ELEC 241: Fundamentals of Electrical Engineering I
Instructor: Don H. Johnson, Duncan Hall 2095, dhj@rice.edu, X4956
Course Overview
- Class meets MWF, 11AM, Duncan Hall 1070
- Instructor Office Hours: Thursdays 1-5 PM, Duncan Hall 2095; by appointment, (almost) anytime.
- Online version of the course can be found here.
- Required Text: Johnson, Fundamentals of Electrical Engineering I
The course’s objectives are to provide, through homework and tutorials, the technical foundations for succeeding courses in electrical engineering and, through the accompanying laboratory, ELEC 240, the practical foundations.
Prerequisites: Math 101 (or 105) and Math 102 (or 106). Co-requisite: ELEC 240.
Course Outline
- Elements of signal and system theory
- Digital and analog information
- Block diagrams: sources, systems, sinks
- Signal and system analysis
- Analog signal processing
- Signal theory: time-domain concepts of amplitude, delay, superposition
- Representation of signals by electronic quantities (electric, optical)
- Elementary circuit theory
- Circuit laws; series and parallel configurations
- Power dissipation
- Equivalent circuits
- Impedance
- Basic analog circuit building block: the op-amp
- Signals in the frequency domain
- Fourier series; signal decomposition; notion of bandwidth
- Fourier transforms: bandwidth, filtering, modulation
- The speech signal
- Sampling theorem
- Signals in the time domain
- Impulses and impulse response
- Convolution
- Digital signal processing
- A/D conversion; amplitude quantization; data rate
- DTFT, DFT, FFT, digital filters, spectrograms
- Speech signal processing
- Information Processing
- Developing information processing algorithms using matrices
- Deriving algorithms that minimize an error criterion
- Supervised learning and classification algorithms
- Analog signal processing
- Digital Information Transmission
- Entropy and Shannon’s Coding Theorem
- Lossless and lossy compression; redundancy
- Channel coding; error correcting codes; transmission rate
- Capacity; Shannon’s Noisy Channel Coding Theorem
Course Objectives
- Mathematically describe and manipulate complex exponential signals and linear, time-invariant systems that operate on them;
- Apply Kirchhoff’s Laws, equivalent circuit models, and transfer functions to analyze voltage and current relationships in passive circuits;
- Apply formal node analysis to analyze the operation of basic op-amp circuits;
- Use Fourier series representation of periodic signals to perform frequency domain analysis of linear time-invariant systems;
- Apply properties of the Fourier transform to signal analysis;
- Develop a time-domain view of signals and systems;
- Show how to covert analog signals into digital signals;
- Analyze the behavior of digital systems on discrete-time signals using the Discrete-Time Fourier Transform (DTFT);
- Calculate the complexity of implementing discrete-time filtering using the Fast Fourier Transform; describe and analyze discrete-time filtering of analog signals;
- Introduce data science notions of developing information processing algorithms without models;
- Construct simple source compression codes and error-correcting codes, and explain their application in digital communication of information;
- Use Shannon’s Source Coding and Channel Capacity Theorems to show how error-free communication becomes possible.
This is the first course in a two course sequence, the second being ELEC 242 taught in the spring semester.
University Disability Accommodation Policy
Any student with a documented disability needing academic adjustments or accommodations is requested to speak with the course instructor during the first two weeks of class. All discussions will remain confidential. If you have a documented disability that may affect academic performance, you should: 1) make sure this documentation is on file with Disability Resource Center (Allen Center, Room 111 / adarice@rice.edu / x5841) to determine the accommodations you need and 2) meet with the instructor to discuss your accommodation needs.