Applied Digital Signal Processing

Course Description

CET4190C – Applied Digital Signal Processing is a 4-credit, upper-division course in the Computer Engineering Technology program offered under the Florida Statewide Course Numbering System (SCNS) taxonomy of Engineering Technologies > Computer Engineering Technology. The course introduces students to the theory and hands-on practice of digital signal processing (DSP), with an emphasis on real-world implementation using DSP hardware and software tools. Students examine discrete-time signals and systems, digital filter design, spectral analysis, and the use of industry-standard platforms such as MATLAB and Texas Instruments DSP processors to analyze and process signals in applied engineering contexts. The course includes a laboratory component in which students implement and verify DSP algorithms on actual DSP hardware.

Learning Outcomes

Required Learning Outcomes

Upon successful completion of this course, students will be able to:

• Analyze and classify discrete-time signals and systems, including linear time-invariant (LTI) systems.
• Apply the Z-transform and inverse Z-transform to analyze discrete-time systems and determine stability.
• Apply the Discrete-Time Fourier Transform (DTFT) and the Discrete Fourier Transform (DFT) to characterize signals in the frequency domain.
• Implement and apply the Fast Fourier Transform (FFT) algorithm for efficient spectral analysis.
• Explain and apply sampling theory, including the Nyquist sampling theorem, aliasing, and analog-to-digital (ADC) / digital-to-analog (DAC) conversion.
• Design and analyze Finite Impulse Response (FIR) digital filters using windowing and frequency sampling methods.
• Design and analyze Infinite Impulse Response (IIR) digital filters using bilinear transformation and impulse invariance techniques.
• Implement DSP algorithms using industry-standard DSP hardware (e.g., Texas Instruments TMS320 series processors) and development tools in a laboratory environment.
• Use MATLAB or equivalent software to simulate, verify, and analyze DSP designs and signals.

Optional Learning Outcomes

Depending on instructor emphasis, students may also be able to:

• Apply multirate DSP concepts including decimation, interpolation, and polyphase filter banks.
• Describe and implement adaptive filtering algorithms such as the Least Mean Squares (LMS) method.
• Perform power spectrum estimation using nonparametric and model-based methods.
• Apply DSP techniques to audio, speech, or image processing applications.
• Analyze the effects of finite word length and quantization on digital filter performance.
• Interface DSP hardware with analog front-end and back-end circuitry for real-time signal acquisition and output.

Major Topics

Required Topics

• Discrete-Time Signals and Systems – Sequences, unit impulse, unit step, sinusoidal signals; LTI systems, convolution, causality, and stability.
• Sampling Theory and ADC/DAC Conversion – Nyquist theorem, aliasing, anti-aliasing filters, reconstruction, quantization, and sample-and-hold circuits.
• Z-Transform – Definition, region of convergence, properties, inverse Z-transform, system function analysis, pole-zero plots.
• Discrete-Time Fourier Transform (DTFT) – Properties, frequency response of LTI systems, magnitude and phase response.
• Discrete Fourier Transform (DFT) and FFT – DFT definition, circular convolution, DFT properties, FFT algorithms (Cooley-Tukey radix-2), computational efficiency.
• FIR Filter Design – Linear phase FIR filters, windowing methods (Hamming, Hanning, Blackman, Kaiser), frequency sampling design.
• IIR Filter Design – Analog prototype filters (Butterworth, Chebyshev), bilinear transformation, impulse invariance, frequency transformations.
• DSP Hardware Implementation – DSP processor architecture, fixed-point vs. floating-point arithmetic, real-time DSP implementation, development tools and debugging.
• Laboratory Practice – MATLAB/Simulink-based simulation; hardware implementation using DSP development kits (e.g., TI TMS320); waveform generation, filtering, and spectral analysis experiments.

Optional Topics

• Multirate Signal Processing – Decimation, interpolation, polyphase decomposition, and filter banks.
• Adaptive Filters – LMS algorithm, applications in noise cancellation and echo suppression.
• Power Spectrum Estimation – Periodogram, Welch method, autoregressive modeling.
• Audio and Speech DSP Applications – Digital audio effects, speech coding, echo cancellation, equalization.
• Image Processing Fundamentals – 2D DFT, spatial filtering, image enhancement.
• Finite Word Length Effects – Coefficient quantization, roundoff noise, limit cycles in IIR filters.

Resources & Tools

• Software: MATLAB with Signal Processing Toolbox (primary); Python with NumPy/SciPy (optional alternative); Code Composer Studio (TI DSP development environment).
• Hardware: Texas Instruments TMS320 series DSP evaluation boards (e.g., TMS320C6748 or TMS320C5535); oscilloscopes, function generators, and spectrum analyzers for lab verification.
• Textbooks (commonly used):
  • Proakis & Manolakis, Digital Signal Processing: Principles, Algorithms, and Applications, 4th ed., Prentice-Hall.
  • Oppenheim & Schafer, Discrete-Time Signal Processing, 3rd ed., Prentice-Hall.
• Online Resources: TI DSP application notes and tutorials; MathWorks MATLAB documentation; FSCJ/MDC library databases.

Career Pathways

Graduates who complete this course are prepared to pursue careers in a broad range of technology industries where digital signal processing is applied. Relevant roles include:

• DSP Engineer / Embedded Systems Engineer – Design and implementation of signal processing algorithms for consumer electronics, telecommunications, and defense systems.
• Communications Systems Technologist – Development and testing of digital communication hardware and software including radio, cellular, and satellite systems.
• Audio/Video Engineer – Application of DSP techniques in broadcast, recording, and streaming media systems.
• Biomedical Equipment Technologist – Signal acquisition and processing for medical devices such as ECG, EEG, and ultrasound systems.
• Test and Measurement Engineer – Use of spectral analysis and filtering tools for electronic system testing and validation.
• Graduate Study Preparation – This course provides a strong foundation for students pursuing a B.S. in Computer Engineering, Electrical Engineering, or related fields at Florida universities including USF, UCF, FAU, FIU, and UF.

Special Information

This is a laboratory course (CET4190C); the "C" suffix in the Florida SCNS designates a combined lecture and laboratory format. Students should expect hands-on lab sessions involving DSP hardware programming, real-time signal acquisition, and performance measurement in addition to lecture content.

This course is part of the Electrical and Computer Engineering Technology (ECET) program at Miami Dade College and similar A.S./B.A.S. programs at Florida state colleges. It is intended as a senior-level capstone elective within the Computer Engineering Technology concentration, and is designed to prepare graduates to function as technical professionals in industries such as transportation, aerospace, defense, networks and communications, and biomedical technology.

Students planning to transfer to a 4-year engineering program should note that completion of this course may satisfy or substitute for undergraduate DSP coursework such as UF's EEL 4750 / EEE 4511C or similar courses, subject to institutional review.