Digital Signal Processing

Start Date: 03/08/2020

Course Type: Common Course

Course Link: https://www.coursera.org/learn/dsp

About Course

Digital Signal Processing is the branch of engineering that, in the space of just a few decades, has enabled unprecedented levels of interpersonal communication and of on-demand entertainment. By reworking the principles of electronics, telecommunication and computer science into a unifying paradigm, DSP is a the heart of the digital revolution that brought us CDs, DVDs, MP3 players, mobile phones and countless other devices. The goal, for students of this course, will be to learn the fundamentals of Digital Signal Processing from the ground up. Starting from the basic definition of a discrete-time signal, we will work our way through Fourier analysis, filter design, sampling, interpolation and quantization to build a DSP toolset complete enough to analyze a practical communication system in detail. Hands-on examples and demonstration will be routinely used to close the gap between theory and practice. To make the best of this class, it is recommended that you are proficient in basic calculus and linear algebra; several programming examples will be provided in the form of Python notebooks but you can use your favorite programming language to test the algorithms described in the course.

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Course Introduction

Digital Signal Processing Digital Signal Processing (DSP) is the study of the fundamental concepts and algorithms for digital signal processing. In a DSP course, the digital signal is the signal processing unit. The digital signal has a wide variety of digital components, so the course concentrates on getting a good understanding of the digital signal, its characteristics, and basic algorithms for digital signal processing. In this course, you will gain an in-depth understanding of digital signal processing and gain a strong foundation in digital signal processing concepts and algorithms. You will also gain a strong foundation in DSP theory and algorithms. This will allow you to analyze and improve your DSP skills in subsequent courses in this specialization. This course is part of the DSP Supply Chain Management specialization. To get the most out of this specialization, you must have access to a computer and a DSP system. We recommend that you take the GIS and Cartography requirements from the specialization and complete them all at once. This course requires the use of MATLAB for analysis of the data. What you'll learn - Explores the fundamentals of a digital signal processor - Performs basic signal processing operations in the MATLAB environment - Understands and uses the common analog and digital control channels of a digital signal processor - Understands and uses the common digital output channels of a digital signal processor - Explains how digital signals and analog frames are linked - Explains how to

Course Tag

Signal Processing Discrete Fourier Transform Data Transmission Ipython Fourier Analysis Convolution Linear Algebra Digital Signal Processing

Related Wiki Topic

Article Example
Digital signal processing Digital signal processing and analog signal processing are subfields of signal processing. DSP applications include audio and speech signal processing, sonar, radar and other sensor array processing, spectral estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control of systems, biomedical engineering, seismic data processing, among others.
Digital signal processing Depending on the requirements of the application, digital signal processing tasks can be implemented on general purpose computers.
Signal processing The concept of discrete-time signal processing also refers to a theoretical discipline that establishes a mathematical basis for digital signal processing, without taking quantization error into consideration.
Digital signal processing Digital signal processing (DSP) is the use of digital processing, such as by computers, to perform a wide variety of signal processing operations. The signals processed in this manner are a sequence of numbers that represent samples of a continuous variable in a domain such as time, space, or frequency.
Digital signal processing Digital signal processing can involve linear or nonlinear operations. Nonlinear signal processing is closely related to nonlinear system identification and can be implemented in the time, frequency, and spatio-temporal domains.
Digital signal processing The main applications of DSP are audio signal processing, audio compression, digital image processing, video compression, speech processing, speech recognition, digital communications, digital synthesizers, radar, sonar, financial signal processing, seismology and biomedicine. Specific examples are speech compression and transmission in digital mobile phones, room correction of sound in hi-fi and sound reinforcement applications, weather forecasting, economic forecasting, seismic data processing, analysis and control of industrial processes, medical imaging such as CAT scans and MRI, MP3 compression, computer graphics, image manipulation, hi-fi loudspeaker crossovers and equalization, and audio effects for use with electric guitar amplifiers.
Digital signal This process is the basis of synchronous logic, and the system is also used in digital signal processing.
Digital signal processing DSP algorithms have long been run on general-purpose computers and digital signal processors. DSP algorithms are also implemented on purpose-built hardware such as application-specific integrated circuit (ASICs). Additional technologies for digital signal processing include more powerful general purpose microprocessors, field-programmable gate arrays (FPGAs), digital signal controllers (mostly for industrial applications such as motor control), and stream processors.
Digital Signal Processing (journal) Digital Signal Processing is a monthly peer-reviewed open access scientific journal covering all areas of signal processing. It as established in 1991 and published by Academic Press, now Elsevier. The editor-in-chief is Ercan E. Kuruoglu (ISTI-CNR, Pisa, Italy).
Digital signal processor A digital signal processor (DSP) is a specialized microprocessor (or a SIP block), with its architecture optimized for the operational needs of digital signal processing.
Digital signal processing Often when the processing requirement is not real-time, processing is economically done with an existing general-purpose computer and the signal data (either input or output) exists in data files. This is essentially no different from any other data processing, except DSP mathematical techniques (such as the FFT) are used, and the sampled data is usually assumed to be uniformly sampled in time or space. For example: processing digital photographs with software such as "Photoshop".
Digital signal processing The application of digital computation to signal processing allows for many advantages over analog processing in many applications, such as error detection and correction in transmission as well as data compression.  DSP is applicable to both streaming data and static (stored) data.
Digital signal processing The most common processing approach in the time or space domain is enhancement of the input signal through a method called filtering. Digital filtering generally consists of some linear transformation of a number of surrounding samples around the current sample of the input or output signal. There are various ways to characterize filters; for example:
Quantization (signal processing) Quantization, in mathematics and digital signal processing, is the process of mapping a large set of input values to a (countable) smaller set. Rounding and truncation are typical examples of quantization processes. Quantization is involved to some degree in nearly all digital signal processing, as the process of representing a signal in digital form ordinarily involves rounding. Quantization also forms the core of essentially all lossy compression algorithms.
Decimation (signal processing) In digital signal processing, decimation is the process of reducing the sampling rate of a signal. Complementary to interpolation, which increases sampling rate, it is a specific case of sample rate conversion in a multi-rate digital signal processing system. Decimation utilises filtering to mitigate aliasing distortion, which can occur when simply downsampling a signal. A system component that performs decimation is called a decimator.
Audio signal processing Historically, before the advent of widespread digital technology, ASP was the only method by which to manipulate a signal. Since that time, as computers and software became more advanced, digital signal processing has become the method of choice.
Digital signal processing The increasing use of computers has resulted in the increased use of, and need for, digital signal processing. To digitally analyze and manipulate an analog signal, it must be digitized with an analog-to-digital converter. Sampling is usually carried out in two stages, discretization and quantization. Discretization means that the signal is divided into equal intervals of time, and each interval is represented by a single measurement of amplitude. Quantization means each amplitude measurement is approximated by a value from a finite set. Rounding real numbers to integers is an example.
Perception Digital PD majors in Digital Signal Processing (DSP), Android, Live-Lite, and mobile technologies.
Digital radio See also software radio for a discussion of radios which use digital signal processing.
Audio signal processing A digital representation expresses the pressure wave-form as a sequence of symbols, usually binary numbers. This permits signal processing using digital circuits such as microprocessors and computers. Although such a conversion can be prone to loss, most modern audio systems use this approach as the techniques of digital signal processing are much more powerful and efficient than analog domain signal processing.