Accurate and automatic quantitative cephalometry analysis is of great importance in orthodontics. The fundamental step for cephalometry analysis is to annotate anatomic-interested landmarks on X-ray images. Computer-aided automatic method remains to be an open topic nowadays. In this paper, we propose an efficient deep learning-based coarse-to-fine approach to realize accurate landmark detection. In the coarse detection step, we train a deep learning-based deformable transformation model by using training samples. We register test images to the reference image (one training image) using the trained model to predict coarse landmarks' locations on test images. Thus, regions of interest (ROIs) which include landmarks can be located. In the fine detection step, we utilize trained deep convolutional neural networks (CNNs), to detect landmarks in ROI patches. For each landmark, there is one corresponding neural network, which directly does regression to the landmark's coordinates. The fine step can be considered as a refinement or fine-tuning step based on the coarse detection step. We validated the proposed method on public dataset from 2015 International Symposium on Biomedical Imaging (ISBI) grand challenge. Compared with the state-of-the-art method, we not only achieved the comparable detection accuracy (the mean radial error is about 1.0-1.6mm), but also largely shortened the computation time (4 seconds per image).

In this study we propose a method to perform personal identification (PI) based on Electroencephalogram (EEG) signals, where the used network is named residual and multiscale spatio-temporal convolution neural network (RAMST-CNN). Combined with some popular techniques in deep learning, including residual learning (RL), multi-scale grouping convolution (MGC), global average pooling (GAP) and batch normalization (BN), RAMST-CNN has powerful spatio-temporal feature extraction ability as it achieves task-independence that avoids the complexity of selecting and extracting features manually. Experiments were carried out on multiple datasets, the results of which were compared with methods from other studies. The results show that the proposed method has a higher recognition accuracy even though the network it is based on is lightweight.

Brain-computer interfaces (BCIs) have been used by users to convey their intentions directly with brain signals. For example, a spelling system that uses EEGs allows letters on a display to be selected. In comparison, previous studies have investigated decoding speech information such as syllables, words from single-trial brain signals during speech comprehension, or articulatory imagination. Such decoding realizes speech recognition with a relatively short time-lag and without relying on a display. Previous magnetoencephalogram (MEG) research showed that a template matching method could be used to classify three English sentences by using phase patterns in theta oscillations. This method is based on the synchronization between speech rhythms and neural oscillations during speech processing, that is, theta oscillations synchronized with syllabic rhythms and low-gamma oscillations with phonemic rhythms. The present study aimed to approximate this classification method to a BCI application. To this end, (1) we investigated the performance of the EEG-based classification of three Japanese sentences and (2) evaluated the generalizability of our models to other different users. For the purpose of improving accuracy, (3) we investigated the performances of four classifiers: template matching (baseline), logistic regression, support vector machine, and random forest. In addition, (4) we propose using novel features including phase patterns in a higher frequency range. Our proposed features were constructed in order to capture synchronization in a low-gamma band, that is, (i) phases in EEG oscillations in the range of 2-50 Hz from all electrodes used for measuring EEG data (all) and (ii) phases selected on the basis of feature importance (selected). The classification results showed that, except for random forest, most classifiers perform similarly. Our proposed features improved the classification accuracy with statistical significance compared with a baseline feature, which is a phase pattern in neural oscillations in the range of 4-8 Hz from the right hemisphere. The best mean accuracy across folds was 55.9% using template matching trained by all features. We concluded that the use of phase information in a higher frequency band improves the performance of EEG-based sentence classification and that this model is applicable to other different users.

Photon upconversion (UC) is a technique to convert long wavelength light into short wavelength light. UC fluorescence by triplet-triplet annihilation (TTA) follows a mechanism involving two kinds of molecules as sensitizer and emitter. In this study, we constructed the photon UC dyes system that was applicable to weak excitation light and convert the red light into yellow light in high efficiency. The present result will be useful for the purpose of application to optical elements and light medical care.

Hazes with various properties spread widely across flat areas with depth continuities and corner areas with depth discontinuities. Removing haze from a single hazy image is difficult due to its ill-posed nature. To solve this problem, this study proposes a modified hybrid median filter that performs a median filter to preserve the edges of flat areas and a hybrid median filter to preserve depth discontinuity corners. Recovered scene radiance, which is obtained by removing hazy particles, restores image visibility using adaptive nonlinear curves for dynamic range expansion. Using comparative studies and quantitative evaluations, this study shows that the proposed method achieves similar or better results than those of other state-of-the-art methods.

A study on quantification of human stress using low beta waves of electroencephalography (EEG) is presented. For the very first time the importance of low beta waves as a feature for quantification of human stress is highlighted. In this study, there were twenty-eight participants who filled the Perceived Stress Scale (PSS) questionnaire and recorded their EEG in closed eye condition by using a commercially available single channel EEG headset placed at frontal site. On the regression analysis of beta waves extracted from recorded EEG, it has been observed that low beta waves can predict PSS scores with a confidence level of 94%. Consequently, when low beta wave is used as a feature with the Naive Bayes algorithm for classification of stress level, it not only reduces the computational cost by 7 folds but also improves the accuracy to 71.4%.

In this paper a new method for noise removal from single-trial event-related potentials recorded with a multi-channel electroencephalogram is addressed. An observed signal is separated into multiple signals with a multi-channel Wiener filter whose coefficients are estimated based on parameter estimation of a probabilistic generative model that locally models the amplitude of each separated signal in the time-frequency domain. Effectiveness of using prior information about covariance matrices to estimate model parameters and frequency dependent covariance matrices were shown through an experiment with a simulated event-related potential data set.

Research on emotion recognition using electroencephalogram (EEG) of subjects listening to music has become more active in the past decade. However, previous works did not consider emotional oscillations within a single musical piece. In this research, we propose a continuous music-emotion recognition approach based on brainwave signals. While considering the subject-dependent and changing-over-time characteristics of emotion, our experiment included self-reporting and continuous emotion annotation in the arousal-valence space. Fractal dimension (FD) and power spectral density (PSD) approaches were adopted to extract informative features from raw EEG signals and then we applied emotion classification algorithms to discriminate binary classes of emotion. According to our experimental results, FD slightly outperformed PSD approach both in arousal and valence classification, and FD was found to have the higher correlation with emotion reports than PSD. In addition, continuous emotion recognition during music listening based on EEG was found to be an effective method for tracking emotional reporting oscillations and provides an opportunity to better understand human emotional processes.

The combination of a halogen-free solvent 1,2,4-trimethylbenzene and unmodified fullerene potentially provides a way to develop environmentally-friendly and cost-effective solution-processed organic photocells. In this paper, the thermal annealing effect on the optical absorption spectra in poly(3-hexylthiophene):unmodified-C$_{60}$ composites with various compositions is reported. It is found that the onset temperature of the absorption spectrum change is higher in the composites with higher fullerene content. It is speculated that strong interaction between the polymer main chain and C$_{60}$ tends to suppress the reorientation of polymer main chains in a composite with high C$_{60}$ content.

In this letter, we propose an improved single image haze removal algorithm using image segmentation. It can effectively resolve two common problems of conventional algorithms which are based on dark channel prior: halo artifact and wrong estimation of atmospheric light. The process flow of our algorithm is as follows. First, the input hazy image is over-segmented. Then, the segmentation results are used for improving the conventional dark channel computation which uses fixed local patches. Also, the segmentation results are used for accurately estimating the atmospheric light. Finally, from the improved dark channel and atmospheric light, an accurate transmission map is computed allowing us to recover a high quality haze-free image.

This letter presents a method of adding a virtual halo effect to an object of interest in video sequences. A modified graph-cut segmentation algorithm extracts object layers. The halo is modeled by the accumulation of gradually changing Gaussians. With a synthesized blooming effect, the experimental results show that the proposed method conveys realistic halo effect.

As sensitive magnetic sensors, magnetometers based on superconducting quantum interference devices can be used for the detection of weak magnetic fields. These signals can be generated by diverse origins, for example, brain electric activity, myocardium electric activity, and nuclear precession of hydrogen protons. In addition, weak current induced in the low-temperature detectors, for example, transition-edge sensors can be detected using SQUIDs. And, change of magnetic flux quantum generated in a superconducting ring can be detected by SQUID, which can be used for realization of mechanical force. Thus, SQUIDs are key elements in precision metrology. In Korea, development of low-temperature SQUIDs based on Nb-Josephson junctions was started in late 1980s, and Nb-based SQUIDs have been used mainly for biomagnetic measurements; magnetocardiography and magnetoencephalography. High-Tc SQUIDs, being developed in mid 1990s, were used for magnetocardiography and nondestructive evaluation. Recently, SQUID-based low-field nuclear magnetic resonance technology is under development. In this paper, we review the past progress and recent activity of SQUID applications in Korea, with focus on biomagnetic measurements.

Magnetoencephalography (MEG) measures very weak neuromagnetic signals using SQUID sensors. Standard MEG analyses include averaged waveforms, isofield maps and equivalent current dipoles. Beamforming MEG analyses provide us with frequency-dependent spatiotemporal information about the cerebral oscillatory changes related to not only somatosensory processing but also language processing. Language dominance is able to be evaluated using laterality of power attenuation in the low γ band in the frontal area. Neuromagnetic signals of the unilateral upper movements are able to be decoded using a support vector machine.

We developed a novel movement-imagery-based brain-computer interface (BCI) for untrained subjects without employing machine learning techniques. The development of BCI consisted of several steps. First, spline Laplacian analysis was performed. Next, time-frequency analysis was applied to determine the optimal frequency range and latencies of the electroencephalograms (EEGs). Finally, trials were classified as right or left based on β-band event-related synchronization using the cumulative distribution function of pretrigger EEG noise. To test the performance of the BCI, EEGs during the execution and imagination of right/left wrist-bending movements were measured from 63 locations over the entire scalp using eight healthy subjects. The highest classification accuracies were 84.4% and 77.8% for real movements and their imageries, respectively. The accuracy is significantly higher than that of previously reported machine-learning-based BCIs in the movement imagery task (paired t-test, p < 0.05). It has also been demonstrated that the highest accuracy was achieved even though subjects had never participated in movement imageries.

The asymmetrical halo and dual-material gate structure is used in the surrounding-gate metal-oxide-semiconductor field effect transistor (MOSFET) to improve the performance. By treating the device as three surrounding-gate MOSFETs connected in series and maintaining current continuity, a comprehensive drain current model is developed for it. The model incorporates not only channel length modulation and impact ionization effects, but also the influence of doping concentration and vertical electric field distributions. It is concluded that the device exhibits increased current drivability and improved hot carrier reliability. The derived analytical model is verified with numerical simulation.

We present a simple technique for enhancing multi-modal images. The unsharp masking (UM) is at first nonlinearized to prevent halos around large edges. This edge-preserving UM is then extended to cross-sharpening of multi-modal images where a component image is sharpened with the aid of more clear edges in another component image.

A solution-processed red-sensitive organic photoconductive device was demonstrated by using soluble nickel-phthalocyanine. We found that a ratio of four nickel-phthalocyanine regioisomers was important factor for the high optical-electrical conversion efficiency. A maximum external quantum efficiency of device of 0.83% was achieved by optimizing the device structure.

Design of the 30 nm FinFETs and Double Gate MOSFETs with the halo structure for suppressing the threshold voltage roll-off and improving the subthreshold swing at the same time is proposed for the first time. The performances of nano scale FinFETs and Double Gate MOSFETs with the halo structure are analyzed using a two-dimensional device simulator. The device characteristics, focusing especially on the threshold voltage and subthreshold slope, are investigated for the different gate length, body thickness, and halo impurity concentration. From the viewpoint of body potential control, it is made clear on how to design the halo structure to suppress the short channel effects and improve the subthreshold-slope. It is shown that by introducing the halo structure to FinFETs and Double Gate MOSFETs, nano-scale FinFETs and Double Gate MOSFETs achieve an improved S-factor and suppressed threshold voltage Vth roll-off simultaneously.

Halo doping profile is used in nanoscale surrounding-gate MOSFETs to suppress short channel effect and improve current driving capability. Analytical surface potential and threshold voltage models are derived based on the analytical solution of Poisson's equation for the fully depleted symmetric and asymmetric halo-doped MOSFETs. The validity of the analytical models is verified using 3D numerical simulation. The performance of the halo-doped MOSFETs are studied and compared with the uniformly doped surrounding-gate MOSFETs. It is shown that the halo-doped channel can suppress threshold voltage roll-off and drain-induced barrier lowering, and improve carrier transport efficiency. The asymmetric halo structure is better in suppressing hot carrier effect than the symmetric halo structure.

Many studies of on-line comprehension of semantic violations have shown that the human sentence processor rapidly constructs a higher-order semantic interpretation of the sentence. What remains unclear, however, is the amount of time required to detect semantic anomalies while concatenating two words to form a phrase with very rapid stimuli presentation. We aimed to examine the time course of semantic integration in concatenating two words in phrase structure building, using magnetoencephalography (MEG). In the MEG experiment, subjects decided whether two words (a classifier and its corresponding noun), presented each for 66 ms, form a semantically correct noun phrase. Half of the stimuli were matched pairs of classifiers and nouns. The other half were mismatched pairs of classifiers and nouns. In the analysis of MEG data, there were three primary peaks found at approximately 25 ms (M1), 170 ms (M2) and 250 ms (M3) after the presentation of the target words. As a result, only the M3 latencies were significantly affected by the stimulus conditions. Thus, the present results indicate that the semantic integration in concatenating two words starts from approximately 250 ms.