Graduate (List)

This course covers topics of interest in communication engineering at the graduate level. Course content is specifically designed by the instructor.

In this course, we introduce the contemporary commercial mobile communication system: LTE and 5G. This course will help the students understand the whole system of mobile communication specifically its internal entities and their inter-working.

This course is to introduce some important topics in the general area of communications and signal processing. Topics may vary from year to year.

This course is designed for students who understand deep learning and convolutional neural networks and studies research topics related to convolutional neural networks. Topics can include residual network, knowledge distillation, network minimization, image-to-image translation, continual learning, domain adaptation, data augmentation, self-supervised learning, meta-learning, attention.

This course provides an overview of neuro-imaging and neuro-image processing techniques. We will discuss the historical and recent development of neuro-imaging technology as well as and neuro-image processing techniques. Neuro-imaging modalities will be discussed with an emphasis on optical imaging. On the imaging processing side, general image processing techniques as well as specialized techniques for processing neuro-images will be discussed.

This lecture provides advanced multimedia processing and learning. Multisensory signal, video, audio, and language are core components of multimedia. Multimodal learning with them is one of the core technologies in multimedia in real-world applications such as intelligent surveillance, smart TVs, and human-machine interface systems. Lecture topics include the basics of multimedia learning, which is image, video, audio, and language representation learning, multimedia fusion schemes, multimedia alignment, and multimedia attention. In addition to the basics of multimodal, students are to participate in term projects and papers reading recently published in multimodal processing and learning.

This course deals with recent techniques based on deep learning for speech pre-processing, recognition, synthesis, and speaker recognition technologies, and makes it possible to extend the area of applications of speech intelligence.

The primary goal of this course is to understand the sound propagation and learn how to extract information from various sound fields. The course covers fundamental principles of sound propagation, beamforming, sound localization, acoustic holography, and related array signal processing techniques for extracting sound information from various environment.

The primary goal of this course is to understand the sound propagation and learn how to extract information from various sound fields. The course covers fundamental principles of sound propagation, beamforming, sound localization, acoustic holography, and related array signal processing techniques for extracting sound information from various environment.

This course introduces selected topics of recent technologies and algorithm related to image processing and imaging systems. (Prerequisite: EE432, EE535)