BackgroundThe aim of the study was to examine the differences of boredom, pain, and surprise. In addition to that, it was conducted to propose approaches for emotion recognition based on physiological signals.MethodsThree emotions, boredom, pain, and surprise, are induced through the presentation of emotional stimuli and electrocardiography (ECG), electrodermal activity (EDA), skin temperature (SKT), and photoplethysmography (PPG) as physiological signals are measured to collect a dataset from 217 participants when experiencing the emotions. Twenty-seven physiological features are extracted from the signals to classify the three emotions. The discriminant function analysis (DFA) as a statistical method, and five machine learning algorithms (linear discriminant analysis (LDA), classification and regression trees (CART), self-organizing map (SOM), Naïve Bayes algorithm, and support vector machine (SVM)) are used for classifying the emotions.ResultsThe result shows that the difference of physiological responses among emotions is significant in heart rate (HR), skin conductance level (SCL), skin conductance response (SCR), mean skin temperature (meanSKT), blood volume pulse (BVP), and pulse transit time (PTT), and the highest recognition accuracy of 84.7 % is obtained by using DFA.ConclusionsThis study demonstrates the differences of boredom, pain, and surprise and the best emotion recognizer for the classification of the three emotions by using physiological signals.

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Flexible electronic devices which are lightweight, thin and bendable have attracted increasing attention in recent years. In particular, solution processes have been spotlighted in the field of flexible electronics, since they provide the opportunity to fabricate flexible electronics using low-temperature processes at low-cost with high throughput. However, there are few reports which describe the characteristics of electronic devices on flexible substrates. In this study, we fabricated flexible thin-film transistors (TFTs) on plastic substrates with channel layers formed by the spin-coating of ZnO nanoparticles and investigated their electrical properties in the flat and bent states. To the best of our knowledge, this study is the first attempt to fabricate fully functional ZnO TFTs on flexible substrates through the solution process. The ZnO TFTs showed n-channel device characteristics and operated in enhancement mode. In the flat state, a representative ZnO TFT presented a very low field-effect mobility of 1.2 x 10(-5) cm(2) V(-1) s(-1), while its on/off ratio was as high as 1.5 x 10(3). When the TFT was in the bent state, some of the device parameters changed. The changes of the device parameters and the possible reasons for these changes will be described. The recovery characteristics of the TFTs after being subjected to cyclic bending will be discussed as well.

In this work, the capacitance characteristics of gold nanoparticle-embedded metal-oxide-semiconductor (MOS) capacitors with Al 2 O 3 control oxide layers are investigated. The capacitance versus voltage (C-V ) curves obtained for a representative MOS capacitor embedded with gold nanoparticles synthesized by the colloidal method exhibit large flat-band voltage shifts, which indicate the presence of charge storages in the gold nanoparticles. Their hysteresis characteristics are dependent on the voltage sweep range. The clockwise hysteresis and rightward shift of the flat band voltages observed from the C-V curves imply that electrons are trapped in a floating gate layer consisting of the gold nanoparticles present between SiO 2 and Al 2 O 3 layers in the MOS capacitor, and that these trapped electrons originate from the top electrode. In addition, the characteristics of the capacitance versus time curves for the gold nanoparticle-embedded MOS capacitor are discussed in this paper.

Resistance switching memory devices constructed on flexible plastic substrates via the spin-coating of titanium oxide solution were characterized in this study. The resistance switching memory device exhibited a ratio of the high resistance to low resistance states of more than 10 2 , and this large resistance ratio was maintained even after 10 4 s. These memory characteristics are comparable to those of resistance switching memory devices based on titanium oxide films deposited on Si substrates. Moreover, the endurance of the flexible memory device investigated by means of a continuous substrate bending test revealed that the ratio of the high resistance to low resistance states was negligibly changed up to two hundred cycles. Its resistance switching characteristics were not degraded by the bending of the substrate, due to its short length channel and the high ductility of the electrode.

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We report an enhancement in light emission efficiency form Si nanocrystal (NC) light-emitting diodes (LEDs) via surface plasmons (SPs) by employing Au nanoparticles (NPs). Photoluminescence intensity of Si NCs with Au NPs was enhanced by 2 factors of magnitude due to the strong coupling of Si NCs and SP resonance modes of Au NPs. The electrical characteristics of Si NC LED were significantly improved, which was attributed to an increase in an electron injection into the Si NCs due to the formation of inhomogeneous Schottky barrier at the SiC-indium tin oxide interface. Moreover, light output power from the Si NC LED was enhanced by 50% due to both SP coupling and improved electrical properties. The results presented here can provide a very promising way to significantly enhance the performance of Si NC LED.

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