Question

a) Choose any one practical application of signal processing and discuss how the signal processing contribute to the overall device performance and implement a matlab programme for any signal processsing applications b) Discuss about how signal processing is utilized in bio medical devices(Take one device and discuss how it works) Include the frequency of signals used in these devices along with its voltage, current and power requirements. And implement the applications of processing of the signals using matlab coding /simulink c) Discuss about how signal processing is utilized in speech and audio processing (Take the entire system and discuss how it works) Include the frequency of signals used in these devices along with its voltage, current and power requirements, implement the audio processing system using matlab simulink/programming d)Discuss how the signal is manipulated in a SONAR/RADAR and detailly analyze how the signal can be utilized for detection of any physical device also implement a circuit utilizing sonar/radar in matlab simulink or implement matlab coding for the same

Answer 1

A)

It is a specialized microprocessor chip, with its architecture optimized for the operational needs of digital signal processing. DSPs are fabricated on MOS microcircuit chips. they're widely utilized in audio signal processing, telecommunications, digital image processing, radar, sonar and speech recognition systems, and in common consumer electronic devices like mobile phones, disk drives and HDTV (HDTV) products.

The goal of a DSP is typically to live , filter or compress continuous real-world analog signals. Most general-purpose microprocessors also can execute digital signal processing algorithms successfully, but might not be ready to continue with such processing continuously in real-time. Dedicated digital signal processor have better power efficiency, thus they're more suitable in portable devices like mobile phones due to power consumption constraints. DSPs often use special memory architectures that are ready to fetch multiple data or instructions at an equivalent time. DSPs often also implement data compression technology, with the discrete cosine transform (DCT) especially being a widely used compression technology in DSPs.

A typical digital processing system
Digital signal processing algorithms typically require an outsized number of mathematical operations to be performed quickly and repeatedly on a series of knowledge samples. Signals from audio or video sensors are constantly converted from analog to digital, manipulated digitally, then converted back to analog form. Many processors applications have constraints on latency; that's , for the system to figure , the DSP operation must be completed within some fixed time, and deferred (or batch) processing isn't viable.

Most general-purpose microprocessors and operating systems can execute DSP algorithms successfully, but aren't suitable to be used in portable devices like mobile phones and PDAs due to power efficiency constraints. A specialized processor will supply a lower-cost solution, with better performance, lower latency, and no requirements for specialised cooling or large batteries.

These better improvements in performance of DSP have led to the introduction of digital signal processing in commercial communications satellites where hundreds or maybe thousands of analog filters, switches, frequency converters are required to receive and process the uplinked signals and prepared them for downlinking, and may get replaced with specialised DSPs with significant benefits to the satellites' weight, power consumption, complexity/cost of construction, reliability and adaptability of operation. for instance , the SES-12 and SES-14 satellites from operator SES launched in 2018, were both built by Airbus Defence and Space with 25% of capacity using DSP.

The design of a DSP is optimized specifically for digital signal processing. Most also support a number of the features as an applications processor or microcontroller, since signal processing is never the sole task of a system.

Example--

Take an example of the signaling system of a digital phone dial. The signals produced by such a system are referred to as dual-tone multi-frequency (DTMF) signals. The sound produced by each dialed number consists of the sum of two sinusoids − or tones − with frequencies taken from two mutually exclusive groups. Each pair of tones contains one frequency of the low group (697 Hz, 770 Hz, 852 Hz, or 941 Hz) and one frequency of the high group (1209 Hz, 1336 Hz, or 1477Hz) and represents a singular symbol. the subsequent are the frequencies allocated to the buttons of a telephone pad:

Generate a DTMF signal and hear it.

[tones, Fs] = helperDTMFToneGenerator();
p = audioplayer(tones,Fs,16);
play(p)
Listening to the signal, you'll tell that a three-digit number was dialed. However, you can't tell which number it had been . Next, check the signal in time and in frequency domain over the 650 to 1500 Hz band. Set the 'Leakage' parameter of the pspectrum function to 1 to use an oblong window and improve frequency resolution.

N = numel(tones);
t = (0:N-1)/Fs;
subplot(2,1,1)
plot(1e3*t,tones)
xlabel('Time (ms)')
ylabel('Amplitude')
title('DTMF Signal')
subplot(2,1,2)
pspectrum(tones,Fs,'Leakage',1,'FrequencyLimits',[650, 1500])

The time-domain plot of the signal confirms the presence of three bursts of energy, corresponding to three pushed buttons. To measure the length of the burst, take the pulse width of the RMS envelope.

env = envelope(tones,80,'rms');
pulsewidth(env,Fs)
ans = 3×1

0.1041
0.1042
0.1047

title('Pulse Width of RMS Envelope')

B) With the growing costs of healthcare, the necessity for mobile health monitoring devices is critical. A transceiver( wireless) gives a price effective thanks to transmit biomedical signals to the varied personal electronic devices, like computers, cellular devices, and other mobile devices. Various kind of biomedical signals are used and produced by these devices, including electroencephalograph (EEG), electrocardiograph (ECG), and electromyography (EMG). By utilizing wireless transmission, the user gains freedom to attach with fewer constraints to their personal devices to look at and monitor their health condition.

Different biomedical signal transceiver device types are often designed. There are several distinctions between the kinds of the devices and their operation. The distinctions are often based upon how the device is powered and the way the device communicates. Despite these design differences, the hardware makeup of a biomedical signal transceiver is extremely standard.

Before going deeper into the small print on the kinds of biomedical signal transceivers, it's important to know how the device will operate. Typically, a biomedical signal transceiver device will have two main components, the transmitter and therefore the receiver. The transmitter has several sub systems, including: signal acquisition, amplification, filtering, and because the name dictates, transmitter. The receiver subsystem will receive the signal from the transmitter, perform any required analysis on the signal, then display the results. The transmitter is break away the receiver, such the transmitter can acquire the bioelectric signal and transmit to a different device for remote viewing and analysis.

Existing biomedical transceivers are are often separated into two groups describing how they're powered; frequency (RF) and battery powered. In RF powered, an inductive link with external controller allows the transmission of power and commands. a standard application of the RF powered transceiver is that the transcutaneous neural recording arrays. In battery-powered transceivers, an onboard battery is employed power source. This battery are often either disposable or rechargeable, counting on the device application. the utilization of A battery allows using higher frequencies for transmission and improved data rates are often achieved.

Biomedical transceivers can communicate either wirelessly or within the traditional wired connection. Not only can the device transmit the biomedical signal, but some devices have communication between the transmitter and receiver for not only biomedical information, but also any feedback or control signals. during this case, both subsystems are acting like transceivers.

Example-- signal processing for ECG Machine

x=load('F001.txt');
y=fft(x)
PS=abs(y).^2;
N=length(x);
fs=1000;
freq=(0:N-1)*fs/N;
plot(freq,PS)

For symmetrical plot of frequency-

freq=(0:ceil(N/2)-1)*fs/N;
PS=PS(1:numel(freq));
plot([-freq freq],[fliplr(PS) PS])

C)

Audio signal processing may be a subfield of signal processing that's concerned with the electronic manipulation of audio signals. These signals are electronic representations of sound waves—longitudinal waves which travel through air, consisting of compressions and rarefactions. The energy contained in audio signals is usually measured in decibels. As we can represented audio signal in either digital or analog format, processing may occur in either domain. ASP operate directly on the electrical signal, while digital processors operate mathematically on its digital representation.

Analog signal processing :-
An analog audio signal may be a continuous signal represented by an electrical voltage or current that's “analogous” to the sound waves within the air. Analog signal processing then involves physically altering the continual signal by changing the voltage or current or charge via electrical circuits.

Historically, before the arrival of widespread digital technology, analog was the sole method by which to control a sign . Since that point , as computers and software became more capable and affordable and digital signal processing has become the tactic of choice. However, in music applications, analog technology is usually still desirable because it often produces nonlinear responses that are difficult to duplicate with digital filters.

Digital signal processing :-
digital representation expresses for audio waveform as a sequence of symbols, usually binary numbers. this allows signal processing using digital circuits like digital signal processors, microprocessors and general-purpose computers. latest audio systems use a digital approach because the techniques of digital signal processing are far more powerful and efficient than analog domain signal processing.

Speech Processing :- Speech processing is that the study of speech signals and therefore the processing methods of signals. The signals processing during a digital representation, so speech processing are often considered a special case of digital signal processing, applied to speech signals. Process of speech processing includes the acquisition, manipulation, storage, transfer and output of speech signals. The input is named speech recognition and therefore the output is named speech synthesis.

Applications :-

Interactive Voice Systems

Virtual Assistants

Voice Identification

Emotion Recognition

Call Center Automation

Robotics

D) Radar and sonar are sensor systems that
use the propagation of waves to detect and
localise targets. In radar, a transmitter with
an oscillator is employed to get radio waves,
and a waveguide links the transmitter to the
antenna. In sonar, electricity is supplied
directly to a hydrophone array which converts
it to sound waves. These transmitted waves
bounce off upon coming into contact with a
target. The reflected waves (or the target’s
inherent sound within the case of passive sonar)
make their way back to the sensor system.
For radar, the received signals normally
go through a coffee noise amplifier before
being down-converted to an intermediate
frequency. On the opposite
hand, the received signals for a sonar go
through a pre-amplifier to shape the signal
(front-end conditioning) before being sent
to the signal processing unit. Subsequent
processing and analysis of the signal received
enable the system to detect the target also
as determine characteristics like range,
bearing, course and speed.

Radar frequencies cover a good spectrum,
from the high frequency range of 3-30MHz
for the aim of over-the-horizon radar
surveillance to the ultra-high frequency range
of 40-300GHz – also referred to as the millimetre
wavelength band – used for very high
resolution and short-range imaging. Fire
control and missile guidance radars operate
between these two extremes, at frequencies
of 8-12GHz.
The detection range of sonar installed on
large shipboard platforms varies from tens
of kilometres (for active sonar) to hundreds
of kilometres (for passive sonar). Smaller
versions of sonar are installed on helicopters
and unmanned surface vehicles, and
miniaturised versions are used for unmanned
underwater vehicles that provide situational
updates in localised areas of operations.
Sonar detection also can be achieved using
the bistatic principle, although this area remains
in the developmental stage.
The spectrum of sonar frequency spans from
a few Kilohertz or less for long-range target
detection to medium range of 10-50kHz
for target detection and classification.
For short-range scanning of high resolution,
higher frequencies of 100-600kHz are
commonly used.
Most static radar and sonar platforms are
capable of operations around the clock, thus
providing a seamless and perpetual field of
security.
CAPABILITIES OF RADAR
AND SONAR
Radar and sonar are capable of distance
measurement through the timing method,
i.e. Range R = cTr/2 where
c is that the speed of
the wave, and Tr is that the time taken for the
signal to visit the target and back.
This method is taken into account more accurate,
although the frequency and phase
modulation of the transmitted signal vary
according to time. While frequency agility
in radar is established and widely available,
the use of several transducer frequencies
in sonar remains within the early stage of
development. At this stage, some sonar
transmissions are capable of being modulated
to operate in additional than one frequency
due to its larger bandwidth. However, the
majority of current sonar transducers can
only use one transduction frequency. This
is because the conversion of electricity
to mechanical sound energy is completed through
the use of specific piezoelectric crystals
which have limited frequency modulating
capabilities.
Radar and sonar also are capable of
measuring the speed of the target within the
following ways:
• Measuring distance and recording where
the target was located a group time ago
• Analysing the Doppler signal where the
target’s motion relative to the transmitter
and receiver produces a change in
frequency (see Figure)

fr = fr-fd f = fe+fd } PHASE HISTORY f = TRANSMITTED FREQUENCY = RECEIVED FREQUENCY fo=DOPPLER FREQUENCY Figure 1. The Doppler effect: Velocity's impact on frequency (Source: European Space Agency)

Radar sensor model:-
This example uses radarDetectionGenerator to get synthetic radar detections. radarDetectionGenerator models the subsequent performance characteristics of automotive radar:

Strengths

Good range accuracy over long detection ranges
Weaknesses
Long range detection
Poor position and velocity accuracy along the cross-range dimension
Shorter detection range for pedestrians and other nonmetallic objects
Close range detection clusters pose a challenge to tracking algorithms

Inability to resolve closely spaced targets at long ranges

FCW Driving Scenario

Create a forward collision warning (FCW) test scenario, which is employed for instance the way to measure a target's position with a typical long-range automotive radar. The scenario include a moving vehicle and a stationary target vehicle placed 150 meters down the road. The ego vehicle has an initial speed of fifty kph before applying its brakes to realize a continuing deceleration of three m/s^2. The vehicle then involves an entire stop 1 meter before the target vehicle's rear bumper.

addpath(fullfile(matlabroot,'toolbox','shared','tracking','fusionlib'));

rng default;
initialDist = 150; vitamin Bc
initialSpeed = 50; % kph
brakeAccel = 3; % m/s^2
finalDist = 1; vitamin Bc
[scenario, egoCar] = helperCreateSensorDemoScenario('FCW', initialDist, initialSpeed, brakeAccel, finalDist);
Forward-Facing Long-Range Radar

Create a forward-facing long-range radar sensor mounted on the ego vehicle's front bumper, 20 cm above the bottom . The sensor produce measurements every 0.1 second and has an azimuthal field of view of 20 degrees and an angle resolution of 4 degrees. maximum range for this is 150 m and its range resolution is 2.5 m. The ActorProfiles property specifies the physical dimensions and radar cross-section (RCS) patterns of the vehicles seen by the radar within the simulation.

radarSensor = radarDetectionGenerator( ...
'SensorIndex', 1, ...
'UpdateInterval', 0.1, ...
'SensorLocation', [egoCar.Wheelbase+egoCar.FrontOverhang 0], ...
'Height', 0.2, ...
'FieldOfView', [20 5], ...
'MaxRange', 150, ...
'AzimuthResolution', 4, ...
'RangeResolution', 2.5, ...
'ActorProfiles', actorProfiles(scenario))
radarSensor =

radarDetectionGenerator with properties:

SensorIndex: 1
UpdateInterval: 0.1000

SensorLocation: [3.7000 0]
Height: 0.2000
Yaw: 0
Pitch: 0
Roll: 0

FieldOfView: [20 5]
MaxRange: 150
RangeRateLimits: [-100 100]

DetectionProbability: 0.9000
FalseAlarmRate: 1.0000e-06

Use get to point out all properties