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Course Criteria
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3.00 Credits
This course extends the classical continuous time control techniques fromEE-3720 to the areas of discrete-time systems and state-space techniques. An independent hardware project is required that demonstrates the principles of control systemanalysis,modeling, and design. Control systems are analyzed,modeled, and designed using frequency response, z-transform and state-space techniques. (prereq: (EE-3720 or EE-371), EE-3220)
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3.00 Credits
This course provides the theory and hands-on experience necessary to enable students to design programmable controller systemapplications. This course highlights the systems approach as an aid to understandingmodern industrial programmable controllers. Coverage begins with a review of controller basics and conventional approaches and proceeds through the concept of programmable logic including the use ofmicroprocessors as controller elements. In addition, programming, input/output elements, peripherals, and standards and codes that govern interfacing aspects are covered. Development, design and understanding of analog input/output devices are also covered. The use of PCs as a device to programPLCs is developed. Thematerial is reinforced by laboratory sessions that provide the opportunity to learn to develop several popular systemapplications. (prereq: EE-2901)
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3.00 Credits
This course introduces students to the basic concepts ofmodeling uncertainty in systems through the use of fuzzy sets. The underlying concepts of fuzzy sets are introduced and their role in such applications as semantic interpreters, control systems and reasoning systems is presented. Students gain firsthand experience of fuzzy sets through programming assignments and a short research project. (prereq: senior standing in CE, EE, or SE)
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3.00 Credits
This course introduces students to the basic concepts ofmodeling and simulating adaptive and learning systems using neural networks. The underlying concepts of neural networks are introduced, as well as a number of common topologies and learning rules used in neural networks. Students gain firsthand experience of neural networks through computer assignments and a short research project. (prereq: CS-150 or equivalent,MA-330 orMA-343, senior standing in EE, EE-2901)
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3.00 Credits
This course introduces the student tomachine vision technology and its applications. Topics include lighting equipments and techniques, image acquisition devices/systems and techniques, and image processing techniques. Interfacingmachine vision systems to other engineering systems are also discussed. Laboratory experiments and a class project include introduction to various kinds of vision systems, image processing techniques, and applications. (prereq: senior standing in EE or CE)
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3.00 Credits
The objective of this course is to provide the student with an overview of topics in the field of artificial intelligence (AI). The course also provides the student with a working knowledge of designing an expert systemand applying expert systemtechnology in designing and analyzing engineering systems. The first part of the course covers historical background, knowledge acquisition and knowledge representation including propositional calculus, predicate calculus, semantic networks, frame systems and production rules. Various search techniques will be discussed. Fuzzy logic systems, neural network systems and computer vision systems will be briefly discussed in the second part of the course. Languages for AI problemsolving such as Prolog and/or LISP will be introduced. The third part of this course will be devoted to the design of expert systems. Applications of expert systems in engineering systemdesign and analysis will be stressed throughout. Case studies will be discussed. Class project is required. Students are encouraged to design expert systems for his/her own engineering applications, and an expert shell will be used to implement the design. (prereq: (CS-150 or CS-1010 or SE-1010 or EE-1910) and, (MA-3260 orMA-262))
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3.00 Credits
Students enrolled in this course are afforded the opportunity to pursue a specialized topic in their chosen field of study. After an approved area of study has been selected, weeklymeetings with the course advisor are required. A final written report, the format of which is left to the discretion of the advisor, is required at the end of the term. (prereq: senior standing and consent of department chairman)
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12.00 Credits
Students enrolled in this course are afforded the opportunity to pursue a specialized topic in their chosen field of study. After an approved area of study has been selected, weeklymeetings with the course advisor are required. A final written report, the format of which is left to the discretion of the advisor, is required at the end of the term. (prereq: for FHL students only)
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3.00 Credits
In this course students are given background in noisemechanisms andmodels as applicable to analog electronics. Topics covered include origin of noise, resistor, BJT, and FET noisemodels, amplifier noise, design of low-noise amplifiers and power supplies, simulation of noise in SPICE, and noisemeasurement systems. An individual project is required. (prereq: EE-3101, consent of instructor)
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3.00 Credits
This course continues investigation of single and cascaded BJT andMOSFET amplifiers. In addition,midband gains, impedances, and frequency responses ofmulti-transistor amplifiers are studied. The effects of classic feedback configurations on amplifier characteristics are included. A significant portion of the course grade is based on the student's performance on assigned design projects. Students are expected to use previously learned design tools such as PSPICE to explore alternatives and verify their designs. The designs are constructed and tested in the laboratory, and documented in formal design reports. An individual project is required. (prereq: EE-3101, EE-3111, consent of instructor)
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