An Overview of Deterministic Digital Signal Processing (DSP) Applications

November 2nd, 2017 by

Digital signal processing (DSP)is a term used to refer to different techniques that are used to improve the reliability and accuracy of digital communications. Deterministic digital signals are signals that can be specified using mathematical equations. Ramp, impulse, exponential, sinusoidal and step are good examples of deterministic digital signals that can be defined in this way. Digital signal processes take voice, audio, pressure, temperature, and video recorded signals and manipulate them mathematically.

Digital signal processing (DSP)

Digital signal processes contain four main components:

  1. Computing Engine: the computing engine handles mathematical manipulations, processes, and calculations through accessing a task or program directly from its memory or the information stored in data memory.
  2. Data Memory: data memory stores information that should be processed and operates in line with program memory.
  3. Program Memory: program memory stores tasks or programs that digital signal processes will rely on to process, manipulate and compress data.
  4. I/O: I/O can be used to serve different duties based on the digital signal processing field it is used for, such as timers, external ports, serial ports or connection with the outside world.

Deterministic digital signal processingapplications

Due to its programmable nature, digital signal processing is used in numerous applications. It allows you to create application-specific processors with ASICs, FPGA, and prototyping. Fast and secure digital signal transmissions are simplified for large databases through the assistance of fast digitized computation and compression security. Additional digital signal processing applications include:

  • – Feature extraction, multiscale analysis and pattern recognition under digital image processing
  • – Data compression, storage, enhancement, transmission and level compression under audio signal processing
  • – VC-2, MPEG, JPEG-200, and H.263/64/65 under video/image compression
  • – Digital communication through FEC Codes, AES, LDPC, and Cryptography
  • – Biomedical signaling and imaging analysis as CAT, MRI, EEG, ECG, cardiac pacemaker, artificial eye and brain chips
  • -Home automation, automatic translation, hands-free computing, robotics, speech-to-text reporter and multimodal interaction under speech recognition

Digital signal processes

The difference among the digital signal processing applications listed above is how the digital signal processor filters inputs. Five different aspects vary from one deterministic digital signal processing application to another: data bus width, ROM size, RAM size, clock frequency and I/O voltage. These aspects determine the arithmetic format, memory organization, data width and speed of the processor in use.

DSP applications are executed using the Harvard architecture or the Von Neumann memory architecture. The Harvard architecture allows a processor to use two data bus sets to access two memory banks simultaneously while the Von Neumann memory architecture allows the use of one data bus at a time,so that operations cannot be executed while instructions are being processed.

Deterministic digital signal processing is highly relied upon to execute daily operations. It is essential for recreating analog signals into digital signals for numerous purposes. It is preferred over the general purpose microprocessor because it consumes less energy, power and time to execute efficient signal processes.



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