Random Processes in Systems
The course provides fundamental knowledge in stochastic systems and statistical signal processing with emphasis on practical engineering applications. The students will be able to model random processes and deeply understand the concepts of stationarity, ergodicity, independence, correlation and power density, as well as noise modeling concepts. They will be able to design an optimum linear system: Wiener filter or matched filter, or to solve a number of problems in communication, automation, and measurements.
- define and describe concepts of stochastic processes in systems
- list examples of random signals problems and applications
- define and explain theory of stochastic theory
- analyze a practical problem requiring use of stochastic process concept
- combine acquired knowledge and propose a solution to the given problem
- evaluate a practical solution obtained using stochastic processes
Forms of Teaching
12 lectures, preseentation of student team projectsExams
Midterm and final exam.Consultations
After lecturesProgramming Exercises
Homeworks.Other Forms of Group and Self Study
Student team project.
|Type||Threshold||Percent of Grade||Threshold||Percent of Grade|
|Homeworks||50 %||10 %||50 %||0 %|
|Seminar/Project||50 %||20 %||50 %||0 %|
|Mid Term Exam: Written||50 %||30 %||0 %|
|Final Exam: Written||50 %||40 %|
|Exam: Written||50 %||50 %|
|Exam: Oral||50 %|
Week by Week Schedule
- Course remarks. Introduction to stochastic processes.
- Stationary and independent stochastic processes.
- Spectral characteristics of stochastic processes. Examples.
- Stochastic signals in linear systems. Examples.
- Stochastic signals in linear systems. Examples of stochastic signals in linear systems. Random process parameter estimation.
- Signal estimation.
- Signal detection.
- Midterm exam.
- Detection of periodic signals in noise. Noise source modeling. Thermal noise, effective noise temperature, noise in cascade systems.
- Power spectral density measurement. Bandlimited noise. System identification by crosscorrelation. Examples of power spectral density measurement.
- Optimal linear systems. Examples of matched and Wiener filters.
- Scalar quantization. Practical applications of theory: noise in communication systems.
- Practical applications of theory. Examples: Practical applications of theory.
- Presentations and discussion of student team projects.
- Final exam.