Digital Signal Processing- An Introduction Training
|Commitment||3 days, 7-8 hours a day.|
|How To Pass||Pass all graded assignments to complete the course.|
|User Ratings||Average User Rating 4.8 See what learners said|
|Delivery Options||Instructor-Led Onsite, Online, and Classroom Live|
Digital Signal Processing- An Introduction Training Course – Hands-on
Digital Signal Processing- An Introduction Training 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.
Digital Signal Processing- An Introduction Training Course – Customize it
- We can adapt this training course to your group’s background and work requirements at little to no added cost.
- If you are familiar with some aspects of this training course, we can omit or shorten their discussion.
- We can adjust the emphasis placed on the various topics or build the training around the mix of technologies of interest to you (including technologies other than those included in this outline).
- If your background is nontechnical, we can exclude the more technical topics, include the topics that may be of special interest to you (e.g., as a manager or policy-maker), and present the training course in manner understandable to lay audiences.
Digital Signal Processing- An Introduction Training Course – Audience/Target Group
The target audience for this training course:
Digital Signal Processing- An Introduction Training Course – Objectives:
Upon completing this training course, learners will be able to meet these objectives:
- 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.
Digital Signal Processing- An Introduction Training – Course Content
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.
Digital Signal Processing- An Introduction Training – 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.