环保英文数字信号专业英语翻译

2021年1月20日发(作者：玛琪)

电子与通信专业英语







Digital
Signal
Processing
（
英文翻
译）










姓名：赵

豪

班级：信工
122

学号：
2012020217

Digital

Signal

Processing
1
、
Introduction
Digital signal processing (DSP) is concerned with the representation of th
e signals by a sequence of numbers or symbols and the processing of these s
ignals. Digital signal processing and analog signal processing are subfields of
signal processing. DSP includes subfields like audio and speech signal proce
ssing, sonar and radar signal processing, sensor array processing, spectral es
timation, statistical signal processing, digital image processing, signal process
ing for communications, biomedical signal processing, seismic data processin
g, etc.
Since the goal of DSP is usually to measure or filter continuous real-world
analog signals, the first step is usually to convert the signal from an analog to
a digital form, by using an analog to digital converter. Often, the required outp
ut signal is another analog output signal, which requires a digital to analog co
nverter. Even if this process is more complex than analog processing and has
a discrete value range, the stability of digital signal processing thanks to error
detection and correction and being less vulnerable to noise makes it advanta
geous over analog signal processing for many, though not all, applications.
DSP algorithms have long been run on standard computers, on specializ
ed processors called digital signal processors (DSP)s, or on purpose- built har
dware such as application-specific integrated circuit (ASICs). Today there are
additional technologies used for digital signal processing including more powe
rful general purpose microprocessors, field- programmable gate arrays (FPGA
s), digital signal controllers (mostly for industrial applications such as motor co
ntrol), and stream processors, among others.
In DSP, engineers usually study digital signals in one of the following do
mains: time domain (one- dimensional signals), spatial domain (multidimensio
nal signals), frequency domain, autocorrelation domain, and wavelet domains.
They choose the domain in which to process a signal by making an informed
guess (or by trying different possibilities) as to which domain best represents t
he essential characteristics of the signal. A sequence of samples from a meas
uring device produces a time or spatial domain representation, whereas a disc
rete Fourier transform produces the frequency domain information that is the f
requency spectrum. Autocorrelation is defined as the cross- correlation of the s
ignal with itself over varying intervals of time or space.
2
、
Signal Sampling
With the increasing use of computers the usage of and need for digital si
gnal processing has increased. In order to use an analog signal on a compute
r it must be digitized with an analog to digital converter (ADC). Sampling is us
ually carried out in two stages, discretization and quantization. In the discretiz
ation stage, the space of signals is partitioned into equivalence classes and q
uantization is carried out by replace the signal with representative signal value
s are approximated by values from a finite set.
The Nyquist-Shannon sampling theorem states that a signal can be exact
ly reconstructed from its samples if the samples if the sampling frequency is g
reater than twice the highest frequency of the signal. In practice, the sampling
frequency is often significantly more than twice the required bandwidth.
A digital to analog converter (DAC) is used to convert the digital signal ba
ck to analog signal.
The use of a digital computer is a key ingredient in digital control systems
.
3
、
Time and Space Domains
The most common processing approach in the time or space domain is e
nhancement of the input signal through a method called filtering. Filtering gen
erally consists of some transformation of a number of surrounding samples ar
ound the current sample of the input or output signal. There are various ways
to characterize filters, for example:
A“linear”
filter is a linear transformation of i
nput samples; other filters are
“non
-
linear.”
Linear filters satisfy the superpositi
on condition, i.e. if an input is a weighted linear combination of different signal
s, the output is an equally weighted linear combination of the corresponding o
utput signals.
A
“causal”
filter uses only previous samples of the input or output signals;
while a
“non
-
causal”
filter uses future input samples. A non-causal filter can u
sually be changed into a causal filter by adding a delay to it.
A“time
-
invariant”
filter has constant properties over time; other filters such
as adaptive filters change in time.
Some filters are
“stable”,
others are
“unstable”.
A stable filter produces an
output that converges to a constant value with time, or remains bounded withi
n a finite interval. An converges to a constant value with time, or remains bou
nded within a finite interval. An unstable filter can produce an output that grow
s without bounds, with bounded or even zero input.
A“Finite
Impulse
Response”
(FIR) filter uses only the input signal, while a
n
“Infinite
Impulse
Response”
filter (IIR) uses both the input signal and previou
s samples of the output signal. FIR filters are always stable, while IIR filters m
ay be unstable.
Most filters can be described in Z-domain (a superset of the frequency do
main) by their transfer functions. A filter may also be described as a difference
equation, a collection of zeroes and poles or, if it is an FIR filter, an impulse r
esponse or step response. The output of an FIR filter to any given input may b
e calculated by convolving the input signal with the impulse response. Filters c
an also be represented by block diagrams which can then be used to derive a
sample processing algorithm to implement the filter using hardware instruction
s.
4
、
Frequency Domain
Signals are converted from time or space domain to the frequency domai
n usually through the Fourier transform. The Fourier transform converts the si
gnal information to a magnitude and phase component of each frequency. Oft
en the Fourier transform is converted to the power spectrum, which is the mag
nitude of each frequency component squared.
The most common purpose for analysis of signals in the frequency domai
n is analysis of signal properties. The engineer can study the spectrum to dete
rmine which frequencies are present in the input signal and which are missing
.
Filtering, particularly in non real-time work can also be achieved by conve
rting to the frequency domain, applying the filter and then converting back to t
he time domain. This is a fast, O (nlogn) operation, and can give essentially a
ny filter shape including excellent approximations to brickwall filters.
There are some commonly used frequency domain transformations. For
example, the cepstrum converts a signal to the frequency domain Fourier tran
sform, takes the logarithm, then applies another Fourier transform. This emph
asizes the frequency components with smaller magnitude while retaining the o
rder of magnitudes of frequency ncy domain analysis is al
so called spectrum or spectral analysis.
5
、

signal processing,
Signal usually need in different example, from a sensor output
signal may be contaminated the redundant electrical
connected to a patient's chest, electrocardiogram (ecg) is measured by the
heart and other muscles activity caused by small voltage to the
strong effect electrical interference from the power supply, signal picked up the

least reduce unwanted part of the , more and more, is by the DSP
technology to extract the signal filter to improve the quality of signal or
important information, rather than the analog electronic technology.
6
、
the development of DSP
The development of digital signal processing (DSP) in the 1960 s to large
Numbers of digital computing applications using fast Fourier transform (FFT),
which allows the frequency spectrum of a signal can be quickly
techniques have not been widely used at the time, because
suitable computing equipment is usually only in university and other research
institutions can be used.
7
、

the digital signal processor (DSP)
In the late 1970 s and early 1980 s the introduction of microprocessor
makes DSP technology is used in the wider l microprocessor,
such as Intel x86 family, however, is not suitable for the calculation of DSP
intensive demand, with the increase of DSP importance in the 1980 s led to
several major electronics manufacturers (such as Texas instruments, analog
devices and MOTOROLA) to develop a digital signal processor chip,
microprocessor, specifically designed for use in the operation of the digital
signal processing requirements type of architecture.(note that abbreviation
DSP digital signal processing (DSP) of different meanings, this word is used in
digital signal processing, a variety of technical or digital signal processor, a
special type of microprocessor chips).As a common microprocessors, DSP is
one kind has its own local instruction code of programmable chip
is able to millions of floating point operations per second, as they are of the
same type more famous universal device, faster and more powerful versions
are can also be embedded in a complex
devices, usually includes analog and digital circuit.
8
、
the application of digital signal processors
DSP technology is widespread in mobile phones, multimedia computers,
video recorders, CD players, hard disk drives and controller of the modem
equipment, and will soon replace analog circuits in TV and telephone
is an important application of signal compression and
compression is used for digital cellular phone, in every
place of the
compression technology not only makes people can talk to each other, and
can be installed on the computer by using the small camera make people
through the monitor to see each other, and these together is the only needs to
be a traditional phone audio CD system, DSP technology to perform
complex error detection and correction of raw data, because it is read from CD.
Although some of the underlying mathematical theory of DSP technology,
such as Fourier transform and Hilbert transform, the design of digital filter and
signal compression, can be quite complex, and the actual implementation of
these technologies needed for numerical computation is very simple, mainly