Multi-color light-emitting transistors composed of organic single crystals

We report a novel concept for multi-color light emission from an ambipolar organic single-crystal transistor using natural optical waveguides, the self-absorption effect, Davydov splitting and the unique alignment of the transition dipole moments. We used 9,10-bis-(2,2-diphenylvinyl)-anthracene single crystals to produce blue and green light from identical single-crystal transistors. We also observed red light, which corresponds to the emission from in-gap states that are caused by impurities. Importantly, each of these different colors corresponds to a distinguishable light polarization, which enables us to tune the emission color by using a light polarizer.     

Efficient global optimization with ensemble and selection of kernel functions for engineering design

In this paper, we investigate the use of multiple kernel functions for assisting single-objective Kriging-based efficient global optimization (EGO). The primary objective is to improve the robustness of EGO in terms of the choice of kernel function for solving a variety of black-box optimization problems in engineering design. Specifically, three widely used kernel functions are studied, that is, Gaussian, Matérn-3/2, and Matérn-5/2 function. We investigate both model selection and ensemble techniques based on Akaike information criterion (AIC) and cross-validation error on a set of synthetic (noiseless and noisy) and non-algebraic (aerodynamic and parameter tuning) optimization problems; in addition, the use of cross-validation-based local (i.e., pointwise) ensemble is also studied. Since all the constituent surrogate models in the ensemble scheme are Kriging models, it is possible to perform EGO since the Kriging uncertainty structure is still preserved. Through analyses of empirical experiments, it is revealed that the ensemble techniques improve the robustness and performance of EGO. It is also revealed that the use of Matérn-kernels yields better results than those of the Gaussian kernel when EGO with a single kernel is considered. Furthermore, we observe that model selection methods do not yield any substantial improvement over single kernel EGO. When averaged across all types of problem (i.e., noise level, dimensionality, and synthetic/non-algebraic), the local ensemble technique achieves the best performance.

     

Outlook and Emerging Semiconducting Materials for Ambipolar Transistors

Ambipolar or bipolar transistors are transistors in which both holes and electrons are mobile inside the conducting channel. This device allows switching among several states: the hole‐dominated on‐state, the off‐state, and the electron‐dominated on‐state. In the past year, it has attracted great interest in exotic semiconductors, such as organic semiconductors, nanostructured materials, and carbon nanotubes. The ability to utilize both holes and electrons inside one device opens new possibilities for the development of more compact complementary metal‐oxide semiconductor (CMOS) circuits, and new kinds of optoelectronic device, namely, ambipolar light‐emitting transistors. This progress report highlights the recent progresses in the field of ambipolar transistors, both from the fundamental physics and application viewpoints. Attention is devoted to the challenges that should be faced for the realization of ambipolar transistors with different material systems, beginning with the understanding of the importance of interface modification, which heavily affects injections and trapping of both holes and electrons. The recent development of advanced gating applications, including ionic liquid gating, that open up more possibility to realize ambipolar transport in materials in which one type of charge carrier is highly dominant is highlighted. Between the possible applications of ambipolar field‐effect transistors, we focus on ambipolar light‐emitting transistors. We put this new device in the framework of its prospective for general lightings, embedded displays, current‐driven laser, as well as for photonics–electronics interconnection.     

Interaction techniques in desktop virtual environment: the study of visual feedback and precise manipulation method

Gesture-based systems allow users to interact with a virtual reality application in a natural way. Visual feedback for the gesture-based interaction technique has an impact on the performance and the hand instability making the manipulation of the object less precise. This paper investigated two new interaction techniques in a virtual environment. It describes the influence of natural and non-natural virtual feedback in the selection process using the GITDVR-G interaction technique, which consists of a grasping visual feedback. The GITDVR-G was evaluated in a virtual knee surgery training system. The results showed that it was effective in terms of the task completion time, and that the participants preferred the natural grasping visual feedback. Besides that, the precise manipulation in a newly-designed interaction technique (Precise GITDVR-G) was evaluated. The Precise GITDVR-G includes a normal manipulation mode and a precise manipulation mode that can be triggered by hand gestures. During the precise manipulation mode, an inset view will appear and move with the selected object to provide a better view to users, while the movements of the virtual hand are scaled down to improve the precision. Four different configurations of the precise manipulation technique were evaluated, and the results showed that the unimanual control method with an inset view performed better in terms of the task performance time and the subjective feedback. The finding suggested that the realistic virtual grasping visual feedback can be applied in a virtual hand interaction technique, and that the inset view feature is helpful in the precise manipulation.     

On efficient global optimization via universal Kriging surrogate models

In this paper, we investigate the capability of the universal Kriging (UK) model for single-objective global optimization applied within an efficient global optimization (EGO) framework. We implemented this combined UK-EGO framework and studied four variants of the UK methods, that is, a UK with a first-order polynomial, a UK with a second-order polynomial, a blind Kriging (BK) implementation from the ooDACE toolbox, and a polynomial-chaos Kriging (PCK) implementation. The UK-EGO framework with automatic trend function selection derived from the BK and PCK models works by building a UK surrogate model and then performing optimizations via expected improvement criteria on the Kriging model with the lowest leave-one-out cross-validation error. Next, we studied and compared the UK-EGO variants and standard EGO using five synthetic test functions and one aerodynamic problem. Our results show that the proper choice for the trend function through automatic feature selection can improve the optimization performance of UK-EGO relative to EGO. From our results, we found that PCK-EGO was the best variant, as it had more robust performance as compared to the rest of the UK-EGO schemes; however, total-order expansion should be used to generate the candidate trend function set for high-dimensional problems. Note that, for some test functions, the UK with predetermined polynomial trend functions performed better than that of BK and PCK, indicating that the use of automatic trend function selection does not always lead to the best quality solutions. We also found that although some variants of UK are not as globally accurate as the ordinary Kriging (OK), they can still identify better-optimized solutions due to the addition of the trend function, which helps the optimizer locate the global optimum.     

Advances in computer–human interaction for detecting facial expression using dual tree multi band wavelet transform and Gaussian mixture model

Neural Computing and Applications - In human communication, facial expressions play an important role, which carries enough information about human emotions. Last two decades, it becomes a very...     

Parallel tsunami simulation and visualization on tiled display wall using OpenGL Shading Language

Tsunami simulation consists of fluid dynamics, numerical computations, and visualization techniques. Nonlinear shallow water equations are often used to model the tsunami propagation. Tsunami inundation is modeled by adding the friction slope to the conservation of momentum. The two-step second-order finite difference MacCormack numerical method can solve these equations. It is well suited for nonlinear equations and simpler for related application development. In addition, the finite difference method can be computed in parallel. The programmable graphics hardware allows general-purpose computing on graphics processing units (GPUs) to solve the MacCormack method in parallel to speed up the simulation. Tsunami simulation data can be loaded as textures data in graphics memory, the computation processes can be written as shader programs using OpenGL Shading Language so that the operations can be computed by graphics processors in parallel. We developed different versions of the tsunami simulation and visualization programs: (i) CPU-based, and (ii) CPU–GPU collaboration to implement the MacCormack numerical method. The performance results showed that graphics hardware accelerated simulation had a significant improvement in the execution time of each computation step. Real-time simulation and visualization are made possible by the programmable shaders. Furthermore, we achieved high-performance parallel visualization on a tiled display wall with a cluster of computers.