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.

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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.

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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.

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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.

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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

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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.

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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).

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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