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Digital Signal Processing (DSP) is a branch of Electronics and Telecommunication Engineering that is concerned with the improvisation and accuracy of digital signals by employing multiple advanced techniques.
Digital Signal Processing works on real-world signals like voice, audio, video, temperature, pressure, or position that have been digitized and then mathematically manipulate them.
Digital Signal Processing applications consists of audio and speech processing, sonar, radar, and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, data compression, video coding, audio coding, image compression, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others.
Digital Signal Processing manipulates various types of signals with the intention of filtering, measuring, or compressing and producing analog signals.
These signals need to be processed so that the data that they contain can be displayed, analyzed, or converted to another type of signal that may be useful. Analog devices detect signals such as sound, light, temperature or pressure and manipulate them. A device like the Analog-to-Digital converter takes the real-world signal and turns it into the digital format of 1's and 0's.
Digital Signal information is translated into a binary format where each bit of data is represented by two distinguishable amplitudes while the Analog signal takes information and translates it into electric pulses of varying amplitude, whereas
Digital Signal information can be used by a computer to control things like security, telephone, home theater systems, and video compression. Signals may be compressed so that they can be transmitted quickly and more efficiently from one place to another.
Signals may also be enhanced or manipulated to improve their quality or provide information that is not sensed by humans. Although real-world signals can be processed in their analog form, processing signals digitally provides the advantages of high speed and accuracy.
Digital Signal Processing is made up of below components:
Program Memory: This component stores the programs the DSP will use to process, compress and manipulate the signal data
Data Memory: This works with the program memory and stores the signal information to be processed.
Compute Engine: This performs the mathematical processes and manipulations by accessing the program from the Program Memory and the signal data from the Data Memory
Input/Output: This works with a variety of things depending on the field it's used to connect to the outside world.
Digital Signal Processing can be used in various manners and applications using different processors.
There are numerous digital signal processors that can accomplish different things, depending on the application being performed. Some of these are audio signal processing, audio and video compression, speech processing and recognition, digital image processing, and radar applications. The difference between each of these applications is how the digital signal processor can filter each input.
There diverse of fields DSP can be applied on and below are major applications:
1. Telecommunications: This involves transferring information from one location to another. This includes many forms of information: telephone conversations, television signals, computer files, and other types of data. To transfer the information, you need a channel between the two locations. This may be a wire pair, radio signal, optical fiber, etc. DSP has revolutionized the telecommunications industry in many areas: signaling tone generation and detection, frequency band shifting, filtering to remove power line hum, etc.
2. Audio Processing: Audio processing comprises of many diverse fields, that are involved in presenting sound to human listeners. Below are major areas audio processing is prominent:
a. Music: High fidelity music reproduction, such as in audio compact discs. This is very familiar to anyone who has compared the musical quality of cassette tapes with compact disks. In a typical scenario, a musical piece is recorded in a sound studio on multiple channels or tracks.
b. Speech Generation/Recognition: This involves synthetic speech, where computers generate and recognize human voice patterns. These are used to communicate between humans and machines.
c. VoIP: This involves voice telecommunications, another name for telephone networks.
3. Echo Location: This involves using signals to detect an object from the surrounding signal direction. This is a common method of obtaining information about a remote object is to bounce a wave off of it. It involves the use of Radar, Sonar, and Echo reflection. For example, radar operates by transmitting pulses of radio waves and examining the received signal for echoes from aircraft. In sonar, sound waves are transmitted through the water to detect submarines and other submerged objects.
4. Image Processing: This involves the use of signals to store, process and manipulate digitized image signals. Images are signals with special characteristics. Images are a measure of a parameter over space (distance), while most signals are a measure of a parameter over time. Another good thing is, they contain a great deal of information. Video Signals are made up of bits of image signals.
The following are a few of the advantages of Digital Signal Processing:
1. Programmability: A digital system can be programmably changed to change the functionality and diversity.
2. Versatility - Ease of upgrading and Flexibility
3. Stability - Less sensitive environmental changes
4. Cost effectiveness
5. Special applications like lossless compression
6. DSP based systems can be easily modified
Digital Signal Processing - Introduction
Digital Signal Processing - Signals-Definition
Digital Signal Processing - Basic CT Signals
Digital Signal Processing - Basic DT Signals
Digital Signal Processing - Classification of CT Signals
Digital Signal Processing - Classification of DT Signals
Digital Signal Processing - Miscellaneous Signals
Digital Signal Processing - Operations Signals - Shifting
Digital Signal Processing - Operations Signals - Scaling
Digital Signal Processing - Operations Signals - Reversal
Digital Signal Processing - Operations Signals - Differentiation
Digital Signal Processing - Operations Signals - Integration
Digital Signal Processing - Operations Signals - Convolution
Digital Signal Processing - Static Systems
Digital Signal Processing - Dynamic Systems
Digital Signal Processing - Causal Systems
Digital Signal Processing - Non-Causal Systems
Digital Signal Processing - Anti-Causal Systems
Digital Signal Processing - Linear Systems
Digital Signal Processing - Non-Linear Systems
Digital Signal Processing - Time-Invariant Systems
Digital Signal Processing - Time-Variant Systems
Digital Signal Processing - Stable Systems
Digital Signal Processing - Unstable Systems
Digital Signal Processing - Solved Examples
Digital Signal Processing - Z-Transform Introduction
Digital Signal Processing - Z-Transform Properties
Digital Signal Processing - Z-Transform Existence
Digital Signal Processing - Z-Transform Inverse
Digital Signal Processing - Z-Transform Solved Examples
Digital Signal Processing - DFT Introduction
Digital Signal Processing - DFT Time Frequency Transform
Digital Signal Processing - DTF Circular Convolution
Digital Signal Processing - DFT Linear Filtering
Digital Signal Processing - DFT Sectional Convolution
Digital Signal Processing - DFT Discrete Cosine Transform
Digital Signal Processing - DFT Solved Examples
Digital Signal Processing - Fast Fourier Transform
Digital Signal Processing - In-Place Computation
Digital Signal Processing - Computer Aided Design
Digital Signal Processing - Exams and Certification
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