No abstract

Brain–computer interfaces are systems capable of mapping brain activity to specific commands, which enables to remotely automate different types of processes in hardware devices or software applications. However, the development of brain–computer interfaces has been limited by several factors that affect their performance, such as the characterization of events in brain signals and the excessive processing load generated by the high volume of data. In this paper, we propose a method based on computational intelligence techniques to handle these problems, turning them into a single optimization problem. An artificial neural network is used as a classifier for event detection, along with an evolutionary algorithm to find the optimal subset of electrodes and data points that better represents the target event. The obtained results indicate our approach is a competitive and viable alternative for feature extraction in electroencephalograms, leading to high accuracy values and allowing the reduction of a significant amount of data.

No abstract

No abstract

No abstract

No abstract

In this paper, we proposed a system to integrate optical and electronic instrumentation devices to predict a mode-locking fiber laser response, using a remote data acquisition with processing through an artificial neural network (ANN). The system is made up of an optical spectrum analyzer (OSA), oscilloscope (OSC), polarimeter (PAX), and the data acquisition automation through transmission control protocol/internet protocol (TCP/IP). A graphic user interface (GUI) was developed for automated data acquisition with the purpose to study the operational characteristics and stability at the passively mode-locked fiber laser (figure-eight laser, F8L) output. Moreover, the evolution of the polarization state and the behavior of the pulses are analyzed when polarization is changed by proper control plate adjustments. The data is processed using deep learning techniques, which provide the characteristics of the pulse at the output. Therefore, the parameter classification-identification is in accordance with the input polarization tilt used for the laser optimization.

No abstract