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Course Criteria
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4.00 Credits
ENG BE 402 or equivalent. The course addresses the interface between cellular and molecular phenomena using methods of engineering system analysis. Topics include storage and processing of genetic information in the cell, the regulation and control of gene action, the analysis of cell surface receptor/ligand binding and trafficking, signal transduction, receptor mediated cell responses, metabolic pathways and control mechanisms, cell proliferation and growth, and some analysis of the immune system. The interpretation and analysis of these systems will be based, as much as possible, on the engineering methodologies taught in traditional signals and systems courses, with some additional training in non-linear systems kinetics and dynamics. The emphasis in the course will be to expose undergraduate and graduate students to molecular/cellular phenomena for which there is sufficient experimental data and mechanistic understanding for the analysis from an engineering perspective. The aim is not just to translate the cellular and molecular systems into engineering terminology, but to attempt to be sufficiently productive for the design of modified biological systems. 4 cr.
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4.00 Credits
ENG EK 424, CAS CH 102, and ENG BE 505 or consent of instructor. Covers the basic properties of biological macromolecules and assemblies including proteins, nucleic acids, and membranes. Among the topics covered are the forces that govern biological structures, how proteins act as catalysts, how membranes act to store energy, and how nucleic acids and proteins are synthesized in cells. Methods for manipulating the living cells to change their properties and to produce specific proteins of nucleic acids are detailed. 4 cr.
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4.00 Credits
CAS CH 102, CAS PY 212, and ENG EK 424. Physical structure and properties of DNA. The physical principles of the major experimental methods to study DNA are explained, among them: X-ray analysis, NMR, optical methods (absorption, circular dichroism, fluorescence), centrifugation, gel electrophoresis, chemical and enzymatic probing. Different theoretical models of DNA are presented, among them the melting (helix-coil) model, the polyelectrolyte model, the elastic-rod model, and the topological model. Theoretical approaches to treat the models, (e.g., the Monte Carlo method) are covered. Special emphasis is placed on DNA topology and DNA unusual structures and their biological significance. Major structural features of RNA are considered in parallel with DNA. The main principles of DNA-protein interaction are presented. the role of DNA and RNA structure in most fundamental biological proceses, replication, transcription, recombination, reparation, and translation is considered. 4 cr.
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4.00 Credits
ENG BE 505 or equivalent; graduate standing or consent of instructor; ordinary differential equations; linear dynamic systems and linear algebra are recommended. Introduction to nonlinear dynamical systems in biomedical engineering. Qualitative, analytical, and computational techniques. Stability, bifurcations, oscillations, multistability, hysteresis, multiple time-scales, chaos. Introduction to experimental data analysis and control techniques. Applications discussed include genetic circuit engineereing, neural processing, cardiac control, posture control, and populations dynamics. 4 cr.
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3.00 Credits
CAS MA 226 and ENG EK 510, and ENG BE 401 or ENG EC 401 and ENG BE 200 or ENG EK 500 and working knowledge of MATLAB; grad prereq: CAS MA 226 and ENG BE 401 or ENG EK 500. Introductory course in computational visual neuroscience. Provides a survey of general neural network models for vision and the computational vision theories and survey of neuroanatomy, neurophysiology, and psychophysics underlying specific problems in vision. Topics addressed include models of visual motion analysis such as optic flow, boundary extraction, and three-dimensional structure and motion, and models of stereopsis. Briefly addresses learning mechanisms and their relationship to brain plasticity. A term project is required for graduate credit. 4 cr. TOP OF PAGE ENG EC 311 Introduction to Logic DesignCoreq: ENG EK 307. Introduction to hardware building blocks used in digital computers. Boolean algebra, combinatorial and sequential circuits: analysis and design. Adders, multipliers, decoders, encoders, multiplexors. Programmable logic devices: read-only memory, programmable arrays. Counters and registers. Includes lab. 4 cr.
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4.00 Credits
ENG EC 311. Introduction to the fundamentals and design of computer systems. Topics covered include computer instruction sets, assembly language programming, arithmetic circuits, CPU design (data path and control, pipelining), performance evaluation, memory devices, memory systems including caching and virtual memory, and I/O. Project using design automation tools. Includes lab. 4 cr.
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4.00 Credits
ENG EK 127. The goal of this course is to introduce engineering students to advanced programming techniques and basic data structures concepts. The course will start with a fast-paced introduction to the fundamentals of object-oriented programming, dynamic memory allocation, and file input/output operations. The stress in this introduction will be on practical programming issues, such as proper programming style and optimization, debugging techniques and compilation, and graphical user interfaces. Students will also be introduced to fundamental data structures, such as linked lists, queues, trees, hash tables, and graphs, and algorithmic analysis techniques in the context of searching and sorting methods. Throughout the course, students will utilize industry-standard programming tools, and examples for theoretical concepts will be provided from contemporary applications. Duplicate credit with CAS CS 111; cannot take both for credit. 4 cr.
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4.00 Credits
ENG EC 327 and CAS MA 193. Introduction to the general concept of algorithms. Efficiency and run-time of algorithms. Various approaches to design of algorithms and their applications to numerous typical numerical and non-numerical problems. 4 cr.
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3.00 Credits
Prereq: CAS MA 225. Introduction to modeling uncertainty in electrical and computer systems. Experiments, models, and probabilities. Discrete and continuous random variables. Reliability models for circuits. Probability distributions. Moments and expectations. Random vectors. Functions of random variables. Sums of random variables and limit theorems. Signal detection and estimation. Basic stochastic processes. Discrete-time Markov chains. State-diagrams. Applications to statistical modeling and interpretation of experimental data in computer, communication, and optical systems. 4 cr.
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3.00 Credits
CAS MA 226 and ENG EK 307. Continuous-time and discrete-time signals and systems. Convolution sum, convolution integral. Linearity, time-invariance, causality, and stability of systems. Frequency domain analysis of signals and systems. Filtering, sampling, and modulation. Laplace transform, z-transform, pole-zero plots. Linear feedback systems. Includes lab. Cannot be taken for credit in addition to ENG BE 401. 4 cr.
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