Patrick Michaud
- Courses1
- Reviews1
- School: Texas A&M University
- Campus: Corpus Christi
- Department: Computer Science
- Email address: Join to see
- Phone: Join to see
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Location:
Corpus Christi, TX - Dates at Texas A&M University: April 2006 - April 2006
- Office Hours: Join to see
Biography
Texas A&M University Corpus Christi - Computer Science
Owner, Pm Informatics, Inc. and Computer Software Consultant
Computer Software
Patrick
Michaud
Dallas/Fort Worth Area
Dynamic leader with 25+ years in project management, software development, system and network administration, and internet-based systems. Experienced in open-source development and building open-source communities. Recognized as an outstanding communicator, presenter, and instructor.
Specialties: Perl, PHP, Linux, Apache, PmWiki, HTML, C, MySQL, TCP/IP, Git, Subversion, open-source software.
Experience
Parrot Foundation
Technical advisor/developer
Patrick worked at Parrot Foundation as a Technical advisor/developer
The Perl Foundation
Lead developer, Rakudo Perl (Perl 6)
Created a Perl 6 regular expression and grammar engine for the Parrot Virtual Machine. Developed the Not Quite Perl (NQP) mini-Perl6 compiler and the Parrot Compiler Toolkit. Established procedures for monthly releases and long-term planning. Mentored developers and built an international team of volunteer contributors to Perl 6 development. Frequent speaker at international open source conferences and workshops. Conducted Perl 6 and Parrot workshops and tutorials. Member of Perl 6 language design team focusing on language implementation and performance issues.
The University of Texas at Dallas
Assistant Director
Patrick worked at The University of Texas at Dallas as a Assistant Director
Pm Informatics, Inc.
Owner, President, CEO
Negotiates sales, support, and development contracts for business and commercial users of PmWiki software.
Texas A&M University-Corpus Christi
Professor, Systems Programmer
Principal investigator or co-investigator for over $4.3 million in grants and contracts focused on application of computer, internet, and GIS technologies to real-world problems in education and health care. Designed and taught new courses in object oriented systems, geographic information science, Unix systems programming, scripting languages, and system and network administration. Assisted in development of undergraduate and graduate Geographic Information Science curricula. Helped design, build and maintain GulfBase.org, a resource database for Gulf of Mexico research.
Education
University of Southwestern Louisiana
MS
Computer science
University of Southwestern Louisiana
Ph.D.
Computer science
Corpus Christi State University
BS
Computer science
Publications
Comparison of Gap Interpolation Methodologies for Water Level Time Series Using PERL/PDL
Revista de Matemática: Teoría y Aplicaciones
Revista de Matematica: Teoria y Aplicaciones, Volume 12, No 1 & 2, pp 157-164 Abstract Extensive time series of measurements are often essential to evaluate long term changes and averages such as tidal datums and sea level rises. As such, gaps in time series data restrict the type and extent of modeling and research which may be accomplished. The Texas A&M University Corpus Christi Division of Nearshore Research (TAMUCC-DNR) has developed and compared various methods based on forward and backward linear regression to interpolate gaps in time series of water level data. We have developed a software system that retrieves actual and harmonic water level data based upon user provided parameters. The actual water level data is searched for missing data points and the location of these gaps are recorded. Forward and backward linear regression are applied in relation to the location of missing data or gaps in the remaining data. After this process is complete, one of three combinations of the forward and backward regression is used to fit the results. Finally, the harmonic component is added back into the newly supplemented time series and the results are graphed. The software created to implement this process of linear regression is written in Perl along with a Perl module called PDL (Perl Data Language). Generally, this process has demonstrated excellent results in filling gaps in our water level time series. The program was tested on existing data under three types of typical weather conditions: calm summers, frontal passages and extreme weather conditions, such as hurricanes. The parameters varied in order to test the accuracy of the methodology included the number of coefficients utilized in the linear regression processes as well as the size of the gaps to be filled. Results are presented for the different weather conditions and the different gap size and coefficient combinations.
Comparison of Gap Interpolation Methodologies for Water Level Time Series Using PERL/PDL
Revista de Matemática: Teoría y Aplicaciones
Revista de Matematica: Teoria y Aplicaciones, Volume 12, No 1 & 2, pp 157-164 Abstract Extensive time series of measurements are often essential to evaluate long term changes and averages such as tidal datums and sea level rises. As such, gaps in time series data restrict the type and extent of modeling and research which may be accomplished. The Texas A&M University Corpus Christi Division of Nearshore Research (TAMUCC-DNR) has developed and compared various methods based on forward and backward linear regression to interpolate gaps in time series of water level data. We have developed a software system that retrieves actual and harmonic water level data based upon user provided parameters. The actual water level data is searched for missing data points and the location of these gaps are recorded. Forward and backward linear regression are applied in relation to the location of missing data or gaps in the remaining data. After this process is complete, one of three combinations of the forward and backward regression is used to fit the results. Finally, the harmonic component is added back into the newly supplemented time series and the results are graphed. The software created to implement this process of linear regression is written in Perl along with a Perl module called PDL (Perl Data Language). Generally, this process has demonstrated excellent results in filling gaps in our water level time series. The program was tested on existing data under three types of typical weather conditions: calm summers, frontal passages and extreme weather conditions, such as hurricanes. The parameters varied in order to test the accuracy of the methodology included the number of coefficients utilized in the linear regression processes as well as the size of the gaps to be filled. Results are presented for the different weather conditions and the different gap size and coefficient combinations.
HARMAN and HARMPRED: Web-Based Software to Analyze Tidal Constituents and Tidal Forecasts for the Texas Coast
American Mathematical Society
Proc. of 19th AMS Conf. on Weather Analysis and Forecasting/15th AMS Conf. on Numerical Weather Prediction Introduction The Conrad Blucher Institute Division of Nearshore Research operates about 60 platforms along the coast of Texas collecting water level measurements and other meteorological parameters such as wind speed, wind direction, and barometric pressure (Michaud 2001). However, tidal forecasts are available only for the small number of stations for which the National Ocean Service (NOS) provides harmonic constants. Harmonic constants are often not published as there are many factors which cause water level to deviate from tidal predictions. There is a great need to be able to obtain harmonic constants and water-level predictions in support of various research projects (e.g., Tissot 2002, Cox 2002, Drikitis 2002). The Conrad Blucher Institute for Surveying and Science at Texas A&M University-Corpus Christi has developed the HarmAn and HarmPred programs to provide tidal forecasts for most of its stations and to make the process of generating harmonic constants and tide forecasts more accessible. The HarmAn program implements NOS’s harmonic analysis method (Zetler 1982) to compute constants from reliable data sets and a set of tidal constituent waves. To calculate harmonic constants, data sets need to be dependable water level readings of at least one year for the target station. HarmPred utilizes the harmonic constants derived by HarmAn (or obtained from other sources) to generate water level predictions. These programs are written in Perl/PDL. Perl is a popular programming language for data manipulation and PDL is a Perl module which provides matrix-based numeric calculations.
