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Electrical and Computer Engineering Commons™
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Articles 1 - 7 of 7
Full-Text Articles in Electrical and Computer Engineering
Weed And Crop Discrimination Through An Offline Computer Vision Algorithm, Phillip J. Putney
Weed And Crop Discrimination Through An Offline Computer Vision Algorithm, Phillip J. Putney
ELAIA
With the recent global interest in organic farming and cultivation, many people are turning away from chemical-based herbicides and moving towards alternate methods to extirpate weeds living amongst their crops. Of the methods proposed, robotic weed detection and removal is the most promising because of its possibility to be completely autonomous. Several robust, fully-autonomous robots have been developed, although none have been approved for commercial use. This paper proposes a weed and crop discrimination algorithm that utilizes an excessive green filter paired with principal component analysis to detect specific spatial frequencies within an image corresponding to different types of weeds …
Vocal Processing With Spectral Analysis, Bradley J. Fitzgerald
Vocal Processing With Spectral Analysis, Bradley J. Fitzgerald
ELAIA
A well-known signal processing issue is that of the “cocktail party problem,” which A well-known signal processing issue is that of the “cocktail party problem,” which refers to the need to be able to separate speakers from a mixture of voices. A solution to this problem could provide insight into signal separation in a variety of signal processing fields. In this study, a method of vocal signal processing was examined to determine if principal component analysis of spectral data could be used to characterize differences between speakers and if these differences could be used to separate mixtures of vocal signals. …
Using Principle Component Analysis Of Spectral Mixtures To Analyze Tertiary And Four End-Member Mixtures Containing Carbonates And Olivine, David Burnett
Pence-Boyce STEM Student Scholarship
CRISM images from Mars are expected to contain carbonates such as magnesite [1]. Prior research has been successfully able to determine the approximate percent composition of phyllosilicates in binary lab mixtures using Principle Component Analysis (PCA) [2]. In order to expand this model to work on CRISM images, one of preliminary steps is allowing the algorithm to work on mixtures with more than two components.
Vocal Processing With Spectral Analysis, Bradley Fitzgerald
Vocal Processing With Spectral Analysis, Bradley Fitzgerald
Honors Program Projects
A well-known signal processing issue is that of the “cocktail party problem”, which refers to the need to be able to separate speakers from a mixture of voices. A solution to this problem could provide insight into signal separation in a variety of signal processing fields. In this study, a method of vocal signal processing was examined to determine if principal component analysis of spectral data may be used to characterize differences between speakers and if these differences may be used to separate mixtures of vocal signals. Processing was done on a set of voice recordings from 30 different speakers …
Vocal Processing With Spectral Analysis, Brad Fitzgerald
Vocal Processing With Spectral Analysis, Brad Fitzgerald
Scholar Week 2016 - present
A method of vocal signal processing was examined to determine if principal component analysis of spectral data may be used to characterize differences between speakers and if these differences may be used to separate mixtures of vocal signals. Processing was done on a set of voice recordings from 30 different speakers in order to create a projection matrix which could be used by an algorithm to identify the source of an unknown recording from one of the 30 speakers. Two different identification algorithms were tested, both of which were generally unable to correctly identify the source of a single vocal …
Spectral Mixture Modeling Using Principle Component Analysis, Joseph S. Makarewicz, Heather D. Makarewicz
Spectral Mixture Modeling Using Principle Component Analysis, Joseph S. Makarewicz, Heather D. Makarewicz
Scholar Week 2016 - present
A method for modeling mixtures between two end-member spectra using principle component analysis and linear regression was presented. The presentation included results from three binary mixture data sets including orthopyroxene-clinopyroxene, kaolinite-montmorillonite, and nontronite-ferrihydrite.
Weed And Crop Discrimination Through An Offline Computer Vision Algorithm, Phillip Jamison Putney
Weed And Crop Discrimination Through An Offline Computer Vision Algorithm, Phillip Jamison Putney
Scholar Week 2016 - present
With the recent global interest in organic farming and cultivation, many people are turning away from chemical-based herbicides and moving towards alternate methods to extirpate weeds living amongst their crops. Of the methods proposed, robotic weed detection and removal is the most promising because of its possibility to be completely autonomous. Several robust, fully-autonomous robots have been developed, although none have approved for commercial use. This paper proposes a weed and crop discrimination algorithm that utilizes an excessive green color filter paired with principal component analysis to detect spatial frequencies within an image corresponding to different types of weeds and …