Lecture

Waveguides: Modes and Propagation

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Covers TE modes in rectangular waveguides, including boundary conditions and dispersion diagrams.

Explores the propagation modes in waveguides, discussing TE and TM modes, field distribution, and Bessel functions.

Covers the propagation modes in rectangular waveguides, focusing on TM modes and discussing basic equations and solutions.

Rectangular Waveguides: TE and TM Modes

Covers guided propagation in rectangular waveguides for TE and TM modes.

Guided Propagation: Theory and Examples

Discusses guided propagation for waves along a structure with invariant geometry and covers examples of TEM, TE, and TM modes.

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Dielectric Waveguides: Modes and Dispersion

Explains TE and TM modes, guided modes, multimodal fields, and waveguide dispersion.

Data Compression and Entropy: Compression

Explores lossless data compression techniques, emphasizing efficient message representation and encoding strategies.

Data Compression: Shannon-Fano Algorithm

Explores the Shannon-Fano algorithm for efficient data compression and its applications in lossless and lossy compression techniques.

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Guided Propagation: Modes and Wave Impedance

Explores guided propagation of electromagnetic waves, covering modes, wave impedance, dispersion, and distortion effects in different structures.

Data Compression: Entropy Definition

Explores data compression through entropy definition, types, and practical examples, illustrating its role in efficient information storage and transmission.

Rectangular Waveguides: TM and TE Modes

Explains the propagation modes in rectangular waveguides for TM and TE modes.

Data Compression and Entropy: Basics and Introduction

Introduces data compression, entropy, and the importance of reducing redundancy in data.

Compression: introduction

Introduces data compression, exploring how redundancy in data can be reduced to achieve smaller file sizes without losing information.

Data Compression and Shannon's Theorem: Huffman Codes

Explores the performance of Shannon-Fano algorithm and introduces Huffman codes for efficient data compression.

Data Compression and Entropy: Conclusion

Covers the definition of entropy, Shannon–Fano algorithm, and upcoming topics.

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Reviews information measures like entropy and introduces mutual information as a measure of information between random variables.

Neural Signal Compression

Explores analog-to-digital conversion, neural signal optimization, multichannel architectures, and on-chip compression techniques in neuroengineering.