Scholarly Work - Computer Science
Recent Submissions
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Sex Parity in Cognitive Fatigue Model Development for Effective Human-Robot Collaboration
(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
(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
(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
(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
(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 the Tandem Duplication Distance is NP-Hard
(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
(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
(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
(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
(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
(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
(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
(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
(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
(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 ... -
Hyperspectral Dimensionality Reduction Based on Inter-Band Redundancy Analysis and Greedy Spectral Selection
(2021-09)Hyperspectral imaging systems are becoming widely used due to their increasing accessibility and their ability to provide detailed spectral responses based on hundreds of spectral bands. However, the resulting hyperspectral ... -
An Affective Computing in Virtual Reality Environments for Managing Surgical Pain and Anxiety
(2019-12)Pain and anxiety are common accompaniments of surgery. About 90% of people indicate elevated levels of anxiety during pre-operative care, and 66% of the people report moderate to high levels of pain immediately after ... -
Hyperspectral imaging and machine learning for monitoring produce ripeness
(2020-04)Hyperspectral imaging is a powerful remote sensing tool capable of capturing rich spectral and spatial information. Although the origins of hyperspectral imaging are in terrestrial remote sensing, new applications are ... -
Maximal Perfect Haplotype Blocks with Wildcards
(2020-05)Recent work provides the first method to measure the relative fitness of genomic variants within a population that scales to large numbers of genomes. A key component of the computation involves finding maximal perfect ... -
Breakpoint distance and PQ-trees
(2020-12)The PQ-tree is a fundamental data structure that has also been used in comparative genomics to model ancestral genomes with some uncertainty. To quantify the evolution between genomes represented by PQ-trees, in this paper ...
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