71 - 80 of 184 results for: ME

This course introduces computational modeling methods for cardiovascular blood flow and physiology. Topics in this course include analytical and computational methods for solutions of flow in deformable vessels, one-dimensional equations of blood flow, cardiovascular anatomy, lumped parameter models, vascular trees, scaling laws, biomechanics of the circulatory system, and 3D patient specific modeling with finite elements; course will provide an overview of the diagnosis and treatment of adult and congenital cardiovascular diseases and review recent research in the literature in a journal club format. Students will use SimVascular software to do clinically-oriented projects in patient specific blood flow simulations. Pre-requisites: CME102, ME133 and CME192.

The main idea for the course is to seek a deeper and more theoretical understanding of some practically useful techniques for modeling and estimation in engineering design. The class will draw from system identification, system modeling theory, statistics and data science disciplines in order to "let the data speak about the system." Prerequisites: ENGR205, EE263, AA212. We will not use any specific materials covered in these subjects, but we assume basic background knowledge of state space, transfer functions, frequency responses, probability, and linear algebra. Intermediate Proficiency in Matlab is preferred (but Python is OK).

Introduction to the mechanical behaviors of biological tissues in health and disease. Overview of experimental approaches to evaluating tissue properties and mathematical constitutive models. Elastic behaviors of hard tissues, nonlinear elastic and viscoelastic models for soft tissues.

The objective of this course is to develop the ability to derive design insights from quantitative data, similar to the expertise of a UX Researcher. You will learn skills necessary to analyze user/consumer analytical data and how to use statistical tools (R for Statistics) to interpret results. Specific insight tools include A|B testing, factor analysis, linear regression, max/diff analysis and customer life value analysis. The ethics of collecting and analysis of consumer/user data will be explored. Class content will be a mix of lectures, exercises, case studies and a summary team-based project. This class compliments ME341 Design Experiments and can be taken concurrently or after ME341.

First we invent our technologies - and then we use our technologies to reinvent ourselves, as individuals, as communities and ultimately, as a planetary society. The result has been a vast wave of astonishing innovations that in turn have generated the profound challenges facing humanity today. You will work with a suite of forecasting methods essential to cultivating innovator's effective foresight, the ability to spot hidden trends, identify new opportunities, develop responsive innovations and anticipate unintended impacts in the face of exponential uncertainty. Our topical focus this quarter will be the Western US megadrought. We will develop an integrated forecast of its long-term trajectory, explore its implications and working as teams, translate our insights into innovation opportunities, encompassing engineering solutions, behavioral insights and policy recommendations. You will learn the basics of technology diffusion, and how to apply a variety of methodologies including scenario planning, field anomaly interaction, cross-impact analysis, expert judgment elicitation and design thinking tools.

A course focusing on creating small scale objects in precious metals, with equal attention given to design and the process of investment casting in the Product Realization Lab. By application only, see notes below.

For master's students. Educational opportunities in high technology research and development labs in industry. Students engage in internship work and integrate that work into their academic program. Following internship work, students complete a research report outlining work activity, problems investigated, key results, and follow-up projects they expect to perform. Meets the requirements for curricular practical training for students on F-1 visas. Student is responsible for arranging own internship/employment and faculty sponsorship. Register under faculty sponsor's section number. All paperwork must be completed by student and faculty sponsor, as the Student Services Office does not sponsor CPT. Students are allowed only two quarters of CPT per degree program. Course may be repeated twice.

For Ph.D. students. Educational opportunities in high technology research and development labs in industry. Students engage in internship work and integrate that work into their academic program. Following internship work, students complete a research report outlining work activity, problems investigated, key results, and follow-up projects they expect to perform. Meets the requirements for curricular practical training for students on F-1 visas. Student is responsible for arranging own internship/employment and faculty sponsorship. Register under faculty sponsor's section number. All paperwork must be completed by student and faculty sponsor, as the student services office does not sponsor CPT. Students are allowed only two quarters of CPT per degree program. Course may be repeated twice.

Computer based solution of systems of algebraic equations obtained from engineering problems and eigen-system analysis, Gaussian elimination, effect of round-off error, operation counts, banded matrices arising from discretization of differential equations, ill-conditioned matrices, matrix theory, least square solution of unsolvable systems, solution of non-linear algebraic equations, eigenvalues and eigenvectors, similar matrices, unitary and Hermitian matrices, positive definiteness, Cayley-Hamilton theory and function of a matrix and iterative methods. Prerequisite: familiarity with computer programming, and MATH51.