Computational Depth-resolved Imaging and Metrology

In this thesis, the main research challenge boils down to extracting 3D spatial information of an object from 2D measurements using light. Our goal is to achieve depth-resolved tomographic imaging of transparent or semi-transparent 3D objects, and to perform topography characterization of rough surfaces. The essential tool we used is computational imaging, where depending on the experimental scheme, often indirect measurements are taken, and tailored algorithms are employed to perform image reconstructions. The computational imaging approach enables us to relax the hardware requirement of an imaging system, which is essential when using light in the EUV and x-ray regimes, where high-quality optics are not readily available. In this thesis, visible and infrared light sources are used, where computational imaging also offers several advantages. First of all, it often leads to a simple, flexible imaging system with low cost. In the case of a lensless configuration, where no lenses are involved in the final image-forming stage between the object and the detector, aberration-free image reconstructions can be obtained. More importantly, computational imaging provides quantitative reconstructions of scalar electric fields, enabling phase imaging, numerical refocus, as well as 3D imaging

Location Estimation from an Indoor Selfie

With the development of social networks and hardware devices, many young people have post a lot of high definition v-logs containing selfie images and videos to commemorate and share their daily lives. We found that the reflected image of corneal position in the high definition selfie image has been able to reflect the position and posture of the selfie taker. The classic localization works estimating the position and posture from a selfie are difficult because they lack the knowledge of the environment. The corneal reflection images inherently carry information about the surrounding environment, which can reveal the location, posture and even height of the selfie taker. We analyze the corneal reflection imaging process in the selfie scenario and design a validation experiment based on this process to estimate the pose of the selfie in several scenarios to further evaluate the leakage of the pose information of the selfie taker

The Binding Mechanism Between Inositol Phosphate (InsP) and the Jasmonate Receptor Complex: A Computational Study

Jasmonates are critical plant hormones, mediating stress response in plants and regulating plant growth and development. The jasmonate receptor is a multi-component complex, composed of Arabidopsis SKP-LIKE PROTEIN1 (ASK1), CORONATINE INSENSITIVE 1 (COI1), inositol phosphate (InsP), and jasmonate ZIM-domain protein (JAZ). COI1 acts as multi-component signaling hub that binds with each component. InsP is suggested to play important roles in the hormone perception. How InsP binds with COI1 and the structural changes in COI1 upon binding with InsP, JA-Ile, and JAZ are not well understood. In this study, we integrated multiple computational methods, such as molecular docking, molecular dynamics simulations, residue interaction network analysis and binding free energy calculation, to explore the effect of InsP on the dynamic behavior of COI1 and the recognition mechanism of each component of the jasmonate receptor complex. We found that upon binding with InsP, JA-Ile, and JAZ1, the structure of COI1 becomes more compact. The binding of InsP with COI1 stabilizes the conformation of COI1 and promotes the binding between JA-Ile or JAZ1 and COI1. Analysis of the network parameters led to the identification of some hub nodes in this network, including Met88, His118, Arg120, Arg121, Arg346, Tyr382, Arg409, Trp467, and Lys492. The structural and dynamic details will be helpful for understanding the recognition mechanism of each component and the discovery and design of novel jasmonate signaling pathway modulators

Quantum Anomaly Detection with a Spin Processor in Diamond

In the processing of quantum computation, analyzing and learning the pattern of the quantum data are essential for many tasks. Quantum machine learning algorithms can not only deal with the quantum states generated in the preceding quantum procedures, but also the quantum registers encoding classical problems. In this work, we experimentally demonstrate the anomaly detection of quantum states encoding audio samples with a three-qubit quantum processor consisting of solid-state spins in diamond. By training the quantum machine with a few normal samples, the quantum machine can detect the anomaly samples with a minimum error rate of 15.4%. These results show the power of quantum anomaly detection in dealing with machine learning tasks and the potential to detect abnormal output of quantum devices.Comment: 10 pages, 8 figure

Hydrophilic domains compose of interlocking cation-? blocks for constructing hard actuator with robustness and rapid humidity responsiveness

Biomimetic actuators have seemingly infinite potential for use in previously unexplored areas. However, large stresses and a rapid water response are difficult to realize in soft actuators, owing to which their practical applicability is currently limited. In this paper, a new method for designing and fabricating humidity-responsive sturdy hard actuator. By combining a rigid matrix and hydrophilic water domains consisting of dynamic interlocking cation-π blocks, high-performance polymer actuator was synthesized that swell rapidly in response to a water gradient in their environment, resulting in unprecedentedly large stresses. More critically, the strong interlocking cation-π blocks reform and the intermolecular distance is reduced when the water is removed, allowing the deformed actuator to revert its original shape. The proposed design principle can potentially be extended to produce different types of sturdy actuators with rapid water responsiveness