He has investigated the evolution of global Internet routing to discover the occurrence of significant events that result in large-scale, repeated changes to the routing system, spanning many years. He has also developed new notions of anomalous routing behavior, and shown how such anomalies can be understood in the context of the economic and engineering goals of ISPs.
Foundations of Bioinformatics I. Introduction to script programming and basic biomolecular sequence analysis. Topics covered include sequence alignment, dynamic programming algorithms, hidden Markov models, and their implementation with a scripting language.
Foundations of Bioinformatics II. Topics in bioinformatics such as phylogeny reconstruction, genome-wide association study analysis, structure and sequence analysis, and machine learning and statistical approaches.
Focus of the course is on a hands-on project on a contemporary bioinformatics problem. Data Analysis in Bioinformatics. Students Object tracking phd thesis learn machine learning methods.
They will apply the methods to various problems in bioinformatics using the Python scikit machine learning library.
Previous programming experience is required, previous knowledge of Python is a plus. This course will introduce students to the practice of analyzing large-scale genomic data generated by recent high throughput bio-techniques. It will cover microarray data and short-read sequencing data.
It presents widely used analytical methods and software. The course includes several case studies on real large-scale genomics datasets.
Students will gain practical experience in large-scale data analysis, which is highly desirable by both industry and academia employers. Data Mining and Management in Bioinformatics. Concepts and principles of data management in bioinformatics. Presents methods for indexing, querying, and mining data obtained from molecular and evolutionary biology.
Programming, Data Structures, and Algorithms. Computer science students cannot use this course for graduate degree credit. Intensive introduction to computer science principles: Programming assignments are included. Foundations of Computer Science. Cannot be used for graduate credit towards the M.
Introduction to the concepts of iteration, asymptotic performance analysis of algorithms, recursion, recurrence relations, graphs, automata and logic, and also surveys the main data models used in computer science including trees, lists, sets, and relations.
Programming assignments are given. Graduate Co-op Work Experience I. Provides on-the-job reinforcement and application of concepts presented in the undergraduate computer science curriculum.
Work assignments are identified by the co-op office and developed and approved by the CIS department in conjunction with the student and employer.
Students must submit, for CIS department approval, a proposal detailing the nature of the intended work. A report at the conclusion of the semester work experience is required.
Students must have the approval of the co-op advisor for the CIS department.
Provides on-the-job reinforcement and application of concepts presented in the undergraduate or graduate computer science curriculum. One immediately prior 3-credit registration for graduate co-op work experience with the same employer.
Requires approval of departmental co-op advisor and the Division of Career Development Services.
Must have accompanying registration in a minimum of 3 credits of course work. Basic constructs and syntax and then the core advanced features. Emphasis is on the latest version of Java, both deprecated methods and newly introduced features are discussed.
This course involves computational methods providing secure Internet communication. Among the topics covered are: Security threats in communication systems; conventional cryptography: Data Structures and Algorithms.
Intensive study of the fundamentals of data structures and algorithms. Presents the definitions, representations, processing algorithms for data structures, general design and analysis techniques for algorithms.
Covers a broad variety of data structures, algorithms and their applications including linked lists, various tree organizations, hash tables, strings, storage allocation, algorithms for searching and sorting, and a selected collection of other algorithms.VISION-BASED DETECTION, TRACKING AND CLASSIFICATION OF VEHICLES USING STABLE FEATURES WITH AUTOMATIC CAMERA CALIBRATION A Dissertation Presented to the Graduate School of Clemson University In Partial Fulﬁllment of the .
Towards Robust Visual Object Tracking: Proposal Selection & Occlusion Reasoning Yang HUA Rapporteur Dr. Patrick PÉREZ Rapporteur Pr.
Deva RAMANAN Contributions of this thesis •Robust model update in the context of long-term tracking •Publication: Y. Hua, K. Alahari, and C. Schmid. •Visual Object Tracking (VOT) Challenge. Object tracking phd thesis, al.
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Tracking and Detecting Objects in Image Sequence Li WANG School of Electrical and Electronic Engineering A thesis submitted to the Nanyang Technological University. Thesis Distributed Tracking and Re-Identification in a Camera Network PhD, , University of California, Santa Barbara, Advisor: srmvision.comath DOI PDF PPT; Distributed Tracking with On-line Multiple Instance Learning in a Camera Network.
Sheng Chen, PhD , Thesis: Object Tracking-by-Segmentation in Videos Eric Marshall, MS , Project: An Empirical Evaluation of Policy Rollout for Clue Jervis Pinto, PhD , Thesis: Incorporating and Learning Behavior Constraints for Sequential Decision Making.