Linearized Reconstruction for Diffuse Optical Spectroscopic Imaging

In this paper, we present a novel reconstruction method for diffuse optical spectroscopic imaging with a commonly used tissue model of optical absorption and scattering. It is based on linearization and group sparsity, which allows recovering the diffusion coefficient and absorption coefficient simultaneously, provided that their spectral profiles are incoherent and a sufficient number of wavelengths are judiciously taken for the measurements. We also discuss the reconstruction for imperfectly known boundary and show that with the multi-wavelength data, the method can reduce the influence of modelling errors and still recover the absorption coefficient. Extensive numerical experiments are presented to support our analysis.Comment: 18 pages, 7 figure

Real-time Online Chinese Character Recognition

In this project, I built a web application for handwritten Chinese characters recognition in real time. This system determines a Chinese character while a user is drawing/writing it. The techniques and steps I use to build the recognition system include data preparation, preprocessing, features extraction, and classification. To increase the accuracy, two different types of neural networks ared used in the system: a multi-layer neural network and a convolutional neural network

Efficient Private ERM for Smooth Objectives

In this paper, we consider efficient differentially private empirical risk minimization from the viewpoint of optimization algorithms. For strongly convex and smooth objectives, we prove that gradient descent with output perturbation not only achieves nearly optimal utility, but also significantly improves the running time of previous state-of-the-art private optimization algorithms, for both $\epsilon$-DP and $(\epsilon, \delta)$-DP. For non-convex but smooth objectives, we propose an RRPSGD (Random Round Private Stochastic Gradient Descent) algorithm, which provably converges to a stationary point with privacy guarantee. Besides the expected utility bounds, we also provide guarantees in high probability form. Experiments demonstrate that our algorithm consistently outperforms existing method in both utility and running time

Adaptive Attitude Control for Foldable Quadrotors

Recent quadrotor vehicles transcended conventional designs, emphasizing more on foldable and reconfigurable bodies. However, the state of the art still focuses on the mechanical feasibility of such designs with limited discussions on the tracking performance of the vehicle during configuration switching. In this paper, we propose a complete control and planning framework for attitude tracking during configuration switching and curbs any switch-based disturbances, which can lead to violation of safety constraints and cause crashes. The control framework includes a morphology-aware adaptive controller with a estimator to account for parameter variation and a minimum-jerk trajectory planner to achieve stable flights while switching. Stability analysis for attitude tracking is presented by employing the theory of switched systems and simulation results validate the proposed framework for a foldable quadrotor's flight through a passageway.Comment: Submitted to IEEE LCSS ; 8 Pages, 6 Figure