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This course presents the methods for analyzing deformable structures in equilibrium. It is fundamental to mechanical analysis and design and is the basis for many advanced courses and professions in mechanical, civil, materials, biomedical and biological engineering.

This course is an introduction to electronic circuits. We start with the basic quantities used to characterize circuit operation (like current, voltage, and power) and then enforce several physical laws to form the basis of our approach to circuit analysis. Networks comprising passive circuit elements such as resistors, inductors, and capacitors will be examined under constant dc, transient, and sinusoidal steady-state conditions. Active components including transistors and Op-Amps will be introduced and used to build simple amplifiers and switching power converters. Many of these ideas will be unified mathematically through the use of Laplace transforms and associated transfer functions. In the lab part of the course, we will learn how to use modern instruments to test circuits, and explore the concepts from lecture applied to real circuits. Finally, we will develop some simple modeling software in MATLAB to numerically predict the results from analysis and experiment.

Intermediate programming in a high-level language and introduction to software engineering. Topics include object-oriented programming (objects, classes, subtypes, encapsulation, polymorphism), program correctness (specifications, invariants, testing), algorithm analysis (asymptotic complexity, big O notation), recursion, data structures (lists, trees, stacks, queues, heaps, hash tables, graphs), iteration and searching/sorting, graph algorithms, and concurrent and event-driven programming (graphical user interfaces, synchronization). Java is the principal programming language.

This course serves as an introduction to thermodynamics and physical chemistry focused on the application to biomolecular systems. Topics include the role of entropy and free energy in determining biological reactions and processes such as enzymatic reactions or molecular interactions, protein folding/unfolding, single molecule mechanics, energy states, and equilibrium distribution of biomolecular and other systems. This course serves as the foundation for the Molecular, Cellular, and Systems Engineering (MCSE) concentration as well as the molecular principles of biomedical engineering course.

Intermediate software design and introduction to key computer science ideas. Topics are similar to those in CS 2110 but are covered in greater depth, with more challenging assignments. Topics include object-oriented programming, program structure and organization, program reasoning using specifications and invariants, recursion, design patterns, concurrent programming, graphical user interfaces, data structures as in CS 2110, sorting and graph algorithms, asymptotic complexity, and simple algorithm analysis. Java is the principal programming language.

Computer systems programming involves developing software to connect the low-level computer hardware to high-level, user-facing application software. This course will provide a strong foundation in the principles, practices, and art of computer systems programming using the C and C++ programming languages. Students will learn procedural programming in C and how to theoretically analyze and practically implement basic data structures and algorithms. Students will transition to C++ to explore object-oriented, generic, functional, and concurrent programming before exploring advanced data structures and algorithms involving trees, tables, and graphs. Students will explore systems programming using the POSIX standard library. The course includes a series of programming assignments for students to put the principles they have learned into practice. For more information, see https://www.csl.cornell.edu/courses/ece2400.

Engineering problems involving material and energy balances. Batch and continuous reactive systems in the steady and unsteady states. Introduction to phase equilibria for multicomponent systems. Examples drawn from a variety of chemical and biomolecular processes.

Quantitative analysis of transport phenomena in physiological systems, including fluid mechanics and mass transfer. Fluid statics, mass and momentum conservation, laminar and turbulent flow, microscale and macroscale analytical methods, mass transport with biochemical reactions, applications to transport in tissue and organs.

Presents the definitions, concepts, and laws of thermodynamics. Topics include the first and second laws, thermodynamic property relationships, and applications to vapor and gas power systems, refrigeration, and heat pump systems. Examples and problems are related to contemporary aspects of energy and power generation and to broader environmental issues.

EAS 2250 provides a broad math-, physics-, and chemistry-based introduction to the earth sciences, including geology, paleontology, oceanography, and atmospheric science. Topics covered include formation of the Earth, the chemistry and physics of the Earth's interior, plate tectonics, weathering and erosion, soil development, stream and groundwater flow, volcanism and crustal deformation, the evolution of life, ocean and atmospheric structure, circulation and heat transport, ocean waves and tides, generation of storms, seawater chemistry, mineral and energy resources, and climate change.

This course provides an introduction to the design and implementation of digital circuits and microprocessors. Topics include transistor network design, Boolean algebra, combinational circuits, sequential circuits, finite state machine design, processor pipelines, and memory hierarchy. Design methodology using both discrete components and hardware description languages is covered in the laboratory portion of the course.

While the environmental challenges and hazards facing society over the next decades are diverse and complex, the next generation of scientists and engineers can look forward to a steadily increasing family of space-based observations that will help us better understand the ongoing changes. This data can enable researchers to highlight potential problems and gain the attention of decision-makers, and to evaluate the efficacy of attempts at mitigation so that we can redirect our efforts into the most useful avenues. Examples of engineering solutions that require evaluation of their outcomes include management and maintenance of coastal infrastructure, flood control or dam-building, and efforts to change land cover/land use. In this course, we will study the key questions facing our planet today, and explore the use of relevant data from current and future satellite missions. We will introduce students to Geographical Information Systems (GIS) and other methods for viewing and manipulating data. We will build from analysis of individual images, including multispectral imagery, and extend to time-series analysis of both optical and microwave data. Students will design and present a capstone project involving satellite data analysis and interpretation.

Students will quantitatively understand and analyze environmental issues such as: the impact of industrial contaminants and excess nutrients on water quality; the global carbon cycle; improving global access to clean water. This course integrates principles from chemistry, biology, math and engineering to understand and solve real-world problems that impact three major environmental compartments: air, water, and soil. Students will solve mass and energy balances beginning with simple, closed systems, then progress through reactive, open systems to describe environmental fate and transport of pollutants, natural environmental cycles and remediation scenarios. Students will be exposed to technical and lay material from interdisciplinary sources to understand the environmental externalities - social, political, economic and cultural - that must be considered when proposing solutions to today's most pressing environmental issues. BE and EnvE students must complete either BEE 2510 or BEE 2600 according to their academic plan. Students who complete both BEE 2510 and BEE 2600 receive engineering credit toward their degree for only one of these courses.

Introduces the physics of biological macromolecules (e.g., proteins, DNA, RNA) to students of the physical sciences or engineering who have little or no background in biology. The macromolecules are studied from three perspectives. First, the biological role or function of each class of macromolecules is considered. Second, a quantitative description of the physical interactions that determine the behavior of biomolecular systems. An introduction is provided to probability and statistical methods used to describe the behavior of biological systems. Finally, techniques that are commonly used to probe these systems, with an emphasis on biotechnology applications, are discussed.

This course examines the physical hardware of quantum information processing, quantum communication, and quantum sensing technologies. Topics include an analysis of qubit attributes and an introduction to the operational principles of physical qubits. Specific systems will include photonic circuits, trapped ions, superconducting quantum circuits, isolated solid-state spins and quantum dots.

Focuses on the integration of biological principles with engineering, math, and physical principles. Students learn how to formulate equations for biological systems in class and practice in homework sets. Topics range from molecular principles of reaction kinetics and molecular binding events to macroscopic applications such as energy and mass balances of bioprocessing and engineering design of implantable sensors. Students will also experience scientific literature searches as related to the biological engineering topics, and critical analysis and evaluation of relevant information sources. BEE students must complete either BEE 2510 or BEE 2600 according to their academic plan. BEE students who complete both BEE 2510 and BEE 2600 receive engineering credit for only one of these courses.

Examines the mechanical properties of materials (e.g., strength, stiffness, toughness, ductility) and their physical origins. Explores the relationship of the elastic, plastic, and fracture behavior to microscopic structure in metals, ceramics, polymers, and composite materials. Discusses effects of time and temperature on materials properties. Emphasizes considerations for design and optimal performance of materials and engineered objects.

Examines the electrical and optical properties of materials. Topics include the mechanism of electrical conduction in metals, semiconductors and insulators; tuning of electrical properties in semiconductors, charge transport across metal/semiconductor and semiconductor/semiconductor junctions, and the interaction of materials with light; semiconductor electronic devices; and the materials science of device fabrication. Applications in microelectronics, solar cells, electronics, and display technologies are discussed. Semiweekly labs provide hands-on experience characterizing electronic materials and devices.

Gives students a working knowledge of basic probability and statistics and their application to engineering. Includes computer analysis of data and simulation. Topics include: random variables, probability distributions, expectation, estimation, testing, experimental design, quality control, and regression.

An introduction to data science for engineers. The data science workflow: acquisition and cleansing, exploration and modeling, prediction and decision making, visualization and presentation. Tools for data science including numerical optimization, the Discrete Fourier Transform, Principal Component Analysis, and probability with a focus on statistical inference and correlation methods. Techniques for different steps in the workflow including outlier detection, filtering, regression, classification, and techniques for avoiding overfitting. Methods for combining domain-agnostic data analysis tools with the types of domain-specific knowledge that are common in engineering. Ethical considerations. Optional topics include classification via neural networks, outlier detection, and Markov chains. Programming projects in Python.

Introduction to numerical methods, computational mathematics, and probability and statistics. Development of programming and graphics proficiency with MATLAB and spreadsheets. Topics include Taylor-series approximations, numerical errors, condition numbers, operation counts, convergence, and stability, probability distributions, hypothesis testing. Included are numerical methods for solving engineering problems that entail roots of functions, simultaneous linear equations, statistics, regression, interpolation, numerical differentiation and integration, and solution of ordinary and partial differential equations, including an introduction to finite difference methods. Applications are drawn from different areas of engineering. A group project uses these methods on a realistic engineering problem.