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

Location: Africa

Units: 6

Semester Offered: Fall

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.

Signals arise in a wide range of applications including bridge pressure sensors, glucose monitors, robotic position sensors, microphones, cameras, and radar/lidar systems. Signals are most often represented digitally, so this course places particular emphasis on Digital Signal Processing (DSP). DSP is integral to modern systems, from consumer BlueTooth devices to commercial spacecraft. Throughout your life, you will continually work with systems dependent on DSP. A functional understanding—including hands-on experience—of how such systems operate, what they have in common, and their practical limitations will greatly enhance your ability to understand and navigate our DSP-enabled world.

Learning objectives

This course, the first of a two-part mini sequence, helps students develop a practical understanding of how DSP systems operate through a combination of lectures and labs. Students from ANY engineering/science background will learn key DSP principles and use AI driven coding techniques to develop Matlab code for a Software Defined Radio (SDR) system. It is not necessary to also take Part II of this sequence in order to benefit from Part I.

Outcomes

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

  1. The common high-level architecture of many DSP systems
  2. 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
  3. The power of representing a signal as a sequence of vectors, each of which contains a block of signal samples
  4. 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
  5. Digital filtering of signals: what it means and why it’s helpful
  6. Techniques for removing noise from digital signals
  7. How bits can be embedded in signals for communication

Content details

We start with the basics of real-world signals and study how they are captured, represented, and manipulated. Ultimately, we use those fundamentals together with other concepts we develop to prototype a communication system built around our SDR.

Prerequisite

Students are expected to be:

  1. Comfortable using calculus;
  2. Possess a basic linear algebra background (chiefly vectors, matrices, the dot product, and the concept of orthogonality);
  3. Have experience using a scripting language such as python; and
  4. Have some exposure to complex numbers. Additional concepts will be developed as needed in a largely intuitive manner, including those of the frequency domain, transforms, and filtering among others. 

Although math is used, this is not a math class.