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Publications by faculty and students, often in collaboration with others.
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Item On-chip integrated quantum emitter with 'trap-enhance-guide': a simulation approach(Optics Express, 2022-12-19) Saha, Samprity; Fomra, Dhruv; Ozgur, Umit; Avrutin, Vitaly; Ndukaife, Justus C.; Kinsey, NathanielTo address the challenges of developing a scalable system of an on-chip integrated quantum emitter, we propose to leverage the loss in our hybrid plasmonic-photonic structure to simultaneously achieve Purcell enhancement as well as on-chip maneuvering of nanoscale emitter via optical trapping with guided excitation-emission routes. In this report, we have analyzed the feasibility of the functional goals of our proposed system in the metric of trapping strength (-8KBT), Purcell factor (>1000-), and collection efficiency (-10%). Once realized, the scopes of the proposed device can be advanced to develop a scalable platform for integrated quantum technology.Item The Pseudo-Pascal Triangle of Maximum Deng Entropy(International Journal of Computers Communications & Control, 2020-02) Gao, X.; Deng, Y.Pascal triangle (known as Yang Hui Triangle in Chinese) is an important model in mathematics while the entropy has been heavily studied in physics or as uncertainty measure in information science. How to construct the the connection between Pascal triangle and uncertainty measure is an interesting topic. One of the most used entropy, Tasllis entropy, has been modelled with Pascal triangle. But the relationship of the other entropy functions with Pascal triangle is still an open issue. Dempster-Shafer evidence theory takes the advantage to deal with uncertainty than probability theory since the probability distribution is generalized as basic probability assignment, which is more efficient to model and handle uncertain information. Given a basic probability assignment, its corresponding uncertainty measure can be determined by Deng entropy, which is the generalization of Shannon entropy. In this paper, a Pseudo-Pascal triangle based the maximum Deng entropy is constructed. Similar to the Pascal triangle modelling of Tasllis entropy, this work provides the a possible way of Deng entropy in physics and information theory.Item Multifunctional metaoptics based on bilayer metasurfaces(Light-Science & Appplications, 2019-09-04) Zhou, You; Kravchenko, Ivan I.; Wang, Hao; Zheng, Hanyu; Gu, Gong; Valentine, JasonOptical metasurfaces have become versatile platforms for manipulating the phase, amplitude, and polarization of light. A platform for achieving independent control over each of these properties, however, remains elusive due to the limited engineering space available when using a single-layer metasurface. For instance, multiwavelength metasurfaces suffer from performance limitations due to space filling constraints, while control over phase and amplitude can be achieved, but only for a single polarization. Here, we explore bilayer dielectric metasurfaces to expand the design space for metaoptics. The ability to independently control the geometry and function of each layer enables the development of multifunctional metaoptics in which two or more optical properties are independently designed. As a proof of concept, we demonstrate multiwavelength holograms, multiwavelength waveplates, and polarization-insensitive 3D holograms based on phase and amplitude masks. The proposed architecture opens a new avenue for designing complex flat optics with a wide variety of functionalities.Item Detecting web attacks with end-to-end deep learning(Journal of Internet Services and Applications, 2019-08-27) Pan, Yao; Sun, Fangzhou; Teng, Zhongwei; White, Jules; Schmidt, Douglas C.; Staples, Jacob; Krause, LeeWeb applications are popular targets for cyber-attacks because they are network-accessible and often contain vulnerabilities. An intrusion detection system monitors web applications and issues alerts when an attack attempt is detected. Existing implementations of intrusion detection systems usually extract features from network packets or string characteristics of input that are manually selected as relevant to attack analysis. Manually selecting features, however, is time-consuming and requires in-depth security domain knowledge. Moreover, large amounts of labeled legitimate and attack request data are needed by supervised learning algorithms to classify normal and abnormal behaviors, which is often expensive and impractical to obtain for production web applications. This paper provides three contributions to the study of autonomic intrusion detection systems. First, we evaluate the feasibility of an unsupervised/semi-supervised approach for web attack detection based on the Robust Software Modeling Tool (RSMT), which autonomically monitors and characterizes the runtime behavior of web applications. Second, we describe how RSMT trains a stacked denoising autoencoder to encode and reconstruct the call graph for end-to-end deep learning, where a low-dimensional representation of the raw features with unlabeled request data is used to recognize anomalies by computing the reconstruction error of the request data. Third, we analyze the results of empirically testing RSMT on both synthetic datasets and production applications with intentional vulnerabilities. Our results show that the proposed approach can efficiently and accurately detect attacks, including SQL injection, cross-site scripting, and deserialization, with minimal domain knowledge and little labeled training data.