04-801-X2   Processing The World’s Information: Foundations

Course description

Acquiring, communicating, and processing information are fundamental activities in modern engineering. Information is carried in signals—physical quantities such as voltage, light intensity, acoustic pressure, etc. that vary over time and space in a manner that reflects the information we care about. The compounding of advances in sensing, digital computing, communications, cloud technologies, and AI have made signal processing a foundational technology in our lives. This course builds an understanding of the principles and techniques involved in working with signals through a combination of lectures and hands-on activities. It is aimed at engineers and scientists from ALL backgrounds. Students from ANY engineering/science background will learn key DSP principles and use AI driven coding techniques to develop code for acquiring, processing, and presenting data of various types. It is not necessary to also take Part II of this sequence in order to benefit from Part I.

Learning objectives

Students successfully completing this mini will have a functional understanding of key concepts

Including:

• The common high-level architecture of many DSP systems
• Signal bandwidth, Analog to Digital conversion (“A/D”, also known as signal sampling), and the errors that can arise when converting an analog signal to a digital representation
• The power of representing a signal as a sequence of vectors, each of which contains a block of signal samples
• The insights that can be gained through first multiplying a signal vector by a matrix (i.e. applying a “transform”) so that signals can be studied in a different domain
• Digital filtering of signals: what it means and why it’s helpful
• Techniques for removing noise from digital signals
• How bits can be embedded in signals for communication

Outcomes

Students will be able to understand systems that employ DSP, communicate competently with experts in the area, improve their expertise in using AI to solve real-world problems, and gain hands on experience developing and analyzing basic DSP techniques.

Content details

Discrete time and value signals, bandwidth, sampling, aliasing quantization noise, FIR filtering, noise filtering, modulation

Prerequisite

Calculus, basic linear algebra, some experience programming with a scripting language such as python, useful: experience using agentic AI, recommended: basic complex number understanding (Euler’s equation)