A manufacturer specification sheet lists the transfer function of a pressure sensor as 45
Solution
Digital signal processing (DSP) is the mathematical manipulation of an information
signal to modify or improve it in some way. It is characterized by the representation of discrete
time, discrete frequency, or other discrete domain signals by a sequence of numbers or symbols
and the processing of these signals. Digital signal processing and analog signal processing are
subfields of signal processing. DSP includes subfields like: audio and speech signal processing,
sonar and radar signal processing, sensor array processing, spectral estimation, statistical signal
processing, digital image processing, signal processing for communications, control of systems,
biomedical signal processing, seismic data processing, etc. The goal of DSP is usually to
measure, filter and/or compress continuous real-world analog signals. The first step is usually to
convert the signal from an analog to a digital form, by sampling and then digitizing it using an
analog-to-digital converter (ADC), which turns the analog signal into a stream of numbers.
However, often, the required output signal is another analog output signal, which requires a
digital-to-analog converter (DAC). Even if this process is more complex than analog processing
and has a discrete value range, the application of computational power to digital 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.[1] DSP algorithms
have long been run on standard computers, on specialized processors called digital signal
processor on purpose-built hardware such as application-specific integrated circuit (ASICs).
Today there are additional technologies used for digital signal processing including more
powerful general purpose microprocessors, field-programmable gate arrays (FPGAs), digital
signal controllers (mostly for industrial apps such as motor control), and stream processors,
among other
Solution
Digital signal processing (DSP) is the mathematical manipulation of an information
signal to modify or improve it in some way. It is characterized by the representation of discrete
time, discrete frequency, or other discrete domain signals by a sequence of numbers or symbols
and the processing of these signals. Digital signal processing and analog signal processing are
subfields of signal processing. DSP includes subfields like: audio and speech signal processing,
sonar and radar signal processing, sensor array processing, spectral estimation, statistical signal
processing, digital image processing, signal processing for communications, control of systems,
biomedical signal processing, seismic data processing, etc. The goal of DSP is usually to
measure, filter and/or compress continuous real-world analog signals. The first step is usually to
convert the signal from an analog to a digital form, by sampling and then digitizing it using an
analog-to-digital converter (ADC), which turns the analog signal into a stream of numbers.
However, often, the required output signal is another analog output signal, which requires a
digital-to-analog converter (DAC). Even if this process is more complex than analog processing
and has a discrete value range, the application of computational power to digital 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.[1] DSP algorithms
have long been run on standard computers, on specialized processors called digital signal
processor on purpose-built hardware such as application-specific integrated circuit (ASICs).
Today there are additional technologies used for digital signal processing including more
powerful general purpose microprocessors, field-programmable gate arrays (FPGAs), digital
signal controllers (mostly for industrial apps such as motor control), and stream processors,
among other
