Font size  SML

Graduate major in Information and Communications Engineering
Instructor(s)
Course component(s)
Lecture    (ZOOM)
Day/Period(Room No.)
Mon5-6(Zoom)  Thr5-6(Zoom)
Group
-
Course number
ICT.S414
Credits
2
2020
Offered quarter
3Q
Syllabus updated
2020/9/18
Lecture notes updated
-
Language used
English
Access Index Course description and aims

After reviewing Fourier analysis, Sampling theorem and Discrete time Fourier transform as the common languages in signal processing, the instructor first introduces first several classical solutions, e.g., Generalized inverses and Best Linear Unbiased Estimator, for the linear inverse problems based on the orthogonal projection theorem and the singular value decomposition. Next, the instructor presents a unified view for many adaptive filtering algorithms and/or Online learning algorithms based on the convex projection theorem and fixed point theorems in Hilbert space. The instructor also introduces powerful ideas in fixed point approximations that are common principles in convex optimization algorithms and their applications to inverse problems. Finally, the instructor introduces several advanced topics, e.g., Hierarchical convex optimization, Subspace tracking and Phase unwrapping, etc.

Student learning outcomes

Along with rapid progress in the computational technology, many powerful algorithms in modern signal processing have been established in the last two decades.
By the end of this course, students will be able to:
1) understand such algorithms in unified ways.
2) understand mathematical ideas behind such algorithms.
3) understand how such algorithms can be applied to real world problems.

Keywords

Fourier analysis, Sampling theorem, Discrete time Fourier transform, Linear inverse problems, Generalized inverse, Best Linear Unbiased Estimator, Adaptive filtering, Convex optimization, Fixed point theorems

Competencies that will be developed

 ✔ Specialist skills Intercultural skills Communication skills ✔ Critical thinking skills ✔ Practical and/or problem-solving skills

Class flow

After reviewing Fourier analysis, Sampling theorem and Discrete time Fourier transform as the common languages in signal processing, the instructor explains systematically powerful signal processing algorithms and their applications to modern inverse problems and adaptive learning problems.

Course schedule/Required learning

Course schedule Required learning
Class 1 Introduction to Signal Processing -From classic and modern What is Signal processing ?
Class 2 Review of Fourier analysis Explain about the basic ideas in Fourier analysis
Class 3 Sampling Theorem, DFT and FFT How are Fourier series expansion and Fourier transform used to derive Sampling theorem ?
Class 4 Hilbert space - Mathematical stage for modern approach What is Hilbert space ?
Class 5 Projection theorem, Generalized inverses, Best linear unbiased estimator Explain about
Class 6 Singular Value Decomposition (SVD) and low rank estimator Explain about
Class 7 Fixed point theorems and convexity Explain about Fixed point theorems and convexity
Class 8 Adaptive learning based on projection theorems 1: Algorithms Explain about Adaptive learning based on projection theorems.
Class 9 Adaptive learning based on projection theorems 2: Applications to classification problems. Explain about applications to classification problems.
Class 10 Fixed point algorithms for nonexpansive operators. Explain about Fixed point algorithms for nonexpansive operators.
Class 11 Convex optimization and its image recovery applications Explain about convex optimization and its image recovery applications
Class 12 Inverse problems and hierarchical optimization Explain about Inverse problems and hierarchical optimization
Class 13 Subspace method and applications (Subspace tracking, Directions of arrivals estimation etc) Explain about subspace method and its applications
Class 14 Phase unwrapping: Algorithms and applications Explain about phase unwrapping: and applications

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.

None

Reference books, course materials, etc.

Handouts will be distributed at the beginning of class if necessary.

Assessment criteria and methods

Learning achievement is evaluated by the quality of the students' presentation, the written reports, etc.

Related courses

• ICT.A402 ： Communications and Computer Engineering I

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Firm understanding is required on linear algebra and multivariate calculus.

Contact information (e-mail and phone)    Notice : Please replace from "[at]" to "@"(half-width character).

isao[at]sp.ce.titech.ac.jp

Office hours

Contact by e-mail in advance to schedule an appointment. 