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Full-Text Articles in Engineering
Generation Of Dna Oligomers With Similar Chemical Kinetics Via In-Silico Optimization, Michael Tobiason, Bernard Yurke, William L. Hughes
Generation Of Dna Oligomers With Similar Chemical Kinetics Via In-Silico Optimization, Michael Tobiason, Bernard Yurke, William L. Hughes
Electrical and Computer Engineering Faculty Publications and Presentations
Networks of interacting DNA oligomers are useful for applications such as biomarker detection, targeted drug delivery, information storage, and photonic information processing. However, differences in the chemical kinetics of hybridization reactions, referred to as kinetic dispersion, can be problematic for some applications. Here, it is found that limiting unnecessary stretches of Watson-Crick base pairing, referred to as unnecessary duplexes, can yield exceptionally low kinetic dispersions. Hybridization kinetics can be affected by unnecessary intra-oligomer duplexes containing only 2 base-pairs, and such duplexes explain up to 94% of previously reported kinetic dispersion. As a general design rule, it is recommended that unnecessary …
Machine Learning-Enabled Regional Multi-Hazards Risk Assessment Considering Social Vulnerability, Tianjie Zhang, Donglei Wang, Yang Lu
Machine Learning-Enabled Regional Multi-Hazards Risk Assessment Considering Social Vulnerability, Tianjie Zhang, Donglei Wang, Yang Lu
Civil Engineering Faculty Publications and Presentations
The regional multi-hazards risk assessment poses difficulties due to data access challenges, and the potential interactions between multi-hazards and social vulnerability. For better natural hazards risk perception and preparedness, it is important to study the nature-hazards risk distribution in different areas, specifically a major priority in the areas of high hazards level and social vulnerability. We propose a multi-hazards risk assessment method which considers social vulnerability into the analyzing and utilize machine learning-enabled models to solve this issue. The proposed methodology integrates three aspects as follows: (1) characterization and mapping of multi-hazards (Flooding, Wildfires, and Seismic) using five machine learning …
Processing Time, Temperature, And Initial Chemical Composition Prediction From Materials Microstructure By Deep Network For Multiple Inputs And Fused Data, Amir Abbas Kazemzadeh Farizhandi, Mahmood Mamivand
Processing Time, Temperature, And Initial Chemical Composition Prediction From Materials Microstructure By Deep Network For Multiple Inputs And Fused Data, Amir Abbas Kazemzadeh Farizhandi, Mahmood Mamivand
Mechanical and Biomedical Engineering Faculty Publications and Presentations
Prediction of the chemical composition and processing history from microstructure morphology can help in material inverse design. In this work, we propose a fused-data deep learning framework that can predict the processing history of a microstructure. We used the Fe-Cr-Co alloys as a model material. The developed framework is able to predict the heat treatment time, temperature, and initial chemical compositions by reading the morphology of Fe distribution and its concentration. The results show that the trained deep neural network has the highest accuracy for chemistry and then time and temperature. We identified two scenarios for inaccurate predictions; 1) There …
Deep Learning Approach For Chemistry And Processing History Prediction From Materials Microstructure, Amir Abbas Kazemzadeh Farizhandi, Omar Betancourt, Mahmood Mamivand
Deep Learning Approach For Chemistry And Processing History Prediction From Materials Microstructure, Amir Abbas Kazemzadeh Farizhandi, Omar Betancourt, Mahmood Mamivand
Mechanical and Biomedical Engineering Faculty Publications and Presentations
Finding the chemical composition and processing history from a microstructure morphology for heterogeneous materials is desired in many applications. While the simulation methods based on physical concepts such as the phase-field method can predict the spatio-temporal evolution of the materials’ microstructure, they are not efficient techniques for predicting processing and chemistry if a specific morphology is desired. In this study, we propose a framework based on a deep learning approach that enables us to predict the chemistry and processing history just by reading the morphological distribution of one element. As a case study, we used a dataset from spinodal decomposition …
An Alternative Approach To Nucleic Acid Memory, George D. Dickinson, Golam Md Mortuza, William Clay, Luca Piantanida, Christopher M. Green, Chad Watson, Eric J. Hayden, Tim Andersen, Wan Kuang, Elton Graugnard, Reza Zadegan, William L. Hughes
An Alternative Approach To Nucleic Acid Memory, George D. Dickinson, Golam Md Mortuza, William Clay, Luca Piantanida, Christopher M. Green, Chad Watson, Eric J. Hayden, Tim Andersen, Wan Kuang, Elton Graugnard, Reza Zadegan, William L. Hughes
Materials Science and Engineering Faculty Publications and Presentations
DNA is a compelling alternative to non-volatile information storage technologies due to its information density, stability, and energy efficiency. Previous studies have used artificially synthesized DNA to store data and automated next-generation sequencing to read it back. Here, we report digital Nucleic Acid Memory (dNAM) for applications that require a limited amount of data to have high information density, redundancy, and copy number. In dNAM, data is encoded by selecting combinations of single-stranded DNA with (1) or without (0) docking-site domains. When self-assembled with scaffold DNA, staple strands form DNA origami breadboards. Information encoded into the breadboards is read by …
Perspective On Coarse-Graining, Cognitive Load, And Materials Simulation, Eric Jankowski, Nealee Ellyson, Jenny W. Fothergill, Michael M. Henry, Mitchell H. Leibowitz, Evan D. Miller, Mone't Alberts, Jamie D. Guevara, Chris D. Jones, Mia Klopfenstein, Kendra K. Noneman, Rachel Singleton, Matthew L. Jones
Perspective On Coarse-Graining, Cognitive Load, And Materials Simulation, Eric Jankowski, Nealee Ellyson, Jenny W. Fothergill, Michael M. Henry, Mitchell H. Leibowitz, Evan D. Miller, Mone't Alberts, Jamie D. Guevara, Chris D. Jones, Mia Klopfenstein, Kendra K. Noneman, Rachel Singleton, Matthew L. Jones
Materials Science and Engineering Faculty Publications and Presentations
The predictive capabilities of computational materials science today derive from overlapping advances in simulation tools, modeling techniques, and best practices. We outline this ecosystem of molecular simulations by explaining how important contributions in each of these areas have fed into each other. The combined output of these tools, techniques, and practices is the ability for researchers to advance understanding by efficiently combining simple models with powerful software. As specific examples, we show how the prediction of organic photovoltaic morphologies have improved by orders of magnitude over the last decade, and how the processing of reacting epoxy thermosets can now be …
A Validated Software Application To Measure Fiber Organization In Soft Tissue, Erica E. Morrill, Azamat N. Tulepbergenov, Christina J. Stender, Roshani Lamichhane, Raquel J. Brown, Trevor J. Lujan
A Validated Software Application To Measure Fiber Organization In Soft Tissue, Erica E. Morrill, Azamat N. Tulepbergenov, Christina J. Stender, Roshani Lamichhane, Raquel J. Brown, Trevor J. Lujan
Mechanical and Biomedical Engineering Faculty Publications and Presentations
The mechanical behavior of soft connective tissue is governed by a dense network of fibrillar proteins in the extracellular matrix. Characterization of this fibrous network requires the accurate extraction of descriptive structural parameters from imaging data, including fiber dispersion and mean fiber orientation. Common methods to quantify fiber parameters include fast Fourier transforms (FFT) and structure tensors, however, information is limited on the accuracy of these methods. In this study, we compared these two methods using test images of fiber networks with varying topology. The FFT method with a band-pass filter was the most accurate, with an error of 0.71 …
An Immersed Boundary Geometric Preprocessor For Arbitrarily Complex Terrain And Geometry, Inanc Senocak, Micah Sandusky, Rey Deleon, Derek Wade, Kyle Felzien, Marianna Budnikova
An Immersed Boundary Geometric Preprocessor For Arbitrarily Complex Terrain And Geometry, Inanc Senocak, Micah Sandusky, Rey Deleon, Derek Wade, Kyle Felzien, Marianna Budnikova
Mechanical and Biomedical Engineering Faculty Publications and Presentations
There is a growing interest to apply the immersed boundary method to compute wind fields over arbitrarily complex terrain. The computer implementation of an immersed boundary module into an existing flow solver can be accomplished with minor modifications to the rest of the computer program. However, a versatile preprocessor is needed at the first place to extract the essential geometric information pertinent to the immersion of an arbitrarily complex terrain inside a 3D Cartesian mesh. Errors in the geometric information can negatively impact the correct implementation of the immersed boundary method as part of the solution algorithm. Additionally, the distance …
Enhanced Dna Sensing Via Catalytic Aggregation Of Gold Nanoparticles, Herbert M. Huttanus, Elton Graugnard, Bernard Yurke, William B. Knowlton, Wan Kuang, William L. Hughes, Jeunghoon Lee
Enhanced Dna Sensing Via Catalytic Aggregation Of Gold Nanoparticles, Herbert M. Huttanus, Elton Graugnard, Bernard Yurke, William B. Knowlton, Wan Kuang, William L. Hughes, Jeunghoon Lee
Materials Science and Engineering Faculty Publications and Presentations
A catalytic colorimetric detection scheme that incorporates a DNA-based hybridization chain reaction into gold nanoparticles was designed and tested. While direct aggregation forms an inter-particle linkagefrom only one target DNA strand, catalytic aggregation forms multiple linkages from a single target DNA strand. Gold nanoparticles were functionalized with thiol-modified DNA strands capable of undergoing hybridization chain reactions. The changes in their absorption spectra were measured at different times and target concentrations and compared against direct aggregation. Catalytic aggregation showed a multifold increase in sensitivity at low target concentrations when compared to direct aggregation. Gelelectrophoresis was performed to compare DNA hybridization reactions …
Multiscaffold Dna Origami Nanoparticle Waveguides, William P. Klein, Charles N. Schmidt, Blake Rapp, Sadao Takabayashi, William B. Knowlton, Jeunghoon Lee, Bernard Yurke, William L. Hughes, Elton Graugnard, Wan Kuang
Multiscaffold Dna Origami Nanoparticle Waveguides, William P. Klein, Charles N. Schmidt, Blake Rapp, Sadao Takabayashi, William B. Knowlton, Jeunghoon Lee, Bernard Yurke, William L. Hughes, Elton Graugnard, Wan Kuang
Electrical and Computer Engineering Faculty Publications and Presentations
DNA origami templated self-assembly has shown its potential in creating rationally designed nanophotonic devices in a parallel and repeatable manner. In this investigation, we employ a multiscaffold DNA origami approach to fabricate linear waveguides of 10 nm diameter gold nanoparticles. This approach provides independent control over nanoparticle separation and spatial arrangement. The waveguides were characterized using atomic force microscopy and far-field polarization spectroscopy. This work provides a path toward large-scale plasmonic circuitry.
Multi-Level Parallelism For Incompressible Flow Computations On Gpu Clusters, Dana A. Jacobsen, Inanc Senocak
Multi-Level Parallelism For Incompressible Flow Computations On Gpu Clusters, Dana A. Jacobsen, Inanc Senocak
Mechanical and Biomedical Engineering Faculty Publications and Presentations
We investigate multi-level parallelism on GPU clusters with MPI-CUDA and hybrid MPI-OpenMP-CUDA parallel implementations, in which all computations are done on the GPU using CUDA. We explore efficiency and scalability of incompressible flow computations using up to 256 GPUs on a problem with approximately 17.2 billion cells. Our work addresses some of the unique issues faced when merging fine-grain parallelism on the GPU using CUDA with coarse-grain parallelism that use either MPI or MPI-OpenMP for communications. We present three different strategies to overlap computations with communications, and systematically assess their impact on parallel performance on two different GPU clusters. Our …
Programmable Periodicity Of Quantum Dot Arrays With Dna Origami Nanotubes, Hieu Bui, Craig Onodera, Carson Kidwell, Yerpeng Tan, Elton Graugnard, Wan Kuang, Jeunghoon Lee, William B. Knowlton, Bernard Yurke, William L. Hughes
Programmable Periodicity Of Quantum Dot Arrays With Dna Origami Nanotubes, Hieu Bui, Craig Onodera, Carson Kidwell, Yerpeng Tan, Elton Graugnard, Wan Kuang, Jeunghoon Lee, William B. Knowlton, Bernard Yurke, William L. Hughes
Materials Science and Engineering Faculty Publications and Presentations
To fabricate quantum dot arrays with programmable periodicity, functionalized DNA origami nanotubes were developed. Selected DNA staple strands were biotin-labeled to form periodic binding sites for streptavidin-conjugated quantum dots. Successful formation of arrays with periods of 43 and 71 nm demonstrates precise, programmable, large-scale nanoparticle patterning; however, limitations in array periodicity were also observed. Statistical analysis of AFM images revealed evidence for steric hindrance or site bridging that limited the minimum array periodicity.
Limitations Of Poole–Frenkel Conduction In Bilayer Hfo2/Sio2 Mos Devices, Richard G. Southwick Iii, Justin Reed, Christopher Buu, Ross Butler, Gennadi Bersuker, William B. Knowlton
Limitations Of Poole–Frenkel Conduction In Bilayer Hfo2/Sio2 Mos Devices, Richard G. Southwick Iii, Justin Reed, Christopher Buu, Ross Butler, Gennadi Bersuker, William B. Knowlton
Materials Science and Engineering Faculty Publications and Presentations
The gate leakage current of metal–oxide– semiconductors (MOSs) composed of hafnium oxide (HfO2) exhibits temperature dependence, which is usually attributed to the standard Poole–Frenkel (P–F) transport model. However, the reported magnitudes of the trap barrier height vary significantly. This paper explores the fundamental challenges associated with applying the P–F model to describe transport in HfO2/SiO2 bilayers in n/p MOS field-effect transistors composed of 3- and 5-nm HfO2 on 1.1-nm SiO2 dielectric stacks. The extracted P–F trap barrier height is shown to be dependent on several variables including the following: the temperature range, method …
Recent Advances In High Density Area Array Interconnect Bonding For 3d Integration, J. M. Lannon, J., C. Gregory, M. Lueck, A. Huffman, D. Temple, Amy J. Moll, William B. Knowlton
Recent Advances In High Density Area Array Interconnect Bonding For 3d Integration, J. M. Lannon, J., C. Gregory, M. Lueck, A. Huffman, D. Temple, Amy J. Moll, William B. Knowlton
Materials Science and Engineering Faculty Publications and Presentations
The demand for more complex and multifunctional micro systems with enhanced performance characteristics for military applications is driving the electronics industry toward the use of best-of-breed materials and device technologies. Threedimensional (3-D) integration provides a way to build complex microsystems through bonding and interconnection of individually optimized device layers without compromising system performance and fabrication yield. Bonding of device layers can be achieved through polymer bonding or metal-metal interconnect bonding with a number of metalmetal systems. RTI has been investigating and characterizing Cu-Cu and CulSn-Cu processes for high density area array imaging applications, demonstrating high yield bonding between sub-I5 11m …
2x1d Image Registration And Comparison, Geng Zheng, Elisa H. Barney Smith, Nader Rafla, Tim Andersen
2x1d Image Registration And Comparison, Geng Zheng, Elisa H. Barney Smith, Nader Rafla, Tim Andersen
Electrical and Computer Engineering Faculty Publications and Presentations
This paper presents a novel 2x1D phase correlation based image registration method for verification of printer emulator output. The method combines the basic phase correlation technique and a modified 2x1D version of it to achieve both high speed and high accuracy. The proposed method has been implemented and tested using images generated by printer emulators. Over 97% of the image pairs were registered correctly, accurately dealing with diverse images with large translations and image cropping.
On The Thermal Activation Of Negative Bias Temperature Instability, Richard G. Southwick Iii, William B. Knowlton, Ben Kaczer, Tibor Grasser
On The Thermal Activation Of Negative Bias Temperature Instability, Richard G. Southwick Iii, William B. Knowlton, Ben Kaczer, Tibor Grasser
Materials Science and Engineering Faculty Publications and Presentations
The temperature dependence of negative bias temperature instability (NBTI) is investigated on 2.0nm SiO2 devices from temperatures ranging from 300K down to 6K with a measurement window of ~12ms to 100s. Results indicate that classic NBTI degradation is observed down to ~200K and rarely observed at temperatures below 140K in the experimental window. Since experimental results show the charge trapping component contributing to NBTI is thermally activated, the results cannot be explained with the conventionally employed elastic tunneling theory. A new mechanism is observed at temperatures below 200K where device performance during stress conditions improves rather than degrades with …
Chip-Scale Nanophotonic Chemical And Biological Sensors Using Cmos Process, Lincoln Bollschweiler, Alex English, R. Jacob Baker, Wan Kuang, Zi-Chang Chang, Ming-Hsiung Shih, William Knowlton
Chip-Scale Nanophotonic Chemical And Biological Sensors Using Cmos Process, Lincoln Bollschweiler, Alex English, R. Jacob Baker, Wan Kuang, Zi-Chang Chang, Ming-Hsiung Shih, William Knowlton
Electrical and Computer Engineering Faculty Publications and Presentations
A monolithic integrated chip-scale surface plasmon resonance (SPR) sensor is demonstrated. The device consists of a pn photodiode covered with a periodic modified thin metal film whose lattice constant is on the order of the wavelength of light. The device performs real-time measurement of resonant wavelengths of enhanced optical transmission due to surface plasmon resonance, which are influenced by the presence of chemical or biological materials at the device’s surface.
Hardware/Software Codesign In A Compact Ion Mobility Spectrometer Sensor Systemfor Subsurface Contaminant Detection, Sin Ming Loo, Jonathan P. Cole, Molly M. Gribb
Hardware/Software Codesign In A Compact Ion Mobility Spectrometer Sensor Systemfor Subsurface Contaminant Detection, Sin Ming Loo, Jonathan P. Cole, Molly M. Gribb
Electrical and Computer Engineering Faculty Publications and Presentations
A field-programmable-gate-array-(FPGA-) based data acquisition and control system was designed in a hardware/software codesign environment using an embedded Xilinx Microblaze soft-core processor for use with a subsurface ion mobility spectrometer (IMS) system, designed for detection of gaseous volatile organic compounds (VOCs). An FPGA is used to accelerate the digital signal processing algorithms and provide accurate timing and control. An embedded soft-core processor is used to ease development by implementing nontime critical portions of the design in software. The design was successfully implemented using a low-cost, off-the-shelf Xilinx Spartan-III FPGA and supporting digital and analog electronics.
Partitioning Of The Degradation Space For Ocr Training, Elisa H. Barney Smith, Tim Andersen
Partitioning Of The Degradation Space For Ocr Training, Elisa H. Barney Smith, Tim Andersen
Electrical and Computer Engineering Faculty Publications and Presentations
Generally speaking optical character recognition algorithms tend to perform better when presented with homogeneous data. This paper studies a method that is designed to increase the homogeneity of training data, based on an understanding of the types of degradations that occur during the printing and scanning process, and how these degradations affect the homogeneity of the data. While it has been shown that dividing the degradation space by edge spread improves recognition accuracy over dividing the degradation space by threshold or point spread function width alone, the challenge is in deciding how many partitions and at what value of edge …
A Study Of Style Effects On Ocr Errors In The Medline Database, Penny Garrison, Diane Davis, Tim Andersen, Elisa Barney Smith
A Study Of Style Effects On Ocr Errors In The Medline Database, Penny Garrison, Diane Davis, Tim Andersen, Elisa Barney Smith
Electrical and Computer Engineering Faculty Publications and Presentations
The National Library of Medicine has developed a system for the automatic extraction of data from scanned journal articles to populate the MEDLINE database. Although the 5-engine OCR system used in this process exhibits good performance overall, it does make errors in character recognition that must be corrected in order for the process to achieve the requisite accuracy. The correction process works by feeding words that have characters with less than 100% confidence (as determined automatically by the OCR engine) to a human operator who then must manually verify the word or correct the error. The majority of these errors …
Text Degradations And Ocr Training, Elisa H. Barney Smith, Tim Andersen
Text Degradations And Ocr Training, Elisa H. Barney Smith, Tim Andersen
Electrical and Computer Engineering Faculty Publications and Presentations
Printing and scanning of text documents introduces degradations to the characters which can be modeled. Interestingly, certain combinations of the parameters that govern the degradations introduced by the printing and scanning process affect characters in such a way that the degraded characters have a similar appearance, while other degradations leave the characters with an appearance that is very different. It is well known that (generally speaking) a test set that more closely matches a training set will be recognized with higher accuracy than one that matches the training set less well. Likewise, classifiers tend to perform better on data sets …