Publications

Showing 80 results for Author: Amir K. Ziabari

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Nov, 2023

Conference Paper

Accurate and Accelerated Neuromorphic Network Design Leveraging A Bayesian Hyperparameter Pareto Optimization Approach

Neuromorphic systems allow for extremely efficient hardware implementations for neural networks (NNs). In recent years, several algorithms have been presented to train spiking NNs (SNNs) for neuromorphic hardware. However, SNNs often provide lower accuracy than their artificial NNs (ANNs) counterparts or require computationally expensive and slow training/inference methods…
Nov, 2023

Conference Paper

Neural network-based single material beam-hardening correction for X-ray CT in Additive Manufacturing

Beam-hardening (BH) artifacts are ubiquitous in X-ray CT scans of dense metal additively manufactured (AM) parts. While linearization approaches are useful for correcting beam-hardened data from single material objects, they either require a calibration scan or detailed system and material composition information. In this paper, we introduce a neural network-based, materia…
Oct, 2023

Conference Paper

Deep Learning Based Workflow for Accelerated Industrial X-Ray Computed Tomography

X-ray computed tomography (XCT) is an important tool for high-resolution non-destructive characterization of additively-manufactured metal components. XCT reconstructions of metal components may have beam hardening artifacts such as cupping and streaking which makes reliable detection of flaws and defects challenging. Furthermore, traditional workflows based on using analy…
Oct, 2022

Conference Paper

Simurgh: A Framework for Cad-Driven Deep Learning Based X-Ray CT Reconstruction

High-resolution X-ray computed tomography (XCT) is an important technique for the inspection of additively manufactured (AM) parts. While XCT is typically used off-line to inspect a subset of manufactured parts, significantly accelerating measurement speed while retaining accuracy would enable use of XCT for in-line inspection to rapidly identify defects in each part as it…
Aug, 2022

Journal

A general synthesis of single atom catalysts with controllable atomic and mesoporous structures

The control of single metal atomic sites has been extensively studied in the field of single atom catalysts. By contrast, the precise control of the mesoporous structure in the matrix material, which directly correlates with mass diffusions and may play a dominant role in delivering industrially relevant reaction rates, has been overlooked. Here we report a general method…
Apr, 2020

Conference Paper

A 2.5d Yolo-Based Fusion Algorithm for 3d Localization Of Cells

Advances in microscopy techniques such as lattice-light-sheet, confocal, two-photon, and electron microscopy have enabled the visualization of 3D image volumes of tightly packed cells, extracellular structures in tissues, organelles, and subcellular components inside cells. These images sampled by 2D projections are often not accurately interpreted even by human experts. A…
Feb, 2020

Conference Paper

PABO: Pseudo Agent-Based Multi-Objective Bayesian Hyperparameter Optimization for Efficient Neural Accelerator Design

The ever increasing computational cost of Deep Neural Networks (DNN) and the demand for energy efficient hardware for DNN acceleration has made accuracy and hardware cost co-optimization for DNNs tremendously important, especially for edge devices. Owing to the large parameter space and cost of evaluating each parameter in the search space, manually tuning of DNN hyperpara…
Feb, 2020

Conference Paper

A Two-Tier Convolutional Neural Network for Combined Detection and Segmentation in Biological Imagery

Deep learning techniques have been useful for modern microscopy imaging techniques to further study and analyze biological structures and organs. Convolutional neural networks (CNN) have improved 2D object detection, localization, and segmentation. For imagery containing biological structures with depth, it is especially desirable to perform these tasks in 3D. Traditionall…

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Publications

Accurate and Accelerated Neuromorphic Network Design Leveraging A Bayesian Hyperparameter Pareto Optimization Approach

Neural network-based single material beam-hardening correction for X-ray CT in Additive Manufacturing

Deep Learning Based Workflow for Accelerated Industrial X-Ray Computed Tomography

Simurgh: A Framework for Cad-Driven Deep Learning Based X-Ray CT Reconstruction

A general synthesis of single atom catalysts with controllable atomic and mesoporous structures

A 2.5d Yolo-Based Fusion Algorithm for 3d Localization Of Cells

PABO: Pseudo Agent-Based Multi-Objective Bayesian Hyperparameter Optimization for Efficient Neural Accelerator Design

A Two-Tier Convolutional Neural Network for Combined Detection and Segmentation in Biological Imagery