Computer Science, PhD
Program Description
The PhD program in Computer Science is designed to cultivate experts in the expansive domain of computer science, anchoring students in rigorous theoretical and practical concepts. For those with a keen interest in spatial analysis and the dynamic interplay between location and computational data, the program offers an optional specialization in Geospatial Informatics.
Student Learning Outcomes
The program's student learning outcomes are for students to:
• Produce innovative research that advances theory or methodology in computer science and optionally in geospatial informatics.
• Advance the science of to create new algorithms and applications for computational challenges in the field of research.
• Develop the professional skills necessary to present research outcomes orally to a professional or general audience as well as in writing for peer reviewed journals and conference proceedings
For Additional Information
Website:
http://gradschool.tamucc.edu/degrees/science/geo_comp_sci.html
Campus Address:
Center for Instruction, Room 301
Phone: (361) 825-2474
Mailing Address:
Program Coordinator, PhD Computer Science
College of Engineering and Computer Science
Texas A&M University-Corpus Christi
6300 Ocean Drive
Corpus Christi, Texas 78412-5825
Admission Requirements
- Persons seeking admission to the COSC program should first contact the program to identify a faculty member willing to serve as their graduate advisor. Applicants will not be admitted to the program without a graduate faculty advisor.
- In addition to meeting all University requirements, students seeking admission to the graduate degree program in Computer Science must submit the following to the Office of Recruitment and Admissions:
- An application and application fee,
- Transcripts from regionally accredited institutions (international students will be required to submit relevant international transcripts),
- An essay (500-1000 words) discussing why you are seeking admission to the program and what your research plans are,
- A curriculum vitae,
- GRE scores (within five years of the date of application), and
- International students must submit TOEFL or IELTS scores and additional documents to the Office of Recruitment and Admissions. http://gradschool.tamucc.edu/international.htm
- A student entering the program is expected to have adequate preparation in computer science, geographic information science, and mathematics. For computer science, this preparation must include successful completion of coursework in a high-level programming language, For geospatial science, students must have successfully completed course work in geospatial data analysis and visualization. In mathematics, students must have successfully completed course work in calculus plus one additional junior level or higher mathematics course such as linear algebra, numerical analysis, or applied probability and statistics.
Students who have not successfully completed the above courses may be required to take leveling courses in any missing subjects before being formally admitted into the program. Leveling coursework does not count towards the total credit hours required for the degree. All leveling courses must be completed with a grade of “B” or better. While taking leveling courses, a student can take regular courses that can be counted towards the degree once admitted into the program formally. However, the total credit hours of such courses must not exceed nine hours.
Program Requirements
There are two pathways that students can take to be admitted into the PhD in Computer Science degree program, those coming in with
- a bachelor’s degree in a related field, and
- a master’s degree in a related field.
Students entering the program with a bachelor’s degree are required to take a minimum of 75 semester credit hours beyond the bachelor’s degree. Of these 75 hours, students must take certain core courses, Graduate Seminar, electives, and research and dissertation credits.
Students entering the program with a master’s degree are required to take a minimum of 57 semester credit hours beyond the master’s degree. Of these 57 hours, students must take certain core courses, Graduate Seminar , electives, and research and dissertation credits.
More details on core, required, elective, and research courses are given below.
Additional courses may be assigned depending on the student’s background. Students must file an approved degree plan by the end of their second semester in the program. A student’s graduate advisory committee must approve the degree plan. All students must pass a final dissertation defense, to be administered by their advisory committee, during their last semester before graduation.
The PhD program, by default, enables a student to become a Computer Science scholar and teacher. There is an optional specialization for those students who want to become specialists in the field of geospatial informatics.
1. Default CS Track
| Code | Title | Hours |
|---|---|---|
| (a) Core Courses (12 hours) 1,2 | ||
| COSC 6334 | Design and Analysis of Algorithms | 3 |
| COSC 6351 | Advanced Computer Architecture | 3 |
| COSC 6352 | Advanced Operating Systems | 3 |
| COSC 6370 | Advanced Software Engineering | 3 |
| (b) Required Courses (3 hours) 3 | ||
| GSCS 6302 | Graduate Seminar | 3 |
| (c) Research Hours | ||
| Minimum of 30 hours of research and defense from the following: | 30 | |
| Research | ||
| Dissertation Research (after advancement to candidacy) | ||
| Dissertation Defense (minimum of 3 sem. hours) | ||
| (d) Electives | ||
| Select 12-30 hours from the following list: 4 | 12-30 | |
| Introduction to Computer Graphics | ||
| Advanced Computer Graphics | ||
| Database Management Systems 5 | ||
| Machine Learning | ||
| Deep Learning | ||
| Human-Computer Interaction | ||
| Advanced Topics in DBMS | ||
| Compiler Design and Construction | ||
| Artificial Intelligence | ||
| Data Communications and Networking | ||
| Theory of Computation | ||
| Wireless Sensor Networks | ||
| Parallel Computing | ||
| Parallel Algorithms | ||
| Mobile Software Development | ||
| Computer Forensics | ||
| Information Assurance | ||
| Network Security 5 | ||
| Applied Cryptography | ||
| Advanced Information Assurance | ||
| Data Analytics | ||
| Selected Topics | ||
| Geospatial Data Structures | ||
| Advanced Geospatial Computing | ||
| Special Topics | ||
| Spatial Systems Science | ||
| Geospatial Programming Techniques | ||
| Programming for Geospatial Data Science | ||
| Spatial Database Design | ||
| Geospatial Data Mining | ||
| UAS for Surveying and Mapping | ||
| Geopositioning Systems and Autonomous Navigation | ||
| Applied Geospatial Statistics | ||
| Cadastral Information Systems Design | ||
| Policy and Legal Aspects of Spatial information Systems | ||
| Advanced Geospatial Analytics | ||
| Geospatial Visualization Design | ||
| Photogrammetric Engineering and Lidar Scanning | ||
| Remote Sensing and Image Analysis | ||
| Advanced Topics | ||
| Total Hours | 57-75 | |
- 1
All students must master the same core knowledge, and this content must be mastered prior to their candidacy exam. The core knowledge can be mastered with the following courses listed above. If any of the core courses have been previously taken by the student, one core course (3 hours) can be replaced by an approved transfer of credit or GSCS 6996 Research. Additional core courses (up to 9 hours) can be replaced by approved COSC electives.
- 2
Previously taken core courses must be deemed equivalent and validated by the syllabus, confirmed on the student’s graduate transcript with a grade of B or better, and approved prior to enabling a transfer of credit or core replacement. Specific requirements must be met for courses that may transfer into a terminal degree at TAMU-CC as defined by the College of Graduate Studies (CGS). Credit used for another graduate degree cannot be applied to a graduate degree at TAMU-CC. Refer to the CGS Doctoral Student Handbook for more details on credit transfers.
- 3
Students should take GSCS 6302 within their first semester or year in the program. Exceptions are if the course is not offered within a given academic year due to low enrollment.
- 4
Students entering with a bachelor’s degree must take at least 30 hours of electives. Student’s entering with a master’s degree must take at least 12 hours of electives. Electives will predominately come from COSC, GSCS, and GSEN graduate courses. Up to 6 hours can be from another graduate program at TAMU-CC with approval.
- 5
Students are required to take COSC 6336 Database Management Systems and COSC 6376 Network Security as part of their elective sequence unless the course (or equivalent) has been previously taken by the student at the graduate level. The student’s transcript must show the course taken with a grade of B or better. Course equivalency will be validated by the course syllabus.
2. Optional Geospatial Informatics Specialization
| Code | Title | Hours |
|---|---|---|
| (a) CS Core (12 hours) 1,2 | ||
| COSC 6334 | Design and Analysis of Algorithms | 3 |
| COSC 6351 | Advanced Computer Architecture | 3 |
| COSC 6352 | Advanced Operating Systems | 3 |
| COSC 6370 | Advanced Software Engineering | 3 |
| Geospatial Informatics Core (6 hours) | ||
| Select two from the following: | 6 | |
| Geospatial Data Structures | ||
| Advanced Geospatial Computing | ||
| Spatial Systems Science | ||
| (b) Required Courses (3 hours) 3 | ||
| GSCS 6302 | Graduate Seminar | 3 |
| (c) Research Hours | ||
| Minimum of 30 hours of research and defense from the following: | 30 | |
| Research | ||
| Dissertation Research (after advancement to candidacy) | ||
| Dissertation Defense (minimum of 3 sem. hours) | ||
| (d) Electives | ||
| Select 6-24 hours from the following list: 4 | 6-24 | |
| Introduction to Computer Graphics | ||
| Advanced Computer Graphics | ||
| Database Management Systems 5 | ||
| Machine Learning | ||
| Deep Learning | ||
| Human-Computer Interaction | ||
| Advanced Topics in DBMS | ||
| Compiler Design and Construction | ||
| Artificial Intelligence | ||
| Data Communications and Networking | ||
| Theory of Computation | ||
| Wireless Sensor Networks | ||
| Parallel Computing | ||
| Parallel Algorithms | ||
| Mobile Software Development | ||
| Computer Forensics | ||
| Information Assurance | ||
| Network Security 5 | ||
| Applied Cryptography | ||
| Advanced Information Assurance | ||
| Data Analytics | ||
| Selected Topics | ||
| Geospatial Data Structures | ||
| Advanced Geospatial Computing | ||
| Special Topics | ||
| Spatial Systems Science | ||
| Geospatial Programming Techniques | ||
| Programming for Geospatial Data Science | ||
| Spatial Database Design | ||
| Geospatial Data Mining | ||
| UAS for Surveying and Mapping | ||
| Geopositioning Systems and Autonomous Navigation | ||
| Applied Geospatial Statistics | ||
| Cadastral Information Systems Design | ||
| Policy and Legal Aspects of Spatial information Systems | ||
| Advanced Geospatial Analytics | ||
| Geospatial Visualization Design | ||
| Photogrammetric Engineering and Lidar Scanning | ||
| Remote Sensing and Image Analysis | ||
| Advanced Topics | ||
| Total Hours | 57-75 | |
- 1
All students must master the same core knowledge, and this content must be mastered prior to their candidacy exam. The core knowledge can be mastered with the following courses listed above. If any of the core courses have been previously taken by the student, one core course (3 hours) can be replaced by an approved transfer of credit or GSCS 6996 Research. Additional core courses (up to 9 hours) can be replaced by approved COSC electives.
- 2
Previously taken core courses must be deemed equivalent and validated by the syllabus, confirmed on the student’s graduate transcript with a grade of B or better, and approved prior to enabling a transfer of credit or core replacement. Specific requirements must be met for courses that may transfer into a terminal degree at TAMU-CC as defined by the College of Graduate Studies (CGS). Credit used for another graduate degree cannot be applied to a graduate degree at TAMU-CC. Refer to the CGS Doctoral Student Handbook for more details on credit transfers.
- 3
Students should take GSCS 6302 within their first semester or year in the program. Exceptions are if the course is not offered within a given academic year due to low enrollment.
- 4
Students entering with a bachelor’s degree must take at least 24 hours of electives and at least 6 hours must come from geospatial informatics (GSEN, GSCS). Student’s entering with a master’s degree must take at least 6 hours of electives and at least 3 hours must come from geospatial informatics. Electives will predominately come from COSC, GSCS, and GSEN graduate courses. Up to 6 hours can be from another graduate program at TAMU-CC with approval.
- 5
Students are required to take COSC 6336 Database Management Systems and COSC 6376 Network Security as part of their elective sequence unless the course (or equivalent) has been previously taken by the student at the graduate level. The student’s transcript must show the course taken with a grade of B or better. Course equivalency will be validated by the course syllabus.
Courses
Computer Science Courses
This course introduces concepts and techniques for image processing. The purpose of this course is to introduce the fundamental techniques and algorithms used for processing and extracting useful information from digital images. The students will learn how to apply the image processing methods to solve real-world problems.
This graduate course introduces concepts and techniques for machine vision. Particular emphasis will be placed on methods used for object recognition, machine learning, content-based image retrieval, image matching, 3D vision, tracking, and motion analysis.
Prerequisite: COSC 6324.
This graduate course provides students with a foundation in basic principles and techniques for computer graphics on modern graphics hardware. Students will gain experience in interactive computer graphics using the OpenGL API. Topics include: graphics hardware, rendering, perspective, lighting, and geometry.
This course covers advanced computer graphics techniques. Students will be introduced to state-of-the-art methods in computer graphics. This course will focus on techniques for real-time rendering and animation.
An advanced course that concentrates on the design and analysis of algorithms used to solve a variety of problems. The methods of design include topics such as: divide-and-conquer, the greedy method, dynamic programming, search and traversal techniques, and backtracking.
A study of contemporary database management concepts. Performance (storage and indexing) and Big Data techniques (management, processing, and analysis) are discussed. Also includes the study of spatial data management.
This course introduces fundamental strategies and methodologies for data mining. Topics include data preprocessing, mining frequent data patterns, classification, clustering, and outlier detection.
In this course, students will learn about the concepts as well as some applications of machine learning (ML) algorithms. The course includes many exercises on how these ML algorithms can be used in practical applications in both industry and basic science. Topics include such as artificial neural networks, fuzzy logic, hybrid systems, search and optimization, classification, clustering, and deep learning. Students will gain experiences on some programming tools and a variety of applications of machine learning algorithms.
This course introduces advanced concepts and techniques for deep learning. Particular emphasis is placed on regularization and optimization of deep learning models, convolutional networks, recurrent neural networks, autoencoders, and generative models. The students will learn how to apply the deep learning methods to solve real-world problems and develop the insight necessary to use the tools and techniques to solve any new problem.
Prerequisite: COSC 6338.
This graduate course introduces concepts and techniques for Human Computer Interaction (HCI). Students will investigate HCI through understanding its historical context and foundational elements. Other topics include the human factor, interaction elements, modeling interactions, scientific foundations of HCI research, and design of HCI experiments.
The study of emerging database technologies. Topics are chosen from data warehousing, distributed databases, spatial databases, and web-based applications.
Prerequisite: COSC 6336.
An overview of computer architecture, which stresses the underlying design principles and the impact of these principles on computer performance. General topics include design methodology, processor design, control design, memory organization, system organization, and parallel processing. Fall.
Introduction to advanced concepts in operating systems and distributed systems. Topics include distributed system architectures, inter-process communication, distributed mutual exclusion, distributed synchronization and deadlock, agreement protocols, distributed scheduling and process management, distributed shared memory, distributed file systems, multiprocessor system architectures and operating systems, recovery, and fault tolerance.
This course introduces the basic concepts and mechanisms traditionally employed in language translators, with emphasis on compilers. Topics include strategies for syntactic and semantic analysis, techniques of code optimization and approaches toward code generation.
Fundamental concepts and techniques for the design of computer-based, intelligent systems. Topics include: a brief history, methods for knowledge representation, heuristic search techniques, programming in LISP or Prolog.
Prerequisite: COSC 5321.
Areas studied include principles of computer-based communication systems, analysis and design of computer networks, and distributed data processing. Fall.
An introduction to theoretical foundations of modern computing. Topics include finite state machine concepts, formal grammars, and basic computability concepts.
Prerequisite: COSC 5321.
This is a graduate level course on wireless sensor networks; one of the fastest developing areas in computer science and engineering. The focus of this course is on the design of optimized architectures and protocols for such unique networks. Topics include the design principles of wireless sensor networks, energy management, MAC protocols, naming and addressing, localization, routing protocols, applications of wireless sensor networks, and associated challenges and measures.
Introduction to the hardware and software issues in parallel computing. Topics include motivation and history, parallel architectures, parallel algorithm design, and parallel performance analysis. Students will be introduced to a variety of parallel computing paradigms including message passing systems and shared memory systems.
Introduces and evaluates important models of parallel and distributed computation. Topics include a selection of parallel algorithms for various models of parallel computation, combinational circuits, parallel prefix computation, divide and conquer, pointer based data structures, linear arrays, meshes and related models, and hypercubes.
Survey of software development on mobile platforms including both native and cross-platform applications with topics such as: prototyping, programming, testing, debugging, and deploying. Coverage of software life cycle on mobile platforms and how mobile hardware differs from traditional computers.
This is a survey of current trends in computer programming. The focus of this course is on the development of computer programs utilizing the latest technologies and paradigms. Topics include state-of-the-art in problem solving and software development, programming techniques and approaches, programming languages, development tools and environments, and software deployment methods.
Prerequisite: COSC 5321.
Areas studied include engineering principles and their application to the design, development, testing, and maintenance of large software systems, tools and processes for managing the complexities inherent in creating and maintaining large software systems. Fall.
This course will introduce students to the fundamentals of computer forensics and various software tools used in cyber-crime analysis. Students will be introduced to established methodologies for conducting computer forensic investigations, as well as to emerging international standards for computer forensics. Applicable laws and regulations dealing with computer forensic analysis will also be discussed.
An introduction to information security and assurance. This course covers the basic notions of confidentiality, integrity, availability, authentication models, protection models, secure programming, audit, intrusion detection and response, operational security issues, physical security issues, personnel security, policy formation and enforcement, access controls, information flow, legal and social issues, classification, trust modeling, and risk assessment.
This course is a study of networking basics and security essentials with respect to information services provided over a computer network. The course covers the technical details of security threats, vulnerabilities, attacks, policies, and countermeasures such as firewalls, honeypots, intrusion detection systems, and cryptographic algorithms for confidentiality and authentication and the development of strategies to protect information services and resources accessible on a computer network.
Prerequisite: COSC 6375.
This course includes an introduction to cryptographic algorithms and protocols for encrypting information securely, techniques for analyzing vulnerabilities of protocols, approaches to digital signatures and information digests, and implementation approaches for the most significant cryptographic methodologies.
This course encompasses a broad range of topics involving information security, communications security, network security, risk analysis, operational security, health information privacy, criminal justice digital forensics, homeland security, the human element and social engineering, and applicable national and international laws. A project and/or research paper will be needed to satisfy the course requirements.
Prerequisite: COSC 6375.
This course will introduce state-of-the-art techniques to process and analyze different types of data, generate insights and knowledge from data, and make data-based decisions and predictions. Real-world examples will be used to familiarize students with the theory and applications. Main topics include data preprocessing, probability theory, tests of hypothesis, and various data analysis techniques (e.g., clustering, classification, prediction/forecasting, etc.) for different types of data including static, time-series, spatial, and spatiotemporal.
This course provides students with a range of experiences in conducting and communicating research. Students will learn major research methods and techniques. Experiences will be gained in all stages of research: reviewing literature, writing a proposal, designing an approach, and reporting results. Critical-reading/writing assignments and class discussions on state-of-the-art research in Computer Science will provide students with major research aspects. Spring
This course is designed to provide an intensive, supervised professional experience in an approved counseling setting. Topics addressed in this course include counselor education, pedagogy, research, supervision, leadership and advocacy, consultation, and training. Students will be expected to earn a total of 300 clock hours and will receive supervision in the five core areas of counseling, supervision, teaching, research/scholarship, and leadership/advocacy. Students repeat the internship for another 300 clock hours and another 3 semester hours of credit. Students must earn a grade of 'B' or better to pass.
Variable content study of specific areas of computer and information systems. May be repeated for credit when topics vary. Offered on sufficient demand.
Geospatial Computer Science Courses
Advanced topic study and presentation by students, faculty, or visiting scientists. Meets one hour weekly. Must be taken three times by all GSCS PhD students.
This is a 3-credit course that is intended to help facilitate the development of a student's dissertation research ideas and to contribute to the student's professional development as a doctoral level researcher in the field of geospatial computer science. The course focuses on developing professional research skills typically not provided in formal coursework such as methods for novel research, literature review, developing a research prospectus, presenting scientific research, research ethics, peer-review process, and professional society engagement. At the outcome, students will have a better understanding of the research process and a foundation to aid their development as a doctoral student and professional scientific researcher.
The representation of spatial data is an important issue in diverse areas including computer graphics, geographic information systems (GIS), robotics, and many others. Choosing an appropriate representation is a key to facilitate operations such as spatial search. This course will focus on representation of point data and object data, which are the important types of spatial data. Various fundamental data structures on spatial data, such as quadtrees, kd-trees, grid structures, kd-trees, and R-trees will be explored. The use of these structures to address some important problems will also be covered.
This course presents principles and methods for visualizing data resulting from measurements and calculations in both the physical sciences and the life sciences. The emphasis is on using 2D and 3D computer graphics to garner insight into multi-dimensional data sets for understanding and solving scientific problems. Topics include visualization software and techniques, human vision attributes and limitations, data encoding, data representation, volume rendering, flow visualization, and information visualization.
Seminar in reading and critical evaluation of academic literature in the field of and fields relating to geospatial computing. Student will design, implement, and evaluate an advanced, contemporary geospatial computing technology to solve a geospatial problem.
The aim of this course is to introduce the principle of positioning indoors/outdoors using sensors and short-range radio frequency signals in smartphones. These sensors will include a GNSS receiver, an accelerometer, a gyroscope, a magnetometer, a barometer, and a camera, why short-range RF signals will include WiFi and Bluetooth signals. The course will concentrate on various positioning algorithms for fusing sensor measurements and RF signal measurements.
Prerequisite: GSCS 5321.
Variable content study of specific areas of geospatial computing science. May be repeated for credit when topics vary. Offered on sufficient demand.
Independent research conducted under supervision of an advisor. Open to Geospatial Computing Science students who have not yet passed the qualifying exam and with consent of their graduate advisor. The course is graded with an S or U, and may be repeated.
Research related to PhD dissertation. Open only to degree candidates having passed the qualifying exam in Geospatial Computing Science with consent of their graduate advisor. The course is graded with an S or U, and may be repeated.
Open only to degree candidates in Geospatial Computing Science with consent of their graduate advisor. Students should enroll in this course during their last semester of the GSCS PhD program. To successfully complete this course the student must pass the dissertation defense as well as have a final copy of the dissertation signed by the full graduate committee and approved for binding and distribution. A grade of Credit/No Credit will be assigned for the class with the possibility to assign the grade of IP or In Progress. If a grade of IP is assigned, the course must be repeated the following semester(s) until the course is passed.
Geospatial Systems Engineering Courses
Geospatial data management and analysis is a fundamental activity in describing, documenting, and modeling the built and natural environment. This course examines the use of various types of geospatial data, including remote sensing data, for characterization of geospatial phenomena. Topics covered include geodetic datums and coordinate systems, digital representation of geospatial data, positional accuracy and error propagation, spatial analysis and modeling techniques, and emerging topics such as geospatial AI. A GIS (ArcGIS Pro) will be used to investigate and visualize patterns and relationships using various types of geospatial data sets. Familiarity with basic concepts of probability and statistics; familiarity with calculus and matrix algebra is helpful for some topics though not required; experience working with ArcGIS or other GIS software is helpful.
Course teaches programming techniques in geospatial fields, such as how to automate GIS tasks using Python and other scripting languages. Automation can make your work easier, faster, and more accurate, and knowledge of a scripting language is a highly desired skill in GIS analysts.
Handling, processing and analyzing spatial data in an open and reproducible way is critical in the emergence of geospatial data science. Various open source packages and tools for geospatial data and process are available and they provide an effective solution for flexibility, reproducibility and transparency in geospatial research and analysis. This course focuses on various programming skills in handling and manipulating spatial data through open source environments. Creating spatial database and queries, exploring spatial data, modeling spatial data, and visualizing spatial data through open source packages will be covered.
This course will focus on spatial database principles and the practical skills of design, implementation, and use of spatial databases. This course will first cover fundamentals of relational database design, and then focus on design and management of spatial databases utilizing geodatabase models. In addition, case studies of geodatabase design models in several applications will also be covered. This course is intended for students who want to design, create, maintain and manipulate data from a geospatial database.
Geospatial data mining is the process of automatically discovering interesting and useful spatial patterns in large geospatial datasets. This course begins by covering fundamental concepts and techniques in data mining. Specific topics covered include classification, association analysis, and cluster analysis. It then focuses on using these data mining techniques for handling spatial, temporal and spatial-temporal data. In addition, the data mining tools to implement applications in geoscience will also be covered.
Introduces the fundamentals of mapping with small Unmanned Aircraft Systems (sUAS) using digital imaging sensors to produce high resolution, accurate geospatial surveying products. The course will cover the full spectrum of UAS mapping including technology, current regulations, operational factors, flight design, photogrammetric data processing, and data fidelity. Supporting concepts will include georeferencing and ground control, 3D reconstruction with structure-from-motion photogrammetry, orthorectification and image mosaicking, accuracy assessment, and current developments in UAS for geomatics. Processing and analysis workflows using commercial and open-source software will be conducted to transform UAS image sequences into geospatial data products, extract analytics, assess results, and optimize output.
Addresses the foundations and computational techniques of Global Navigation Satellite Systems (GNSS) and inertial measurement units (IMUs) for autonomous navigation applications. Specifically, the course will cover concepts and principles of GNSS signal structures and the derivation of observables; error sources and corrections; point, differential, and kinetic positioning techniques; IMU linear and angular dynamics modeling; mechanization of inertial navigation and error propagation; global/local coordinate frames and conversion; and filtering techniques for GNSS/IMU integration. The course also covers current and future capabilities of emerging geopositioning systems as they relate to autonomous navigation and mobile devices.
This course will focus on geospatial statistics methods particularly multivariate statistics and applications of the statistical procedures to research geospatial problems. Research on geospatial problems often requires the application of multivariate statistical methods to produce new insight. Various existing statistic software is available to conduct multivariate statistical analysis, however, the interpretation of the results rely on solid understanding of statistic principles and theories. This course is intended for students who want to apply statistical methods to research geospatial problems.
A review of the evolution of European cadastral systems and land records traditions and alternatives. Examination of the goals and purposes of land tenure systems with attention to social, political, legal, economic, organizational, and technical issues. Exploration of U.S. modernization efforts and the problems of developing countries.
A study of the current and emerging status of computer law in electronic environments. Covers issues related to: privacy, freedom of information, confidentiality, copyright, and legal liability; the impact of statue and case law on use of digital databases and spatial databases; and research of legal options of conflicts related to spatial data. Additional description: study of specific court cases specific to Texas boundary law. Introduction into International Boundaries and Treaties.
This course will focus on the theory, techniques, and applications of advanced geospatial analytics. Topics covered include spatial point patterns, network analysis, area objects and spatial autocorrelation, and spatial interpolation. New approaches to geospatial analytics will also be covered. This course emphasizes the methods and the applied side of geospatial analytics that can be useful in students' own theses or projects for their current or potential employers.
This course will ensure that students understand and apply cartographic theory for visual communication and visual thinking, and be able to create, evaluate, and critique reference and thematic maps using GIS software. Fall.
A study of the analytical and systems engineering foundations of airborne photogrammetry and geodetic imaging technologies for 2D and 3D mapping of natural and built environments. The course covers principles of digital imaging, camera calibration, stereo and multi-view photogrammetry, analytical photogrammetry, structure-from-motion, light detection and ranging (lidar) systems, and emergent scanning and imaging approaches. The course also details photogrammetric and lidar data processing, point cloud analysis, and applications.
Addresses the interpretation, processing and analysis techniques of remotely sensed data acquired by orbital and sub-orbital platforms. Physical principles and imaging mechanisms, remote sensing systems, data characteristics, image processing, and information extraction methods will be covered. Topics include passive optical imaging with multispectral, hyperspectral, and thermal sensing; active imaging with radar sensing; image corrections and rectification; spatial/frequency transforms and image filtering; image classification and feature extraction; and image processing with machine learning techniques. Applications in the course will be focused on geomatics and monitoring of natural and built environments.
Variable content study of specific areas of geospatial surveying engineering. May be repeated for credit when topics vary. Offered on sufficient demand.
Seminar in reading and critical evaluation of academic literature in the fields relating to geospatial engineering. Research methods for geospatial engineering will be introduced. Student will design, implement, and evaluate an advanced, contemporary geospatial engineering technology to solve a geospatial problem.
Study in areas of current interest. A maximum of 6 SCH of approved Directed Independent Study may count toward the MS degree.