EMOTION RECOGNITION FROM MULTI-CHANNEL EEG SIGNALS BY EXPLOITING THE DEEP BELIEF-CONDITIONAL RANDOM FIELD FRAMEWORK

Emotion Recognition From Multi-Channel EEG Signals by Exploiting the Deep Belief-Conditional Random Field Framework

Recently, much attention has been attracted to automatic emotion recognition based on multi-channel electroencephalogram (EEG) signals, with the rapid development of machine learning methods.However, IV and Instrument Stands traditional methods ignore the correlation information between different channels, and cannot fully capture the long-term dep

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Correlative imaging of ferroelectric domain walls

Abstract The wealth of properties in functional materials at the nanoscale has attracted tremendous interest over the last decades, spurring the development of ever more precise and ingenious characterization techniques.In ferroelectrics, for instance, scanning Swim Bottom probe microscopy based techniques have been used in conjunction with advance

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Photogrammetry, from the Land to the Sea and Beyond: A Unifying Approach to Study Terrestrial and Marine Environments

The series of technological advances that occurred over the past two decades allowed photogrammetry-based approaches to achieve their actual potential, giving birth to one of the most popular and applied procedures: structure from motion (SfM).The technique expanded rapidly to different environments, from the early ground-based and aerial applicati

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