3D Medical Image Acquisition

This lecture overviews 3D Medical Image Acquisition that has many applications in medical imaging and diagnosis. It covers the following topics in detail: 3D Computed Tomography (including Cone Beam Tomography and Micro-Computed Tomography). 3D Magnetic Resonance Tomography (including Functional MRI Magnetic Resonance Elastography, Diffusion MRI). 3D Ultrasonography. 3D Nuclear Tomography, Single-Photon Emission Tomography (SPECT) Positron Emission Tomography (PET), Magnetic Particle Imaging (MPI). Photoacoustic Tomography (PAT). Non-contact 3D Surface Imaging, notably Structured-light scanning (SLS) and Stereophotogrammetry. 3D Medical Optical Imaging, including Diffuse Optical Tomography, Optical Coherence Tomography and Confocal Laser Scanning Microscopy (CLSM).

2D Medical Image Acquisition

This lecture overviews 2D Medical Image Acquisition that has many applications in Medical diagnosis and treatment, as well as in Biology, Biomedical Engineering and Dentistry. It covers the following topics in detail: Radiography, Conventional Radiography (X-Ray), Fluoroscopy. Ultrasonography. Nuclear Scan, Scintigraphy. Elastography. Photoacoustic Imaging, Photoacoustic Microscopy (PAM).

Cryptography

This lecture overviews Cryptography that has many applications in Communications and Blockchain. It covers the following topics in detail: Symmetric Key Cryptography, Asymmetric Key Cryptography, Hash Functions, Secure Hash Algorithms, Merkle Hash Binary Tree, Homomorphic Encryption, Zero Knowledge Proof.

Wireless Communication Networks

This lecture overviews Wireless Communication Networks  that has many applications in autonomous systems. It covers the following topics in detail: 4G networks, Quality of Service in 4G networks, 5G networks, 5G technology components, Quality of Service in 5G networks, Internet of Things (IoT).

Federated Learning

This lecture overviews  that has many applications in distributed Machine Learning and privacy protection.  It covers the following topics in detail: Centralized/Decentralized Learning, Federated Learning principles and platforms, Federated Learning Algorithms (Federated Averaging Algorithm, FedProx algorithm, FedMA algorithm) and Privacy Principles & Technologies, notably: Differential Privacy, Homomorphic Encryption, Zero-knowledge Proof Technologies, Secure Multiparty Computation.

Transfer Learning

This lecture overviews Transfer Learning (TL) that has many applications in DNN training and adaptation, Image Understanding, Text Mining, Activity Recognition, Bioinformatics, Transportation. It covers the following topics in detail: Definition of TL, Categorization of TL: Instance-based (Noninductive, Inductive), Feature-based, Model-based, Relation-based, Heterogeneous TL, Negative Transfer, TL with Deep Learning, Fundamental TL Research Issues, Applications of TL.