Predicting Driver's mental workload using physiological signals: A functional data analysis approach.

This study investigates the impact of advanced driver-assistance systems on drivers' mental workload. Using a combination of physiological signals including ECG, EMG, EDA, EEG (af4 and fc6 channels from the theta band), and eye diameter data, this study aims to predict and categorize drivers' mental workload into low, adequate, and high levels. Data were collected from five different driving situations with varying cognitive demands. A functional linear regression model was employed for prediction, and the accuracy rate was calculated. Among the 31 tested combinations of physiological variables, 9 combinations achieved the highest accuracy result of 90%. These results highlight the potential benefits of utilizing raw physiological signal data and employing functional data analysis methods to understand and assess driver mental workload. The findings of this study have implications for the design and improvement of driver-assistance systems to optimize safety and performance.

Triethanolamine doped multilayer MoS2 field effect transistors.

Chemical doping has been investigated as an alternative method of conventional ion implantation for two-dimensional materials. We herein report chemically doped multilayer molybdenum disulfide (MoS2) field effect transistors (FETs) through n-type channel doping, wherein triethanolamine (TEOA) is used as an n-type dopant. As a result of the TEOA doping process, the electrical performances of multilayer MoS2 FETs were enhanced at room temperature. Extracted field effect mobility was estimated to be ∼30 cm2 V-1 s-1 after the surface doping process, which is 10 times higher than that of the pristine device. Subthreshold swing and contact resistance were also improved after the TEOA doping process. The enhancement of the subthreshold swing was demonstrated by using an independent FET model. Furthermore, we found that the doping level can be effectively controlled by the heat treatment method. These results demonstrate a promising material system that is easily controlled with high performance, while elucidating the underlying mechanism of improved electrical properties by the doping effect in a multilayered scheme.

The Influence of Cu Lattices on the Structure and Electrical Properties of Graphene Domains during Low-Pressure Chemical Vapor Deposition.

The influence of various Cu lattices on the texturing of graphene domains during low-pressure chemical vapor deposition was investigated in a large area. The results show that the sizes and shapes of graphene domains grown on Cu(111) substrates match well with those of the underlying Cu(111) domains, which seem to be quasi-single-crystalline. In contrast, on other Cu substrates such as (100) and more intermediate domains, graphene islands with poly-domains (ca. 85 %) are significantly nucleated, eventually merging into polycrystalline graphene. Within the overall channel-length range, graphene from a Cu foil shows a higher resistance compared to graphene from a Cu(111) domain, with the extracted average channel resistances being 34.51 Ω µm(-1) for Cu(111) and 66.17 Ω µm(-1) for the Cu foil.

Low-frequency noise in multilayer MoS2 field-effect transistors: the effect of high-k passivation.

Diagnosing of the interface quality and the interactions between insulators and semiconductors is significant to achieve the high performance of nanodevices. Herein, low-frequency noise (LFN) in mechanically exfoliated multilayer molybdenum disulfide (MoS2) (~11.3 nm-thick) field-effect transistors with back-gate control was characterized with and without an Al2O3 high-k passivation layer. The carrier number fluctuation (CNF) model associated with trapping/detrapping the charge carriers at the interface nicely described the noise behavior in the strong accumulation regime both with and without the Al2O3 passivation layer. The interface trap density at the MoS2-SiO2 interface was extracted from the LFN analysis, and estimated to be Nit ~ 10(10) eV(-1) cm(-2) without and with the passivation layer. This suggested that the accumulation channel induced by the back-gate was not significantly influenced by the passivation layer. The Hooge mobility fluctuation (HMF) model implying the bulk conduction was found to describe the drain current fluctuations in the subthreshold regime, which is rarely observed in other nanodevices, attributed to those extremely thin channel sizes. In the case of the thick-MoS2 (~40 nm-thick) without the passivation, the HMF model was clearly observed all over the operation regime, ensuring the existence of the bulk conduction in multilayer MoS2. With the Al2O3 passivation layer, the change in the noise behavior was explained from the point of formation of the additional top channel in the MoS2 because of the fixed charges in the Al2O3. The interface trap density from the additional CNF model was Nit = 1.8 × 10(12) eV(-1) cm(-2) at the MoS2-Al2O3 interface.