Digital signal processing (DSP) involves using digital technology to process analog signals. It converts analog signals into digital data that can be manipulated and analyzed. DSP has applications in areas like audio processing, image processing, radar, and mobile phones. The key components of DSP systems are program memory, data memory, a compute engine, and input/output interfaces. DSP emerged in the 1960s and was initially used for applications like radar, sonar, and space exploration. It later expanded into commercial uses with the growth of personal computers and consumer electronics.

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1. Introduction
Digital signal processing (DSP) is the use of digital processing, such as by computers or
more Specialized digital signal processors, to perform a wide variety of signal processing
operations. The digital signals processed in this manner are a sequence of numbers that
represent samples of a continuous variable in a domain such as time, space, or frequency. In
digital electronics, a digital signal is represented as a pulse train, which is typically generated
by the switching of a transistor. Digital signal processing and analog signal processing are
subfields of signal processing. DSP applications include audio and speech processing, sonar,
radar and other sensor array processing, spectral density estimation, statistical signal
processing, digital image processing, data compression, video coding, audio coding, image
compression, signal processing for telecommunications, control systems, biomedical
engineering, and seismology, among others.
Digital Signal Processing converts signals from real world sources (usually in analog form)
into digital data that can then be analyzed. Analysis is performed in digital form because once
a signal has been reduced to numbers, its components can be isolated and manipulated in
more detail than in analog form.
When the DSP has finished its work, the digital data can be turned back into an analog signal
with improved quality. A DSP can filter noise from a signal, amplify frequencies and
suppress others.

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What is Signal?
Any physical phenomenon that carries or convey information from one place to other and
represents as a function of independent variables such as time, distance, etc.
Type of Signal
Signal Processing is the analysis, interpretation and manipulation of like sound, images, time-
varying measurement values and sensor data etc. Types of signal processing:
1. Analog signal processing
2. Digital signal processing

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2. Analog Signal and Digital Signal
Analog and digital signals are the types of signals carrying information. The major difference
between both signals is that the analog signals that have continuous electrical signals, while
digital signals have non-continuous electrical signals. The difference between analog and
digital signal can be observed with the various examples of different types of waves.
Fig. Analog Vs Digital
Analog Signal
The analog signals were used in many systems to produce signals to carry
information. These signals are continuous in both values and time. The use of analog signals
has been declined with the arrival of digital signals. In short, to understand the analog signals
all signals that are natural or come naturally are analog signals.
Digital Signal
Unlike analog signals, digital signals are not continuous, but signals are discrete in value and
time. These signals are represented by binary numbers and consist of different voltage values.

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Difference Between Analog and Digital Signal
Fig. Difference Between Analog and Digital Signal

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3. Components of DSP (Digital signal processing)
DSP contains these key components:
1. Program Memory: Stores the programs the DSP will use to process data
2. Data Memory: Stores the information to be processed
3. Compute Engine: Performs the math processing, accessing the program from the
Program Memory and the data from the Data Memory
4. Input/Output: Serves a range of functions to connect to the outside world
Fig . Components of DSP

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4. Root of DSP (Digital signal processing)
Digital Signal Processing is distinguished from other areas in computer science by the unique
type of data it uses: signals. In most cases, these signals originate as sensory data from the
real world: seismic vibrations, visual images, sound waves, etc. DSP is the mathematics, the
algorithms, and the techniques used to manipulate these signals after they have been
converted into a digital form. This includes a wide variety of goals, such as: enhancement of
visual images, recognition and generation of speech, compression of data for storage and
transmission, etc. Suppose we attach an analog-to-digital converter to a computer and use it
to acquire a chunk of real world data.
The roots of DSP are in the 1960s and 1970s when digital computers first became available.
Computers were expensive during this era, and DSP was limited to only a few critical
applications. Pioneering efforts were made in four key areas: radar & sonar, where national
security was at risk; oil exploration, where large amounts of money could be made; space
exploration, where the data are irreplaceable; and medical imaging, where lives could be
saved. The personal computer revolution of the 1980s and 1990s caused DSP to exploded
with new applications. Rather than being motivated by military and government needs, DSP
was suddenly driven by the commercial marketplace. Anyone who thought they could make
money in the rapidly expanding field was suddenly a DSP vender. DSP reached the public in
such products as: mobile telephones, compact disc players, and electronic voice mail.

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Fig. Roots of DSP

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5. Application
1) Image Processing
2) Speech Processing
3) Biomedical
4) Radar
5) Cellular Mobile Phones
 Image processing
Image processing is a method to perform some operations on an image, in order to get an
enhanced image or to extract some useful information from it. It is a type of
signal processing in which input is an image and output may be image or
characteristics/features associated with that image.
Fig . Image processing

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 Speech Processing
Speech recognition software works by breaking down the audio of a speech recording into
individual sounds, analysing each sound, using algorithms to find the most probable word fit
in that language, and transcribing those sounds into text.
Fig .Speech processing

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 Biomedical
Biomedical signals are observations of physiological activities of organisms, ranging from
gene and protein sequences, to neural and cardiac rhythms, to tissue and organ
images. Biomedical signal processing aims at extracting significant information
from biomedical signals.
Fig .Biomedical

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 Radar
Radar transmits radio signals at distant objects and analyzes the reflections. Data gathered
can include the position and movement of the object, also radar can identify the object
through its "signature" - the distinct reflection it generates. There are many forms of RADAR
- such as continuous, CW, Doppler, ground penetrating or synthetic aperture; and they're used
in many applications, from air traffic control to weather prediction.
Fig. Radar

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 Cellular Mobile Phones
In the most basic form, a cell phone is essentially a two-way radio, consisting of a radio
transmitter and a radio receiver. When you chat with your friend on your cell phone,
your phone converts your voice into an electrical signal, which is then transmitted via radio
waves to the nearest cell tower.
Fig. Cellular Mobile Phones

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6. Advantages of DSP (Digital signal processing)
1) Less overall noise: Since the signals are digital and inherently possess a low
probability of getting mixed with unwanted signals, the entire system benefits. Thus,
DSPs don’t really have as much noise to deal with comparatively.
2) Error detection and correction is possible in DSP: Again, the presence of digital
signal means we have access to many error detection and correction features. For
example, we can use parity generation and correction as a detection and correction
tool.
3) Data storage is easier: Yet again, an advantage because of digital signals. You know
how easy it is to store digital data, right? We can choose from a wide plethora
of digital memories. However, analog data needs to be stored in tapes and stuff like
that. It’s harder to transport and recreate with 100% fidelity.
4) Encryption: Digital signals are easy to encrypt. So this one counts as a win for the
entire DSP system too.
5) Easier to process: Digital signals can easily undergo mathematical changes as
compared to their analog counterparts.
6) More data transmission: Time-division multiplexing is a great tool available for
digital systems to transmit more data over unit time and over a single communication
path.
7) Easier to modify: To modify an analog processing system, you need to change
components, test, and verify the changes. With digital processing systems, you just
need to change a few commands or alter a few lines of code.
8) DSP systems can work on frequencies of a broader range: There are some natural
frequencies, like seismic frequencies that detect earthquakes. These signals have very
low frequencies. Traditional analog signals might not even detect these signals.
However, digital signal processing systems are adept at picking up even the tiniest of
disturbances and also process them easily.
9) Cost: When working at scale, DSPs are cheaper.

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7. Disadvantages of DSP (Digital signal processing)
1) Complexity: As we saw in the block diagram above, there are a lot of elements
preceding and following a Digital Signal Processor. Stuff like filters and converters
add to the complexity of a system.
2) Power: A digital signal processor is made up of transistors. Transistors consume
more power since they are active components. A typical digital signal processor may
contain millions of transistors. This increases the power that the system consumes.
3) Learning curve and design time: Learning the ins and outs of Digital Signal
processing involves a steep learning curve. Setting up digital processing systems thus
takes time. And if not pre-equipped with the right knowledge and tools, teams can
spend a lot of time in setting up.
4) Loss of information: Quantization of data that is below certain Hz causes a loss in
data according to the Rate-Distortion Theory.
5) Cost: For small systems, DSP is an expensive endeavour. Costing more than
necessary.

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8. Future Scope of Digital Signal Processing
Application of Digital Signal Processing:
The main applications of DSP are in the fields like biomedicine, seismology, digital
communications, SONAR, RADAR, speech processing, video compression, digital image
processing, audio compressing and audio signal processing. Some of the examples are
transmission and speech compression in forecasting seismic data processing, weather
forecasting, sound reinforcement applications, room correction of sound in hi-fi, mobile
phones, etc…
Skills needed for a career in Digital Signal Processing:
To make a career in signal processing, candidates must be aware of languages like C++, C
and assembly level programming. A Digital Signal Processing (DSP) professional must be
well-verses in time constraints, signal processing algorithms and time processing.
Professionals in this field must be eligible to ensure quality services and therefore, they will
have to work under system constraints like limited memory and limited processor speeds
without complaining about the unavailability of resources.
Career in Digital Signal Processing:
Signal Processing, particularly Digital Signal Processing, which is a highly value-added
software market with a small differentiation opens up new career options to the professionals
with appropriate qualification. According to the predication made by the NASSCOM, it is
going to the career with high growth prospects. Indian graduates in this field stand to have an
edge over the graduates of other countries since Indian students are skilled in number-
crunching as compared to any other country graduates.
Government job in digital signal processing:
When it comes to public sector jobs in the field of digital signal processing, candidates can
find job opportunities in police department, Doordarshan, All India Radio, BSNL and Army
Signal Core. These professionals can also try for job opportunities in Broadcast Engineering
Consultants India Limited for fetching a good job.

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9. Conclusion
As appears from the above synthetic considerations, the area of digital signal processing is
very important at the present time and yet more in the near future. Practically, with the fast
improvement of digital signal processors (increased processing speed, size reduction and
lower cost), near all possible applications can be covered extending from low to high
frequency signals and from static to dynamic images. Indeed, a continuous trend is
represented by the multidimensional signal processing: i.e. more and more 3-D processing
algorithms are currently used (as in communications, remote sensing and robotics). Looking
forward to the long term future, optical, signal processing will become very important in
particular for increasing the processing speed, being performed not only in analog form (as
available also at the present time), but also in digital form (with the implementation of an
optical digital computer).

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10. Reference
Website
 https://onlinecourses.nptel.ac.in
 https://www.allaboutcircuits.com/technical-articles/an-introduction-to-digital-signal-
processing/
 https://www.journals.elsevier.com/digital-signal-processing/news
 https://www.skyfilabs.com/blog/list-of-good-digital-signal-processing-projects
 https://www.slideshare.net
 https://byjus.com/
Books
 Digital signal processing By A. Anand Kumar
 Digital signal processing: Fundamental and Application By Lizehe Tan, Jean Jiang
 Understanding Digital Signal Processing By Orhan Gazi
 Digital signal processing Fundamental By Vijay Madisetti
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