Objective
Introduction: television standards, digital image representation, statistical models, basic lossless and lossy coding techniques; advanced coding techniques: wavelet coding, synthetic-natural hybrid coding, post-processing techniques; image coding standards: JPEG, JPEG 2000; video coding standards: H.261, H.263, MPEG1, MPEG2, MPEG4; HDTV standard.

Syllabus

Learning Outcome
By the end of the course, students should be able to

• know how video images are formed, perceived by sensors and represented in various formats.
• understand the characterization and sampling of video signal in space and time, and its modeling using different physical models.
• analyze and design the various building blocks of a video codec (encoder/decoder).
• apply the latest video coding standards, such as MPEG-4, H.264, and AVS, in multimedia services.

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Objective

1. To introduce the concepts of optimization, and to understand its connections and impacts in signal processing.
2. To provide students with opportunities to have hands-on experience in using optimization software to solve signal processing problems, or even problems related to their own research.
3. To learn advanced optimization techniques, and to have case studies with emerging or important applications in signal processing.

Syllabus

Learning Outcome

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Objective
This course provides an introduction to deep learning. Students taking this course will learn the theories, models, algorithms, implementation and recent progress of deep learning, and obtain empirical experience on training deep neural networks. The course starts with machine learning basics and some classical deep models (including convolutional neural network, deep belief net, and auto-encoder), followed by optimization techniques for training deep neural networks, implementation of large-scale deep learning, multi-task deep learning, transferred deep learning, recurrent neural networks, applications of deep learning to computer vision and speech recognition, and understanding why deep learning works. The students taking are expected to have some basic background knowledge on calculus, linear algebra, probability, statistics and random process as a prerequisite.

Syllabus
This course provides an introduction to deep learning. Students taking this course will learn the theories, models, algorithms, implementation and recent progress of deep learning, and obtain empirical experience on training deep neural networks. The course starts with machine learning basics and some classical deep models (including convolutional neural network, deep belief net, and auto-encoder), followed by optimization techniques for training deep neural networks, implementation of large-scale deep learning, multi-task deep learning, transferred deep learning, recurrent neural networks, applications of deep learning to computer vision and speech recognition, and understanding why deep learning works. The students taking are expected to have some basic background knowledge on calculus, linear algebra, probability, statistics and random process as a prerequisite.

Learning Outcome
Understand the fundamental concepts, theories and algorithms of deep learning. 2. Understand why deep learning outperforms other machine learning methods when the training data is in large scale 3. Understand the principles of designing different deep models for various applications 4. Implement and train deep neural networks with deep learning toolboxes 5. Obtain empirical experience of designing and training deep models in practical applications.

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Objective
Review of semiconductor fundamentals: electron and hole, Fermi energy, generation and recombination, p-n junction, hopping, field-effect. Introduction to organic and polymeric semiconductors: morphology, molecular packing, conformation, electronic structures, optical and electrical properties. Application of organic/polymeric thin films: OLEDs, OTFTs, PLEDs, photodetectors and sensors. Fabrication methods for flexible electronics: sputtering, CVD, VPD, inkjet printing, screen printing, roll-to-roll printing, spraying coating, etc. Introduction to OLED/PLED based display technology: passive matrix OLED and active matrix OLED display techniques. Basic principles of photovoltaic devices: absorption, photo-electric conversion, conversion efficiency, loss mechanism, carrier collection, device characterization. Introduction to solar cell technology: monocrystalline solar cells; dye-sensitized solar cells; organic solar cells.

Syllabus

Learning Outcome
By the end of the course, students should be able to

• Gain the fundamental knowledge and skills in understanding the operation principles of flexible electronic devices and photovoltaic devices, and note the scope and limitation of flexible electronics and solar cell technology.
• Apply the learned knowledge and skills in solid state devices for analysis in various types of flexible devices and solar cells and their basic functionalities, basic device characterization techniques, and advanced device fabrication methods.
• Understand the technological impact of flexible electronics and solar cell technology to the society.
• Understand the basic physical principles and the engineering know-how of flexible electronics and solar cell technology for further specialization in areas related to display technology, solid state lighting technology, photovoltaic technology.

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