Introduction to Signal processing:-

Digital signal processing (DSP) is concerned with the representation of 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

EATHER forecasting for the future is one of the most

important attributes to forecast because agriculture sectors
as well as many industries are largely dependent on the weather
conditions. It is often used to predict and warn about natural
disasters that are caused by abrupt change in climatic conditions.
At macro level, weather forecasting is usually done using the
data gathered by remote sensing satellites. Weather parameters
like maximum temperature, minimum temperature, extent of
rainfall, cloud conditions, wind streams and their directions, are
projected using images taken by these meteorological satellites to
assess future trends. The satellite-based systems are inherently
costlier and require complete support system. Moreover, such
systems are capable of providing only such information, which is
usually generalized over a larger geographical area

Weather forecasting is the application of science and technology to predict the state of
the atmosphere for a future time and a given location. Human beings have attempted to predict
the weather informally for millennia, and formally since at least the nineteenth century. Weather
forecasts are made by collecting quantitative data about the current state of the atmosphere and
using scientific understanding of atmospheric processes to project how the atmosphere will
evolve.
Applications of DSP
DSP technology is nowadays commonplace in such devices as mobile phones,
multimedia computers, video recorders, CD players, hard disc drive controllers and
modems, and will soon replace analog circuitry in TV sets and telephones. An
important application of DSP is in signal compression and decompression. Signal
compression is used in digital cellular phones to allow a greater number of calls to be
handled simultaneously within each local "cell". DSP signal compression technology
allows people not only to talk to one another but also to see one another on their
computer screens, using small video cameras mounted on the computer monitors, with
only a conventional telephone line linking them together. In audio CD systems, DSP
technology is used to perform complex error detection and correction on the raw data
as it is read from the CD.

Although some of the mathematical theory underlying DSP techniques, such as

Fourier and Hilbert Transforms, digital filter design and signal compression, can be
fairly complex, the numerical operations required actually to implement these
techniques are very simple, consisting mainly of operations that could be done on a
cheap four-function calculator. The architecture of a DSP chip is designed to carry out
such operations incredibly fast, processing hundreds of millions of samples every
second, to provide real-time performance: that is, the ability to process a signal "live"
as it is sampled and then output the processed signal, for example to a loudspeaker or
video display. All of the practical examples of DSP applications mentioned earlier,
such as hard disc drives and mobile phones, demand real-time operation.

The major electronics manufacturers have invested heavily in DSP technology.

Because they now find application in mass-market products, DSP chips account for a
substantial proportion of the world market for electronic devices. Sales amount to
billions of dollars annually, and seem likely to continue to increase rapidly.

gital signal processing is the technique used to analyse various digital signals and obtain information
form the same. It is also used for transfer of information from one place to another and also involves
conversion in between analogue and digital signals.

It finds its application in various areas ranging from broadcasting to medicine.

Let us have a look at some of the applications of the same.

Biomedical Applications: DSP is used extensively in the field of biomedicine. In it, the various signals
that are generated by the different organs in the human body are measured in order to find
information regarding the health of the same. For example, in case of electrocardiograms, the electric
signals generated by the heart are measured. Similarly, the activity of the brain is monitored by
electroencephalograms.
Automatic Control: These days, many gadgets are available that can perform their tasks automatically.
These devices contain various components that can take inputs depending on the surrounding
conditions. These are conveyed to the control unit of the device where they are processed and the
necessary action is taken. For example, a device like the thermostat increases its resistance in
proportion to temperature. This can be used to stem the current in a machine whenever the
temperature rises.
Broadcasting: DSP is used on a large scale for the broadcast of both television and radio programs. In
the process of recording the audio itself, a large amount of processing of the sound waves takes place
in order to enhance the same. Then, the signals are converted into digital format and are broadcasted
and are received at the respective receivers where they are again converted into the analogous format
and then, are filtered to remove the noise etc. Thus, the output of the radio, TV etc. is generated.

Telecommunication: DSP is used to the greatest extent in this field. The various conversations that
one carried out these days are through the means of DSP which is used in the transfer of the signals
from one point to the other. Various methods are available to transfer these audio signals. For
example, ifsatellites are used then, the audio waves are first converted into electromagnetic waves
and then transferred over a wireless medium. On the other hand, in case of optical fibres, the waves
are converted into light waves and are then transferred through these fibres.
Navigation: DSP is used to a great extent in navigation. Devices or systems such as SONAR or Radar
work primarily on the basis of DSP. For example, SONAR makes use of sound waves (signals) in order to
calculate the depth. On the other hand, radars make use of radio waves in order to communicate the
locations of various objects in a particular radius.

Signal processing is an area of electrical engineering and applied mathematics that deals with

operations on or analysis of signals, in either discrete or continuous time, to perform useful operations on
those signals. Signals of interest can include sound, images, time-varying measurement values
and sensor data, for example biological data such aselectrocardiograms, control system signals,
telecommunication transmission signals such as radio signals, and many others. Signals are analog or
digital electrical representations of time-varying or spatial-varying physical quantities. In the context of
signal processing, arbitrary binary data streams and on-off signals are not considered as signals, but only
analog and digital signals that are representations of analog physical quantities.

Contents
 [hide]

1 Typical operations and applications

2 History

3 Mathematical topics embraced by signal

processing

4 Categories of signal processing

o 4.1 Analog signal processing

o 4.2 Discrete time signal processing

o 4.3 Digital signal processing

5 Fields of signal processing

6 See also

7 Notes and references

8 External links

[edit]Typical operations and applications

Processing of signals includes the following operations and algorithms with application examples:[1]

 Filtering (for example in tone controls and equalizers)

 Smoothing, deblurring (for example in image enhancement)
 Adaptive filtering (for example for echo-cancellation in a conference telephone, or denoising for
aircraft identification by radar)
 Spectrum analysis (for example in magnetic resonance imaging, tomographic
reconstruction and OFDM modulation)
 Digitization, reconstruction and compression (for example, image compression, sound coding and
other source coding)
 Storage (in digital delay lines and reverb)
 Feature extraction (for example speech-to-text conversion)
 Modulation (in modems)
 Wavetable synthesis (in modems and music synthesizers)
 Prediction
 System identification and classification
 A variety of other operations

In communication systems, signal processing may occur at OSI layer 1, the Physical

Layer (modulation, equalization, multiplexing, etc) in the seven layer OSI model, as well as at OSI layer 6,
the Presentation Layer (source coding, including analog-to-digital conversion and data compression).

[edit]History

According to Alan V. Oppenheim and Ronald W. Schafer, the principles of signal processing can be found
in the classical numerical analysis techniques of the 17th century. They further state that the
"digitalization" or digital refinement of these techniques can be found in the digital control systems of the
1940s and 1950s.[2]

[edit]Mathematical topics embraced by signal processing

 Linear signals and systems, and transform theory

 Calculus
 Vector spaces and Linear algebra
 Functional analysis
 Probability and stochastic processes
 Detection theory
 Estimation theory
 Optimization
 Programming
 Numerical methods
 Iterative methods
[edit]Categories of signal processing
[edit]Analog signal processing
Analog signal processing is for signals that have not been digitized, as in classical radio, telephone, radar,
and television systems. This involves linear electronic circuits such as passive filters, active
filters, additive mixers, integrators and delay lines. It also involves non-linear circuits such
as compandors, multiplicators (frequency mixers and voltage-controlled amplifiers),voltage-controlled
filters, voltage-controlled oscillators and phase-locked loops.

[edit]Discrete time signal processing

Discrete time signal processing is for sampled signals that are considered as defined only at discrete
points in time, and as such are quantized in time, but not in magnitude.

Analog discrete-time signal processing is a technology based on electronic devices such as sample and
hold circuits, analog time-division multiplexers, analog delay lines and analog feedback shift registers.
This technology was a predecessor of digital signal processing (see below), and is still used in advanced
processing of gigahertz signals.

The concept of discrete-time signal processing also refers to a theoretical discipline that establishes a
mathematical basis for digital signal processing, without taking quantization errorinto consideration.

[edit]Digital signal processing

Digital signal processing is for signals that have been digitized. Processing is done by general-
purpose computers or by digital circuits such as ASICs, field-programmable gate arrays or
specialized digital signal processors (DSP chips). Typical arithmetical operations include fixed-
point and floating-point, real-valued and complex-valued, multiplication and addition. Other typical
operations supported by the hardware are circular buffers and look-up tables. Examples of algorithms are
the Fast Fourier transform (FFT), finite impulse response (FIR) filter,Infinite impulse response (IIR)
filter, Wiener filter, Adaptive filter and Kalman filter.

[edit]Fields of signal processing

 Statistical signal processing — analyzing and extracting information from signals and noise based
on their stochastic properties
 Audio signal processing — for electrical signals representing sound, such as speech or music
 Speech signal processing — for processing and interpreting spoken words
 Image processing — in digital cameras, computers, and various imaging systems
 Video processing — for interpreting moving pictures
 Array processing — for processing signals from arrays of sensors
 Time-frequency signal processing — for processing non-stationary signals[3]
 Filtering — used in many fields to process signals
 Seismic signal processing
 Data mining

Apart from those mentioned above, digital signal processing has various otherapplications. For
example, it is used in cars, remote controls, seismic analysis etc. Thus, DSP proves to be one of
the most useful techniques developed in the modern times.
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 Weather Forecasting System -

UK Meteorological Office
Fixed location radar is used by the UK Meteorological office to aid in forecasting of the
weather. They sought to upgrade the system using DSP to enhance the system's
resolution while retaining the existing RF stages.

They had a good understanding of how their radar should work, and came to us for
advice on a processing engine with sufficient performance to run their signal processing
algorithms in real time. Our applications team analysed the system, looking for the best
solution.

Once analysis of the RADAR's DSP processing and interfacing requirements was
complete, we recommended a system including PCI-based DSP processing boards, a
multi-processing DSP engine, a digital I/O module and both ADC and DAC modules to
interface to the existing systems. This recommendation was accepted.

We supplied fully-configured hardware for their system, as we do for all customers.

These include:

 I/O Boards
 Processing Boards (with DSP or FPGA modules as required)
 Cable Harnesses
 Example Application - relevant to the application
 Development Tools
 System configuration document

Our evaluations ensured that the system we provided was ideal for the Met Office, and as
a result, development proceeded smoothly.  A field trial of the system was completed
successfully, and the system is now deployed and in constant use - 24 hours a day,
every day.

We are grateful for the kind permission of the UK Meteorological Office in allowing us to
describe their system, and acknowledge the assistance they provided in preparing this
article.