The diffusivity of 4- [[4′-(dimethylamino)phenyl]azo]benzoic acid (p-MR) in poly(methyl methacrylate) (PMMA)/toluene solution was found to be much lower than in polystyrene solution due to the hydrogen bonding interaction between the diffusant and the polymer matrix. In order to study the effect of the H-bonding interaction in a quantitative manner, the content of MMA units in the polymer matrix capable of H-bonding with p-MR was varied using mixtures of polystyrene (PS) and PMMA as well as PS/PMMA random and diblock copolymers. In the mixture or diblock copolymer systems, the diffusion behavior of the probe could be well represented by a phenomenological model which assumes the additivity of the diffusional activation barriers due to hydrodynamic interaction and H-bonding interaction. On the other hand, we observed far more effective H-bonding in random copolymers than in mixtures or in block copolymers. The diffusivity of p-MR in a solution of random copolymers with MMA content of 40% or higher was found to be similar to pure PMMA. This peculiar behavior of p-MR diffusion in random copolymers may reflect the difference in binding efficiency of the copolymers and in the microscopic environment for the diffusion path of p-MR.

The active appearance models (AAMs) provide the detailed descriptive parameters that are useful for various autonomous face analysis problems. However, they are not suitable for robust face tracking across large pose variation for the following reasons. First, they are suitable for tracking the local movements of facial features within a limited pose variation. Second, they use gradient-based optimization techniques for model fitting and the fitting performance is thus very sensitive to initial model parameters. Third, when their fitting is failed, it is difficult to obtain appropriate model parameters to re-initialize them. To alleviate these problems, we propose to combine the active appearance models and the cylinder head models (CHMs), where the global head motion parameters obtained from the CHMs are used as the cues of the AAM parameters for a good fitting or re-initialization. The good AAM parameters for robust face tracking are computed in the following manner. First, we estimate the global motion parameters by the CHM fitting algorithm. Second, we project the previously fitted 2D shape points onto the 3D cylinder surface inversely. Electronic supplementary materialThe online version of this article (http://dx.doi.org/10.1007/s11263-007-0125-1) contains supplementary material, which is available to authorized users. Third, we transform the inversely projected shape points by the estimated global motion parameters. Fourth, we project the transformed 3D points onto the input image and computed the AAM parameters from them. Finally, we treat the computed AAM parameters as the initial parameters for the fitting. Experimental results showed that face tracking combining AAMs and CHMs is more pose robust than that of AAMs in terms of 170% higher tracking rate and the 115% wider pose coverage.

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Although two-dimensional (2D) nanomaterials are promising candidates for use in memory and synaptic devices owing to their unique physical, chemical, and electrical properties, the process compatibility, synthetic reliability, and cost-effectiveness of 2D materials must be enhanced. In this context, amorphous boron nitride (a-BN) has emerged as a potential material for future 2D nanoelectronics. Therefore, we explored the use of a-BN for multilevel resistive switching (MRS) and synaptic learning applications by fabricating a complementary metal-oxide-semiconductor (CMOS)-compatible Ag/a-BN/Pt memory device. The redox-active Ag and boron vacancies enhance the mixed electrochemical metallization and valence change conduction mechanism. The synthesized a-BN switching layer was characterized using several analyses. The fabricated memory devices exhibited bipolar resistive switching with low set and reset voltages (+0.8 and −2 V, respectively) and a small operating voltage distribution. In addition, the switching voltages of the device were modeled using a time-series analysis, for which the Holt’s exponential smoothing technique provided good modeling and prediction results. According to the analytical calculations, the fabricated Ag/a-BN/Pt device was found to be memristive, and its MRS ability was investigated by varying the compliance current. The multilevel states demonstrated a uniform resistance distribution with a high endurance of up to 104 direct current (DC) cycles and memory retention characteristics of over 106 s. Conductive atomic force microscopy was performed to clarify the resistive switching mechanism of the device, and the likely mixed electrochemical metallization and valence change mechanisms involved therein were discussed based on experimental results. The Ag/a-BN/Pt memristive devices mimicked potentiation/depression and spike-timing-dependent plasticity-based Hebbian-learning rules with a high pattern accuracy (90.8%) when implemented in neural network simulations.

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