Signal processing is a scientific field applied to mathematics, electrical engineering, and systems engineering. Signal processing gave birth to the EKG, a machine which can can predict and help prevent heart attacks, and other heart conditions. It is so accurate, that it can let doctors know in advance what the chances are for a patient to survive a surgery or if a medication such as or chemotherapy could do more harm rather than help a patient.
What is digital signal processing? Digital signal processing (DSP) involves the representation of time signals through a sequence of numbers or symbols, and the processing of said signals. DSP includes audio and speech processing, sonar and radar processing, sensor array processing, spectral estimation, statistical processing, digital image processing, processing for communications, control of systems, biomedical signal processing, seismic data processing, and more. Analog signal processing (ASP), on the other hand, is processing of signals that is conducted on analog signals by analog means, where analog refers to something that is mathematically represented as a set of continuous values. ASP is different from DSP in that the latter uses a series of discrete quantities to represent signal. Analog values are usually represented as a voltage, electric current, or electric charge. Now, while image signal processing typically refers to digital processing, optical and analog processing are also a possibility.
When it comes to DSP, the main signal processing applications are audio processing, audio compression, digital image processing, video compression, speech processing, speech recognition, digital communications, radar, sonar, seismology and biomedicine. The analysis of signals and the automation of repetitive tasks of recognition are relevant to several areas such as bioengineering, biometrics, industrial inspection, agroindustry, artificial vision, bioacoustics and seismology. Professionals in those fields require advanced data and signal analysis techniques to understand better the nature of their objects of study and the relationships between the variables involved in their corresponding processes. Additionally, pattern recognition methods are able to provide them with advanced and reliable techniques to automate clas
sification or identification procedures and assist them in complicates decisions such as diagnoses and forecasts.
As a subfield of applied mathematics, processing of signals touches upon topics like linear signals and systems, and transform theory; system identification and classification; calculus; differential equations; vector spaces and linear algebra; functional analysis; probability and stochastic processes; detection theory; estimation theory; optimization; programming; numerical methods and iterative methods.
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