Introduction to the physical foundations of electronics, including electrostatic and magnetostatic fields and basic properties of classical dielectrics and magnetic materials; electron behavior as described by quantum theory, classical and quantum pictures of current flow in electrical conductors, and semiconductor materials (composition, structure, electronic and optical properties). Practical examples will draw from electromagnetics and contemporary materials and device applications.

Embedded systems sense, actuate, compute, and communicate to accomplish tasks in domains such as medical, automotive, and industrial controls. In this course, students will learn the fundamentals of using microprocessor-based embedded systems to solve problems in these domains. By the end of the course, students will be able to choose appropriate hardware based on application requirements, execute and optimize programs on simple microcontrollers, and interface these controllers to other subsystems.

Probability: Experiments, models and probabilities; conditional probability and independence; single discrete and single continuous random variables; Gaussian random variables; expectation; pairs of random variables; random vectors; sums of random variables and the Central Limit Theorm. Statistics: Parameter estimation and confidence intervals; hypothesis testing, estimation of random variables.

Probability: Experiments, models and probabilities; conditional probability and independence; single discrete and single continuous random variables; Gaussian random variables; expectation; pairs of random variables; random vectors; sums of random variables and the Central Limit Theorm. Statistics: Parameter estimation and confidence intervals; hypothesis testing, estimation of random variables.

Probability: Experiments, models and probabilities; conditional probability and independence; single discrete and single continuous random variables; Gaussian random variables; expectation; pairs of random variables; random vectors; sums of random variables and the Central Limit Theorm. Statistics: Parameter estimation and confidence intervals; hypothesis testing, estimation of random variables.

Probability: Experiments, models and probabilities; conditional probability and independence; single discrete and single continuous random variables; Gaussian random variables; expectation; pairs of random variables; random vectors; sums of random variables and the Central Limit Theorm. Statistics: Parameter estimation and confidence intervals; hypothesis testing, estimation of random variables.

Continuous-time signal and system representations. Linear time invariant systems, impulse responses, convolution. Frequency-domain analysis of continuous-time signals and systems: Fourier series, Fourier Transforms, frequency responses, filtering. Laplace Transforms for systems analysis: transient responses, transfer functions, stability. Sampling, aliasing, reconstruction. Applications: modulation, filter design, feedback systems.

Continuous-time signal and system representations. Linear time invariant systems, impulse responses, convolution. Frequency-domain analysis of continuous-time signals and systems: Fourier series, Fourier Transforms, frequency responses, filtering. Laplace Transforms for systems analysis: transient responses, transfer functions, stability. Sampling, aliasing, reconstruction. Applications: modulation, filter design, feedback systems.

Continuous-time signal and system representations. Linear time invariant systems, impulse responses, convolution. Frequency-domain analysis of continuous-time signals and systems: Fourier series, Fourier Transforms, frequency responses, filtering. Laplace Transforms for systems analysis: transient responses, transfer functions, stability. Sampling, aliasing, reconstruction. Applications: modulation, filter design, feedback systems.

Continuous-time signal and system representations. Linear time invariant systems, impulse responses, convolution. Frequency-domain analysis of continuous-time signals and systems: Fourier series, Fourier Transforms, frequency responses, filtering. Laplace Transforms for systems analysis: transient responses, transfer functions, stability. Sampling, aliasing, reconstruction. Applications: modulation, filter design, feedback systems.