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Objective & Syllabus

This course aims to provide students with a general understanding of various computational techniques that enable machines to understand different types of data and signals, including text, speech, image and video. The course content covers the methods that are used to analyze, classify and detect the underlying information, properties and modalities inherent in complex signals. Students will learn the theories, models, algorithms and operation of machine learning tools, which have been successfully developed and deployed for speech/audio and image/video processing applications. Specifically, the basics and recent progress of deep learning techniques will be introduced.

Learning Outcome

Upon completion of this course, students will be able to:

• Describe the properties of different types of signals
• Explain the fundamental concepts, theories and algorithms of machine learning
• Describe the advantages, limitations and trends of deep learning techniques
• Apply machine learning algorithms to solve a given problem of signal processing
• Use machine learning tools to implement a system of audio/visual signal processing

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Objective

Syllabus
This course deals with methodologies to design VLSI circuits for DSP algorithms used in a wide range of signal processing applications. Architectural techniques to optimize for speed, power consumption or size include pipelining, retiming, unfolding, folding and systolic array. The course also introduces a hardware description language(HDL), and shows the example of using HDL to design a signal processing system. Practical work will be arranged for students to gain first hand experience of designing and implementing DSP algorithms.

Learning Outcome

1. Use signal diagram to describe digital signal processing algorithms
2. Understand different architectural transforms to optimize a VLSI DSP circuit for speed, power consumption or size.
3. Understand a Hardware Description Language and has the ability to use the HDL to design VLSI DSP circuits
4. Gain the experience of using FPGA Design Tools

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Objective & Syllabus

This course introduces the key concepts and issues of innovation, technology and management in the context of modern engineering practices. The new wave of socio-technological development is viewed as an integration and convergence of innovations and technology in communication, work, entertainment and others in our daily life. The objective of the course is to provide students a general overview and roadmap of creating cutting edge innovation and evolving digital economy. It helps students to establish a deep understanding about how engineering practice works, and how it affects and reshapes our communities and society, and about how to become best engineering performers. The best practices of intellectual property (IP) rights, protection, enforcement, and IP management from a technology perspective will also be introduced. Through case studies, students will appreciate the decision process on the type of technology to be developed, the development process of the technology, and how to turn new technology into real products. The aspects of funding, market study and commercialization will be covered.

Learning Outcome

Upon completion of this course, students will be able to:

• Appreciate the importance of best engineering practices in innovation and technology development
• Describe the roles of innovation and technology in modern society and economy
• Explain the creation, evolving, marketing and commercialization processes of a new technology
• Make independent judgement on technology need and trends
• Formulate a strategy to exploit a technology with IP protection
• Use IP information for planning and decision making
• Establish an IP management strategy for an organization

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Objective

Syllabus
Introduction to health informatics, fundamentals in electro-physiologies, reviews on bio-electronics including ion channels, bio-sensor principles, e-textile sensing, flexible-stretchable-printable bioelectronics, organic transistors, wearable design by "MINDS" (Miniaturization, Intelligence, Networking, Digitization, and Standardization), wearable medical devices and systems, wearable robotics, implantable therapeutic systems, sensor informatics, applications, and topics of current interests.

Learning Outcome

• To understand the basic concepts of health informatics.
• To understand the basic principles of bio-sensors for wearable device design.
• To learn the fundamentals of flexible-stretchable-printable bioelectronics.
• To know how to design wearable devices and systems.
• To familiar with applications in mobile health and home healthcare.

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