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- Signal processing techniques are used to optimize transmissions, digital storage efficiency, correcting distorted signals, improve subjective video quality, and to detect or pinpoint components of interest in a measured signal.
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May 15, 2024 · Digital Signal Processing (DSP) is a fundamental technology that has revolutionized the way, manipulate, and analyze digital signals across various domains. Using some computational algorithms and techniques, DSP gives flexibility and precision that matches to basic analog signal processing methods.
Signal processing techniques are used to optimize transmissions, digital storage efficiency, correcting distorted signals, improve subjective video quality, and to detect or pinpoint components of interest in a measured signal.
- How Is Signal Processing done?
- Signal Processing Applications
- Types of Signal Processing
- FFT Analysis
- Modal Analysis
- Mathematics
- Signal Processing Tools
- Summary
Historically, signal processing was performed entirely in the analog domain. For example, signal filters were built using discrete components like resistors, capacitors, and inductors. This is still done, of course, but since the late 20th century, data of all types have become increasingly digitized. As a result, signal processing has necessarily ...
Signal processing is applied across numerous industries and applications, including: 1. Audio compression and signal processing 2. Data acquisition and signal processing 3. Digital image and graphics processing 4. Video compression and signal processing 5. Speech recognition and processing 6. RADAR, SONAR, and LiDAR signal processing and signal opt...
Filtering
Filtering is one of the most basic and important signal-processing techniques. It has been used since the very beginning of analog recording, to enhance our ability to see the true signal by suppressing “noise” and other interferences. Filtering was (and is) used for applications from adjusting the bass and treble response of your music player to adjusting the channel frequency of a radio, and improving sound quality on telephone lines. In DAQ systems, analog filters were used to attenuate si...
Filter prototypes
No filter is perfect. They can impose phase delays, ripple, ringing, and other distortions within the passband. The trick is to choose the correct filter prototype that works best for the type of signals being processed. You will hear of filter prototypes such as Bessel, Butterworth, Elliptic, and Chebyshev, to name a few. Over the years, engineers have developed numerous filtering prototypes in order to provide the best possible results for various applications:
What’s better, analog or digital filtering?
In applications that require a simple, unchanging filter in the analog domain, it can be cheaper and easier to use an analog filter design. However, if it is required to have filters that are adaptable to a variety of applications, or to run in the digital domain, the digital approach is far less expensive and occupies a lot less space. Digital filtering can also be more accurate since it eliminates the tolerances of discrete components in the analog domain (resistors, capacitors, etc). In th...
Converting a signal from the time domain to the frequency domain allows us to see things in it that are not possible via direct observation. In the complex AC waveform shown below, the signal is composed of numerous frequencies: It is difficult to know which frequencies are contained within the signal from this time vs. history graph. The solution ...
Modal tests and analysis are indispensable tools to determine natural frequencies, damping ratios, and mode shapes of any structure. You “excite” a structure with an impact hammer or one or more multiple modal shakers, then measure the responses using accelerometers mounted on it. When you strike a structure with an impact (impulse) hammer, you exc...
Because data is digital in today’s DAQ systems, there are a host of mathematical processes that can be performed on it. It is well beyond the scope of this article to cover them all, but here is just a short summary of some of the more popular math functions available in popular DAQ software programs: 1. Statistics: RMS, average, min, max, standard...
There are a number of signal-processing tools available to engineers and scientists today. Here is a short list of some of the better-known and popular ones:
Signal processing is applied across many industries and sectors, including acoustics, modal testing of structures, video processing, geophysics, automotive engineering, aerospace engineering, energy, medical imaging, RADAR, SONAR, LIDAR, and more. Each one applies the signal processing methods that are most germane to their particular requirements.
Digital Signal Processors (DSP) take real-world signals like voice, audio, video, temperature, pressure, or position that have been digitized and then mathematically manipulate them. A DSP is designed for performing mathematical functions like "add", "subtract", "multiply" and "divide" very quickly.
Digital signal processing (DSP) is the use of digital processing, such as by computers or more specialized digital signal processors, to perform a wide variety of signal processing operations.
The key reason why digital signal processing systems have a technological advantage today is the computer: computations, like the Fourier transform, can be performed quickly enough to be calculated as the signal is produced, and programmability means that the signal processing system can be easily changed. This flexibility has obvious appeal ...
What is digital signal processing used for? The theory behind DSP is quite complex. DSP can clarify or standardize digital signals, but it can also perform various other tasks, such as filtering, compression and modulation.
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