Smart City
Automatic recycling system for residential units
AP034 »
Our team introduces an automatic classification system for wastes.
If garbage is not classified, it can cause environmental problems, and if it is classified well, resources can be saved through recycling.
Our system can classify not only good-conditioned garbage (plastic, cans, bottles, etc.), but also bad-conditioned garbage.
Data Management
Image and Video Upscaling and Downscaling using FPGA
AP035 »
With time the rate at which we are producing data is increasing at a very tremendous rate. And it seems that this trend will continue with our advancements in technologies and user requirements. One of the big portions of the world’s overall data transmission and data storage is our Videos and Images. Security and surveillance, entertainment, streaming, and roughly every other industry use this kind of data for their applications and the demand for data is increasing than ever. But this increasing demand for data causes two major (global) issues: first, during transmission, they can take a lot of bandwidth of our network, and second that they tend to take a lot of storage space since we need a lot of data points to effectively utilize them for our needs.
Here, this project will be showing the downscaling and upscaling algorithms that are already used in many applications for quite a few years and implementing these algorithms on FPGA and cloud effectively to get the best possible results. Initially, data can be taken from a low-resolution input(or can be downscaled) and then transmitted/stored. When we have to use it, upscaling can be done. These algorithms show very promising results while using fewer resources as to if directly high-resolution input be used from the beginning.
This will give us more bandwidth and storage to work with for our applications, and as computation will be done on FPGA, it will be easily scalable. Edge computing like this will effectively increase productivity and will help in a sustainable advancement in technology. For the continuous advancement in technology and sustainable growth, we have to use our resources efficiently and intelligently and I hope that this project can play a small part in this.
Health
FPGA based Covid-19 detection using Lung Ultrasound Image
AP037 »
With the onset of the Covid-19 pandemic, there has been a tremendous impact on the lives of people globally. The global tally of the no. of infected cases is 229,293,200 and the total death toll is 4,705,498 as of September 20, 2021, and this is just the no. of accounted cases. Apart from this, the covid-19 genome sequence is continuously mutating which resulted in the generation of different and more dangerous types of variants like the delta and delta plus to name a few. The globe witnessed the horrific scenes caused due to the shortage of resources in terms of healthcare during the unprecedented first and second waves. The leading scientists have already predicted the onset of the third wave which is expected to start in the last quarter of the year 2021.
To tackle the third wave, the governments have started the vaccination campaign for the people. But still, as per the earlier predictions the third wave is inevitable and the third world countries that are lagging in terms of medical infrastructure would be affected the most in the oncoming third wave. Reverse Transcription-Polymerase Chain Reaction (RT-PCR) is considered as the standard reference for the Covid-19 based testing but it has certain disadvantages like higher testing costs and longer processing time for the collected samples. Considering the factors like lack of essential infrastructure required for the testing and the capital required, we are hereby proposing a novel approach as a preparatory measure for detecting the presence of Covid-19 using Lung Ultrasound Imaging which in turn can be further deployed on a real-time basis with the help of Intel FPGA Cloud Connectivity Kit and Azure IoT Suite.
Compared to CT scans and X-Ray based detections, Ultrasound Imaging is low cost and radiation-free detection method. In this proposed approach, we would conduct a thorough literature review on the existing deep learning models which has been developed for predicting the covid-19. Based on the earlier approaches, a new model would be proposed which will be extensively trained with available datasets for addressing the limitations offered by the earlier models. Upon the satisfactory performance of the model in terms of accuracy, precision, and computation time, it would be deployed onto the FPGA Cloud Connectivity kit for real-time application. With the help of Azure IoT support, the Ultrasound Images from different centers can be obtained and the test results can be again diverted back to the respective centers at a significantly less amount of time as compared to the RT-PCR test.
This proposed approach will help set up a nationwide/worldwide low-cost testing facility with a significantly less amount of capital being invested. With this approach, we are intending to address the two major challenges prevailing in the present scenario with the first one being high testing costs and improved timing for generating the test results. This approach would in turn be helpful to combat the oncoming third wave of this Covid-19 global pandemic.
Health
FPGA for Healthcare and Wellness
AP041 »
Real-time analysis of medical diagnostics using AI is crucial in healthcare systems. Advanced sensors with deep learning networks need to analyze the diagnostics in set time constraints. The need for high-speed real-time systems is imperative. Generic computer architecture slows down this process. Using low-latency networks with FPGAs will decrease the analysis time by reducing idle cycles, and working on resource utilization. Digital image processing (DIPs) fares better on FPGAs.
Through time, FPGAs are increasingly being used for computationally intensive tasks. Image processing is one such task. To improve the performance of Digital Image Processing systems, it is necessary to implement them on hardware instead of software. FPGAs are inherently good in parallel processing because of the architecture. Incidentally, image processing tasks like feature detection and extractions are highly parallelizable-which makes FPGAs the ideal candidates for this task. We have seen how the healthcare systems had been overwhelmed during the pandemic. Early detection plays a crucial role during the onslaught of highly contagious diseases. Containment and early diagnosis can reduce the losses inflicted during a pandemic. Using the FPGAs and cloud storage, the team can create a robust image detection system to detect the presence of the disease, and use the cloud to distribute relevant information to the concerned doctors. Image detection with CT, MRI, X-Ray scans helps in detecting the disease earlier instead of later. The same image detecting algorithm can be used for similar respiratory diseases that have overlapping symptoms, with minimal changes. Additionally, the cloud ensures that data from across the world is shared with the relevant specialists-removing barriers in healthcare. The accelerometers and sensors will be used to get general information about the body which is important for overall health; for example- temperature sensors to detect temperature, accelerometers for gait analysis and mobility(mobility and gait are important factors to ensure the health and well-being of older patients- fall risk assessment and balance evaluation being a few examples) etc. All the sensors will be used to monitor important aspects of the patient’s health.
This project is a step towards a broad spectrum well-being platform for patients from all walks of life, making healthcare more accessible among the masses.
Smart City
Realtime energy monitoring and reporting
AP043 »
This project aims to develop an always-on device that measures raw energy usage at the ingress power line (current, voltage), processing of this realtime data (PF etc.) and transmitting the processed data via WiFi to the internet or locally.
Scope is as below,
FPGA:
- Code that collects and process raw values from ADC.
- Code that interfaces with the ESP32
ESP32:
- Code that interfaces with the cloud
Server backend:
- Code that runs on the cloud that collects data from these ESP32s.
Frontend:
- Simple dashboard that displays current and historical energy usage.
Hardware scope is as bellow:
1. Analog interfacing circuit that interfaces the FPGA to the powerline.
Other: Internet of Things : Home/Industrial Automation and Security system on FPGA with hardware acceleration
AI based Smart Home Assistant and Security System
AP045 »
With the rapid emergence of IoT-based technologies in the recent era, the need for high-performance edge computing to handle data and run AI-based algorithms has increased. The objective of moving the computation from the cloud to the edge devices for compute-intensive tasks, provides increased reconfigurability, low latency, and scalability. In this project, we use Intel’s DE-10 Nano SoC FPGA, by leveraging the ARM Processor to run Linux Server, Docker containers, etc., required to host the IoT system for the premise, we offload the AI-based object detection task from the HPC to the FPGA to accelerate it. The system design uses the ARM Processor to run the Linux Server and connect to the home network hosting the Home-assistant framework, NodeRED, Grafana, and other services. On the other hand, every switch box in the house is equipped with a Wi-Fi-based Custom ESP8266 based Relay switches running Tasmota firmware which is connected to the same network. Similar edge deployments are made for getting temperature, water level in tanks, etc., All these data are communicated to the Home Assistant server running on the HPC. Using Azure Event hub integration we send the data to the Azure IoT Hub. The security system consists of CCTV Cameras which are connected to DVR and using ONVIF the stream is captured in our application for object detection and processing. We use the FPGA for Accelerating the YOLOv2 object detection algorithm. We process the results and in the event of an intruder/vehicle/motion detection, the necessary alarms/lightings/or any other actions like email/SMS/mobile alerts, etc. can be set up according to the end-user requirements. The use of FPGA in this kind of security system allows to train new data sets for uses cases like but not limited to automatic distinguished detection of house members to perform actions like opening the garage doors/preventing false-positive security alarms/ lighting based on motion detection etc., We believe that by using SoC FPGAs especially DE-10 Nano, along with Microsoft Azure IoT, we would be able to accomplish a reconfigurable, scalable and low-cost IoT setup that can be deployed in real-time.
Water Related
Smart Water Conservation System
AP046 »
The project is aimed to improve water distributor's capacity so everyone can use water even during dry and drought season.
Food Related
Smart and Safe Livestock Farm Monitoring System
AP047 »
The project aims to provide real time counting and monitoring of poultry or livestock which are freed to graze on the farm's grass. Using FPGAs parallel execution, the system will be able to automate the farm operations. With image processing, the farm will also identify and alarm for any predators coming inside the farm such as snakes, lizards or wolves. The system will connect to Microsoft Azure where it sends those head count for real time information to the owner. The system will also connect to cloud services to get the weather conditions and keep the farm animals safe.
Water Related
Sustainable Aquaphonics
AP048 »
Singapore is a highly industrialized country which is one of the top countries with high population density. Trees and vegetation can be found in every block but is limited in every multi-storey residential building like HDBs and Condominiums. The projects aims to promote local hdb/condo residents to implement and improve their existing aquariums into a sustainable aquaponics which helps to reduce the carbon footprint and reduce the water usage.
Autonomous Vehicles
Enhancing Vehicular Safety Using Cloud Based IOT
AP051 »
In this project we propose to build smart vehicle monitoring and assistance systems using cloud computing in vehicular Ad Hoc networks. Increasing number of on road vehicles has become a major source of unintended accidents. Developing intelligent transportation system using cloud based connected vehicular networks can provide better estimate of time of arrival and localization matrix of vehicles in a given range of interest. We propose to build a safety enhancement feature to cater to accidents that occur due to random stoppage of vehicles and random opening of doors. Sensors will be used to detect and estimate events related to stoppage and door opening and create an event token. This event token will be correlated with the time of arrival and localization matrix of vehicles in a given radius of the vehicle from where the token was generated. Based upon this correlation a warning system and door enable disable system will be implemented to avoid collision. Additionally, temperature and humidity sensors will be deployed inside the vehicle to automatically or partially open the windows for maintaining proper air flow and ventilation.
Other: Agriculture
Pursuit Futurology in Smart Agriculture using FPGA
AP052 »
The proposed system is a system which will closely monitor the parameters of a field on a regular basis round the clock for cultivation of crops or specific plant species which could maximize their production over the whole crop growth season and to eliminate the difficulties involved in the system by reducing human intervention to the best possible extent.
We mainly have Two Blocks for this Project:
a. HPS (Hard Processor System):
Our system will take multi-inputs from multiple sensors such as humidity, light intensity, temperature and IR sensor and give those parameters as input to the HPS automatic training module (as initial values). For certain time the process of taking multiple input will be repeated over time. These will be an input to the HPS. Post processing the data will give as input to the FPGA part.
b. FPGA (Field Programmable Gate Array (Terasic DE10-Nano FPGA)):
By taking the input data in HPS as input to the FPGA part of the board, the system will pre-process the data. Then, it compares both the data and make the result. The result will be sent to the display as a message
Our main aim of the Digital Farming is to improve agricultural yield and reduce potential environmental risks, while benefits are:
1. Water level sensing.
2. Atmospheric temperature sensing.
3. Atmospheric humidity sensing.
4. Detection of pest in the field and automatically start the pest repeller if pest is detected using IR sensor.
5. Suggest best measures to be taken by the farmer.
1. Water Level Sensing:
An advanced water level sensing sensor will be fixed in the field for monitoring the water level. Based on the information given to the system, our system will alert the farmer along with the water level percentage through message.
2. Atmospheric Temperature sensing:
A temperature sensing device will be fixed at the field for checking the temperature of the present atmosphere. Our employed FPGA will decide whether the atmospheric temperature is sufficient for field growth or not and send the information through message.
3. Atmospheric Humidity sensing:
An atmospheric humidity sensing sensor will be placed at the field for checking the humidity present in the atmosphere. The output of the sensor will be given as input to FPGA for checking humidity is high or normal for crops growing. Our system will send this Atmospheric humidity level to the farmer through message.
4. Detection of pests such as rodents:
Here using IR sensors detects the entry and exit of unwanted pests such as rodents.
5. Diagnosis and Suggest some measure to be taken by farmer:
After detecting the sensor inputs from the field, our system will send the actions to be taken and suggest some precautionary measures.
This project is divided into four phases: -
Identifying the appropriate sensor for measuring temperature and relative humidity. Temperature sensor to be used is RTD so that low-cost aim can be successful with best stability.
Design of controller using FPGA, sensor interface card, isolation circuit for input and output, output relay card.
Identify if there any pests enter into the premisses/field and take the necessary action if so.
Development of a user interface and the controlling software.
Since a FPGA(Terasic DE10-Nano FPGA) is used as the heart of the system, it makes the set-up low-cost and effective nevertheless. As the system also employs an LCD display for continuously alerting the user about the condition inside the greenhouse, the entire set-up becomes user friendly.
Food Related
Farm Management System
AP053 »
The project aims at developing an efficient farm management system that would prove to be resourceful in identifying pests on crops, report the nutrient and moisture content of soil, and accordingly irrigate the farm. For an efficient implementation, the system is integrated in a moving bot whose brain is the DE-10 Nano Cyclone V SoC FPGA board. For the time being, we are implementing our design on kharif crops, which can be further expanded to include rabi crops as well after suitable changes. For an efficient implementation, our project has further sub divisions as controlling the wheels of the bot, path tracking, analyzing the nutrients of the soil, image processing for detecting pests and irrigation system.