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This study presents a transparent and soft haptic actuator based on an electroactive polymer designed to create abundant haptic sensations in flexible/deformable next-generation devices and to convey them to users. The haptic sensations are created in the form of vibrations and vary depending on the amplitude and frequency of the vibrations. The haptic actuator consists of a transparent and soft dielectric layer and two transparent and soft ionic conductive layers. The dielectric layer is sandwiched between the two ionic conductive layers, and is compressed in the thickness direction when an electric field is applied to the two ionic conductive layers. When the applied electric field is removed, the proposed haptic actuators are rapidly restored to their initial configuration. Owing to this effect, the actuator can easily create vibrations that are sufficiently strong for human perception. In this study, three different dielectric elastomers (silicone rubber, poly urethane, and acrylic polymer) are used for the dielectric layer. Experiments are conducted to investigate the haptic performance of the proposed soft vibrotactile actuators using an accelerometer. In the case of the silicone rubber-based actuator, the measured acceleration at the resonant frequency (80 Hz) is 1.408 g (g = 9.8m/s2) and the response time is approximately 2.8 ms. Furthermore, the perceived intensity of the stimuli generated by the proposed actuator is verified by perceptual experiments. The results indicate that the proposed actuator can selectively stimulate human mechanoreceptors with sufficient perceptual strength.

Quantum modular multiplication circuit is one of the basic quantum computation circuits which are basic functions in quantum algorithms. However, since quantum-quantum modular multipliers require a high cost reversible modular inversion routine for modular multiplication, researchers have been unable to propose a feasible quantum-quantum modular multiplier. In this paper, we proposed efficient quantum-classical modular multipliers and the first quantum-quantum modular multipliers that do not require a reduction stage by transforming the partial product used in multiplication utilizing bit-shift operation. Then, we calculated quantum resource complexity and analyzed it compared to other quantum modular multipliers and utilized ETRI (Electronics and Telecommunications Research Institute) Qcrypton to analyze quantum resource complexity in the practical quantum computing situation. The proposed quantum modular multipliers show an improvement of 50% in terms of gates and circuit depth compared to the most recently proposed high-performance quantum modular multipliers.

Sleep staging is an important part of clinical neurology. However, it is still performed manually by technical experts and is labor-intensive and time-consuming. To overcome these obstacles in the manual sleep staging process, a large number of machine learning-based classifiers with hand-engineered features have been proposed. Additionally, combinations of a deep neural network (DNN) have been recently highlighted as the state-of-the-art classifiers in view of their effectiveness for automatic sleep staging. In spite of the existence of a large number of these types of classifiers, to-this-date, no prior DNN-based approach has attempted sleep-stage classification using pediatric electroencephalographic (EEG) signals. In this paper, we propose a novel end-to-end classifier based on a multi-domain hybrid neural network (HNN-multi) approach consisting of a convolutional neural network and bidirectional long short-term memory for automatic sleep staging with pediatric scalp EEG recordings. To find effective temporal, spatial, and domain-specific conditions, we investigated noticeable changes in the classification performance corresponding to: 1) the length of input signals; 2) the number of channels; and 3) the types of input signals in the time and frequency domains. Our HNN-based classifier yielded the best performance metrics using 30-s time series in combination with an instantaneous frequency using a 19-channel, three-stage classification, with overall accuracy, F1 score, and Cohen's Kappa, equal to 92.21%, 0.90, and 0.88, respectively. We suggest that an effective combination of temporal and spatial time-domain clues with time-varying frequency domain information plays a pivotal role in pediatric, automatic sleep staging. Sufficiently reasonable performance of our HNN-based approach coping with the highly complicated pediatric EEG signatures hopefully sheds light on the clinical feasibility of the DNN-based automatic sleep staging for pediatric neurology.

This paper presents an investigation of the electromagnetic signature and the coupling mechanism of quadcopter drones with incident electromagnetic (EM) wave and radar cross section (RCS) analysis. Coupling analysis is performed based on the dominant coupling path: when an incident EM wave with a magnitude of 50 kV/m contacts a commercial quadcopter drone, its motor power wires are identified as the dominant coupling path. Higher coupling voltages are obtained for frequencies that have large impedance values at both ends of the load on the motor power wire. This induced voltage can affect the integrated circuit chip on a printed circuit board, as well as parallel plate resonances. Furthermore, the RCS of a quadcopter drone is measured in the frequency range of 0.5-3 GHz. The internal-component vulnerability characteristics of quadcopters can spike at specific frequencies with high RCS values and can be analyzed with or without motor power wires. We verified these hypotheses via 2D inverse synthetic aperture radar images, and we analyzed the results by comparing the empirical and full-wave simulation values.

Instantaneous drowsiness ( i.e., lapse or micro-sleep) during various activities such as driving or construction causes enormous socioeconomic losses. Thus, a virtuous cycle system that monitors a subject’s drowsiness can improve work efficiency and safety. We propose a novel framework to detect instantaneous drowsiness with only a two-second length of electroencephalography (EEG). To achieve reliable performance, we use multitaper power spectral density for feature extraction along with extreme gradient boosting as a machine learning classifier. In addition, we introduce a novel phenotype labeling of instantaneous drowsiness by combining both task dependent and independent measures of alertness (psychomotor vigilance task and electrooculography technique, respectively). The results show that our techniques outperform others used in previous studies. We also identified which spectral components ( $\theta $ , $\alpha $ , and $\gamma $ ) and channels (Fp1, Fp2, T3, T4, O1, O2, and electrocardiogram) play important roles in our drowsiness detection framework. To verify the applicability for a mobile environment, we implemented our framework on a wireless EEG, as well as on a wired EEG. We hereby present our successful results.

Electrocardiogram (ECG) signals from mobile sensors are expected to increase the availability of authentication in the emerging wearable device industry. However, mobile sensors provide a relatively lower quality signal than the conventional medical devices. This paper proposes a practical authentication procedure for ECG signals that collected via one-chip-solution mobile sensors. We designed a cascading bandpass filter for noise cancellation and suggest eight fiducial features. For classification-based authentication, we use the radial basis function kernel-based support vector machine showing the best performance among nine classifiers through experimental comparisons. In spite of noisy ECG signals in mobile sensors, we achieved 4.61% of the equal error rate (EER) on a single heartbeat, and 1.87% of EER on 15 s testing time on 175 subjects, which is a reasonable result and supports the usability of low-cost ECGs for biometric authentication.