Browsing by Author "Zhang, Peng"

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    Interfacial Dynamics and Applications in Optofluidics
    Zhang, Peng (Virginia Tech, 2016-05-27)
    High quality (Q) factor whispering gallery modes (WGMs) can induce nonlinear effects in liquid droplets through mechanisms such as radiation pressure, light scattering, thermocapillarity, Kerr nonlinearity, and thermal effect. However, such nonlinear effects have yet to be thoroughly investigated and compared in the literature. In this study, we first investigate a micron-sized liquid spherical resonator and present an approximated solution for the resonator interface deformation due to the radiation pressure. We then derive an analytical approach that can exactly calculate the droplet deformation induced by the radiation pressure. The accuracy of the analytical solution is confirmed through numerical analyses based on the boundary element method. We show that the nonlinear optofluidic effect induced by the radiation pressure is stronger than the Kerr effect and the thermal effect under a large variety of realistic conditions. Using liquids with ultra-low and experimentally attainable interfacial tension, we further confirm the prediction that it may only take a few photons to produce measurable WGM resonance shift through radiation pressure induced droplet deformation. Similar to the radiation pressure, the scattering force in the droplet can induce a rotational fluid motion which also leads to the interface deformation. The interface deformation can also be produced by the thermocapillarity as a result of the WGM energy absorption and temperature increase. In this study, we provide a numerical scheme to calculate the fluid motion and quantify the nonlinearity induced by the optical scattering force and thermocapillarity. The magnitude of the optofluidic nonlinearities induced by the radiation pressure, thermocapillary effect, light scattering and Kerr effect are compared. We show that the radiation pressure due to the WGM produces the strongest nonlinear optofluidic effect.
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    Radiation pressure-induced nonlinearity in a micro-droplet
    Lee, Aram; Zhang, Peng; Xu, Yong; Jung, Sunghwan (2020-04-27)
    In recent years, some of the most interesting discoveries in science and engineering emerged from interdisciplinary areas that defy the traditional classification. One recent and extensively studied example is the advent of optomechanics that explores the radiation pressureinduced nonlinearity in a solid micro-resonator. Instead of using a solid resonator, we studied a liquid droplet resonator in which optical pressure could actively interact with the fluid interface. The droplet resonator supported high-quality whispering gallery modes along its equatorial plane, which produced a radiation pressure that counterbalances the interfacial tension, resulting in a droplet with damped harmonic oscillation. A major goal of this study was to demonstrate that such a novel and all-liquid platform could lead to a single-photon-level nonlinearity at room temperature. If successful, such a highly nonlinear system may lead to new research paradigms in photonics, fluid mechanics, as well as quantum information science. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
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    Towards Interpretable Vision Systems
    Zhang, Peng (Virginia Tech, 2017-12-06)
    Artificial intelligent (AI) systems today are booming and they are used to solve new tasks or improve the performance on existing ones. However, most AI systems work in a black-box fashion, which prevents the users from accessing the inner modules. This leads to two major problems: (i) users have no idea when the underlying system will fail and thus it could fail abruptly without any warning or explanation, and (ii) users' lack of proficiency about the system could fail pushing the AI progress to its state-of-the-art. In this work, we address these problems in the following directions. First, we develop a failure prediction system, acting as an input filter. It raises a flag when the system is likely to fail with the given input. Second, we develop a portfolio computer vision system. It is able to predict which of the candidate computer vision systems perform the best on the input. Both systems have the benefit of only looking at the inputs without running the underlying vision systems. Besides, they are applicable to any vision system. By equipped such systems on different applications, we confirm the improved performance. Finally, instead of identifying errors, we develop more interpretable AI systems, which reveal the inner modules directly. We take two tasks as examples, words semantic matching and Visual Question Answering (VQA). In VQA, we take binary questions on abstract scenes as the first stage, then we extend to all question types on real images. In both cases, we take attention as an important intermediate output. By explicitly forcing the systems to attend correct regions, we ensure the correctness in the systems. We build a neural network to directly learn the semantic matching, instead of using the relation similarity between words. Across all the above directions, we show that by diagnosing errors and making more interpretable systems, we are able to improve the performance in the current models.