Objective
Review MOS device properties and electrical models. Basic analog circuit building blocks including simple and cascode current sources, active loads, common source and common drain amplifiers, DC biasing networks, and differential amplifiers. Analog sub-systems building blocks including CMOS OTA op-amp, OCA, comparators, A/D, D/A, and switching capacitor circuits. Selected topics in CMOS RF circuits.

Prerequisite: ELEG3210. (ONLY suitable for undergraduate students enrollment.)

Syllabus

• CMOS technology
• MOS transistor model
• CMOS CS amplifier
• CMOS CD amplifier
• CMOS CG amplifier
• Differential amplifier
• Noise
• Current mirror
• Single stage opamp
• Two stage opamp
• Special purpose opamp
• Comparator
• Converter
• Switching capacitor circuit

Learning Outcome

• Understand different types of CMOS technologies.
• Perform design, layout and simulation of different types of CMOS opamps.
• Understand the limitations of different types of CMOS opamps.

Back to the List

Objective
By the end of the course, students should be able to:

• Know the different physiological measurement and imaging principles
• Have hands-on experience in designing a type of medical instrumentation; and,
• Know the future trend in related topics.

Syllabus

Learning Outcome

Back to the List

Objective

Syllabus
Introduction to RFIC technologies; Transceiver Architectures; RFIC components; Computer-Aided Design tools; RFIC design with examples: LNA, Mixer, oscillator etc.

Learning Outcome
By the endof the course, students should be able to

• Have an overallpicture of RFIC design and technology.
• Understand the basicoperating principles of RF transceiver architectures.
• Understand the highfrequency modeling of RFIC components.
• Understand thedesign principles of RFIC circuitry.
• Perform design andsimulation of RFIC.

Back to the List

Objective

Syllabus
This course will provide graduate students with a panorama of functional analysis and approximation theory in multiple dimensions, adopting a systematic dual point of view (functions defined through a collection of measurements, weak formulations). The emphasis will be laid on the simplest, albeit modern mathematical concepts and mechanisms, with a view to avoid extraneous formalism and more abstract (e.g., topological) considerations. This knowledge will be used to devise methods for solving exactly or approximately various inverse problems; e.g., resulting from partial differential equations.

This knowledge will be used to model engineering problems (e.g., data acquisition, sampling), to devise methods for solving exactly or approximately the inverse problems that are related (e.g., resulting from partial differential equations), and to analyse the error resulting from the approximations.

Course contents: elements of

• measure theory, Lebesgue integral, Hilbert spaces
• generalized functions/distributions (delta, etc.), duality
• Fourier, convolutions
• functionals, Lagrangians, variational calculus
• partial differential equations, integral equations, Green functions
• approximations in partial differential equations
• approximation of functions, Ritz-Galerkin approximations
• splines, finite elements, wavelets, Gabor functions
• interpolation of uniform and scattered data, radial basis functions
• examples in electromagnetism, continuum mechanics, image processing etc.

Learning Outcome
The goal of this course is, for the students, to learn the most important mathematical ideas that are needed to be able to solve mathematically and numerically a number of multivariate problems that arise in engineering fields (including physics, signal processing). It is not meant to provide very detailed, complete mathematical coverage of each theory encountered, though.

At the end of this course, the students are expected to be able to

1. Understand the working principle of the theory of measure/integration (Lebesgue)
2. Understand and apply generalized functions in an engineering/physics context
3. Understand and apply the Fourier transformation in multiple dimensions
4. Understand and apply variational calculus
5. Understand and apply exact approaches for solving partial differential equations
6. Understand and apply numerical approaches for solving partial differential equations (approximation theory)
7. Apply a part of the knowledge learnt in a Matlab project (computer implementation)

Back to the List