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ISO 9001:2015
Project Code: RBTEP1601
Abstract: Recently, there has been an increasing interest in the field of human interactive robotics. Contrary to otherwise complex and resource hungry algorithms, we in this work have presented a computationally low cost algorithm for a human following robotic application. Instead of detecting the human, the algorithm makes use of a specific colour tag placed on the human subject which is detected by a camera mounted on the robot. Sensors including range sensor, magnetometer and optical encoders are utilized in tandem to assist the human following process. The method is tested on a custom built robotic platform running Raspberry pi minicomputer. We have performed and presented the results of several experiments for the evaluation of our method.
For IEEE Paper & Synopsis contact: 9886468236 / 7022390800
Project Code: RPI001
Abstract: The proposed system is developed for tracking temperature at
various places. The proposed system is a Temperature
Monitoring system that allows us to continually check the
temperature of a given place it may be a room, heater, oven or
furnace. We have improved some features of over the old
system available in the market, i.e. the data is stored in
Database & can be accessed from all over the World. This
proposed system is divided primarily into two sections that are
the equipment and programming. The equipment part
comprises of Temperature Sensor, Jumper Wires,
Programmable Logic Controller, and Analog Card &
Raspberry Pie 3. Raspberry pi takes data and saves it in the
MySQL database. The MySQL database is connected to a User
Interface which is a webpage, the data onto the database is
extracted & is displayed on the user-friendly Webpage which
can be accessed from all over the World. Our proposed system
is ready to work at Industrial furnaces with equal efficiency.
For IEEE Paper & Synopsis contact: 9886468236 / 7022390800
Project Code: RPI002
Abstract: This paper aims to design and develop an
Automated Wall Painting Robot which helps to reduce manual
efforts on painting and accomplish cost effective painting
accessories. Here we have proposed a robot controlled via
Raspberry Pi board. The autonomous robot can be controlled
using simple python program. It is used to eliminate the
human exposure in dangerous environments and very effective
on time management. Also it completes a painting job without
an error. At last, it is expected that the conceptual model of the
wall painting robot would be efficiently used in various home
and industry applications in wall finishing and maintenance of
other giant architectural and civil structures.
For IEEE Paper & Synopsis contact: 9886468236 / 7022390800
Project Code: RPI003
Abstract: Vehicle’s plate number is a unique identity by
which individual vehicle can be identified. Vehicle plate
recognition system helps to capture a vehicle plate number,
extract the numbers on the plate and check the details of the
car owner. As the number of car owners in a country
increases, identifying and charging unlawful vehicles on the
road has been a tedious work for law enforcement agents. In
this paper, we present an automatic vehicle plate recognition
system using Raspberry pi. A Camera was incorporated to
help in capturing the plate number images and it is interfaced
to a Raspberry pi processor for authentication. Using the Open
Computer Vision (Open CV) and Optical Character
Recognition (OCR), the system can extract numbers from the
captured plate image and completely automate the license plate
recognition. The experimental results from several testing in
different locations and conditions show that the system
performed better than most of the baseline studies considered.
For IEEE Paper & Synopsis contact: 9886468236 / 7022390800
Project Code: RPI004
Abstract: The surveillance of border areas or any other
secured places using security guards at each and every moments
is difficult. It is made possible using digital cameras, a device
which is used widely now a days for surveillance purposes. In
such a case, the following paper defines the border surveillance
system. This is done by computer vision. The requirements
includes a camera, Raspberry-pi, Arduino-UNO and Buzzer. The
digital camera is used to capture the live movements. The
attained information/records are sent as an input to the
raspberry pi which uses a computer vision (open CV) software to
detect the objects and faces as positive and negative. The
corresponding coding is done using python. If any mismatched
objects or face (negative) is detected, automatically a signal is
sent from the pi module. The desired signal is sent to the
authority or monitoring room using transmitter. At one end, the
Raspberry-pi module is connected with a monitor, transmitter
and at the other end, a receiver with Arduino and buzzer. When
any signal is received by a receiver, it is passed to the Arduino,
which in turn triggers the buzzer connected to the Arduino and
gives an alert signal or sound as it was programmed. In addition
to this, the live streaming video can be seen in the
monitor/display connected to the raspberry-pi. This surveillance
system using computer vision can also be used at various places
which are being under surveillances and are secured
For IEEE Paper & Synopsis contact: 9886468236 / 7022390800
Project Code: RPI005
Abstract: In order to provide home intelligent control
function for the elderly more efficiently and conveniently, this
paper designed a smart home system controller based on
raspberry PI with simple hardware structure and low
development cost. The design is implemented by raspberry PI
development board and python language, and the fatigue
detection algorithm is implemented by OpenCV visual library,
Dlib library and EAR algorithm. Through temperature and
humidity sensor, infrared extended version, LCD display screen
and other hardware intelligent control indoor environment. The
test results show that the fatigue state can be accurately detected
and the electrical control function can be realized. This design
enhanced the intelligence of household control, and met the life
demand of energy-saving convenience.
For IEEE Paper & Synopsis contact: 9886468236 / 7022390800
Project Code: RPI006
Abstract: Increasing the number of accidents on the road,
the government have to make the latest innovations to reduce the
high number of accidents every year. Supporting the rules that
have been making by the government through the Minister of
Transportation, a camera needs that to read the speed of a
passing vehicle and takes the pictures if the vehicle exceeds a
predetermined speed limit. In this study produce a tool to detect
the maximum speed of vehicles on the highway with an infrared
sensor based on raspberry pi 3 b + by using Python Software
with physics methods. The tool will store the data (image) when
the vehicle speed exceeds the specified limits, store data in the
form of vehicle speed, the date and time the vehicle is violating in
real time, for testing the maximum vehicle limits of 45 km / hour
the tool works well and the resulting vehicle photos are clear.
Then, the authorities can utilize for the data (Police and
Transportation Agency).
For IEEE Paper & Synopsis contact: 9886468236 / 7022390800
Project Code: RPI007
Abstract: In today’s world, where the need for security is
paramount and biometric access control systems are gaining
mass acceptance due to their increased reliability, research in this
area is quite relevant. Also with the advent of IOT devices and
increased community support for cheap and small computers like
Raspberry Pi its convenient than ever to design a complete
standalone system for any purpose. This paper proposes a Facial
Biometric System built on the client-server paradigm using
Raspberry Pi 3 model B running a novel local descriptor based
parallel algorithm. This paper also proposes an extended version
of Local Gradient Hexa Pattern with improved accuracy. The
proposed extended version of LGHP improved performance as
shown in performance analysis. Extended LGHP shows
improvement over other state-of-the-art descriptors namely LDP,
LTrP, MLBP and LVP on the most challenging benchmark facial
image databases, i.e. Cropped Extended Yale-B, CMU-PIE,
color-FERET, LFW, and Ghallager database. Proposed system is
also compared with various patents having similar system design
and intent to emphasize the difference and novelty of the system
proposed.
For IEEE Paper & Synopsis contact: 9886468236 / 7022390800
Project Code: RPI008
Abstract: In the present day in our daily life, we all depend
on the Internet for web browsing, e-mail, and peer-to-peer
services to fulfill our needs. The word Internet means
Internetworking of things but IoT (Internet of Things) means a
physical object that had a feature of Internet protocol address
and that will make the communication between the object and
other internet-enabled devices. Here to provide security
between the communicating devices without any delay is the
main important factor. To provide more security to the existing
Face recognition and detection system in homes and banks we
propose a new system that will extend the current system. In this
paper, we have briefly described the requirement to make such
a system and Face recognition Algorithm for Authentication
purposes and sending the data using Telegram bot.
For IEEE Paper & Synopsis contact: 9886468236 / 7022390800
Project Code: RPI009
Abstract: Nowadays, Fog computing is facing the requirements
of time-sensitive applications in the IoT-cloud continuum. These
requirements are decisive for mission-critical applications like
structural health monitoring. In this paper, a portable Fog
computing infrastructure, known as FogPi, is presented. This
infrastructure has been designed around Raspberry Pi, which
offers a low-cost and scalable solution for running containerized
applications. FogPi allows the deployment, management, and
orchestration of Docker containers and is especially suitable for
environments where the limited Internet connection and reduced
budgets limit the adoption of Fog and Edge deployments.
For IEEE Paper & Synopsis contact: 9886468236 / 7022390800
Project Code: RPI010
Abstract: Recently, the current available surveillance technology
still lacking in many aspect especially in terms of price and the
flexibility of the alert system. In this modern living styles, illegal
activities detection can be done through surveillance system. Due
to the greater awareness in home security, home surveillance
system offers great solution in providing efficient home security.
Thus, this project is about proposing an intelligent home
surveillance system with the use of Raspberry Pi. Whenever
intrusion detected, the image of the intruder will be captured using
a camera fasten to the Raspberry Pi device. Meanwhile, a buzzer
represents an alarm that will be triggered once the intruder is
captured in the frame of the camera. The captured video will be
stored in SD Card which later can be used as evidence and prompt
action can be taken to be reported to the responsible party.
For IEEE Paper & Synopsis contact: 9886468236 / 7022390800
Project Code: RPI011
Abstract: Programmable Logic Controllers (PLCs) still are
the state-of-the-art regarding the industrial automation control,
but the Industry 4.0 advent is imposing new requirements, e.g.,
related to the capability to acquire and process data on realtime at the edge computational layer. On the other hand, the
current availability of cheaper and more powerful processors
opens new windows to develop low-cost and more advanced
industrial controllers aligned with the Industry 4.0 principles.
In this context, an important challenge is to improve the current
state-of-the-art PLCs by taking into consideration the low-cost
but powerful computational boards that will allow to embed
IoT technologies and data analytics. This work describes the
development of a low-cost but powerful industrial controller
based on the use of the single-board computer Raspberry
Pi, which allows executing logic control programs codified in
IEC 61131-3, IEC 61499, or even in Java or Python, while
maintaining the industrial requirements. The proposed platform
was experimentally used to control an automation process based
on a Fischertechniks platform.
For IEEE Paper & Synopsis contact: 9886468236 / 7022390800
Project Code: RPI012
Abstract: From smart industries to smart cities, sensors in the
modern world plays an important role by covering a large number
of applications. However, sensors get faulty sometimes leading to
serious outcomes in terms of safety, economic cost and reliability.
This paper presents an analysis and comparison of the
performances achieved by machine learning techniques for realtime drift fault detection in sensors using a low-computational
power system, i.e., Raspberry Pi. The machine learning
algorithms under observation include artificial neural network,
support vector machine, naïve Bayes classifier, k-nearest
neighbors and decision tree classifier. The data was acquired for
this research from digital relative temperature/humidity sensor
(DHT22). Drift fault was injected in the normal data using
Arduino Uno microcontroller. The statistical time-domain
features were extracted from normal and faulty signals and pooled
together in training data. Trained models were tested in an online
manner, where the models were used to detect drift fault in the
sensor output in real-time. The performance of algorithms was
compared using precision, recall, f1-score, and total accuracy
parameters. The results show that support vector machine (SVM)
and artificial neural network (ANN) outperform among the given
classifiers.
For IEEE Paper & Synopsis contact: 9886468236 / 7022390800
Project Code: RPI013
Abstract: Current technological advances have made
possible for object tracking activity to become more intelligent.
In order to track objects, the camera must be equipped with a
computing device that can process video images. A Raspberry
Pi embedded computer is chosen because of its smaller size,
making it suitable to embed into devices such as camera
surveillance. It is used to process the image recorded by the
camera so that the camera angle can follow the movement of
objects. The image processing is performed using the
Histogram Oriented Gradients and Support Vector Machine
method which is implemented in the Raspberry Pi. Based on
the test results, the best accuracy is achieved using the
threshold at 175 with the best distance of 6 meters.
For IEEE Paper & Synopsis contact: 9886468236 / 7022390800
Project Code: RPI014
Abstract: The human communication is totally based
on speech and text. So visually impaired people can
gather information from voice. With the help of this
project visually impaired people can read the text
present in the captured image. In this Project we use
Raspberry Pi Camera and this help to take pictures and
that picture is converted into scan image for further
process by using Imagemagick software. The output of
Imagemagick software is in the form of scanned image
this scan image is giving as an input to the Tesseract
OCR (Optical Character Recognition) software to
convert image into the text. For transformation of text
into speech we use TTS (Text to Speech) engine.
Experimental results shows that the analysis of different
captured images and it will be more helpful to blind
people.
For IEEE Paper & Synopsis contact: 9886468236 / 7022390800
Project Code: RPI015
Abstract: Visually Impaired people report numerous
difficulties with accessing printed text. The current scenario of
reading for blind people is with the help of the Braille system,
which is a code-system of dots represent letters of an alphabet.
Not all books are written in Braille, thus a visually impaired
person is limited to a countable number of books. Hence, there is
a need to make a reading device that enables better manageable
eyes-free operation (reading). The current work proposes a
wearable reader that captures real-time images of printed text
from a book using a high-resolution miniature camera. The
images of the printed text are processed to convert it as a
computerized text using raspberry pi microcontroller. Vibration
motors were embedded in the device that guides the user to
orient with the direction of reading in case they get deviated from
the current text-line. The computerized text can be heard as a
voice by the user. The device can be worn in finger and gain
access to a various number of learning resources and can be
widely used by the blind people for their studies.
For IEEE Paper & Synopsis contact: 9886468236 / 7022390800
Project Code: RPI016
Abstract: Population explosion leads to an
unprecedented increase in the number of physical
objects or vehicles on road. As a result, the number of
road accidents increases due to a very heavy traffic
flow. In this paper, traffic flow is monitored by using
computer vision paradigm, where images or sequence of
images provides a betterment on the road view. In order
to detect vehicles, monitor and estimate traffic flow
using low cost electronic devices, this research work
utilizes camera module of raspberry pi along with
Raspberry Pi 3. It also aims to develop a remote access
using raspberry-pi to detect, track and count vehicles
only when some variations occur in the monitored area.
The proposed system captures video stream like vehicles
in the monitored area to compute the information and
transfer the compressed video stream for providing
video based solution that is mainly implemented in
Open CV by Python Programming. The proposed
method is considered as an economical solution for
industries in which cost-effective solutions are
developed for traffic management.
For IEEE Paper & Synopsis contact: 9886468236 / 7022390800
Project Code: RPI017
Abstract: Real-time Python refers to using Python in realtime feedback control experiments by combining an Arduino
microcontroller with a computer. This paper uses a Raspberry
Pi to improve upon a previous method that combined a laptop
and an Arduino. The primary improvement is switching from
serial to i2
c for communication between the Arduino and
Python, which significantly reduces the latency in communications. The reduction in latency allows the digital control
frequency to increase from 200 Hz to 500 Hz. The latency
improvements are verified by oscilloscope measurements. The
new i2
c based approach is applied to vibration suppression
control for a 3D printed beam.
For IEEE Paper & Synopsis contact: 9886468236 / 7022390800
Project Code: RPI018
Abstract: Internet of Things will have a great impact on
human’s lives and particularly in education, it determines how
to implement new technologies to motivate and assist students
on their studies. This paper provides description and evaluation
of the course focused on the Internet of Things. The topics of
both lectures and labs are designed according to the formulated
goals. The final project of the course is discussed in details with
several examples solved by students. The project based learning
method is carefully evaluated by students and analyzed in order
to enhance the quality of the course in the future.
For IEEE Paper & Synopsis contact: 9886468236 / 7022390800
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