Browsing by Author "Talukder, Niloy"

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    A Node-RED-Based MIPS-32 Processor Simulator
    (Springer, 2022-03) Anderson, Ethan; Abrar Jahin, S. M.; Talukder, Niloy; Chu, Yul; Lee, John J.; Electrical and Computer Engineering, School of Engineering and Technology
    Processor simulators are imperative tools that well facilitate the understanding of modern processors. Therefore, numerous attempts have been made to develop better simulators, and some of them have been very widely used. There exist several categories of such simulators in terms of simulation speed, cycle accuracy, functional validation, cache focus, multiprocessor target, behavioral visualization, and education purpose. Our recent study focuses on developing a simulator with the following objectives: (i) to help students understand the organization and operation of processor faster and (ii) to provide students with much easier ways to build their own simulators while learning. Along the study, this paper describes our recent development of a Node-RED-based MIPS-32 processor simulator. Its functionality includes 5-stage pipeline visualization, 2-phase clocking (i.e., mimicking master/slave behavior), various cache configuration, cache statistics visualization, operand forwarding for the resolution of data dependency, and branch prediction mechanisms. Our study demonstrates the feasibility of good simulator implementations using Node-RED.
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    Tissue Cytometry With Machine Learning in Kidney: From Small Specimens to Big Data
    (Frontiers, 2022) El-Achkar, Tarek M.; Winfree, Seth; Talukder, Niloy; Barwinska, Daria; Ferkowicz, Michael J.; Al Hasan, Mohammad; Computer and Information Science, School of Science
    Advances in cellular and molecular interrogation of kidney tissue have ushered a new era of understanding the pathogenesis of kidney disease and potentially identifying molecular targets for therapeutic intervention. Classifying cells and identifying subtypes and states induced by injury is a foundational task in this context. High resolution Imaging-based approaches such as large-scale fluorescence 3D imaging offer significant advantages because they allow preservation of tissue architecture and provide a definition of the spatial context of each cell. We recently described the Volumetric Tissue Exploration and Analysis cytometry tool which enables an interactive analysis, quantitation and semiautomated classification of labeled cells in 3D image volumes. We also established and demonstrated an imaging-based classification using deep learning of cells in intact tissue using 3D nuclear staining with 4',6-diamidino-2-phenylindole (DAPI). In this mini-review, we will discuss recent advancements in analyzing 3D imaging of kidney tissue, and how combining machine learning with cytometry is a powerful approach to leverage the depth of content provided by high resolution imaging into a highly informative analytical output. Therefore, imaging a small tissue specimen will yield big scale data that will enable cell classification in a spatial context and provide novel insights on pathological changes induced by kidney disease.