Digital Signal Processing- An Introduction
This 3-day course provides an overview of digital signal processing (DSP) tools and techniques used to analyze digital signals and systems while also treating the design of DSP systems to perform important DSP operations such as signal spectral estimation, frequency selective filtering, and sample rate conversion. In contrast to typical DSP courses that needlessly focus on mathematical details and intricacies, this course emphasizes the practical tools utilized to create state-of-the-art DSP systems commonly used in real-world applications.
MATLAB is used throughout the course to illustrate important DSP concepts and properties, permitting the attendees to develop an intuitive understanding of common DSP functions and operations. MATLAB routines are used to design and implement DSP filter structures for frequency selection and multirate applications.
The course is valuable to engineers and scientists who are entering the signal processing field or as a review for professionals who desire a cohesive overview of DSP with illustrations and applications using MATLAB. A comprehensive set of notes and references as well as all custom MATLAB routines used in the course will be provided to the attendees.
Bringing a laptop with Matlab loaded would be helpful -BUT NOT REQUIRED- as there will be Matlab demonstrations throughout the course.
What you will learn:
- Compute and interpret the frequency-domain content of a discrete-time signal.
- Design and implement finite-impulse response (FIR) and infinite-impulse response (IIR) digital filters, to satisfy a given set of specifications.
- Apply digital signal processing techniques learned in the course to applications in multirate signal processing.
- Utilize MATLAB to analyze digital signals, design digital filters, and apply these filters for a practical DSP system.
- Discrete-Time Signals & Systems. Frequency concepts in continuous- and discrete-time. Fourier Series and Fourier Transforms. Linear time-invariant systems, convolution, and frequency response.
- Sampling. The Sampling Theorem, Aliasing, and Sample Reconstruction. Amplitude Quantization and Companding.
- The Discrete Fourier Transform (DFT) and Spectral Analysis. Definition and properties of the DFT, illustrated in MATLAB. Zero-padding, windowing, and efficient computational algorithms – the Fast Fourier Transforms (FFTs). Circular Convolution and Linear Filtering with the FFT. Overlap-add and overlap-save techniques.
- Design of Digital Finite-Impulse Response (FIR) Filters. Filter Specifications in Magnitude and Phase. Requirements for linear phase. FIR filter design in MATLAB with Windows and Optimum Equiripple techniques.
- Design of Digital Infinite-Impulse Response (IIR) Filters. The z-transform and system stability. Butterworth, Chebyshev, and Elliptic filter prototypes. IIR filter design in MATLAB using impulse invariance and the Bilinear Transformation.
- Applications in Multirate Signal Processing. Signal decimation and interpolation. Sample rate conversion by a rational factor. Efficient implementation of narrowband filters. Polyphase filters.
REGISTRATION: There is no obligation or payment required to enter the Registration for an actively scheduled course. We understand that you may need approvals but please register as early as possible or contact us so we know of your interest in this course offering.
SCHEDULING: If this course is not on the current schedule of open enrollment courses and you are interested in attending this or another course as an open enrollment, please contact us at (410)956-8805 or email@example.com. Please indicate the course name, number of students who wish to participate. and a preferred time frame. ATI typically schedules open enrollment courses with a 3-5 month lead-time. To express your interest in an open enrollment course not on our current schedule, please email us at firstname.lastname@example.org.
Dr. Brian Jennison is a Principal Engineer at the Johns Hopkins University Applied Physics Laboratory, where he has worked on signal processing efforts for radar, sonar, chemical detectors, and other sensor systems. He holds M.S. and Ph.D. degrees in Electrical Engineering from Purdue University and a B.S. degree in Electrical Engineering from the Missouri University of Science and Technology. He currently serves as Chair of the Electrical and Computer Engineering program for the Johns Hopkins University Engineering for Professionals, where he has taught courses in signals and systems, multi-dimensional and multi-rate digital signal processing.
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