown and meaningful groups.

Visualizing the cluster structure of high-dimensional data is a non-trivial task that must be able to deal with the large dimensionality of the input data. Unlike hard clustering structures, visualization of fuzzy clusterings is not as straightforward because soft clustering algorithms yield more complex clustering structures. Here is introduced the concept of membership networks, an undirected weighted network constructed based on the fuzzy partition matrix that represents a fuzzy clustering. This simple network-based method allows understanding visually how elements involved in this kind of complex data clustering structures interact with each other, without relying on a visualization of the input data themselves. Experiment results demonstrated the usefulness of the proposed method for the exploration and analysis of clustering structures on the Iris flower data set and two large and unlabeled financial datasets, which describes the financial profile of customers of a local bank.

In this work, we perform an approach to emotion recognition from Electroencephalography (EEG) single channel signals extracted in four (4) mother-child dyads experiment in developmental psychology. Single channel EEG signals are decomposed by several types of wavelets and each subsignal are processed using several window sizes by performing a statistical analysis. Finally, three types of classifiers were used, obtaining accuracy rate between 50% to 87% for the emotional states such as happiness, sadness and neutrality.

In this paper the authors are dealing with the detection of different mechanical faults (unbalance and misalignment) under a wide range of working conditions of speed and load. The conditions tested in a test bench are similar to the ones that can be found in different kinds of machines like for example wind turbines. The authors demonstrate how to take advantage of the information on vibrations from the mechanical system under study in a wide range of load and speed conditions. Using such information the prognosis and detection of faults is faster and more reliable than the one obtained from an analysis over a restricted range of working conditions (e.g. nominal).

This work presents the development of an angular resampling algorithm for applying in conditions of high speed variability, as occurs in wind turbines, and the results obtained when applied to simulated signals, bearings diagnostic test-beds and wind turbines. The results improve the accuracy of similar resampling algorithms offered by the consulted bibliography. This algorithm is part of the wind turbine diagnostic system developed by the authors.

Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets., Projects, CENIT-2008-1028, TIN2011-24046, IPT-2011-1265-020000 and DPI2009-06124-E/DPI of the Spanish Ministry of Economy and Competitiveness

Fault diagnosis in machines that work under a wide range of speeds and loads is currently an active area of research. Wind turbines are one of the most recent examples of these machines in industry. Conventional vibration analysis applied to machines throughout their operation is of limited utility when the speed variation is too high. This work proposes an alternative methodology for fault diagnosis in machines: the combination of angular resampling techniques for vibration signal processing and the use of data mining techniques for the classification of the operational state of wind turbines. The methodology has been validated over a test-bed with a large variation of speeds and loads which simulates, on a smaller scale, the real conditions of wind turbines. Over this test-bed two of the most common typologies of faults in wind turbines have been generated: imbalance and misalignment. Several data mining techniques have been used to analyze the dataset obtained by order analysis, having previously processed signals with angular resampling technique. Specifically, the methods used are ensemble classifiers built with Bagging, Adaboost, Geneneral Boosting Projection and Rotation Forest; the best results having been achieved with Adaboost using C4.5 decision trees as base classifiers.

Instruction queues consume a significant amount of power in a high-performance processor. The wakeup logic delay is also a critical timing parameter. This paper compares a commonly used CAM-based instruction queue organization with a new pointer-based design for delay and energy efficiency. A design and pre-layout of all critical structures in 70nm technology is performed for both organizations. The pointer-based design is shown to use 10 to 15 times less power than the CAM-based design, depending on queue size, for a 4-wide issue, 5GHz processor. The results also demonstrate the importance of evaluating all steps of instruction queue access: allocation, issue and wakeup rather than wakeup alone, especially for power consumption.