Smart City
Solar Follower
AS028 »
A sensor based solar farm that can be installed on top of a house.
Health
Musconnect
AS029 »
Neuromuscular diseases are one of the most common and yet hardest to diagnose anomalies. Most neuromuscular diseases are diagnosed at a very late stage where no cure can help the patient.
But what if this can change? The goal of Musconnect is to monitor almost 100 muscles in a patient simultaneously and in real-time. This mass of data is gathered over a period of time using microelectrodes that are connected to a patient. The electrodes data is transmitted to the FPGA, processed using the MCU and uploaded and stored in Azure. Using data analysis, the device will be able to predict any deterioration in muscle activities far before any doctor or patient can realize.
Moreover, Due to the ability of the device to monitor 100 major muscles across a patient’s body, the diagnostic will be able to pinpoint the exact location of the muscle abnormalities.A doctor can then decide the best treatment option rather than going through several expensive and inaccurate scans.
Since muscle activities are saved on the cloud, a patient/doctor will be able to monitor improvements in the treatments by comparing muscle contractions before and after diagnosis.
This is a major step in the medical field that will help millions of people around the world with a high degree of accuracy.
Other: Disaster Prevention
Early Warning Alert System for Forest Fires
AS030 »
We are proposing a low-cost and low power wide-area sensor network that will quickly alert the authorities in case of a forest fire. The project will utilize a mesh network of low-power LoRa transmitter nodes connected to Temperature sensors. The network will be inactive until the temperature at any one node goes above a programmable specified limit, in which case the particular node will send an alert to the FPGA through the network. The FPGA will decide if the alert is real based on fire data modeling of the sensors and alert the authorities with the exact location of the fire.
Other: Monitoring fire in rural areas
Fangorn
AS031 »
Fangorn project aims to develop a drone surveillance system to detect and monitor outbreaks of forest fires.A camera with an infrared sensor and some sensors (temperature, smoke, pressure) will be used to monitor and capture important data, which will be used in conjunction with computer vision techniques and the power of FPGA multiprocessing to classify whether or not there is a focus of fire in the area.
Smart City
Fast Image Deblurring Reconstruction using Generative Adversarial Networks
AS032 »
Deblurring is the process of removing blurring entities from the image. In recent times, with the advent of machine learning there has been tremendous effort from the research community to come up with new deblurring techniques. However, the state-of-the-art deblurring technique still takes hours of time to construct proper deblurring effect. Therefore, in this project the objective is to construct proper deblurring image instantly. In order to accomplish that we will be using Generative Adversarial Networks (GAN). We have come up with a solution to speedup the GAN training. We will be deploying our solution into the cloud connectivity kit and also make use of Microsoft Azure, in order to generate accelerated deblurring image reconstruction.
Our project will have multiple applications starting from Smart City, Autonomous Vehicles, Industrial etc, as it involves creating proper visible images from blurring entities.
Food Related
Soil quality smart monitoring
AS033 »
Making use of Analog Device's EVAL-CN0398-ARDZ board, Intel's cloud connectivity kit, and Azure's IoT services, build a smart station to measure and analize various parameters related to soil quality for smart agriculture. Providing hardware and software solutions for a complete system.
Smart City
Smart Trashcan
AS034 »
The practice of recycling helps reduce pollution, greenhouse gas emissions, and the amount of waste that is disposed in landfills. The majority of Americans unfortunately does not embrace the three Rs (reduce, reuse, recycle). The lack of adequate knowledge for sorting and recycling materials is one of the biggest barriers to being green. Recycling is a behavior that can be improved through technology. This proposed project is centered around the creation of a smart trash can prototype designed to create awareness among students at Queens University and in its neighboring community on the importance of correctly sorting waste items. The smart-trash can has both a hardware and a software component. The project will specifically focused on developing a working prototype and deep learning (DL) model using the Intel FPGA Cloud Connectivity kit in combination with Microsoft Azure IOT. The model is able to correctly classify different types of disposable and recyclable food service items (paper cups, paper boxes, paper trays, food containers, etc.) commonly found in the Queens University’s cafeteria and around campus. The classification is used by the hardware to provide a visual prompt to indicate the bin for a particular waste item. This can lead to improving the process of pre-sorting recyclable materials once the smart trash can is fully deployed on campus.
Health
Implementation of Deep Learning Based Neural Network Algorithm for Intracranial Hemorrhage Detection
AS035 »
This project introduces the concept of deep learning based automatic intracranial hemorrhage recognition & detection from CT Scans images dataset. Purpose of this is to remove role of man from detection purpose to increase efficiency and save time and manpower. CT Scans are used widely in medical for detecting the type of hemorrhage etc.
The task of detecting and identifying hemorrhage is very sensitive as it is a serious health problem requiring often intensive medical treatment. It requires very high precision and accuracy in very short time. Several techniques are available for detection but this project will focus on training a deep learning convolutional neural network model that is trained to detect images and identify the type of hemorrhage. CNNs are more useful because they automatically extract features of targets unlike machine learning algorithms.
Intracranial hemorrhage, bleeding that occurs inside the cranium, is a serious health problem requiring rapid and often intensive medical treatment.
Diagnosis requires an urgent procedure. When a patient shows acute neurological symptoms such as severe headache or loss of consciousness, highly trained specialists review medical images of the patient’s cranium to look for the presence, location and type of hemorrhage. The process is complicated and often time consuming.
We will implement a new algorithm on CT Scans dataset for detecting the Hemorrhage to increase the efficiency of the output results and we will also make it fast enough to give the output in short time in order to save the life of patient.
We will use machine learning and deep learning techniques and different image processing methods to identify the types of hemorrhage. We will use different image identification and processing algorithms, if required the combination of different Neural network and image processing algorithms like YOLO, Faster RCNN and Tensorflows and Keras etc. will be used in order to increase the efficiency of the output results.
Health
FPGA based Body Area Anomaly Detection System Design for Healthcare
AS036 »
Currently, wireless communication network applications are becoming part of our daily life that cannot be ignored. One of the most important is healthcare application. Physiological and health condition of a person can be detected, processed, transported, and stored easily in (wireless) body area network (W)(BAN). WBANs have several applications including in sports, interactive gaming, military, and security. For instance, aged people show larger dependence on the healthcare system because of age related diseases like cardiac ailments, respiratory problems, arthritis, neurological diseases, and dementia. According to World Health Organization (WHO), by 2047 people 60 and above will be 2 billion, up from 841 million in 2008. According to the US Bureau of the Census, in US alone elderly people are expected to 70 million by 2025, the healthcare expenditure is about $5.4 trillion, which will represent 20% of the GDP. Hence, smart and interactive wireless healthcare system can leverage both the health and economic issues of users.
The three-tier communication architecture of WBAN consists of, (i) BAN node: each node is integrated with biosensors (ECG, SpO2, temperature, etc.) to record patient’s dynamic body parameters and movements; (ii) LPU (Local Processing Unit): to gather data from BSNs and provide to the physicians. It also a router between BAN nodes and the central server using Bluetooth and Wi-Fi for short range and mobile networks for long-range transmission. (iii) back-end infrastructure: which consists of (a) CS (Central Sever), which feeds the patient data to the PD (Patient Database) and (b) Physician Workstation.
The challenges in WBAN are, reliable data transmission, node mobility support and fast event detection, timely delivery of data, power management, and security. Medical data networks are increasingly exposed to external attacks. Safety and privacy of medical data must be guaranteed all the way from the sensor nodes to the back-end services. On the other hand, energy efficiency issue exists at different sensor nodes, and communication and data processing subsystems.
This work focuses on developing a new FPGA based body area anomaly detection system using machine learning techniques by training the behavioral change of body area environment (i.e., indoor and outdoor). Because FPGA is an integrated circuit that contains a large resource of logic gates and memory, it is possible to implement parallel digital computation and executions. This leads to low latency and minimum energy consumption. Hence, we propose system on Chip (SoC) (i.e., Microcontroller Unit (MCU), Central Processing Unit (CPU), and FPGA) at both ends of WBAN system. It enables a wide range of healthcare applications such as ubiquitous health monitoring (UHM), computer assisted rehabilitation, emergency medical response system (EMRS), and promoting healthy living styles.
The objective of this work is to design an energy efficient and secure WBAN for pervasive healthcare system, which reduce patients visit to hospitals. Specifically, to transmit and store medical data securely keeping the privacy, authenticity, availability, and integrity of medical data at the three layers of the WBAN architecture, and to model a power efficient WBAN during data processing and transmission.
Smart City
Smart off sensor
AS037 »
To be able to determine with a higher likelihood when to be be able to turn off electronics, lights, set air conditioning higher and not to interrupt someone at home simultaneously.
Autonomous Vehicles
Drone package delivery safety in turbulent atomospheric conditions in confined areas like cities
AS038 »
We believe the wide adoption of drones to replace current CO2 emitting delivery vans will contribute to a significant reduction of carbon additions to the atmosphere. However, if drones are not able to be adopted safely this valuable reduction of carbon emissions will not be realized. Our project combines the technologies of an FPGA, embedded processor, analog sensors and cloud communications to enable the wide and safe adoption of drones to replace traditional CO2 emitting delivery systems.
We will demonstrate an FPGA + Processor + Sensor technology that enables a scout drone to detect atmospheric upsets such as turbulence generated around buildings in windy conditions or city thermals. Atmospheric upsets of drones can cause drone loss of control (LOC), collisions between drones, and collisions with buildings or people. The scout demonstration will send near real-time turbulence location data via the cloud to cargo drones to ensure safe delivery of packages with a reduced hazard to third parties.
The key technology of turbulence and upset detection for prevention of LOC has already been developed by Foale Aerospace Inc and has been developed as a solar powered sensor system that can be attached to flying vehicles without aircraft wiring or integration. We were awarded 3rd prize by the Experimental Aircraft Association Founders Prize competition to produce solutions to prevent Loss of Control, by expert judges at Air Venture 2021 at Oshkosh, Wisconsin.
We propose to add FPGA signal processing to improve performance, reduce detection times and reduce false positive signals from our system. We propose a cloud interface will allow near real-time (a few seconds latency) hazardous conditions detected by a light scout drone to change the flight path of a cargo drone and prevent a hazardous or unsafe outcome.
We have experience in Verilog, Quartus and Modelsim targeting a Terasic DE0-Nano with a Raspberry Pi Zero processor interface written in Python, as well as Yosys/Arachne-pnr/IceStorm toolchain for Trenz Icezero iCE40 FPGA. We have 30 years of programming experience with C and C++. We will use this experience to breadboard a flight system onto the Terasic-Intel-Analog Devices-Microsoft InnovateFPGA platform. Real flight data recorded during light drone aircraft flights in turbulent and calm conditions will be used to demonstrate the identification of atmospheric conditions that cause aircraft upsets using the InnovateFPGA platform as if it were mounted on a scout drone. Communication hazard bulletins via the cloud to the cargo drone will be demonstrated via a wi-fi link to a raspberry pi based processor mounted on a wheeled rover, to demonstrate hazard bulletin reception and responsive action by a cargo drone in flight.
Water Related
River Guardian
AS039 »
The high level of river pollution in industrial and metropolitan environments negatively impacts the ecosystem and raises the government's cost of maintenance and cleaning of those areas. A significant number of rivers, though, are never or rarely cleaned since there is not enough data about their pollution level, nor where the garbage foci are. Hence, a significant portion of all the river waste worldwide is never discovered. It remains unattended, increasing environmental degradation and furthering the impunity of bad actors that pollute rivers without having their actions put to judgment.
We propose a river waste monitoring system composed of an UAV equipped with an image capturing and processing device based on a FPGA. The proposed system also includes support stations with solar panels that will send the collected data to a cloud application while also sending data back to the UAV about its energy status.
The UAV will be capable of flying over the river waters and the riverbank in search of waste. It will also have a small compartment onboard, that will be used to collect water samples, which will then be sent back to a support station for testing.
The support stations will aid the UAVs by collecting solar energy to charge them and analyzing the water collected by the UAVs.
The cloud application will have a dashboard that will display the garbage accumulation spots on a map alongside pictures of the trash and historical data.
The FPGA is an essential part of the system because it will provide the computing power and precision needed for the obstacle and waste recognition algorithm to work in real-time while also being energy-efficient.
The project's expected outcome is to have a relatively low-cost, self-sustainable autonomous system that can be easily deployed on various rivers and efficiently map the river and riverbank area for garbage accumulation spots while also assessing the water quality. This system will provide the local government and agencies with real-time and detailed data about the river's health and waste accumulation spots. Furthermore, the data gathered will be valuable in policies and efforts to restore the river's condition and educate the local community about correct waste disposal practices and other ways of ensuring the nearby river's well-being.