Food Related
SmartCook
AP042 »
Cooking is a strenuous, complex, and tedious process. We often tend to keep something to cook and forget. And sometimes when just beginning to learn cooking we don't exactly know that which dish takes what time to cook. Whatever the case, these result in the charring of the food in simpler cases and kitchen fires in the more appalling ones. So why not make it more advanced and simple for the user by automating it? The project idea focuses on the development of a product that will act as a monitoring and alert system for cooking processes. Though there are many existing products like automatic rice cookers, microwave ovens, etc. But all of them are application-specific, bulky, and expensive. There are also hazards associated with many of them thus making the user skeptical about using them. Our product is an add-on plug-and-play device that you can use with any of your existing utensils, thus making it a cheaper, more user-friendly, and versatile option. The design of the clipper is one of its unique features which have been applied for a patent. The uniquely designed clipper will have an in-built microcontroller and various sensors to monitor the food item being cooked. Depending on the sensor data and factors like the quantity of the food item, type, etc. the device will ring a buzzer to alert the user and also send a notification on the Smartphone. This will make arduous and complex cooking procedures simple for beginners. The user needs to plug or attach the device to the utensil in which they are going to cook the food item by simply clipping it on the wall of the vessel. Then, the user can select the parameters of the dish he/she wishes to cook from an app on the Smartphone. Then the magic of Internet-of-things comes into play. The device is all set to accomplish its task and will alert the user whenever the food requires attention, whether it is stirring in the middle, reducing the flame, or turning the gas off as the dish is ready. It will prevent unnecessary wastage of food due to getting burnt, prevent unfortunate fires in the household, and lead to a more comfortable experience for the user. Controlling the flame and auto-turning off of the gas stove is also an added feature that can be utilized as a part of cooking automation also allowing to remotely controlling the device. This device makes use of modern-day advanced technologies like the internet of things, automation, and furthermore like machine learning to make it more adaptable. The best part about the device as a solution to this frequent problem is not just its cost-effectiveness and simplicity of use, but sustainability and eco-friendliness. The device is powered by a small battery and does not consume a lot of power. It can further be made completely eco-friendly by removing the battery and using technology to draw power from the ambient heat around the stove which essentially gets wasted, enabling the use of a wasted energy form. It will be a great everyday product with all the households as target customers due to its price and ease of use.
Health
FPGA IMPLEMENTATION OF DEEP LEARNING MODEL FOR RADIOGRAPHIC EXAMINATION
AP057 »
The healthcare vertical today is patient centric and data driven with the advances in IOT and Artificial Intelligence. A need for early detection and diagnosis for any contiguous diseases or infections is required which is generally performed through radiographic analysis. Deep learning in the field of radiologic image processing reduces false-positive and negative errors in the detection and diagnosis of disease and offers a unique opportunity to provide fast, cheap, and safe diagnostic services to patients.
Deep learning has the potential to augment the use of chest radiography in clinical radiology, but challenges include poor generalizability, spectrum bias , and difficulty comparing across studies. Recently, several clinical applications of CNNs have been proposed and studied in radiology for classification, detection, and segmentation tasks. The Deep learning deterministic model predicts the radiograph into three classes such as Normal, Covid and Viral Pneumonia. The probabilistic model predicts the radiograph into Normal, Cardiomegaly, mass and other abnormalities using Class activation maps(CAM). The main objective is to develop a deep learning–based reconfigurable architecture that can classify normal and abnormal results from chest radiographs with major thoracic diseases including pulmonary malignant neoplasm, active tuberculosis, pneumonia, pneumo-thorax, covid-19 etc, and to validate the performance on Intel FPGA.
Other: Smart glass for visually impaired
Smart Text Detection Glass
AP067 »
The main aim of our "Smart Text Detection Glass" is to help visually impaired people to read the text. It provides the solution for them to complete even education despite all the difficulties faced by them. Our project is divided into four sections they are Text detection, Text recognition, Text to Speech Conversion and text translation. The task of our glass is to scan any text image and convert it into audio text so the audio can be listened to by the user through headphones. The technologies we are going to use in this project are OCR, Google Text to Speech. The text detection from images is done by using OpenCV and Optical Character Recognition with Tesseract OCR Engine.OpenCV is a real-time computer vision programming functioning library that is used for the digital image or video processing. Optical Character Recognition is the technology in which the Handwritten, typed or printed text into machine-encoded text. The OCR process involves five stages they are preprocessing, image segmentation, feature extraction, image classification and post-processing. We are also using an Efficient and Accurate Scene Text Detector (EAST) which uses Convolutional Neural Network Algorithm. Efficient And Accurate Scene Text Detector is a method and also simple and powerful technology that allows detecting a text in natural scenarios with high accuracy and efficiency. As we are converting the text to audio we are using Text to speech technology by using gTTS library. Now the text is translated to Tamil by using Google Translation Services Library. All the software part is done by using Python compiler. In that case, we are using IDLE with Python 3.9.7. We are embedding this software with hardware by using Raspberry PI Model 3b+. Our Prototype plan includes the glasses with a webcam and headphones. The Raspberry pi is fitted on the user's arm. The Raspberry pi is fitted with a push-button in it. When the user presses the push button the picture is captured in the webcam fitted in the glass frame. The image is then processed and text characters present in the image is extracted and then translated the text to Tamil . Finally the translated text is converted into audio output which will be heard in the headphones. In this work we planning to implement the EAST convolutional neural network algorithm in FPGA hardware as it involves more number of numerical calculations and it is time consuming one if it done in sequential manner. So we prefer FPGA to perform this task.
Food Related
Reduction in food wastage
AP071 »
IoT sensors are placed near areas of harvesting and making varieties of dishes that collect data like expiry date and temperature. This data can be sent to the cloud where it can be processed and the decision can be made on either to store the food or distribute it to the needy if the expiry date is approaching very soon.
Health
Sign Language Interpreter
AP072 »
It has two robotic hands along with arms which will work as a sign language interpreter. Sign language interpreter helps people to communicate with the people who are born deaf or have hearing loss. Over 5% of the world's population or 430 million people are facing hearing loss (432 million adults and 34 million children). It is estimated that by 2050 over 700 million people (one in every ten people) will have disabling hearing loss. So, it will be necessary to find ways to communicate with such people. Our sign language interpreter will be able to perform maximum 40-50 signs which includes words and greetings (i.e. hello, goodbye) on the basis of input given by mobile application. By this we can make communication between a normal person and a deaf person easier. For instance, a person wants to communicate with a person who can’t hear. And the person also don’t know the sign language. In that case, he/she can simply give its input to the mobile application and the robotic arm will communicate his/her message to the deaf by performing the sign language.
Smart City
Advanced Automatic Number Plate Recognition System
AP075 »
Recognizing the vehicle number plates is a much needed system. This system is already applicable in so many countries but we want to make some advancements in it. So here we are proposing a vehicle number plate recognition system that automatically recognizes vehicle number plates using image processing. We will set a zone or the area, when the vehicle is entered in the parking area the camera automatically detect its number plate. The camera will also detect the type of vehicle, make and model. This will serve as a new benchmark in security systems as well as automatic toll deduction mechanism.
Autonomous Vehicles
Adaptive Cruise Control
AP079 »
Our objective is to build an Adaptive cruise control (ACC) System that will help the vehicles to maintain a safe distance and to stay within the speed limit. This system will control the car's speed automatically that will assist the driver and make his involvement in driving the vehicle to minimum.
Other: Sustainable Agriculture
SGAIA - Smart Greenhouse Automation using IOT and AI
AP084 »
The proposed greenhouse system is a fully automated system that has all the measures for the functioning of a green house. The greenhouse’s main brain would be the Terasic DE10-Nano FPGA Cloud Connectivity Kit. It would be responsible for all the activities like collecting sensor data, gathering images for analysis of the growth of the plants, operate actuators and also to connect to Azure IOT cloud to send the sensor data. There would be many rows of plants in the greenhouse and each row of plants would be allotted for a specific crop type. The sensor network connected to the main board would be used to collect data like to receive sensing information, including environmental parameters and plant phenotyping data. There would various Analog Devices plug-in cards connected for collected temperature, humidity, pressure, light intensity, gas, soil moisture, pH values. All these values would be recorded using a data logger. The recorded data would be used for processing and post-data analysis. There would be over the head cameras connected to collect information on the growth of the plants using pattern recognition. All the analysis using deep learning will take place on the edge on the DE 10-Nano board. It would result in low communications costs and improved response times. The analysis results would be transmitted to the Azure IOT cloud services so as to make them available for the users through cloud dashboards and smartphone applications. There would be robotic sliding arm connected over the row of plants which would can used for various activities like seeding of the plant seeds, delivering minerals like NPK(Nitrogen-Phosphorus-Potassium) directly to the roots of the plants, analyzing the plants for diseases in them and also for the removal of weeds. The robot would be a gantry system with three degrees of freedom. The end effector of the gantry robot would be attached with types of actuators for uses like watering, nutrients delivering system. The whole farm would be inside a structure made of glass or fiber. The roofs of the greenhouse would be fitted with sun shades which can be operated on the basis of sunlight required. The structure would be fitted with ventilation fans to regulate the flow of air. The structure would help to control factors like excessive sunlight, pests and other external factors that might affect the growth of plants.
Food Related
Herbal plant recognition and monitoring
AP088 »
The project aims to recognize common herbal plant in the Philippine setting, both grown by gardeners and in the wild. Our project also aims to check the viability of the area for growing these herbs by monitoring how many are growing in the area/presence of clustering, plant part status such as checking leaf condition such as drooping and wilting, and presence and absence of invasive plants/animals that can affect the survival of the herb using computer vision. Other parameters such as humidity and sunlight will also be monitored using ADI boards provided. The data collected will be sent to Microsoft Azure platform where partner NGOs and stakeholders can view the data and insights generated to help make sound decisions that will benefit the community they're helping.
Marine Related
Biodiversity
AP092 »
I want to create an IoT based solution for the given SGP project Idea "Mauritius: improving Livelihoods of Communities- Oyster Farming for Jewelry Making in Rodrigues" which will collect all the problem statement mentioned data and share the data as well as specific high swell, theft warning through the azure cloud platform.
Autonomous Vehicles
Autonomous Pick & Place Robot
AP095 »
The idea of this project is to create an autonomous pick and place robot. This robot will do its movement through line follower robot using IR sensors. The main function of this robot is to pick an object and place it on a desired place; it will select its destination through QR code scanning e.g. there’s a box at X place and a rack at Y place, the robot will scan the QR code of the box at X place. After scanning, next step will be the selection of placing the carried object; robot will select its dropping place after going through the program designed in it using Arduino. Now that our path and dropping place has been selected, object will be placed at Y place as the scanned box belongs to it.
Other
Crisis
AP096 »
One of the major issues that we are facing today is overpopulation which results in food crisis