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We then provide an analysis on different frequency bands and brain regions to evaluate their suitability for driver vigilance estimation. Lastly, an analysis on the role of capsule attention, multimodality, and robustness to noise is performed, highlighting the advantages of our approach.Previous results demonstrated that neuromuscular electrical stimulation (NMES) with various configurations could induce different activity at both the central and peripheral levels. Although NMES generating different peripheral movements have been studied, it is still unclear whether the difference in NMES-induced cortical activity is due to movement- or stimulation- related differences. Because NMES-induced cortical activity impacts motor function recovery, it is essential to know when NMES with various configurations evoke the same movement, whether the induced cortical activity is still different. Four NMES configurations 1) Eight-let Frequency Trains, 2) Doublet frequency trains (DFT), 3) Constant-frequency trains with narrow-pulse, and 4) wide-pulse, were delivered to the right biceps brachii muscle in nine healthy young adults. We adjusted the intensities of these NMES to evoke the same elbow flexion and compared the cortical activities over sensorimotor regions. Selleck Lartesertib Our results showed that the four NMES patterns induced different beta-band Event-Related Desynchronization (ERD), with the DFT providing the strongest ERD value given the same NMES-induced elbow flexion (p less then 0.05). This difference is possibly due to NMES with different configuration activated in the amount of afferent proprioceptive fibers. Our pilot study suggests that the NMES-induced beta-band ERD may be an additional factor to consider when selecting the NMES configuration for a better motor function recovery.We present a new family of active surfaces for the semiautomatic segmentation of volumetric objects in 3D biomedical images. We represent our deformable model by a subdivision surface encoded by a small set of control points and generated through a geometric refinement process. The subdivision operator confers important properties to the surface such as smoothness, reproduction of desirable shapes and interpolation of the control points. We deform the subdivision surface through the minimization of suitable gradient-based and region-based energy terms that we have designed for that purpose. In addition, we provide an easy way to combine these energies with convolutional neural networks. Our active subdivision surface satisfies the property of multiresolution, which allows us to adopt a coarse-to-fine optimization strategy. This speeds up the computations and decreases its dependence on initialization compared to singleresolution active surfaces. Performance evaluations on both synthetic and real biomedical data show that our active subdivision surface is robust in the presence of noise and outperforms current state-of-the-art methods. In addition, we provide a software that gives full control over the active subdivision surface via an intuitive manipulation of the control points.In the low-photon imaging regime, noise in the image sensors is dominated by shot noise, best modeled statistically as Poisson distribution. In this work, we show that the Poisson likelihood function is very well matched with the Bayesian estimation of the "difference of log of contrast of pixel intensities." More specifically, our work is rooted in statistical compositional data analysis, whereby we reinterpret the Aitchison geometry as a multi-resolution analysis in the log-pixel domain. We demonstrate that the difference-log-contrast has wavelet-like properties that correspond well with the human visual system, while being robust to illumination variations. We derive a denoising technique based on an approximate conjugate prior for the latent Aitchison variable that gives rise to an explicit minimum mean squared error estimation. The resulting denoising technique preserves image contrast details that are arguably more meaningful to human vision than the pixel intensity values themselves.LiNbO3 (LN) or LiTaO3 (LT) thin plates bonded to quartz are novel types of layered substrates for temperature-compensated surface acoustic wave (TCSAW) devices. In SAW resonators with Al electrodes arranged on LT/quartz and LN/quartz substrates, improved temperature behavior due to opposite signs of temperature coefficients of frequency (TCF) in quartz substrates and LT or LN plates can be combined with high Q-factors if the quartz orientation is optimized. The quartz orientation areas that enabled high Q-factors were deduced from analyzing the quartz anisotropy and the characteristics of shear horizontally polarized (SH) waves were calculated in the optimal orientations as functions of the quartz cut angle and plate thickness. The simulation results illustrated by contour plots of the wave characteristics revealed the presence of zero lines on the plots demonstrating TCFs at resonant (TCFR) and anti-resonant (TCFA) frequencies in both layered structures. The strong quartz anisotropy explained anomalous temperature behavior of SAW resonators and the existence of LT/quartz structures with TCFA = TCFR facilitating the design of SAW devices with improved thermal stability in the passband. In the LT/quartz structures, two TCFs vanished simultaneously at certain plate thickness and quartz orientation, while in the LN/quartz zero lines did not intersect but low TCF = -(10-20) ppm/°C combined with electromechanical coupling exceeding 18% were obtained numerically in some structures. The effect of inverting the propagation direction or cut angle in one of the combined materials on the wave characteristics was discussed and numerically estimated.Organ segmentation from medical images is one of the most important pre-processing steps in computer-aided diagnosis, but it is a challenging task because of limited annotated data, low-contrast and non-homogenous textures. Compared with natural images, organs in the medical images have obvious anatomical prior knowledge (e.g., organ shape and position), which can be used to improve the segmentation accuracy. In this paper, we propose a novel segmentation framework which integrates the medical image anatomical prior through loss into the deep learning models. The proposed prior loss function is based on probabilistic atlas, which is called as deep atlas prior (DAP). It includes prior location and shape information of organs, which are important prior information for accurate organ segmentation. Further, we combine the proposed deep atlas prior loss with the conventional likelihood losses such as Dice loss and focal loss into an adaptive Bayesian loss in a Bayesian framework, which consists of a prior and a likelihood.