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Teacher: Prof. Dr. Saleem Zaroubi
Teaching assistant: Bharat Kumar Gehlot The aim of this course is to introduce the students to the basics of Statistical Signal Processing with emphasis on the application of this field to data and image analysis. Such methods play a crucial role in the analysis and interpretation of data in almost every field of science. The course will present the general mathematical and statistical framework of Statistical Signal Processing with special emphasis on examples from Astronomy and physics. The course will cover the topics of Random vectors and processes, Estimation theory, Moments analysis, Filtering and Sampling theory. Problem sets and computer assignments are substantial and integral part of the course. Literature The course will rely on the lecture slides and will not follow a specific book in detail. However, there are two books that give a general overview of the material that will be covered in the course.1. Fundamentals of Statistical Signal Processing: Estimation Theory v. 1 (Prentice Hall Signal Processing Series), by Steven M. Kay 2. Discrete Random Signals and Statistical Signal Processing (Prentice Hall Signal Processing), by Charles W. Therrien Grading: The homework assignments and computer project are an integral and mandatory part of the course. The student must submit a substantial number of the homework (80%) and all computer projects in order to be allowed in the final exam.The final grade will be composed as follows: 40% Homework, 20% Computer projects 40% Final exam. Course Material:
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