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Course Criteria
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3.00 Credits
Prerequisite(s): CS 100, CS 100L, CS 120, CS 120L, & CS 180 This course introduces students to microprocessor architecture as well as the knowledge required to directly address and program the microprocessor and the various hardware devices connected to it. Since the resulting code is usually faster than similar code written in a high-level language such as C or C++, low-level programming has great importance in improving the response speed of real-time interactive programs. In this course, students program a microprocessor used to control a hand-held gaming device. The processor used is typically an 8-bit machine, which is easier to understand than 32 or 64-bit machines, but uses the same principles. Topics include registers, instruction set, addressing modes, the stack, I/O ports, interrupts, graphics, animation, collision detection, scrolling, and windowing. There is also a brief introduction to the instruction sets used on larger machines.
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3.00 Credits
Prerequisite(s): CS 225 or CS 270, CS 280, & MAT 200 This course provides students with an introduction to the analysis of algorithms, specifically proving their correctness and making a statement about their efficiency. Topics for discussion may include loop invariants, strong mathematical induction and recursion, asymptotic notation, recurrence relations, and generating functions. Students will examine examples of algorithm analysis from searching and sorting algorithms.
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3.00 Credits
Prerequisite(s): CS 300 This course deals with the efficient representation and processing of complex 3D scenes in order to avoid bottlenecks in the use of the CPU and the GPU. Specific topics include a variety of spatial data structures (binary space-partitioning trees, octrees, kd-trees, and grid data structures), several object-culling methods (occlusion, viewport, and portal), and finally the construction and uses of bounding volumes and their hierarchies for collision detection and related geometric operations.
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3.00 Credits
Prerequisite(s): CS 225 or CS 270 This course covers a wide range of topics in software engineering from the practical standpoint. It encompasses project management issues as well as technical development principles and methods. Topics include system architecture, security, methodologies and notation, UML, object oriented analysis and design, requirements analysis, implementation, verification, validation, maintenance, and software engineering standards. Risk management and iterative design receive special emphasis. Student teams will apply acquired knowledge to a substantial project.
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3.00 Credits
Prerequisite(s): CS 280 The course will be taught at the upper division/ graduate level and will bring image analysis and image processing into a unified framework that provides a useful paradigm for both computer vision and image processing applications. Course material covers methods students can apply in creating special effects with digital images and preparing graphics information for either human or computer interpretation. Course content covers both image processing, which transforms an image, and computer vision, which extracts a measurement or description.
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3.00 Credits
Prerequisite(s): CS 225 This course will introduce students to a wide range of concepts and practical algorithms that are commonly used to solve game AI problems. Case studies from real games will be used to illustrate the concepts. Students will have a chance to work with and implement core game AI algorithms. Topics covered will include the game AI programmer mindset, AI architecture (state machines, rule-based systems, goal-based systems, trigger systems, smart terrain, scripting, message passing, and debugging AI), movement, pathfinding, emergent behavior, agent awareness, agent cooperation, terrain analysis, planning, and learning/adaptation.
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3.00 Credits
Prerequisite(s): CS 280 This course deals with constructing computer programs that automatically improve with experience. Observed events are used to inductively construct decision trees, which can be used by computer-controlled game characters to change behaviors. Students will explore concept learning, partial ordering, reinforcement learning, conditional probability, Bayesian learning, the evaluation of hypotheses and instance-based learning. Types of neural networks examined include perceptrons, backpropagation, radial basis functions, and adaptive resonance theory. We demonstrate the effectiveness of genetic algorithms and show the power of a neuro-genetic approach. The class concludes by looking at inductive analytical learning.
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3.00 Credits
Prerequisite(s): Permission of instructor The content of this course will change each time it is offered. It is for the purpose of offering a new or specialized course of interest to the faculty and students that is not covered by the courses in the current catalog.
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3.00 Credits
Prerequisite(s): CS 250 & CS 280 This course covers data compression techniques for still images and multimedia. Students will learn the theory behind data compression and how it is used in specific formats. Methods covered include run-length encoding, Huffman coding, dictionary compression, transforms, and wavelet methods. Students will learn these techniques by examining various popular graphic file formats, such as BMP, JPEG, DXTn, and MPEG.
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3.00 Credits
Prerequisite(s): CS 300, GAT 300, & MAT 300/500 3D animation and modeling play significant roles in computer simulation and video game software. This course introduces students to algorithms for specifying and generating motion for graphical objects. It addresses practical issues, surveys accessible techniques, and provides straightforward implementations for controlling 3D moving entities with different characteristics.
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