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Search Publications by: Timothy Blattner (Fed)

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Displaying 1 - 11 of 11

Trojan Detection Evaluation: Finding Hidden Behavior in AI Models

October 10, 2020
Author(s)
Michael Paul Majurski, Derek Juba, Timothy Blattner, Peter Bajcsy, Walid Keyrouz
Neural Networks are trained on data, learn relationships in that data, and then are deployed to the world to operate on new data. For example, a traffic sign classification AI can differentiate stop signs and speed limit signs. One potential problem is

Scatter corrections in x-ray computed tomography: a physics-based analysis

May 22, 2019
Author(s)
Zachary H. Levine, Timothy Blattner, Adele Peskin, Adam L. Pintar
Fundamental limits for the calculation of scattering corrections within X-ray computed tomography (CT) are found within the independent atom approximation from an analysis of the cross sections, CT geometry, and the Nyquist sampling theorem, suggesting

A Design Tool for High Performance Image Processing on Multicore Platforms

March 23, 2018
Author(s)
Timothy J. Blattner, Jiahao Wu, Walid Keyrouz, Shuvra S. Bhattacharyya
Design and implementation of smart vision systems often involve the mapping of complex image processing algorithms into efficient, real-time implementations on multicore platforms. In this paper, we describe a novel design tool that is developed to address

Model-based Dynamic Scheduling for Multicore Implementation of Image Processing Systems

October 5, 2017
Author(s)
Timothy J. Blattner, Walid Keyrouz, Jiahao Wu, Shuvra S. Bhattacharyya
In this paper, we present a new software tool, called HTGS Model-based Engine (HMBE), for the design and implementation of multicore signal processing applications. HMBE provides complementary capabilities to HTGS (Hybrid Task Graph Scheduler), which is a

A Hybrid Task Graph Scheduler for High Performance Image Processing Workflows

June 22, 2017
Author(s)
Timothy J. Blattner, Walid Keyrouz, Mary C. Brady, Shuvra S. Bhattacharyya, Milton Halem
Designing applications for scalability is key to improving their performance in hybrid and cluster computing. Scheduling code to utilize parallelism is difficult, particularly when dealing with data dependencies, memory management, data motion, and

A Hybrid Task Graph Scheduler for High Performance Image Processing Workflows

December 16, 2015
Author(s)
Timothy J. Blattner, Walid Keyrouz, Milton Halem, Shuvra S. Bhattacharyya, Mary C. Brady
The scalability of applications is a key requirement to improving performance in hybrid and cluster computing. Scheduling code to utilize parallelism is difficult, particularly when dealing with dependencies, memory management, data motion, and processor

A Hybrid CPU-GPU System for Stitching Large Scale Optical Microscopy Images

September 12, 2014
Author(s)
Timothy Blattner, Walid Keyrouz, Joe Chalfoun, Bertrand C. Stivalet, Mary C. Brady, Shujia Zhou
Researchers in various fields are using optical microscopy to acquire very large images, 10K--200K of pixels per side. Optical microscopes acquire these images as grids of overlapping partial images (thousands of pixels per side) that are then stitched

A Hybrid CPU-GPU Approach to Fourier-Based Image Stitching of Optical Microscopy Images

March 3, 2013
Author(s)
Walid Keyrouz, Timothy J. Blattner, Bertrand C. Stivalet, Joe Chalfoun, Mary C. Brady, Shujia Zhou
We present a hybrid CPU-GPU approach for the Fourier-based stitching of optical microscopy images. This system achieves sub-minute stitching rates with large grids; it stitches a grid of 59x42 tiles in 26 seconds on a two-CPU (8 physical cores) & two-GPU