Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
  • Faculty of Engineering
  • Dept.of Electrical and Electronics Engineering(Eng
  • Course Structure Diagram with Credits
  • Random Signals
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications
  • ECTS Credit Load

Course Introduction Information

Code - Course Title EEM409 - Random Signals
Course Type Area Elective Courses
Language of Instruction İngilizce
Laboratory + Practice 3+0
ECTS 5.0
Course Instructor(s) PROFESÖR DOKTOR TANSU FİLİK
Mode of Delivery Lecturing, sample applications
Prerequisites There is no prerequisite or co-requisite for this course.
Courses Recomended There is no recommended optional programme component for this course.
Required or Recommended Resources Henry Stark & John W. Woods "Probability, Random Processes with Applications to Signal Processing, 3rd edition
Recommended Reading List Simon Haykin, Communication Systems, 4th editionLeon W. Couch, II () Digital and Analog Communication Systems, 7th edition, Prentice HallJohn G. Proakis & Masoud Salehi, Communication Systems Engineering, 2nd editionAthanasios Papoulis, “Probability, Random Variables and Stochastic Processes”, 3rd edition.Dimitri P. Bertsekas & John N. Tsitsiklis, “Introduction to Probability”, 2nd edition.C.W. Therrien, “Discrete random signals and statistical signal processing”, 1992.
Assessment methods and criteria 1 midterms, 1 final exam, homeworks
Work Placement There is no internship or practical training requirement within the scope of this course.
Sustainability Development Goals

Content

Weeks Topics
Week - 1 Review of the Theory of Random Variables and Random Vectors
Week - 2 Review of the Theory of Random Variables and Random Vectors
Week - 3 Review of the Theory of Random Variables and Random Vectors
Week - 4 Random Processes and Correlation Functions.
Week - 5 Stationarity and Ergodicity
Week - 6 Review of Fourier Transforms
Week - 7 Power Spectrum for WSS Signals
Week - 8 LTI Systems with Random Inputs
Week - 9 Gaussian and Poisson Processes, Poisson Impulses
Week - 10 Noise (White: Thermal, Shot)
Week - 11 Sampling of Random Processes
Week - 12 Bandlimited/Bandpass White Noise
Week - 13 Hilbert Transforms & Bandpass Signals
Week - 14 Narrowband Noise

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Question & Answer
  • Drill - Practise
  • Competences
  • Effective use of a foreign language
  • Work in teams
  • Problem solving
  • Elementary computing skills

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 25
Homework 1 25
Final Exam 1 50
Toplam (%) 100
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