Digital Signal Processing Training Introduction 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 Digital Signal Processing Introduction 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 Digital Signal Processing 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 multi-rate applications.
The Digital Signal Processing Introduction 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 Digital Signal Processing Introduction course will be provided to the attendees.
- 3 days of Digital Signal Processing Training Introduction with an expert instructor
- Digital Signal Processing Introduction Electronic Course Guide
- Certificate of Completion
- 100% Satisfaction Guarantee
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Upon completing this Digital Signal Processing Training Introduction 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 multi-rate signal processing.
- Utilize MATLAB to analyze digital signals, design digital filters, and apply these filters for a practical DSP system.
- We can adapt this Digital Signal Processing Training Introduction course to your group’s background and work requirements at little to no added cost.
- If you are familiar with some aspects of this Digital Signal Processing Introduction course, we can omit or shorten their discussion.
- We can adjust the emphasis placed on the various topics or build the Digital Signal Processing Introduction course 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 Digital Signal Processing course in a manner understandable to lay audiences.
The target audience for this Digital Signal Processing Introduction course:
There are no formal prerequisites for this Digital Signal Processing Training Introduction course.
- 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 Training Introduction
- 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.