Persistent Link:
http://hdl.handle.net/10150/184465
Title:
Long-range predictors for saccadic eye movements.
Author:
Wu, Chao-Yen.
Issue Date:
1988
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
To predict the final eye position in the middle of a saccadic eye movement will require long-range prediction. This dissertation investigated techniques for doing this. Many important results about saccadic eye movements and current prediction techinques were reviewed. New prediction techinques have been developed and tested for real saccadic data in computer. Three block processing predictors, two-point linear predictor (TPLP), five-point quadratic predictor (FPQP), and nine-point cubic predictor (NPCP), were derived based on the matrix approach. A different approach to deriving the TPLP, FPQP, and NPCP based on the difference equation was also developed. The difference equation approach is better than the matrix approach because it is not necessary to compute the matrix inversion. Two polynomial predictors: the polynomial-filter predictor 1 (PFP1), which is a linear combination of a TPLP and an FPQP, and the polynomial-filter predictor 2 (PFP2), which is a linear combination of a TPLP, and FPQP, and an NPCP, were also derived. Two recursive predictors: the recursive-least-square (RLS) predictor and the least-mean-square (LMS) predictor, were derived. Results show that the RLS and LMS predictors perform better than TPLP, FPQP, NPCP, PFP1, and PFP2 in the prediction of saccadic eye movements. A mathematical way of verifying the accuracy of the recursive-least-square predictor was developed. This technique also shows that the RLS predictor can be used to identify a signal. Results show that a sinusoidal signal can be described as a second-order difference equation with coefficients 2cosω and -1. In the same way, a cubic signal can be realized as a fourth-order difference equation with coefficients 4, -6, 4, and -1. A parabolic signal can be written as a third-order difference equation with coefficients 3, -3, and 1. And a triangular signal can be described as a second-order difference equation with coefficients 2 and -1. In this dissertation, all predictors were tested with various signals such as saccadic eye movements, ECG, sinusoidal, cubic, triangular, and parabolic signals. The FFT of these signals were studied and analyzed. Computer programs were written in systems language C and run on UNIX supported minicomputer VAX11/750. Results were discussed and compared to that of short-range prediction problems.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Saccadic eye movements -- Forecasting.; Human engineering.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Systems and Industrial Engineering; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Bahill

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleLong-range predictors for saccadic eye movements.en_US
dc.creatorWu, Chao-Yen.en_US
dc.contributor.authorWu, Chao-Yen.en_US
dc.date.issued1988en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractTo predict the final eye position in the middle of a saccadic eye movement will require long-range prediction. This dissertation investigated techniques for doing this. Many important results about saccadic eye movements and current prediction techinques were reviewed. New prediction techinques have been developed and tested for real saccadic data in computer. Three block processing predictors, two-point linear predictor (TPLP), five-point quadratic predictor (FPQP), and nine-point cubic predictor (NPCP), were derived based on the matrix approach. A different approach to deriving the TPLP, FPQP, and NPCP based on the difference equation was also developed. The difference equation approach is better than the matrix approach because it is not necessary to compute the matrix inversion. Two polynomial predictors: the polynomial-filter predictor 1 (PFP1), which is a linear combination of a TPLP and an FPQP, and the polynomial-filter predictor 2 (PFP2), which is a linear combination of a TPLP, and FPQP, and an NPCP, were also derived. Two recursive predictors: the recursive-least-square (RLS) predictor and the least-mean-square (LMS) predictor, were derived. Results show that the RLS and LMS predictors perform better than TPLP, FPQP, NPCP, PFP1, and PFP2 in the prediction of saccadic eye movements. A mathematical way of verifying the accuracy of the recursive-least-square predictor was developed. This technique also shows that the RLS predictor can be used to identify a signal. Results show that a sinusoidal signal can be described as a second-order difference equation with coefficients 2cosω and -1. In the same way, a cubic signal can be realized as a fourth-order difference equation with coefficients 4, -6, 4, and -1. A parabolic signal can be written as a third-order difference equation with coefficients 3, -3, and 1. And a triangular signal can be described as a second-order difference equation with coefficients 2 and -1. In this dissertation, all predictors were tested with various signals such as saccadic eye movements, ECG, sinusoidal, cubic, triangular, and parabolic signals. The FFT of these signals were studied and analyzed. Computer programs were written in systems language C and run on UNIX supported minicomputer VAX11/750. Results were discussed and compared to that of short-range prediction problems.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectSaccadic eye movements -- Forecasting.en_US
dc.subjectHuman engineering.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineSystems and Industrial Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorBahillen_US
dc.contributor.committeememberSchultzen_US
dc.contributor.committeememberDietrichen_US
dc.contributor.committeememberLaSalleen_US
dc.contributor.committeememberHasanen_US
dc.identifier.proquest8822437en_US
dc.identifier.oclc701330734en_US
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