Password Protected
Circuit Breaker
System using Arduino

Vol. 24 l Sep-Nov 2020

Our Vision: “To become a center of excellence in the fields of
technical education & research and create responsible citizens”



Cognitive Computing
Cognitive computing is based on simulation of the
human thought process. It uses an amalgamation
of different techniques like artificial intelligence,
neural networks, machine learning, natural language
processing, sentiment analysis and contextual
awareness to find a solution to day-to-day problems
just like humans. Cognitive computing redefines the
class of problems that a computer can solve.

applicable at that moment. They are capable of
identifying and comprehending contextual syntax,
domain, regulations, user’s profile, and objective.
Some of these cognitive computing systems use
both structured and unstructured data along with
sensory inputs like visual, gestural, auditory etc.
The unstructured data doesn’t have a predefined data
model as is the case with structured data.

Computing has evolved a lot since its inception. The
first era of computing started with Charles Babbage
who introduced the concepts of programmable
computer, navigational calculation and invented the
mechanical computer. The second era experienced
digitally programmable computers and different
programming languages. The third era consists
of cognitive computing techniques capable of
imitating human behavior and reasoning to solve
complex problems. There is a subtle difference
in the approach of solving problems using AI and
cognitive computing. AI tries to solve problems by
finding patterns in the data space. It tries to draw
conclusions based on these discovered patterns and
makes decisions. Cognitive systems don’t make
decisions, rather they only supplement decision
making. They try to simulate the human thought
process to find solutions to complex problems.
Cognitive systems provide information for humans
to take decisions. Cognitive computing is a subset of
artificial intelligence.

There are several applications of cognitive
computing. Vantage software is one such product
that helps investment bankers to analyze huge data
and provide suggestions as to where the clients’
funds could be invested. LifeLearn provides a
veterinary decision-support tool named Sofie which
gives recommendations instantly, enabling busy
vets to save time and look after patients with quality
consideration. Malware attacks are prevented by
using big data and machine learning algorithms.
Cognitive Engine has the ability to multiply the
value of all the IT investments by a combination of
all the data and the processes of an organization, by
cognitive intelligence, and suggest steps that hold the
highest revenue impact on the company. Cogito is a
multilingual cognitive software that provides humans
the ability to evaluate and comprehend conversations
in different languages. Its core algorithms are based
on natural language processing.

Cognitive systems are adaptive, dynamic in data
gathering, understanding goals, and requirements.
These systems are highly interactive and are able
to communicate with humans in natural language.
Several chatbots have already achieved this ability
of communicating with consumers without human
intervention. These systems remember previous
interactions and provide information that is

Cognitive computing provides and improves the
computer interaction that impersonates the working
of the human brain and thereby helps in better
decision-making processes.It provides customized
results and deep insights by analysing vast data
from different sources. Cognitive computing is
an extremely powerful tool which is capable of
providing us with excellent solutions and further
extending the horizons of computation.

Dr. Pamela Chaudhury
Dept. of CSE


DD Feature

Detecting Diabetic Retinopathy using Deep Learning
Abstract : Diabetic Retinopathy is one of the major causes of blindness around the world. This disease affects those people
who suffer from diabetes and mainly aged diabetes patients. Many hospitals around the world try their best to conduct
research and prevent blindness caused due to diabetic retinopathy. Being able to detect diabetic retinopathy before it is
too advanced is a major challenge. This work focuses on detecting whether a patient has reached the stage of diabetic
retinopathy or not and if yes, then which stage. The deep learning neural network is used to for this purpose classify the
images of eyes of patients.

Keywords: Deep learning, neural networks, image classification, medical imaging, diabetic retinopathy, medical
imaging analysis, machine learning.

Diabetic retinopathy is the most common form of
diabetic eye disease. Diabetic retinopathy usually
only affects people who have had diabetes (diagnosed
or undiagnosed) for a significant number of years
[1]. Retinopathy can affect all diabetics and becomes
particularly dangerous, increasing the risk of blindness,
if it is left untreated. The risk of developing diabetic
retinopathy is known to increase with age as well as with
less well controlled blood sugar and blood pressure level.
According to the NHS, 1280 new cases of blindness
caused by diabetic retinopathy are reported each year
in England alone, while a further 4,200 people in the
country are thought to be at risk of retinopathy-related
vision loss [2].
Diabetic retinopathy occurs when changes in blood
glucose levels change the retinal blood vessels. In some
cases, these vessels will swell up (macular oedema) and
leak fluid into the rear of the eye. Diabetic retinopathy
can gradually become more serious and progress from
background retinopathy to seriously affecting vision
and can lead to blindness. Like many conditions of this
nature, the early stages of diabetic retinopathy may occur
without symptoms and without pain. An actual influence
on the vision will not occur until the disease advances.
Symptoms of retinopathy to look out for include: Sudden
changes in vision / blurred vision, Eye floaters and spots,
Double vision, Eye pain
As, diabetic retinopathy is a leading cause of blindness
among working-age adults, early detection of this
condition is critical for good prognosis. Many adults
who are suffering from diabetes have a high probability
of being diagnosed with diabetic retinopathy at some
stage of their life. In general, diabetic retinopathy is
curable if detected at the early stages. But if the patient

is diagnosed at a very severe stage, then it may not
be curable at all [3]. This work focuses on detecting
whether a person suffering from diabetes has diabetic
retinopathy or not. The power of deep neural networks
from the field of deep learning is employed for this
purpose. In specific, the CNN (Convolutional Neural
Network) architecture is used.
A large set of retina images were taken using fundus
photography under a variety of imaging conditions.
Fundus photography involves photographing the rear of
an eye; also known as the fundus [4]. Specialized fundus
cameras consisting of an intricate microscope attached to
a flash enabled camera are used in fundus photography.
The main structures that can be visualized in a fundus
photo are the central and peripheral retina, optic discand
macula [5]. Fundus photography can be performed
with colored filters, or with specialized dyes including
fluorescein and indocyanine green. An example of how
the images look like is given in fig.1

Figure 1. A retina image taken using fundus Photography


The main objective of the work is to use different types of
images, different learning algorithms, and different neural
network architectures and compare which combination
gives the best results for detection of diabetic retinopathy.
Using the current traditional cures and remedies the
medical system is taking much time than needed to know
about Diabetic retinopathy. We proposed a system in
which the model can be trained using deep neural network
on varieties of retina images and then by validating on the
images weget better results can be otained. This takes less
time than the usual traditional detection methodologies.

The data set is collected from the Kaggle website [6]. The
dataset contains retina scan images taken using fundus
photography under a variety of imaging conditions. The
dataset contains 3,662 images for training 1,928 images
for testing. The default images provided on the website
are colored images. They are very high-resolution
images some ranging to around 4k dimensionality.

Train directory contains all the images of retina that we
train and validate the neural network model on. Train CSV
file contains the labels corresponding to the images in the
train directory. Similar is the structure for the test data as
well. But the test CSV file does not contain the labels

In this work, the severity of diabolic retinopathy is
classified by the use of neural networks. The classification
is done by ResNet34 [4]. This model as shown in fig
3contains 34 CNN layers. The first layer uses 7*7 filter
and the next layers use 3*3. It uses Global average
pooling layer and a 5-way fully-connected layer with
Softmax in the end. ResNet34 solves the degradation
problem i.e. the accuracy gets saturated and degrades
rapidly on the increase in networkdepth.

The dataset has been divided into a train directory, test
directory, train.csv file, and test.csv file. The data is
divided into five classes based on the severity of diabetic
retinopathy. The following are the five classes:
0- No DR
1- Mild
2- Moderate
3- Severe
4- Proliferative DR
The numbering is done based on increasing severity
where 0 means no diabetic retinopathy and 4 means the
highest stage, that is, proliferative diabetic retinopathy.
In the dataset, 1,805 images with no diabetic retinopathy,
370 images with mild, 999 images with moderate, 193
images with severe and 295 images with proliferative
diabetic retinopathy were present. The number of images
consider per class ratio is shown in fig. 2.

Figure 2. Number of Images per Class


Figure 3. ResNet34 blocks

For the first stage of the experimentation the ResNet34
is trained for 20 epochs and for 3 epochs after finding
the optimal learning rate of the neural network. This
process is repeated for all the three different colored
datasets (colored, grayscale, andgaussian).

C. Gaussian Filtered Images Results
For grayscale images, the lowest error rate after 20
epochs of training is 0.221311 with best training loss
of 0.478799 and best validation loss of 0.578836 as
shown in figure 6.

A. Colored Images Results
During the initial training of colored images, for the first
20 epochs the best error rate is 0.195355. After learning
rate optimization the best error rate of 0.178962 is
achieved. The lowest training loss is 0.441782 and
validation loss is 0.541847 as shown in figure 4.

Figure 6. Gaussian Filtered Images Training Loss After 20

Figure 4. Colored Images Traning Loss after 20 Epochs

B. Grayscale Images Results
For grayscale images, the lowest error rate after 20
epochs of training is 0.196721 with best training loss
of 0.480602 and best validation loss of 0.520497 as
shown in figure 5.

D. Training Result Tables
The following tables show the results of training on
colored, grayscale, and gaussian filtered retina images.
We have trained each of the image sets for 20 epochs. In
the following tables 1,2 and 3the results of the last three
epochs are shown




Table 1. Last three epoch results for colored retina images




Table 2. Last three epoch results for grayscale retina images
Figure 5. Grayscale Images Training Loss After 20 Epochs




Table 3. Last three epoch results for Gaussian filtered retina images


From the above tables, it can be observed that the
training loss after 20 epochs is the least for colored
retina images (Table 1). For the error rate, the least
error rate in the case of training grayscale images
is obtained. But the training loss is more in case of
grayscale images than in colored images. The neural
network seems to be performing the worst in the case
of gaussian filtered images when taking the error
rate as a result metric. Also, the validation loss is the
highest in this case. The results show that perhaps,
training a bigger neural network model on a mixture of
all images may provide much better results.
E. Validation Results
The validation results provide a good insight of how
good/bad the neural network model is performing
while validating on the dataset. Figures 7, 8, and
9 shows the confusion matrix interpolation plot Figure 8. Confusion Matrix for Grayscale Images Validation
for the validation of the retina images. The figures
correspond to colored images validation, grayscale
images validation, and gaussian filtered images
validation results respectively.It may be clearly
observe that in all cases, the ResNet34 neural network
model is perfectly classifying the No_DRclass. This
is because, this class has the most number of images
(almost 1800 images) and it is getting enough images
for that class to learn the patterns. Whereas, for the
other classes, the number images are quite less, and
below 1000 images per class as well.

Figure 9. Confusion Matrix for Gaussian Filtered Images

Figure 7. Confusion Matrix for Colored Images Validation


The validation mistakes in some cases are quite
profound as well. For example, in the case of colored
retina images validation, the neural network model
is classifying 18 Moderate class as Proliferate_
DRclass. And in total, it is misclassifying 131
images out of 732 validation images. In the case
of grayscale validation images, the neural network
model is misclassifying 143 images out of 732
validation images.Here, validation results are
worse than the colored images validation results. In
the case of gaussian filtered validation images, the
neural network model is misclassifying 146 images
out of 732 validation images. The validation results

are the worst here. Mostly because, after applying
gaussian filtering to the retina images, they are
losing many of the pixel and color features.
Therefore, the neural network model is finding it
difficult to learn all the important features.
F. Results from the Larger Dataset
This dataset contains 35,126 images and is 35
gigabytes in size. For this specific dataset, we
trained two different neural networkmodels:
ResNet34, ResNet50. We are showing the best
set of results that were obtained by training the
ResNet50 model. The SGD optimizer is used with
image augmentation and learning rate scheduler for
hyperparameter tuning.

The presented methods using deep learning and neural
networks on the retina images achieves 78% validation
accuracy. This is 3% higher than manual method of
detection. Although the increase in accuracy is not
too much but it can be further improved using larger
neural networks for training and using more robust
image processing techniques. Also, using larger images
for training the neural network may help capture and
extract more useful features.
We express our sincere gratitude to our project
guideDr. Ramakrushna Swain of CSE department for
his guidance in carrying out this projectwork.


[1] Goodfellow et al., “Convolutional Networks”, in
Deep Learning, pp. 326 - 366.
[2] Panwar et al., “Fundus Photography in the 21st
Century—A Review of Recent Technological
Advances and Their Implications for Worldwide
Healthcare”. Telemedicine and e-Health, March

Figure 10. Accuracy plot for ResNet50 trained for 40
epochs with SGD optimizer and with data augmentation

[3] Mingyuan Xin and Yong Wang. “Research
on image classification model based on deep
convolution neural network”.EURASIP Journal
on Image Processing.
[4] JaakkoSahlsten, Joel Jaskari, JyriKivinen, Lauri
Turunen, Esajanio, KustaaHietala, Kimmo
Kaski, “Deep Learning Fundus Image Analysis
for Diabetic Retinopathy and Macular Edema
Grading,” in Science Reports, 2019.
Dilip Singh Sisodia, Shruti Nair, Pooja
Khobragade. “Diabetic Retinal Fundus Images:
Preprocessing and Feature Extraction For Early
Detection of Diabetic Retinopathy”. Biomedical
and Pharmacology Journal.
[6] “Kaggle Diabetic Retinopathy Detection,” 2015.
[Online]. Available: https://www.kaggle. com/c/

Figure 11. Loss plot for ResNet50 trained for 40 epochs with
SGD optimizer and with data augmentation

Harshit Verma, Rashmiranjan Pradhan,
Sovit Ranjan Rath, Jogesh Agrawal
8th Sem. IT


Automation and Monitoring for the Military
Abstract : : This project can be used by the military force to seek help during any war or sudden attack. So, this project aims to improve
the communication and provide back-up for them. The basic idea behind this project is to keep a count on the pulse rate of the force
men and the location of all the troops and if any disturbance, a signal and with the location would be send to the control unit and even
to the other troops to be alert and provide back up and aids to them with the help of IOT techniques, RF module and GSM technology.
Keywords: Internet of things (IOT),Global System for Mobile Communications (GSM), radio frequency (RF),defence readiness
condition (DEFCON), Valid Transmission (VT), Global Positioning System (GPS)

Keywords: Internet of things (IOT),Global System for Mobile Communications (GSM), radio frequency
(RF),defence readiness condition (DEFCON), Valid Transmission (VT), Global Positioning System (GPS)

In past centuries communicating a message usually
required someone to go to the destination, bringing the
message. Drums, horns, flags, and riders on horseback
were some of the early methods the military used to send
messages over distances.Then came the era of Electric
telegraph by Samuel F.B. Morse. In his successful
demonstration of electric communication. Then in 19th
century the military used handheld trans-receiver. As there
are many technology development seen in every field so
there should be some development in the military field also
so there can be better communication within the soldier
troops and the main station. In the all past attacks we saw
that due to lack in communication within our soldiers we
lost many of our great assets This technology that we are
developing could help us in doing the same [1].
Drums, horns, flags, and riders on horseback were some
of the early methods the military used to send messages
over distances. In the middle 20th century radio equipment
came to dominate the field. Many modern pieces of military
communications equipment are built to both encrypt
and decode transmissions and survive rough treatment
in hostile climates [2]. They use different frequencies
to send signals to other radios and to satellites. Military
communications - are activities, equipment, techniques,
and tactics used by the military in some of the most
hostile areas of the earth and in challenging environments
such as battlefields, on land, underwater and also in air.
Military communications include command, control and
communications and intelligence and were known as
the C3I model before computers were fully integrated
[3]. The U.S. Army expanded the model to C4I when it
recognized the vital role played by automated computer
equipment to send and receive large, bulky amounts of

data. The advent of distinctive signals led to the formation
of the signal corps; a group specialized in the tactics of
military communications. The signal corps evolved into a
distinctive occupation where the signaller became a highly
technical job dealing with all available communications
methods including civil ones. In the modern world,
most nations attempt to minimize the risk of war caused
by miscommunication or inadequate communication.
As a result, military communication is intense and
complicated, and often motivates the development of
advanced technology for remote systems such as satellites
and aircraft, both manned and unmanned, as well as
computers. Computers and their varied applications
have revolutionized military communications. Although
military communication is designed for warfare, it also
supports intelligence-gathering and communication
between adversaries, and thus sometimes prevents war.
There are six categories of military communications: the
alert measurement systems, cryptography, military radio
systems, nuclear command control, the signal corps,
and network-centric warfare [4]. The alert measurement
systems are various states of alertness or readiness for
the armed forces used around the world during a state
of war, act of terrorism or a military attack against a
state. They are known by different acronyms, such as
DEFCON, or defence readiness condition, used by the
U.S. Armed Forces. Cryptography is the study of methods
of converting messages to a form unreadable except to
one who knows how to decrypt them [5,6]. This ancient
military communications art gained new importance with
the rise of radio systems whose signals travelled far and
were easily intercepted. Cryptographic software is also
widely used in civilian commerce.

DD Feature
The whole project that is done can be divided into mainly
2 parts: Main and Base station
1. Main station :
Fig. 1 shows the main station.

Fig. 1 shows the main station.

2. Base station:
Fig. 2 shows the base station

Fig. 2 Base station

The microcontroller and other devices get power supply
from AC to Dc adapter through 7805, 5 volts regulator.
The adapter output voltage will be 12V DC non-regulated
[7]. The 7805/7812 voltage regulators are used to convert
12 V to 5V/12V DC. Fig. 3 shows the power supply circuit.

A. Regulated Power Supply
A power supply containing means of maintaining
essentially constant output voltage or output current under
changing load conditions.
B. Bridge Rectifier
A bridge rectifier can be made using four individual diodes,
but it is also available in special packages containing the
four diodes required. It is called a full wave rectifier because
it uses the entire AC wave (both positive and negative
section). 1.4 volt is used up in the bridge rectifier because
each diode uses 0.7 volt when conducting and there are
always two diode conducting. The maximum current they
can pass rates bridge rectifiers and the maximum reverse
voltage they can withstand (this must be at least three times
the supply RMS voltage so the rectifier can withstand the
peak voltages.
C. Smoothing
Smoothing is performed by a large value electrolyte
capacitor connected across the DC supply to act as a
reservoir, supplying current to the output when the varying
dc voltage from the rectifier is falling. The diagram shows
the unsmoothed varying dc and the smoothed DC [8,9].
The capacitor charges quickly near the peak of the varying
DC, and then discharges as it supplies current to the output.
D. Regulator
Voltage regulator IC shown in fig. 4 available with fixed
(typically 5, 12, and 15 volts) or variable output voltages.
The maximum current they can pass also rates them.
Negative voltage regulators are available, mainly for use
in dual supplies. Most regulators include some automatic
protection from excessive current and overheating.

Fig. 4 Regulator

Fig. 3 Power Supply Circuit

The AT89C51 is a low-power, high-performance
CMOS 8-bit microcontroller with 8K bytes of insystem programmable Flash memory. The device is
manufactured using Atmel’s high-density non-volatile
memory technology and is compatible with the industrystandard 80C51 instruction set and pin out. The on-chip
Flash allows the program memory to be reprogrammed
in-system or by a conventional non-volatile memory

programmer. By combining a versatile 8-bit CPU with
in-system programmable Flash on a monolithic chip,
the Atmel AT89S52 is a powerful microcontroller which
provides a highly-flexible and cost-effective solution
to many embedded control applications. The AT89S52
provides the following standard features: 8K bytes
of Flash, 256 bytes of RAM, 32 I/O lines, Watchdog
timer, two data pointers, three 16-bit timer/counters, a
six-vector two-level interrupt architecture, a full duplex
serial port, on-chip oscillator, and clock circuitry [10].
A. Decoder
HT12D shown in Fig. 5, is a decoder at standby mode
initially i.e, oscillator is disabled and a HIGH on DIN
pin activates the oscillator. Thus the oscillator will be
active when the decoder receives data transmitted by an
encoder. The device starts decoding the input address
and data. The decoder matches the received address
three times continuously with the local address given to
pin A0 – A7. If all matches, data bits are decoded and
output pins D8 – D11
are activated. This
valid data is indicated
by making the pin VT
(Valid Transmission)
continue till the address
code becomes incorrect
or no signal is received.
Fig. 5 HT12D
B. Encoder:
Fig. 6 shows HT12E a 212 series encoder IC (Integrated
Circuit) for remote control applications. It is commonly
used for radio frequency (RF) applications. By using
the paired HT12E encoder and HT12D decoder we
can easily transmit and receive 12 bits of parallel data
serially. HT12E simply converts 12bit parallel data in to
serial output which can
be transmitted through
a RF transmitter. These
12bit parallel data is
divided in to 8 address
bits and 4 data bits. By
using these address
pins we can provide
8 bit security code for
data transmission and
multiple receivers may
be addressed using the
Fig. 6 HT12E
same transmitter.

The proposed model was run using MATLAB/
SIMULINK.Comparison between the performances
of the proposed system and the conventional system
is carried out in terms of power generated using the
PV units.When the irradiation values are changed to
simulate partial shading scenario; the power produced
by the conventional converter is reduced drastically.
Whereas, in power supplied by the proposed system is
comparatively more.
When the irradiation of a single unit is set to 600 W/sq.m
from 650 W/sq.m, the total power generated from all the
four PV units is around 600 W for the proposed system
while it is only 400 W for the conventional system. Such
small variations are very frequent in normal operation
hence it can amount to considerable difference in
the energy extracted. For further larger difference in
insolation, the results are more and more promising with
the proposed method.
The proposed method involving MISO converter does
not require bypass diodes across individual PV units.
Designed for global market, SIM900 is a Tri-band
GSM/GPRS engine that works on frequencies EGSM
900 MHz, DCS 1800 MHz and PCS1900 MHz SIM300
provides GPRS multi-slot class 10 capability and support
the GPRS coding schemes CS-1, CS-2, CS-3 and CS-4.
With a tiny configuration of 40mm x 33mm x 2.85
mm, SIM300 can fit almost all the space requirement in
your application, such as Smart phone, PDA phone and
other mobile device. The GSM 07.05 commands are for
performing SMS and CBS related operations. SIM300 II
supports both Text and PDU modes.
The Global Positioning System (GPS) is a U.S.-owned
utility that provides users with positioning, navigation,
and timing (PNT) services. This system consists of
three segments: the space segment, the control segment,
and the user segment. The U.S. Air Force develops,
maintains, and operates the space and control segments.
GPS is a system. It’s made up of three parts: satellites,
ground stations, and receivers.Satellites act like the stars
in constellations—we know where they are supposed to
be at any given time.The ground stations use radar to
make sure they are actually where we think they are.A
receiver, like you might find in your phone or in your
parents car, is constantly listening for a signal from these
satellites. The receiver figures out how far away they are
from some of them.

C. Heart Rate
Pulse Sensor is a low cost, very small size a plug-andplay heart rate sensor for MCU boards. It can be used by
students, artists, athletes, makers, and game & mobile
developers who want to easily incorporate live heart-rate
data into their projects. Fig. 7 shows a heartbeat sensor.
Pulse Sensor Amped adds amplification and noise
cancellation circuitry to the hardware. It’s noticeably
faster and easier to get reliable pulse readings. Pulse
Sensor works with either a 3V or 5V MCU.


Fig. 8 and 9 shows the hardware results.

Fig. 8 Hardware Result

Fig. 9 Hardware results

Fig. 7 Heart beat sensor

A Color-Coded Cable, with a standard male header
connectors. Plug it straight into an MCU or a Breadboard.
No soldering is required. An Ear Clip, perfectly sized to
the sensor. It can be hot-glued or epoxied to the back of
the sensor to get reading from an ear lobe [11].
The AC supply was given to the power supply circuit
where it was stepped down to 0-18 v. Further the voltage
was converted to DC by passing through bridge rectifier.
The output of the bridge rectifier was pulsating DC, so
to convert it to pure DC an electrolyte capacitor was
used. This current is then passed through 7805 and 7812
voltage dividers to get 5v and 12v respectively. The
supply was given to the micro controller circuit to turn it
on. This microcontroller gets clock pulse through crystal
oscillator. The microcontroller gets the output from the
sensor and the GPS. This send the result to the RFID
transmitter. The RFID receives the information decodes
the analog signal and send this to the microcontroller.
The information is displayed in the LCD. All these
information’s are send to the headquarters through
SIM900 in form of text message.

The rate of death of soldiers in India in last 5 years rose
to 93% death of security personnel in terror attacks. By
implementing this project we would at least decrease
this rate or save some by providing immediate help
Abhishek A,Debashis Majhi,
Indrani Singh, Aurochit Mohanty,
Suvendu Kar, Lopamudra Rath
Dept. of EEE


Design of a Miniaturized Circular
Microstrip Patch Antenna for 5G Applications
Abstract : In this paper, a novel proximity-coupled fed microstrip Circular Patch Antenna (CPA) is proposed for 5G applications.
The proposed CPA resonates at a frequency of 3.5 GHz. The simulation of the proposed CPA is done using High Frequency Structure
Simulator (HFSS) software. The parametric optimization feature available in HFSS Software is used for determining the optimized
dimensions of the proposed CPA. The optimized compact CPA is having a substrate of length 30 mm, and width 45 mm. The radius of
the radiating patch is 13.005 mm. The proposed antenna is having an excellent S11 characteristic and impedance matching. The S11 is
found to be -40.2827 dB. The CPA also has a substantial gain of 5.8 dB. The VSWR is nearly ideal (1.02) and the CPA has an efficiency
of 88.40% and serves a bandwidth of 200 MHz. With such alluring features, the proposed CPA can be a suitable candidate for a variety
of 5G applications such as the Internet of Things (IoT), Machine to Machine (M2M) Communication, etc.

Keywords: —Circular patch antenna, 5G, HFSS, proximity-coupled feed, bandwidth, gain, efficiency, optimization

5G is one of the most recent technologies these days and
moreover, everyone is curious about this technology. It has
a number of benefits like seamless coverage, higher data
rate, low latency, and high reliability. Moreover, the quality
of video services is also expected to improve. As per a
study done by the Telecom Regulatory Authority of India
(TRAI) in [1], it has been found that the mobile data usage
per month in India has increased from 39 petabytes in June
2016 to 4178 petabytes in September 2018. Moreover,
the demand for higher data rates has increased for which
it is expected that within a few years it is necessary to
switch from the current generation to a higher generation,
to a higher frequency band. 5G uses higher frequency
ranges for its applications for which the microstrip patch
antennas are the best candidates. Moreover, the microstrip
patch antennas are economic, light-weight and easy to
manufacture. As the frequency is increased, the size of the
devices reduces. Therefore, the antennas can be expected
to be compact and miniaturized so that they can be used in
different hand-held devices.
The spectrum is the lifeline of any communication. These
are of three different categories – low frequency, medium
frequency, and high frequency. Based upon different
requirements and applications, 5G communication
technology uses the medium and high frequencies for the
communication purpose for higher data rates and high
system capacity in dense deployments. There are three
pioneer bands for 5G technology, been targeted or allocated
in India. These are – 700 MHz, 3.5 GHz and 26/28 GHz
[1]. Out of these three pioneer bands, the proposed antenna
in this paper resonates at 3.5 GHz.


Literature surveys were done on 5G antennas and different
feeding techniques. The following are some of the

In [2], the 5G antenna operates at 38 GHz and 54
GHz. The antenna has a high gain of 6.9 dB and 7.4 dB
at 38 GHz and 54 GHz respectively. However, the S11
parameter is found to be -15.5 dB and -12 dB for 38 GHz
and 54 GHz respectively. Moreover, the bandwidth at 38
GHz and 54 GHz is observed to be 1.94 GHz and 2.05
GHz respectively. The model also illustrated a 5G antenna
array for different resonating frequencies at 38 GHz, 47.7
GHz, and 54 GHz with S11 parameter values -13.5 dB,
-22.5 dB, and -18 dB respectively.
A comparison among different techniques for feeding for
simple rectangular, circular and triangular patch antenna
for a frequency of 2.45 GHz is made in [3]. It is observed
that when the shape of the patch is changed, there is no such
significant change in parameters like gain, directivity, and
efficiency. However, when the different types of feeding
techniques were applied on the patch, it is observed that by
using proximity coupled feeding technique, the directivity
remained almost the same but the gain and efficiency
In [4], a compact and miniaturized rectangular patch
antenna (RPA) is proposed. It resonates at a frequency of
10.5 GHz with the S11 parameter value as -18.27 dB, a
gain of 4.46 dB and a bandwidth of 380 MHz. It has an
acceptable VSWR value of 2.13 dB, which is suitable for
wireless applications.
An RPA resonating at 3.5 GHz frequency is proposed for
5G applications [5]. The antenna is a miniaturized one
with a dimension of 30 mm × 45 mm. It has a maximum
gain of 7 dB. However, the S11 parameter is observed to

DD Feature
be -20.38 dB. The bandwidth, in this case, is found to be
100 MHz.
The substrate plays a vital role in the case of proximitycoupled feeding technique. In [6], it is observed that in
this technique, if the substrate having higher relative
permittivity is kept at the top and the substrate with lower
relative permittivity is kept at the bottom, the gain will be

The different views of the proposed CPA are as shown in
Fig. 1. The feeding of the microstrip patch antennas can
be done in different ways. Some of them are microstrip
line feed, coaxial probe feed, inset feed, aperturecoupled feed, proximity coupling, etc. [7]. The proposed
antenna makes the use of the proximity-coupled feeding
technique. This feeding technique has numerous
advantages such as larger bandwidth and low spurious

The CPA is the most accepted design next to a rectangular
patch antenna. The antenna consists of a thin metallic
circular strip on the top of a substrate.
The initial radius of the patch with Rogers RT/Duroid 5880
as the substrate is found using the following equation [7].

{1 +
[ln( ) + 1.7726]
ðε r F



8.791× 1





fr εr


R = Radius of the circular patch in cm.
h = Height of the substrate in cm.
εr = Dielectric constant of the substrate.
fr = Resonant frequency of the patch in Hz.

Fig. 1. CPA with Proximity-coupled
feed (a) Top view (b) Side view (c)
Front view

Later, the CPA is designed with two substrate layers using
proximity coupled feeding technique. The dimensions
of the CPA and the proximity feed are optimized using
parametric optimization in-built in HFSS. The enhanced
parameters of the CPA are as shown in Table I.

The proximity-coupled feed line is given between the
two substrates – FR4 Epoxy (εr = 4.4) and Rogers RT/
Duroid 5880 (εr = 2.2). In order to increase the gain, the
FR4 Epoxy is used as the top substrate and Rogers RT/
Duroid 5880 substrate is used as the bottom substrate
[6]. The proximity-coupled feed line is as shown in Fig.

Table I. Dimension Of Circular Patch Antenna

Length of ground plane/
substrate (L)
Width of ground plane/
substrate (W)
Radius of the patch (R)
Height of the bottom
substrate (hb)
Height of the top
substrate (ht)
Length of the proximity
feed (Lf)
Width of the proximity
feed (Wf)

Value (mm)

Fig. 2 Proximity – coupled feed given between the two
layers of substrates


The design and the simulation of the proposed antenna
are done via ANSYS HFSS software. It resonates at a
frequency of 3.5 GHz. The CPA has an excellent S11
parameter with a value of –40.2827 dB. The bandwidth
of the proposed CPA is 200 MHz. According to the
TRAI [1], the bandwidth of 3.5 GHz 5G band extends
from 3.3 GHz to 3.6 GHz. The proposed CPA, in this
case, has a bandwidth of 200 MHz and it extends from
3.4 GHz to 3.6 GHz, which is suitable for the 5G band
as mentioned. The S11 characteristic of the CPA is as
shown in Fig. 3

Fig. 4 Impedance characteristics of the proposed CPA
resonating at 3.5 GHz

The CPA has an excellent impedance matching and
therefore, maximum signal transmission can take place.
The impedance characteristic of the proposed CPA is as
shown in Fig. 4.
The radiation pattern of the proposed microstrip circular
patch antenna is as shown in Fig. 5. The plot signifies
that the radiation pattern is almost omnidirectional in
Fig. 3 (dB) versus Frequency (GHz) of the proposed CPA
resonating at 3.5 GHz

For an antenna, the impedance must be matched properly
in order to satisfy the maximum power transfer theorem
[7]. The antenna is excited via a feed line having an
impedance of 50 Ohm. According to the results, the
proposed CPA has a real part of 50.99 Ohm and an
imaginary part of 0.15 Ohm

Fig. 5. Radiation Pattern of the proposed CPA resonating
at 3.5 GHz


The gain of the CPA in E-Plane is 5.8263 dB and that of
the H-Plane is 5.8133 dB. The antenna is highly efficient
with an efficiency of 88.40 %.
Ideally, the VSWR value should be 1 and in this case,
the CPA has a VSWR value of 1.02, which is nearly
ideal. So, the reflected power will be less than 1% and
therefore, there will be a maximum transmission of
the signals. The VSWR characteristic of the CPA is as
shown in Fig. 6.

[1] “Enabling 5G in India”– Telecom Regulatory Authority
of India.
[2] D. Imran et al., "Millimeter wave microstrip patch
antenna for 5G mobile communication," 2018
International Conference on Engineering and Emerging
Technologies (ICEET), Lahore, 2018, pp. 1-6.
[3] A. Elfatimi, S. Bri, and A. Saadi, "Comparison between
techniques feeding for simple rectangular, circular
and triangular patch antenna at 2.45 GHz," 2018
4th International Conference on Optimization and
Applications (ICOA), Mohammedia, 2018, pp. 1-5.
[4] S. Verma, L. Mahajan, R. Kumar, H. S. Saini, and N.
Kumar, "A small microstrip patch antenna for future
5G applications," 2016 5th International Conference
on Reliability, Infocom Technologies and Optimization
(Trends and Future Directions) (ICRITO), Noida, 2016,
pp. 460-463.
[5] R. Li, Q. Zhang, Y. Kuang, X. Chen, Z. Xiao, and J.
Zhang, "Design of a Miniaturized Antenna Based on Split
Ring Resonators for 5G Wireless Communications,"
2019 Cross Strait Quad-Regional Radio Science and
Wireless Technology Conference (CSQRWC), Taiyuan,
China, 2019, pp. 1-4.
[6] W. S. T. Rowe and R. B. Waterhouse, "Investigation into
the performance of proximity coupled stacked patches,"
IEEE Transactions on Antennas and Propagation, vol.
54, no. 6, pp. 1693-1698, June 2006.
[7] C. A. Balanis, Antenna Theory: Analysis and Design,
3rd Ed., John Wiley & Sons, 2005.

Fig. 6. VSWR characteristics of the proposed CPA

In this paper, a CPA has been designed for 5G
applications. The design and simulation are done using
HFSS Software. The antenna resonates at a frequency
of 3.5 GHz. The S11 parameter of the antenna is
–40.2827 dB. The antenna has an impedance of 50.99
– j 0.15 Ohm. The impedance matching is excellent
which satisfies the maximum power transfer theorem.
Moreover, it has a maximum gain of 5.8263 dB and an
efficiency of 88.40%. The VSWR value is 1.02, which
is nearly ideal. Therefore, the reflection will be less
and maximum signal transmission can take place. The
CPA has a bandwidth of 200 MHz, within 3.4 GHz to
3.6 GHz which satisfies the TRAI standard. The simple
structure, compact size, good radiation characteristics,
proper impedance matching, and excellent gain make
this CPA suitable for different 5G applications.

Ninaad Patnaik
Aditya Ravi
Smarak Behera

Dept. of ECE


Profile of a Scientist

Alan Havey Guth, a native of New Jersey, is an American
theoretical physicist and cosmologist. He discovered
and developed the theory of cosmic inflation and he
won the 2014 Kavli Prize for pioneering the theory of
cosmic inflation.
Guth’s first step to developing his theory of inflation
occurred at Cornell in 1978. On the night of December
6, 1979 Alan Guth had the “spectacular realization” that
would soon turn cosmology on its head. He imagined
a mind-bogglingly brief event, at the very beginning
of the big bang, during which the entire universe
expanded exponentially, going from microscopic to
cosmic size. That night was the birth of the concept of
cosmic inflation. Such an explosive growth, supposedly
fueled by a mysterious repulsive force, could solve in
one stroke several of the problems that had plagued the
young theory of the big bang.
Guth studied particle physics during the initial phase
of his education but his interest toward cosmic
dragged him to study physical cosmology. Alan Guth
skipped his final year of high school to begin studies
at the Massachusetts Institute of Technology in 1964.
After his PhD in physics in 1971, he began a series
of postdoctoral positions at Princeton, Columbia,
and Cornell universities, and the Stanford Linear
Accelerator Center. While at Cornell, he began

collaborating with colleague Henry Tye on the creation
of magnetic monopoles in the early universe, and it was
this work which led to his proposal of an inflationary
universe. Guth continues to work on inflation, including
the possibility of igniting inflation in a hypothetical
laboratory to create a new universe and whether
inflation is eternal - it’s always going on, somewhere
in the universe.
Guth has been awarded the Franklin Medal for Physics,
the Eddington Medal, the Isaac Newton Medal, the
Dirac Prize, and the Gruber Prize in Cosmology, and has
been elected to the U.S. National Academy of Sciences
and the American Academy of Arts and Sciences.
As he once said in an interview-“The Big Bang theory
says nothing about what banged, why it banged, or
what happened before it banged”, he dedicated himself
towards explaining certain flaws of Big bang theory,
the cosmic inflation, expanding of the universe etc. In
his book “A Universe in your Backyard”, he said-“It
becomes very tempting to ask whether, in principle,
it’s possible to create a universe in the laboratory or
a universe in your backyard by man-made processes”.
He also explained the number of useful purpose served
by cosmic inflation to the world and his contributions
are incomparable.
Tanmaya Bal
7th SEM ,ECE-C


DD Feature

Blood Cell Counting using YOLO
Abstract : Blood cell count is an important test in medical diagnosis to evaluate overall health condition. Traditionally
blood cells are counted manually using haemocytometer along with other laboratory equipment’s and chemical compounds, which is a time-consuming and tedious task. In this work, the YOLO framework has been trained with a modified
configuration BCCD Dataset of blood smear images to automatically identify and count red blood cells, white blood cells,
and platelets. Overall the computer-aided system of detection and counting enables us to count blood cells from smear
images in a few seconds, which is useful for practical applications.

Keywords: Medical image processing, Blood image classification, , Blood cell count, Blood smear images, Blood
cells detection, YOLO

With the development of machine learning techniques,
image classification and object detection applications are
becoming more robust and more accurate. As a result,
machine learning based methods are being applied in
different fields. Particularly, deep learning methods are
being applied in different medical applications such as
abnormality detection and localization in chest X-rays,
automatic segmentation of the left ventricle in cardiac
MRI, and detection of diabetic retinopathy in retinal
fundus photographs. Thus, it is worth to look into deep
learning based methods that can be applied to identify and
count the blood cells in the smear images
Blood cell count is an important test often requested
by medical professionals to evaluate health condition.
Traditional manual blood cell counting system using
haemocytometer is highly time consuming and erroneous
and most of the cases accuracy vastly depends on the skills
of a clinical laboratory analyst. Therefore, an automated
process to count different blood cells from a smear image
will greatly facilitate the entire counting process.
The main three types of cells that constitute blood are
red blood cells (RBCs), white blood cells (WBCs), and
platelets.[1, 2] RBCs also known as erythrocytes are
the most common type of blood cell, which consists
of 40–45% of blood cells. Platelets also known as
thrombocytes are also in huge number in blood. WBCs
also known as leukocytes are just 1% of total blood cells.
RBCs carry oxygen to our body tissues and the amount
of oxygen tissues receives is affected by the number of
RBCs. WBCs fight against infections and platelets help
with blood clotting. The count of these cells determines
the ability of an organism to resist a particular infection
and capability of the body system. The normal count of

these cells is different for men, women, and children, etc.
Low count of WBCs indicates the presence of infection
while high count indicates an existence of infection,
leukemia or tissue damage. An abnormal count of RBCs
leads to anemia which results in mental tiredness, illness,
weakness, dizziness. So the blood count helps to evaluate
the health of person and detect the disorders.
YOLO (You Only Look Once) is an object detection
system which helps in determining the location
of certain objects present in the image, as well as
classifying those objects [3]. In this system a single
convolutional network predicts the bounding boxes and
the class probabilities for these boxes making it a faster
object detection system. YOLO trains on full images and
directly optimizes detection performance. This model
has several benefits over traditional methods of object
detection. YOLO is extremely fast. Because in this model
simply a neural network runs on a new image at test time
to predict detections. YOLO reasons globally about the
image when making predictions. YOLO sees the entire
image during training and testing and implicitly encodes
contextual information about classes as well as their
appearance. YOLO learns generalized representations
of objects. When trained on natural images and tested
on artwork, YOLO performs well compared to other
detection methods.
B.Working of YOLO
First, an image is taken and YOLO algorithm is applied.
The image is divided into any number grids, depending
on the complexity of the image [4]. Once the image


is divided, each grid undergoes classification and
localization of the object. The objectness score of each
grid is found. If there is no proper object found in the
grid, then the objectness and bounding box value of the
grid will be zero or if there found an object in the grid
then the objectness will be 1 and the bounding box value
will be its corresponding bounding values of the found
object. Anchor
boxes are used to increase the accuracy of object
detection. The working steps of YOLO is shown in fig.1.

Fig.1: Working of YOLO

C. Grid Cells
YOLO divides the input image into an S×S grid. Each
grid cell predicts only one object [3]. For example,
yellow grid cell predict the person object whose
center falls inside the grid cell. Each grid cell predicts a
fixed number of boundary boxes.
For each grid cell,
It predicts B boundary boxes and each box has one box
confidence score.
It detects one object only regardless of the number of
boxes B.
It predicts C conditional class probabilities (one per
class for the likeliness of the object class).
D. Boundary Box Prediction
Each boundary box contains 5 elements: (x, y, w, h)
and a box confidence score. x and y are offsets to the
corresponding cell. w and h are the width and height of
boundary box respectively[3]. The confidence score is
how likely the box contains an object and how accurate
is the boundary box. The conditional class probability
is the probability that the detected object belongs to a
particular class. If two or more grids contain the same
object then the center point of the object is found and
the grid which has that point is taken. For this, to get the


accurate detection of the object two methods can be used
and those are Intersection over Union (IoU) and NonMax Suppression.
E. Network Architecture
YOLO v3 uses a variant of Darknet, which originally
has 53 layer network trained on Imagenet. For the task
of detection, 53 more layers are stacked onto it giving
us a 106 layer fully convolutional layer underlying
architecture for YOLO v3 [4]. Darknet-53 is the feature
extractor and mainly composes of 3 × 3 and 1× 1 filters
with skip connections like the residual network in
ResNet. Darknet-53 has less BFLOP (billion floating
point operations) than ResNet-152, but achieves the
same classification accuracy at two times faster rate.
F. Prediction at three scales
Most classifiers assume output labels are mutually
exclusive. It is true if the outputs are mutually exclusive
object classes. For example, the output labels may be
pedestrian and person which is not mutually exclusive[3].
Therefore, YOLO v3 uses independent logistic classifiers
to calculate the likeliness of the input belongs to a specific
label. YOLOv3 uses binary cross- entropy loss for each
label to calculate the classification loss.


A publicly available dataset of annotated blood cell
images called Blood Cell Count Dataset (BCCD) is
used. It is a small-scale dataset for blood cells detection.
The data set contains of roughly around 400 microscopic
images of blood along with xml files containing the
annotations and information about bounding boxes [4].
We have to identify and localize, in an image, whether
a cell is RBC, WBC or Platelet. It has a total of 364
annotated smear images, but the dataset has some crucial
flaw. After splitting the dataset into training (300) and
testing (64) parts, it is found that one annotation file
in the test set does not include any RBC, although the
image contains RBCs. Moreover, three annotations file
exhibit very low RBC than actual. So, we remove four
fallacious files and the total size of the test set becomes
60. For the validation set, we randomly pick 60 training
images with annotations.

DD Feature


Fig. 6. VSWR characteristics of the proposed CPA

Using deep learning object detection method the different
types of blood cells are detected. Among the different
object detection algorithms you only look once (YOLO) is
chosen which gives faster and accurate results compared
to other algorithms. The block diagram for blood cell
identification and counting is shown in Fig. 2.
YOLO framework is trained to identify and count
RBCs, WBCs, and platelets from blood smear images.
The images are transformed into grayscale images and
looking at the structures and features of the cells they are
identified and further classified. To improve the counting
accuracy, KNN algorithm is used. Then the trained model
is tested with other images to observe the correctness of
the method.
The original implementation of the Tiny YOLO
configuration was trained for 20 different classes. To
adopt it for blood cells identification, it is modified for
three classes consisting of WBC, RBC, and platelets. Due
to modifying the class number, the number of filters in
the final convolutional layer in the CNN architecture is
required to be changed as well. Of the 360 images of the
dataset, 300 annotated blood smear images were used
for training and 60 for testing [4]. During training, loss
and moving average loss was recorded. Two different
learning rates were used: 10-5 and 10-7. The weights
were recorded and the model was evaluated. The weights
are then used for testing purpose. We use our model to
count the different cells in the validation dataset with
different confidence threshold. For counting RBCs,
confidence threshold is set to 0.55, for WBCs it is 0.35
and for platelets it is set to 0.25.

The test image is imported along with the trained weights.
Blood Cells are predicted and counted. Our model is also
used to detect and count blood cells from high- resolution
blood cell smear images. These test images are of the size
of 3872 x 2592 which is way higher than the size of our
trained images of 640 x 480. So, to match the cell size
of our trained images we divide those images into grid
cells and run in each grid cell and then combine all the
prediction results. In some cases platelets are counted
twice. To improve counting accuracy of platelets, we use
KNN algorithm in each platelet and determine its closest
platelet and then using the intersection of union (IoU)
between two platelets we calculate their extent of overlap.
If the overlap is greater than ten percent, we ignore that
cell to avoid the extra count.
YOLO framework is trained to identify and count
RBCs, WBCs, and platelets from blood smear images.
The images are transformed into grayscale images and
looking at the structures and features of the cells they are
identified and further classified. To improve the counting
accuracy, KNN algorithm is used. Then the trained model
is tested with other images to observe the correctness of
the method.
The original implementation of the Tiny YOLO
configuration was trained for 20 different classes. To
adopt it for blood cells identification, it is modified for
three classes consisting of WBC, RBC, and platelets. Due
to modifying the class number, the number of filters in
the final convolutional layer in the CNN architecture is
required to be changed as well. Of the 360 images of the
dataset, 300 annotated blood smear images were used
for training and 60 for testing [4]. During training, loss
and moving average loss was recorded. Two different
learning rates were used: 10-5 and 10-7. The weights
were recorded and the model was evaluated. The weights
are then used for testing purpose. We use our model to
count the different cells in the validation dataset with
different confidence threshold. For counting RBCs,
confidence threshold is set to 0.55, for WBCs it is 0.35
and for platelets it is set to 0.25.
The test image is imported along with the trained weights.
Blood Cells are predicted and counted. Our model is also
used to detect and count blood cells from high- resolution
blood cell smear images. These test images are of the size
of 3872 x 2592 which is way higher than the size of our
trained images of 640 x 480. So, to match the cell size
of our trained images we divide those images into grid
cells and run in each grid cell and then combine all the
prediction results. In some cases platelets are counted


twice. To improve counting accuracy of platelets, we use
KNN algorithm in each platelet and determine its closest
platelet and then using the intersection of union (IoU)
between two platelets we calculate their extent of overlap.
If the overlap is greater than ten percent, we ignore that
cell to avoid the extra count.

tested on a different dataset of higher resolution, where
it has performed satisfactorily. With the accuracy and the
detection performance of the proposed method, it can be
said that, the method can ease up the manual blood cell
identification and counting process.


We would like to express our gratitude to our project
guide, Mr. Nihar Ranjan Nayak, of CSE department for his
guidance in carrying out this work.


1. Manish Chablani, YOLO-You only look once, real
time object detection explained

Fig.1: Working of YOLO

The outputs of predictions on test image and classified
output of HRI is shown in fig.3 and fig.4 respectively. The
algorithm detected 17 RBC, 1 WBC and 2 Platelets.

2. N. Guo, L. Zeng, and Q. Wu, A method based on
multispectral imaging technique for white blood cell
segmentation, Computers in Biology and Medicine,
vol. 37, no. 1, pp. 70–76, 2007.
3. N. Hazlyna and M. Y. Mashor, Segmentation
technique for acute leukemia blood cells images using
saturation component and moving l-mean clustering
procedures, International Journal of Electrical, Electronic
Engineering and Technology, vol. 1, pp. 23– 35, 2011.
4. P. R. Tabrizi, S. H. Rezatofighi, and M. J. Yazdanpanah,
-Using PCA and LVQ neural network for automatic
recognition of five types of white blood cells,‖ in
Proceedings of the 32nd Annual International Conference
of the IEEE Engineering in Medicine and Biology Society
(EMBC ‘10), pp. 5593–5596, September 2010.

Fig.1: Working of YOLO


In this work, a machine learning approach to automatically
identify and count blood cells from a smear image based
on YOLO algorithm is presented. To improve accuracy, the
method employed KNN and IOU based method to remove
multiple counting of the same object. The proposed method
is evaluated on publicly available datasets. It is observed
for test dataset that, our method accurately identifies RBCs,
WBCs, and Platelets with an accuracy of 96.09%, 86.89%,
96.36% manually. The proposed method has also been


Neha Gupta, Swagata Upadhyay,
Arpita Panda, Sarthak Padhi
8th sem. CSE

Innovation in BioTechnology

CRISPR-Cas9 – Another step towards change!
Genome editing is a set of technologies that gives scientists the
ability to shape an organism’s DNA. This group of technologies
has allowed genetic material to be attached, detached, or
metamorphosed at particular locations in the gene sequence.
Many approaches to genome editing have been developed over
the years. The most recent one is known as “CRISPR-Cas9”. It
stands for: Clustered Regularly Interspaced Short Palindromic
Repeats and CRISPR-associated protein 9. The CRISPRCas9 system has generated a lot of enthusiasm in the scientific
community because it is swift, economical, precise and much
greater in efficient than other existing genome editing methods.
The idea of CRISPR-Cas9 was cultivated from a naturally
occurring genome editing system in bacteria called the
“Escherichia Coli” (acronym being “E. coli”). The bacteria
capture and mirror snippets of DNA (Deoxyribonucleic acid)
from foreign micro-organisms like viruses, fungi, protozoans,
etc., and use them to create DNA segments known as CRISPR
arrays. The CRISPR arrays aid the bacteria to “recall” the viruses
(or closely related ones). If the viruses try to infect again, then
the bacteria produce RNA segments from the CRISPR arrays
to target the viruses’ DNA. The bacteria then use Cas9 or a
similar enzyme to snip the DNA apart, which disables the virus.
The CRISPR-Cas9 system has a similar mode of operation.
Scientists create a small fragment of RNA (Ribonucleic acid)
with a short “guide” sequence that latches to a specific target
sequence of DNA in a genome. The RNA also binds to the Cas9
enzyme. As in bacteria, the modified RNA is used to recognize
the DNA sequence, and the Cas9 enzyme attacks the DNA
at the targeted location. Although Cas9 is the enzyme that is
used most often, other enzymes like Cpf1 (Cas12a) and C2c2
(Cas13) can also be used. Once the DNA is cut, researchers
use the cell’s own DNA repair mechanism to attach or detach
pieces of genetic material, or alter the DNA by replacing an
existing segment with a customized DNA sequence.

Genome editing is one of the best possible methods for the
prevention and treatment of deadly human diseases. Most
research on genome editing is currently done to understand
diseases using cells and animal models. The same methods
of editing are also being performed on the dreaded “Corona
Virus” and its mutations to learn the pattern and mutation cycle.
Such a research gives an idea about the genome sequence in
order to develop a safe and effective cure. Scientists are still in
the process of determining whether this approach is safe and
effective for use in people. It is being employed in research
on a wide variety of diseases, including single-gene disorders
such as cystic fibrosis, haemophilia, and sickle cell anaemia.
It also holds promise for the treatment and prevention of
more complex diseases, such as cancer, heart disease, mental
illness, and human immunodeficiency virus (HIV) infection.
There was a time when sequencing the human genome was
a massive project at the cutting edge of science. Indeed,
many thought it to be the pinnacle of biological research.
Today, sequencing one’s genome has never been cheaper,
dropping from $95million to just $950 over the past ten
years, CRISPR-Cas9 being the main reason behind it. With
greater understanding of our genetics comes greater capacity
for their manipulation. Gene editing currently stands as one
of the most exciting areas within the biotech industry. Many
of the proposed applications involve editing the genomes of
somatic (non-reproductive) cells but there has been a lot of
interest in and debate about the potential to edit germline
(reproductive) cells as well. The main reason behind the
debate being the changes made in germline cells that will
be passed on from generation to generation. By contrast, the
use of CRISPR-Cas9 and other gene editing technologies in
somatic cells is uncontroversial. They have already been used
to cure human disease on a small number of exceptional and/
or life-threatening cases.

CRISPR-Cas9 gene editing imaged for first time ( Courtesy: https://www.
Gene Editing Process by CRISPR_Cas9 (Courtesy: / news/46282/crispr-cas9-gene-editing-imaged-for-first-time/) tools-and-resources/feature-articles/crispr-cas9-and-targeted-genome-editing-anew-era-in-molecular-biology)


CRISPR-Cas9 is indeed one of the greatest breakthroughs
in the world of biotechnology. Derived from the natural
process of gene sequencing by E. coli, it sure has
revolutionized the scientific world and will soon be
administered routinely in humans. Many researches are
still focusing on its use in animal and/or isolated human
cells. Its main aim being to be used routinely used in order
to treat human diseases. Though the system is still not
completely accurate, work is being done till date in order

to perfect this system of targeting a particular fragment
and successfully operating it. When its all said and done,
CASPR-Cas9 will surely become a step towards change,
towards attaining a world free of several deadly diseases.

Soumyakanta Panda
5Th sem. EEE

Innovations for COVID-19
Indian company Techmax Solution designs
toucheless elevator panels to fight COVID-19
Thanks to this panel, called “Sparshless”, no one
needs to touch elevator buttons anymore. According
to the company, it can work with all existing lifts, no
modification is required. Instead of disinfecting the
lift every day, this innovation could easily help stop
the spread of the virus in apartments, hotels, malls,
hospitals, offices and such public places.
Japanese start-up Donut Robotics designs smart
masks that connect to phones via Bluetooth
The face mask, called 'C-Mask,' can be
worn over regular, fabric-based masks. It connects to
an app via Bluetooth, enabling it to transcribe speech
to text messages, which are then sent via the user’s
smartphone. It can also translate from Japanese into
eight other languages and amplify the user’s voice, in
case they are not heard through the mask.

(learning content, certifications, job-seeking tools), as
well as from inside the rest of the company, and make
them available for free or cheaply to those who are
interested in up-skilling or retraining for new careers.
New MIT robot using UV light to kill coronavirus could
be used to disinfect warehouses, schools, and offices
The system has already been used to sanitise the
Greater Boston Food Bank. In tests, the robot covered
a 4,000 square foot area of the warehouse within 30
minutes, providing enough light to neutralise around
90% of coronavirus particles. Tele-operators have
first to teach the robot a route around the site, then
it follows waypoints around a map of the venue. The
next step is enabling the robot to adapt to changes in
its environment.

Microsoft launches digital skills initiative, making
available its educational content, to help those hit
by COVID-19
Microsofts new global skills initiative aims at bringing
more digital skills to 25 million people worldwide by
the end of the year. Microsoft plans to combine existing
and new resources from its LinkedIn and GitHub units



DD Feature

Increasing the Number of Fin and Studying its Impact
on Various Parameters of the Fin-FET
Abstract : In this paper, we systematically examined the impact of inserting a second gate by hollowing out the fin, on the ac performance
parameters including total gate capacitance(Cgg), RC delay (CggVDD/ION), cutoff frequency (fT), energy (E),Total power (PTotal),
and leakage power (PLeakage) of hybrid FinFETs at the supply voltage, VDD with on-current ION. The RC delay, energy, and total
power consumption are the primary factors limiting the operating frequency of the high-performance devices. Therefore, these electrical
parameters are needed to be addressed in the architectural level of the fin based devices. In this paper, a calibrated numerical device
simulation tool is used to achieve the best device performances of 50-nm hybrid FinFETs. From the simulated current–voltage (I–V)
and capacitance–voltage (C–V) characteristics of hybrid FinFETs, the parameters Cgg, CggVDD/ION, fT, CV2, PTotal, and PLeakage
are extracted to analyze the effect of the second gate inserted in the fin on the performance matrices of these devices. In addition, this
paper proposes an optimum structural configuration for 50-nm hybridFinFET architecture for digital application perspective.

A metal–oxide–semiconductor field-effect transistor is a fieldeffect transistor where the voltage determines the conductivity
of the device. It is used for switching or amplifying signals. The
ability to change conductivity with the amount of applied voltage
can be used for amplifying or switching electronic signals. [1]
A MOSFET is by far the most common transistor in digital
circuit, as hundreds of thousands or millions of them may be
included in a memory chip or microprocessor. Since they can
be included in a memory chip or microprocessor. And they
can be made from either P-type or N-type semiconductors,
complementary pairs of MOS transistors can be used to make
switching circuits with very low power consumption, in the form
of CMOS logic .MOSFETs are particularly useful in amplifiers
due to their input impedance being nearly infinite which allows
the amplifier to capture almost all the incoming signal. The main
advantage is that it requires almost no input current to control the
load current, when compared with bipolar transistors [2].
STRUCTURE OF MOSFET: It is a four-terminal device with
source, gate, drain and body terminals. The body is frequently
connected to the source terminal, reducing the terminals to three.
It works by varying the width of a channel along which charge
carriers flow (electrons or holes).The charge carriers enter the
channel at source and exit via the drain. The width of the channel
is controlled by the voltage on an electrode is called gate which
is located between source and drain. It is insulated from the
channel near an extremely thin layer of metal oxide. A metalinsulator-semiconductor field-effect transistor or MOSFET is a
term almost synonymous with MOSFET between the drain and
source, the current flows freely between the source and drain and
the gate voltage controls the electrons in the channel. If we apply
negative voltage, a hole channel will be formed [3].

Fig. 1. Structure of the MOSFET

A fin field-effect transistor (Fin-FET) is a multi gatedevice, a
MOSFET (metal-oxide-s) shown in fig 1 is built on a substrate
where the gate is placed on two, three, or four sides of the
channel or wrapped around the channel, forming a double
gate structure. Fin-FET is a type of non-planar transistor, or
“3D” transistor as shown in fig.2. It is the basis for modern
Nona electronics semiconductor device fabrication [2].

Fig. 2 Structure of FINFET




• Higher output current per input voltage.
• Higher switching speed and lower power
consumption due to lower equivalent input
capacitance and channel quantization effects.
• Better on/off contrast due to channel
quantization effect.
• Channel quantization effect also reduce short-
channel effects due to more effective physical
separation of the source and drain regions.
The parameters on which the performance of the device
will be verified upon are its analog parameters which are
On-drain current(Ion)and OFF-drain current(Ioff), analog
parameters helps us to know the optimum point at which the
device should work as well as they give us better idea on the
better usage of the device, the second parameter on which
the device is checked are its linearity parameters, under
linearity parameters comes the trans conductance and voltage
intercept point, third comes the radio frequency parameters
in it determines the frequency at which the device will work
as well as it gives the cut-off frequency at which the device
will produce normal output. The other parameters at which
the impact of the two layer gate will depend are the energy,
power and Rc-delay parameters for this the lower is the value
the more efficient is the device These are the parameters on
which the device will be judged upon and will be studied.

Fig. 2 Structure of FINFET

In basic fin, electrons are utilized only in the part of the substrate
where the fin is acting. So, all the electrons can’t be utilized. In
order to utilize the amount of electrons to the fullest, we are
applying this technique. But the difference from the above
technique is that we are not applying a gate in between the fins.

Here the important parameters like drain current, cutoff
frequency, RC delay, energy and power are calculated
of different structures.


Fig. 2 Structure of FINFET

In basic finet, electrons are utilized only in the part of the substrate
where the fin is acting. So, all the electrons can’t be utilized. In
order to utilize the amount of electrons to the fullest, we are
applying this technique where we are using gate in between the
two fins [3]. The structure of Finfet is shown in figure 3.


Fig. 2 Structure of FINFET

From the above figure 5 it is clear that the drain current for
a constant voltage is increasing when the number of fin is
increasing. That implies the tri-fin device provides more
current compared to dual-fin and the dual fin device produces
more current compared to basic finFET.



Fig.9. Comparison of RC-Delay plot
Fig.6. Comparison of frequency plot

The cut-off frequency is found to be larger in case of basic finFET
as shown in fig 6. The dual-fin having lesser frequency compared to
that of basic. The tri-fin structure works for even lesser frequency.


The above comparison result shows that the RC delay
in case of tri-fin material is less compared to dual-fin
and basic finFET as shown in fig 9.

We have successfully implemented two methodologies which
are, first, we have two fins. Second, we have three fin. Out
of the two methods that we implemented, the first method in
which we have two fins showed the maximum current output
as we are using a gate in between the two fins which help us
to accumulate more electrons. Thus, helps in improvising the
device parameters.

Fig.7. Comparison of energy plot

The energy plot shown here in fig. 7 describes the energy
consumed in the basic structure is more than the dual-fin and trifin structure.


1. P. Elakkumanan , C. Thondapu , and R. Sridhar, “A gate
leakage reduction strategy for sub-70 nm memory circuits,”
in IEEE Proceedings of the Workshop on Implementation of
High Performance Circuits, 2004, pp. 145–148.
2. G. Prasad, “Novel low power 10T SRAM cell on 90nm
CMOS,” in 2nd International Conference on Advances in
Electrical, Electronics, Information, Communication and
Bio-Informatics (AEEICB), 2016. IEEE, 2016, pp. 109–114.
3. G. Razavipour ,A.Afzali-Kusha, and M.Pedram , “Design
and analysis of two low-power SRAM cell structures,” IEEE
transactions on very large scale integration (VLSI) systems,
vol. 17, no. 10, pp. 1551–1555, 2009.

Fig.8. Comparison of power plot

Siddhartha, Marmik Maharana, AbhijeetSahu,
Manjeet Shekhar, Debraj Bhattacharjee,
SamarpitaPattnaik, Chandana Dash, Jyoti Singh.
Dept. of AE & I

The power plot shown in fig. 8 here describes the power consumed
in the basic structure is more than the dual-fin and tri-fin structure.


PhD Synopsis

For the last few decades, MOSFETs are being
continuously scaled down. As a result of this
continuous scaling, several deleterious shortchannel effects (SCEs) become prominent and start
deteriorating the performance of traditional planar
bulk MOSFETs. In order to reduce SCEs, various
nonplanar multiple-gate device structures have been
proposed in recent years to improve the electrostatic
control of the channel by the gate terminal. Among
them, Double Gate (DG), Surrounding gate (SRG)
and FinFET technology based on Silicon-oninsulator (SOI) technology has been the forerunner
of the CMOS technology in the last decade offering
superior CMOS devices with higher speed, higher
density, excellent radiation hardness and reduced
second order effects for submicron VLSI applications.
Recent experimental studies have invigorated interest
in fully depleted (FD) SOI devices because of their
potentially superior scalability relative to bulk silicon
CMOS devices.
In this study, for the first time the effect of dimension
scaling on different figure of merits for digital,
analog and RF performance parameter such as drain
induced barrier lowering (DIBL), subthreshold
slope, threshold voltage roll-off, Transconductance,
Resistance, Intrinsic Gain, Cut-off
maximum Frequency of oscillation, Gain-BandWidth Product are need to be studied by developing
simple computationally efficient physics-based

analytical models and extensive simulation studies.
Therefore, in this work, our main objective is to
understand and analyze the design structure of novel
nanoscale devices, exhibiting superior performance
over traditional bulk MOSFETs in terms of device
physics and scaling effects to become a competitive
contender for usage in next generation system-onchip applications and to provide incentive for further
experimental exploration.
In this work, the remedies of prominent short-channel
effects exhibited by deca-nanometer MOSFETs are
studied by means of analytical modelling and the
extensive numerical simulation studies. Unique
features offered by different novel device structures
such as InAs-based TFETs, gate-engineered
MOSFETs and junctionless transistor structure have
been studied to address the challenge of increasing
short-channel effects and to show the efficacy to
obtain improved analog/RF performance for deepsubmicron VLSI integration.

Dr Biswajit Baral
Dept. of ECE


Environmental Awareness & Concerns

Coal pollution linked to fatalities in India

IMF(International Monetary Fund) chief Christine Lagarde
said pollution from coal generation plants causes about 70,000
premature deaths every year in India. According to her,
environmental problems do not just end with climate change.
Lagarde said she believes the world is facing economic,
environmental and social crisis. “The planet is warming rapidly,
with unknown and possibly dire consequences down the line.
Across too many societies, the gap between the haves and havenots is getting wider and strains are getting fiercer,” she said.

Stressing that climate change is clearly one of the great challenges
of our time, Lagarde said it is a present reality for the world’s
poorest and most vulnerable people. In India, the thermal power
generation is 62.1% out of the total, of which 53.6% is from coal,
whereas it is significantly less from Renewable Energy Sources
(RES). Looking into the present environmental conditions RES
should be given more priority than thermal sources, especially
coal-based plants, with a vision that at least half of the country’s
energy production should come from RES by the next decade.


Publication Cell
Tel: 99372 89499 / 8260333609

Editorial 02
DD Feature


Profile of a Scientist


PhD Synopsis


Awareness & Concerns


Editorial Team
Dr. Jaideep Talukdar
Dr. Pamela Chaudhury
Dr. Lopamudra Mitra
Dr. Bhagyalaxmi Jena
Nalini Singh
Dr. Soumya Priyadarsini Panda
Dr. Priyanka Kar
Student Members
Tanmaya Bal
Rohit Kumar Nayak
Media Services
G. Madhusudan
Sujit Kumar Jena


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