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)

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Objective

This course is an in-depth exploration of audio processing using neural networks. Starting with an introduction to audio problems, the course covers a range of topics including audio features and human labels, filtering and digital signal processing for audio processing, audio and music tagging with convolutional neural networks, audio and music transcription with recurrent neural networks, audio compression, bridging audio and language with sequence-to-sequence models, symbolic music generation, audio and music generation with pipelines, vocoder, and autoregressive models, audio and music generation with VAEs and diffusion models, controllable audio and music generation from texts and multiple modalities, and open problems and future directions in the field.

這門課程探討音頻和音樂處理。这门课從音頻和音樂問題的介紹開始，涵蓋音頻特徵和人類標籤、數字信號處理、使用卷積神經網絡進行音頻和音樂標記、使用循環神經網絡進行音頻和音樂轉錄、音頻壓縮、使用序列到序列模型连接转录音頻。這門課程音頻探討和音樂生成：包括符號音樂生成、聲碼器、自回歸模型、使用VAE和擴散模型進行音頻和音樂生成、從文本和多種模式控制音頻和音樂生成。

Syllabus

1. Introduction: Audio and music problems.
2. Audio features and human labels.
3. Filtering, digital signal processing for audio processing (Assignment 1)
4. Audio and music tagging with convolutional neural networks.
5. Audio and music transcription with recurrent neural networks.
6. Audio compression.
7. Bridging audio and language: audio and music caption with sequence-to-sequence models (Assignment 2).
8. Symbolic music generation.
9. Audio and music generation: pipelines, vocoder, and autoregressive models.
10. Audio and music generation with probabilistic models, such as diffusion models.
11. Controllable audio and music generation from texts and multiple modalities (Assignment 3).
12. Open problems and future.

.

Learning Outcome

Through a combination of lectures, assignments, and projects, students will gain hands-on experience working with state-of-the-art tools and techniques for audio and music processing. By the end of the course, students will have a solid foundation in the latest techniques for audio and music processing using neural networks, and will be able to apply these techniques to real-world problems in the field.

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Objective

The processing techniques used to manufacture materials and components is a very broad activity encompassing materials science, mechanical engineering, electronic engineering, chemistry and economics. Commercial processing is now accomplished by automated and computer controlled equipment yet the engineers and researchers must understand the basic principles to program and control the parameters. This course is to develop the scientific base and fundamental nature of some common processes. The aim of this course is to provide students with the basic understanding of manufacturing processes, and the relationships between process design and fundamental concepts in transport phenomena, and properties.

用于製造材料同組件嘅加工技術係一項非常廣泛嘅活動，包括材料科學，機械工程，電子工程，化學和經濟學。 商業處理而家由自動化和電腦控制嘅設備完成，但工程師和研究人員必須了解編程和控制參數嘅基本原理。 本課程旨在發展一些常見過程的科學基礎和基本性質。 本課程嘅目的係為學生提供對製造過程嘅基本理解，以及過程設計與傳遞現象和屬性中嘅基本概念之間嘅關係。

Syllabus
The following topics will be covered:

MODULE 1 : PROCESSES FOR PARTICULATE MATERIALS AND BULK MATERIALS
a: Powder synthesis
Methods for powder production and blending. Particle shape and size distribution. Properties of powders.
b: Compaction of Powders
Various densities. Pressing. Isostatic pressing. Metal injection molding. Sintering and mechanisms. Processing ceramics, cermets and composite. Properties of sintered products.

MODULE 2: VACUUM PROCESSES
a: Vacuum
Introduction to vacuum technology, systems, pumps and gauges. Units of pressure. Deposition chambers, target and substrate preparation.
b: Physical vapor deposition
Physical vapour deposition processes and sputtering. Polycrystalline and epitaxial film production. Effects of substrates
c: Chemical Vapor Deposition
Films and nanostructures from gas precursors. Atomic layer deposition. Nanoscale control of film chemistry.
d. Growth progress and Microstructure control
Zone structure model, amorphous and crystallinity control, pore control and defects evolutions

MODULE 3: SOLUTION PROCESSES
a: Basic Principles
Precursor solutions, reaction rates, ligand additives for size and shape control.
b:Sol Gel
The principal of Sol gel and the related process. Morphology control, Chemical reaction of sol gel, the advantages and disadvantages of sol gels.
c: Chemical Solution Deposition — Basic Principles
Basics of sol-gel, chelate and related processes. Film formation: spin coating, dip coating and spray coating. Spray pyrolysis. Examples of chemical synthesis. Examples of chalcogenide semiconductor synthesis.

Learning Outcome

At the end of this course the students will be able to
1. Explain the relationships between basic concepts and design of the processing route.
2. Apply basic concepts to predict the material behaviour during processing.
3. Predict the structure and properties of the end products
4. List the advantages and limitations of each process.
5. Choose the best process for a particular component, device or material. 

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Objective

RISC-V is the processor of choice after Intel x86 and ARM processor. RISC-V is an open core and is fully configurable to support a wide scope of applications with varying performance requirements. This course will cover basic processor design. RISC-V instruction set architecture will be described along with an elementary pipelined implementation. Based on the elementary design, different techniques to increase performance will be examined from different levels of cache to instruction level and data level parallelism. This course can be seen as a computer organization course based on RISC-V.

RISC-V 是繼 Intel x86 和 ARM 處理器之後的首選處理器。 RISC-V 是一個開放核心，完全可配置，可支援具有不同效能要求的廣泛應用。本課程將涵蓋基本的處理器設計。將描述 RISC-V 指令集架構以及基本的管線實作。基於基本設計，將從不同層級的快取到指令級和資料級並行性研究提高效能的不同技術。本課程可視為一門基於RISC-V的電腦組成課程。

Syllabus

Basic Computer Organization
RISC-V ISA
RISC-V Pipelined Datapath
Instruction Hazards
Memory Organization and Cache
Virtual Memory
Instruction Level Parallelism
Data Level Parallelism

Learning Outcome

1. Understand the basic concept of computer organization
2. Understand RISC-V ISA and appreciate the merit of different control implementations
3. Use memory organization to increase performance
4. Use different levels of parallelism to increase performance
5. Gain ability to configure a RISC-V

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