Reinforced Proofreading of Image Segmentation for Connectomics

Department of Computer Science and EngineeringManual connectome reconstruction is a challenging task because of large-scale image data, therefore, an automatic pipelines are needed. Recently, with the usage of deep learning in computer vision, automatic segmentations of electron microscopy (EM) image data are acquired but have the high error rates including merge and split errors, which means it still requires correction through human proofreading. In this thesis, I propose a novel fully automatic proofreading system for 2D segmentation base on reinforcement learning. By mimicking the human proofreading process, the proposed system uses Locator, Merger and Splitter agents for error detection and correction tasks. With an input segmentation image, the Locator agent detects erroneous patches on the input image and then feeds them to Merger and Splitter for correcting split and merge errors respectively. To showcase my system performance, I evaluate it on CREMI data set.ope

A Generalized Convolution with a Weight Function for the Fourier Cosine and Sine Transforms

A generalized convolution with a weight function for the Fourier cosine and sine transforms is introduced. Its properties and applications to solving a system of integral equations are considered

On the regularization of solution of an inverse ultraparabolic equation associated with perturbed final data

In this paper, we study the inverse problem for a class of abstract ultraparabolic equations which is well-known to be ill-posed. We employ some elementary results of semi-group theory to present the formula of solution, then show the instability cause. Since the solution exhibits unstable dependence on the given data functions, we propose a new regularization method to stabilize the solution. then obtain the error estimate. A numerical example shows that the method is efficient and feasible. This work slightly extends to the earlier results in Zouyed et al. \cite{key-9} (2014).Comment: 19 pages, 4 figures, 1 tabl

Thermoresistance of p-Type 4H–SiC Integrated MEMS Devices for High-Temperature Sensing

There is an increasing demand for the development and integration of multifunctional sensing modules into power electronic devices that can operate in high temperature environments. Here, the authors demonstrate the tunable thermoresistance of p‐type 4H–SiC for a wide temperature range from the room temperature to above 800 K with integrated flow sensing functionality into a single power electronic chip. The electrical resistance of p‐type 4H–SiC is found to exponentially decrease with increasing temperature to a threshold temperature of 536 K. The temperature coefficient of resistance (TCR) shows a large and negative value from −2100 to −7600 ppm K−1, corresponding to a thermal index of 625 K. From the threshold temperature of 536–846 K, the electrical resistance shows excellent linearity with a positive TCR value of 900 ppm K−1. The authors successfully demonstrate the integration of p–4H–SiC flow sensing functionality with a high sensitivity of 1.035 μA(m s−1)−0.5 mW−1. These insights in the electrical transport of p–4H–SiC aid to improve the performance of p–4H–SiC integrated temperature and flow sensing systems, as well as the design consideration and integration of thermal sensors into 4H–SiC power electronic systems operating at high temperatures of up to 846 K