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Lecture Signals, systems & inference – Lecture 19: Einstein-Wiener-Khinchin theorem, PSD applications, modeling filters

The following will be discussed in this chapter: Einstein-Wiener-Khinchin theorem, periodogram averaging (illustrating the Einstein-Wiener-Khinchin theorem), respiratory model, modeling filters.

6/18/2020 1:33:53 AM +00:00

Lecture Signals, systems & inference – Lecture 18: Power Spectral Density (PSD)

The following will be discussed in this chapter: iid signal x[n], uniform in [-0.5,+0.5]; extracting the portion of x(t) in a specified frequency band; questions (warm-up for Quiz 2!); periodograms (e.g., a unit-intensity “white” process); periodogram averaging (illustrating the Einstein-Wiener-Khinchin theorem).

6/18/2020 1:33:46 AM +00:00

Lecture Signals, systems & inference – Lecture 17: LTI filtering of WSS processes

Lecture Signals, systems & inference – Lecture 17: LTI filtering of WSS processes. The following will be discussed in this chapter: iid signal x[n], uniform in [-0.5,+0.5]; DTFT magnitude |X(ejΩ)|, 0 to 2π; transform magnitudes for 4 realizations of a ±1 Bernoulli process.

6/18/2020 1:33:40 AM +00:00

Lecture Signals, systems & inference – Lecture 16: Wide-sense stationary processes; LTI filtering of WSS processes

Lecture Signals, systems & inference – Lecture 16: Wide-sense stationary processes; LTI filtering of WSS processes. The following will be discussed in this chapter: Random process; iid signal x[n], uniform in [-0.5,+0.5]; y=h*x, with h[n] = δ[n] + δ[n-1]; y=h*x, with h[n] = (0.5) n u[n]; |H| when h[n]=(0.5) n u[n].

6/18/2020 1:33:34 AM +00:00

Lecture Signals, systems & inference – Lecture 15: Normal equations random processes

The following will be discussed in this chapter: Zestimates, LMMSE for multivariate case, Geometric picture, applying orthogonality gives the “normal equations”, estimating mean vector and covariance matrix from data, random variable, random process,...

6/18/2020 1:33:28 AM +00:00

Lecture Signals, systems & inference – Lecture 14: LMMSE estimation, orthogonality

The following will be discussed in this chapter: LMMSE estimator: first step (obtaining unbiasedness), LMMSE estimator: second step (solve reduced problem), LMMSE estimator as projection, putting it all together, orthogonality relations, extension to multivariate case,...

6/18/2020 1:33:21 AM +00:00

Lecture Signals, systems & inference – Lecture 13: Vector picture for first- and second-order statistics; MMSE and LMMSE estimation

The following will be discussed in this chapter: Covariance and correlation, correlation coefficient, ageometric picture, geometric interpretation of correlation coefficient, orthogonality.

6/18/2020 1:33:15 AM +00:00

Lecture Signals, systems & inference – Lecture 12: Probabilistic models, random variables

Lecture Signals, systems & inference – Lecture 12: Probabilistic models, random variables. The following will be discussed in this chapter: Sample space and events, random variable, joint pdf, marginal pdfs.

6/18/2020 1:33:09 AM +00:00

Lecture Signals, systems & inference – Lecture 11: State feedback, observer-based feedback

Lecture Signals, systems & inference – Lecture 11: State feedback, observer-based feedback. The following will be discussed in this chapter: System (“plant”), a good model, observer configuration, observer-based controller.

6/18/2020 1:33:03 AM +00:00

Lecture Signals, systems & inference – Lecture 10: Observers, state feedback

Lecture Signals, systems & inference – Lecture 10: Observers, state feedback. The following will be discussed in this chapter: System (“plant”), a good model, observer configuration, observer performance (with measurement noise), observer for ship heading error, state feedback.

6/18/2020 1:32:57 AM +00:00

Lecture Signals, systems & inference – Lecture 9: Observers for state estimation

The following will be discussed in this chapter: Hidden modes of composite systems: series (cascade) connections, hidden modes of composite systems: feedback and parallel connections, performance of real-time simulation, observer configuration, observer performance (with no measurement noise),...

6/18/2020 1:32:50 AM +00:00

Lecture Signals, systems & inference – Lecture 8: Matrix exponential, ZIR+ZSR, transfer function, hidden modes, reaching target states

The following will be discussed in this chapter: Modal solution of driven DT system, underlying structure of LTI DT statespace system with L distinct modes, reachability and observability, hidden modes,...

6/18/2020 1:32:44 AM +00:00

Lecture Signals, systems & inference – Lecture 7: Full modal solution, asymptotic stability, reachability and observability

The following will be discussed in this chapter: Modal solution of CT system ZIR, asymptotic stability of CT system, the DT case: linearization at an equilibrium, modal solution of driven DT system, underlying structure of LTI DT statespace system with L distinct modes, reachability and Observability,...

6/18/2020 1:32:38 AM +00:00

Lecture Signals, systems & inference – Lecture 6: Modal solution of undriven CT LTI state-space models

The following will be discussed in this chapter: Glucose-insulin system, UVA/Padova model (FDA approved!), linearization at an equilibrium yields an LTI model, phase plane trajectories, complex eigenvalue pairs (CT case).

6/18/2020 1:32:32 AM +00:00

Lecture Signals, systems & inference – Lecture 5: State-space models, equilibrium, linearization

Lecture Signals, systems & inference – Lecture 5: State-space models, equilibrium, linearization. The following will be discussed in this chapter: State variables are (relevant) “memory” variables, defining properties of CT state-space models and the governing equations, linearization at an equilibrium yields an LTI model,...

6/18/2020 1:32:26 AM +00:00

Lecture Signals, systems & inference – Lecture 4: State-Space Models

The following will be discussed in this chapter: Exponential unit sample response, General transfer function of a causal DT LTI system with distinct poles, delay-adder-gain system, defining properties of DT state-space models.

6/18/2020 1:32:20 AM +00:00

Lecture Signals, systems & inference – Lecture 3: Energy spectral density

Lecture Signals, systems & inference – Lecture 3: Energy spectral density. The following will be discussed in this chapter: Inner (dot) product of signals, transform of reversed signal, transform of inner product, energy, energy spectral density (ESD), cross (energy) spectral density, noise-free signal,...

6/18/2020 1:32:14 AM +00:00

Lecture Signals, systems & inference – Lecture 2: Transforms

The following will be discussed in this chapter: DT convolution to z-transform (and system function), DT convolution to DTFT (and frequency response), using frequency response to specify response to sinusoidal inputs, frequency response (DTFT of unit sample response),...

6/18/2020 1:32:08 AM +00:00

Lecture Signals, systems & inference – Lecture 1: Introduction

This lecture introduces the signals, systems & inference. Topics include: Weather prediction, the measurements, the interventions, blood pressure regulation, time-based capnography, mechanistic model for capnography and the governing equations,...

6/18/2020 1:32:01 AM +00:00

Lecture Digital communication systems - Lecture 23

The learning objectives for this chapter include: recognize the global leaders in newspapers, radio, and television; distinguish among the four main theories of government-press relationships; categorize media systems by ownership patterns and degree of government control; understand how politics, culture, geography, history, and economics affect a country's media system.

6/18/2020 1:31:55 AM +00:00

Lecture Digital communication systems - Lecture 22

The main contents of this chapter include all of the following: Defining advertising, a brief history of advertising, advertising in the digital age, organization of the consumer advertising industry, producing advertising, economics, business-to-business advertising.

6/18/2020 1:31:49 AM +00:00

Lecture Digital communication systems - Lecture 21

The following will be discussed in this chapter: The press, the law, and the courts; protecting news sources; covering the courts; reporters’ access to information; defamation; invasion of privacy; copyright; obscenity and pornography; regulating broadcasting; regulating cable TV; the telecommunications act of 1996; regulating advertising.

6/18/2020 1:31:43 AM +00:00

Lecture Digital communication systems - Lecture 20

The learning objectives for this chapter include: distinguish among the types of informal controls on the media, explain the most important ethical principles, explain what the standards departments and performance codes are, discuss the relationship between the media and their advertisers vis-à-vis ethical practices, understand the pros and cons of pressure groups.

6/18/2020 1:31:37 AM +00:00

Lecture Digital communication systems - Lecture 19

The following will be discussed in this chapter: Investigating mass communication effects, effects on knowledge and attitudes, media effects on behavior: a short history, the impact of televised violence, encouraging prosocial behavior, other behavior effects, research about the social effects of the internet, communication in the future: social impact.

6/18/2020 1:31:31 AM +00:00

Lecture Digital communication systems - Lecture 18

The learning objectives for this chapter include: recognize the global leaders in newspapers, radio, and television; distinguish among the four main theories of government-press relationships; categorize media systems by ownership patterns and degree of government control; understand how politics, culture, geography, history, and economics affect a country's media system.

6/18/2020 1:31:25 AM +00:00

Lecture Digital communication systems - Lecture 17

The learning objectives for this chapter include: distinguish among the types of informal controls on the media, explain the most important ethical principles, explain what the standards departments and performance codes are, discuss the relationship between the media and their advertisers vis-à-vis ethical practices, understand the pros and cons of pressure groups.

6/18/2020 1:31:18 AM +00:00

Lecture Digital communication systems - Lecture 16

The following will be discussed in this chapter: The press, the law, and the courts; protecting news sources; covering the courts; reporters’ access to information; defamation; invasion of privacy; copyright; obscenity and pornography; regulating broadcasting; regulating cable TV; the telecommunications act of 1996; regulating advertising.

6/18/2020 1:31:12 AM +00:00

Lecture Digital communication systems - Lecture 15

The main contents of this chapter include all of the following: Defining advertising, a brief history of advertising, advertising in the digital age, organization of the consumer advertising industry, producing advertising, economics, business-to-business advertising.

6/18/2020 1:31:06 AM +00:00

Lecture Digital communication systems - Lecture 13

This chapter includes contents: Deciding what is news, news reporting in the digital age, categories of news and reporting, the news flow, the wire services, media differences in news coverage, readership and viewership.

6/18/2020 1:30:59 AM +00:00

Lecture Digital communication systems - Lecture 12

This chapter includes contents: A brief history of the computer, the internet, structure and features of the internet, the evolving internet, economics, feedback, social implications, the future: the evernet, the internet and the web.

6/18/2020 1:30:53 AM +00:00