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Recent Submissions

  • Hyperspectral Band Selection for Multispectral Image Classification with Convolutional Networks 

    Morales, Giorgio; Sheppard, John; Logan, Riley; Shaw, Joseph (IEEE, 2021-07)
    In recent years, Hyperspectral Imaging (HSI) has become a powerful source for reliable data in applications such as remote sensing, agriculture, and biomedicine. However, hyperspectral images are highly data-dense and often ...
  • Counterfactual Explanations of Neural Network-Generated Response Curves 

    Morales, Giorgio; Sheppard, John (IEEE, 2023-06)
    Response curves exhibit the magnitude of the response of a sensitive system to a varying stimulus. However, response of such systems may be sensitive to multiple stimuli (i.e., input features) that are not necessarily ...
  • Metamorphic Testing For Machine Learning: Applicability, Challenges, and Research Opportunities 

    Rehman, Faqeer Ur; Srinivasan, Madhusudan (IEEE, 2023-07)
    The wide adoption and growth of Machine Learning (ML) have made tremendous advancements in revolutionizing a number of fields i.e., manufacturing, transportation, bio-informatics, and self-driving cars. Its ability to ...
  • An Empirical Internet Protocol Network Intrusion Detection using Isolation Forest and One-Class Support Vector Machines 

    Shu Fuhnwi, Gerard; Adedoyin, Victoria; Agbaje, Janet O. (The Science and Information Organization, 2023-01)
    With the increasing reliance on web-based applications and services, network intrusion detection has become a critical aspect of maintaining the security and integrity of computer networks. This study empirically investigates ...
  • Sex Parity in Cognitive Fatigue Model Development for Effective Human-Robot Collaboration 

    Kalatzis, Apostolos; Hopko, Sarah; Mehta, Ranjana K.; Stanley, Laura; Wittie, Mike P. (IEEE, 2022-10)
    In recent years, robots have become vital to achieving manufacturing competitiveness. Especially in industrial environments, a strong level of interaction is reached when humans and robots form a dynamic system that works ...
  • Low-frequency Inductive Loop and Its Origin in the Impedance Spectrum of a Graphite Anode 

    Thapa, Arun; Gao, Hongwei (The Electrochemical Society, 2022-11)
    Graphite is a well-known anode material for commercial lithium-ion batteries, and its physical and electrochemical properties have been studied extensively. However, the origin of an inductive loop observed in the low-frequency ...
  • Improved Yield Prediction of Winter Wheat Using a Novel Two-Dimensional Deep Regression Neural Network Trained via Remote Sensing 

    Morales, Giorgio; Sheppard, John W.; Hedgedus, Paul B.; Maxwell, Bruce D. (MDPI AG, 2023-01)
    In recent years, the use of remotely sensed and on-ground observations of crop fields, in conjunction with machine learning techniques, has led to highly accurate crop yield estimations. In this work, we propose to further ...
  • Improving RNA Assembly via Safety and Completeness in Flow Decompositions 

    Khan, Shahbaz; Kortelainen, Milla; Cáceres, Manuel; Williams, Lucia; Tomescu, Alexandru I. (Mary Ann Liebert Inc, 2022-12)
    Decomposing a network flow into weighted paths is a problem with numerous applications, ranging from networking, transportation planning, to bioinformatics. In some applications we look for a decomposition that is optimal ...
  • Efficient Minimum Flow Decomposition via Integer Linear Programming 

    Dias, Fernando H.C.; Williams, Lucia; Mumey, Brendan; Tomescu, Alexandru I. (Mary Ann Liebert Inc, 2022-11)
    Minimum flow decomposition (MFD) is an NP-hard problem asking to decompose a network flow into a minimum set of paths (together with associated weights). Variants of it are powerful models in multiassembly problems in ...
  • Computing a consensus trajectory in a vehicular network 

    Zou, Peng; Qingge, Letu; Yang, Qing; Zhu, Binhai (Springer Science and Business Media LLC, 2022-09)
    In this paper, we study the problem of computing a consensus trajectory of a vehicle given the history of Points of Interest visited by the vehicle over a certain period of time. The problem arises when a system tries to ...
  • Computing the Tandem Duplication Distance is NP-Hard 

    Lafond, Manuel; Zhu, Binhai; Zou, Peng (Society for Industrial & Applied Mathematics, 2022-03)
    In computational biology, tandem duplication is an important biological phenomenon which can occur either at the genome or at the DNA level. A tandem duplication takes a copy of a genome segment and inserts it right after ...
  • Dispersing and grouping points on planar segments 

    He, Xiaozhou; Lai, Wenfeng; Zhu, Binhai; Zou, Peng (Elsevier BV, 2022-09)
    Motivated by (continuous) facility location, we study the problem of dispersing and grouping points on a set of segments (of streets) in the plane. In the former problem, given a set of n disjoint line segments in the ...
  • Gray Spectralon polarized reflectance deviations from Lambertian 

    Field, Nathaniel J.; Brown, Jarrod P.; Card, Darrel B.; Welsh, Chad M.; Van Rynbach, Andre J.; Shaw, Joseph A. (SPIE, 2022-06)
    While Spectralon panels are largely assumed to be ideal Lambertian surfaces, their actual polarized reflective responses deviate from the ideal by at least a small amount at illumination and viewing angles off surface ...
  • Designing multi-phased CO2 capture and storage infrastructure deployments 

    Jones, Erick C.; Yaw, Sean; Bennett, Jeffrey A.; Ogland-Hand, Jonathan D.; Strahan, Cooper; Middleton, Richard S. (Elsevier BV, 2022-08)
    CO2 capture and storage (CCS) is a climate change mitigation strategy aimed at reducing the amount of CO vented into the atmosphere by capturing CO emissions from industrial sources, transporting the CO via a dedicated ...
  • Safety in multi-assembly via paths appearing in all path covers of a DAG 

    Caceres, Manuel; Mumey, Brendan; Husic, Edin; Rizzi, Romeo; Cairo, Massimo; Sahlin, Kristoffer; Tomescu, Alexandru I. Ioan (Institute of Electrical and Electronics Engineers, 2021-01)
    A multi-assembly problem asks to reconstruct multiple genomic sequences from mixed reads sequenced from all of them. Standard formulations of such problems model a solution as a path cover in a directed acyclic graph, ...
  • Flow Decomposition with Subpath Constraints 

    Williams, Lucia; Tomescu, Alexandru I. loan; Mumey, Brendan (Institute of Electrical and Electronics Engineers, 2022-01)
    Flow network decomposition is a natural model for problems where we are given a flow network arising from superimposing a set of weighted paths and would like to recover the underlying data, i.e.,decompose the flow into ...
  • Scalable Algorithms for Designing CO2 Capture and Storage Infrastructure 

    Whitman, Caleb; Yaw, Sean; Hoover, Brendan; Middleton, Richard (Springer Science and Business Media LLC, 2022)
    CO2 capture and storage (CCS) is a climate change mitigation strategy that aims to reduce the amount of CO2 vented into the atmosphere from industrial processes. Designing cost-effective CCS infrastructure is critical in ...
  • Reduced-cost hyperspectral convolutional neural networks 

    Morales, Giorgio; Sheppard, John W.; Scherrer, Bryan; Shaw, Joseph A. (2020-09)
    Hyperspectral imaging provides a useful tool for extracting complex information when visual spectral bands are not enough to solve certain tasks. However, processing hyperspectral images (HSIs) is usually computationally ...
  • Hyperspectral Band Selection for Multispectral Image Classification with Convolutional Networks 

    Morales, Giorgio; Sheppard, John W.; Logan, Riley D.; Shaw, Joseph A. (2021)
    In recent years, Hyperspectral Imaging (HSI) has become a powerful source for reliable data in applications such as remote sensing, agriculture, and biomedicine. However, hyperspectral images are highly data-dense and often ...
  • Two-dimensional deep regression for early yield prediction of winter wheat 

    Morales, Giorgio; Sheppard, John W. (2021-11)
    Crop yield prediction is one of the tasks of Precision Agriculture that can be automated based on multi-source periodic observations of the fields. We tackle the yield prediction problem using a Convolutional Neural Network ...

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