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* Deadline to register is October 31, 2021. Teams can still edit your proposals during judging period.
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.

Marine Related
Oyster Farm Autonomous Monitor

AS040 »

An oyster farm monitor system to detect when cleaning or repair is needed.

The Oyster Farm Autonomous Monitor project will consist of an anchored buoy system that will continuously monitor an oyster farm under the water using video analytics and other sensor data. The data, processed locally on the edge, will determine when oyster bed cleaning or repairs are due as well as produce alerts in the case of extreme swells or possible theft. Other sensor data such as pH level, water temperature, ambient temperature, and visibility are also added to the AI models to help produce future predictions well in advance of needed cleanings.

Mixing the team member's backgrounds of hardware engineering, marine biology, and consumer product architecture, The Educated Robot team has accepted the GEF SGP Biodiversity proposal of "Mauritius: Improving Livelihoods of Communities - Oyster Farming for Jewelry Making in Rodrigues" to present one solution to help our world. This unique use case is a perfect real world example highlighting the capabilities of edge computing using Intel Edge-centric FPGAs for advanced image processing. Analog Devices plug-in cards are used to gather additional diverse sensor data to be stored and analyzed using Microsoft Azure Cloud Services.

Water Related
Wastewater Detection

AS046 »

Our project will be based around wastewater detection in rivers and lakes. Specifically we will be testing in the Pittsburgh rivers region because in times of heavy rainfall, waste tends to end up in the rivers due to infrastructure problems. Our device will be able to tell the concentration of wastewater in the location where the sensor is placed.

Smart City
Blending Tech with TEK

AS047 »

As we move forward into a post-climate-change future, temperature regulation proves to be increasingly important for human survival and comfort, which makes up a significant portion of a building’s energy consumption. As such, our project focuses on improving the energy efficiency of temperature regulation in buildings through the use of Traditional Ecological Knowledge and smart-home design (with the help of the Azure cloud).
Ventilation, cooling, and heating account for 33% of energy use in U.S. commercial buildings, which relies upon conventional systems of cooling down air via refrigerants and pushing the air into rooms. However, there are different methods of cooling that utilize more passive properties of airflow. The Eastgate Centre in Zimbabwe is the biggest example of this.
The Eastgate Centre was designed with biomimicry in mind, to emulate the ability of termites to cool down and ventilate their nests while using less energy than conventional systems. It succeeds in this: despite still using high-powered fans, the Eastgate Centre uses only 10% of the energy compared to similarly-sized buildings using air conditioning, and uses 65% of the energy while actively cooling.
However, the Eastgate Centre’s design leaves some room for improvement, such as the usage of fans. Termites are able to do what they do by selectively opening and closing vents in the nest to maximize the flow of cool air. Our project improves upon the Eastgate design by more closely imitating the termite model and utilizing an FPGA-run system that uses actuator-operated windows to control airflow and further reduce energy consumption. It can accomplish this by sensing the temperature outside and inside of the building at the actuator locations, and opening and closing the windows accordingly. By opening the windows when it is cooler outside, the building takes advantage of cross-ventilation to passively draw in cool air without the use of fans. It will not be the same temperature on every side of the building; thus, the system opens the windows only at the specific sensor, and keeps it closed on the hotter sides.
An additional problem with both conventional AC and with the Eastgate Centre design is that they are reactive rather than predictive systems. Our design connects to the Azure cloud to obtain weather forecasts, which it then uses to pre-cool the building. This not only means the cooling system doesn’t work as hard during a specific period of time, but this also helps reduce energy use during an electric grid’s peak hours. It is also easier for the system to cool the building while it is still relatively cool than to do so when it has already become hot.
Overall, our system improves upon previous designs to reduce energy consumption when cooling buildings in three ways: by utilizing passive cross-ventilation as a cooling method, by taking into account the weather forecast, and by using the FPGA to monitor and manage all of these components together. By using an embedded system (the FPGA and the cloud) we are able to create a more proactive and energy efficient system than conventional AC systems.

Autonomous Vehicles
Gogreen 2020

AS002 »

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Water Related
Maji Safi Mazingira Safi - Clean water Clean environment

AS003 »

Nairobi city ; as was formally known as a place of cool waters gets its freshwater from 3 main rivers and dam ; Ngong river ,Nairobi river and Ndakaini dam.
These rivers pass through the industrialized and informal(slum areas) where polution is very rampard . One notorius area is the dandora dumbsite in the poorest slum area in the east .
The rivers are full of industrial chemicals , human wastes,used oil plastics etc .
Maji Safi Mazingira safi is a collaborative project between the Focuslense (intelligent imaging and mapping company) ,the local government environment agancy NEMA; National Environment Management Agency and local community to help reduce pollution ,identify the water pollutants ,chemicals ,plastics etc . The solution involves installation of water ph , chemical level and image (video ) monitoring systems along the river, especially where pollution is rampant.
an FPGA kit is used with remote sensors and cloud connectivity to record chemical and pollution levels in the water and transmit to Azure cloud IoT . Azure AI image and video analytics is also used to analyze sources of pollution.

Focuslense electronics is an engineering company established in 2016 to help tackle challanges affecting poor and marginalized communities around Nairobi ,using computer vision ,AI ,IoT and 3D printing technology .It consists of a team of 5 software,hardware engineers,data scientists and a consultaing city planner.

Industrial
High-Performance Bioinspired Binary Adder

AS004 »

Most of the innovations that use bioinspiration are capable of achieving high-performance in contrast with standard proposals. In this context, we propose a novel bioinspired binary adder to accelerate the latency of the binary addition at reduced cost. With this in mind, we designed a generic n-bit binary adder using VHDL hardware description language and terasic FPGAs. The simulation results have depicted that the proposed adder is faster than the LPM_ADD_SUB megafunction as n goes to infinity. Also, the simulations have demonstrated that the adder consumes less area resources than the megafunction. Since the binary addition is one of the most important operations carried out in the ALUs, this adder proposal will accelerate the digital processing of many digital applications.

Smart City
FPGA Catalyst

AS006 »

Not yet determined

Data Management
Cactus Fish

AS007 »

Utilizing Microsoft Azure to implement IoT solutions on DE10

Smart City
Project Watchdog

AS008 »

Project Watchdog is an FPGA-based smart home security camera. Existing solutions such as Google Nest and Amazon Ring require an internet connection and a monthly subscription to operate properly. They send the video to an external server and then perform all processing on that server, increasing bandwidth and latency. Furthermore, these devices are rendered useless without an internet connection. Watchdog will perform all video capture and analysis right on the device, regardless of an internet connection. The device will run inference using an AI model that has been trained to identify people and animals. When any people or animals are detected, that snippet will be uploaded to Azure cloud storage, for easy online access to these clips. The video footage will be stored on a micro-SD card on the FPGA board, which can be accessed if the full video needs to be viewed. Ultimately, Watchdog will perform identically to the state-of-the-art consumer solutions, but will use significantly less bandwidth and latency while not requiring an internet connection. The FPGA solution will be paired with a temperature sensor to provide a complete picture of the environment to the user.

Water Related
Pool Purity

AS010 »

Swimming Pools provide the perfect evaluation testbed for monitoring and control of water chemistry. PH and Chlorine levels must be monitored and controlled for the health and safety of the swimmers. Given the destabilizing effects of solar exposure, air temperature, and rainfall, additional chemistry supports stabilizing the Chlorine and PH levels. Additionally, pumps circulate the pool water through filters which impact energy consumption. Using a 28 thousand gallon pool, we intend to demonstrate the use of FPGA’s for data collection and Cloud computing for control and monitoring of water chemistry processes. Stretch goals will include using weather forecasts to anticipate and hopefully minimize both chemical and energy usage.
Because other water applications have similar issues, this work will apply to other consumers; people, livestock, and agriculture are prime examples.

Marine Related
Artun Özdemir

AS011 »

Disabled