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.
Smart City
Agrocision ( Agriculture and precision)
EM026 »
PROBLEM
Farmers are receiving less yields than expected because they are subject to the unpredictable climatic changes which is resulting in Zimbabwe importing food and slowly losing its bread basket status.
SOLUTION
Agrocision offers farm management using information technology to ensure that plants, animals and the soil receive exactly what they need to foster optimum health and productivity through analyzing and reacting to data gathered from inter and intra-field variability of crops.
Target Market
Large Scale and Small-scale Farmers in Zimbabwe.
Industrial Effectiveness
Agrocision will help the government in implementing industry 5.0 in the farming sector as the system uses Internet of Things (IOT) and it will also be effective in paving way for Smart Cities.
Economic Feasibility
Agrocision will be available in two modes, outright purchase and subscription to curter for everyone.
Approximate Budget for one unit: USD $1500
Expected Breakeven: After 20 units
Expected Timeline: 2 Months
Smart City
NDVI device for building building monitoring
EM031 »
We have developed a platform to measure and visualize Normal Difference Vegetation indices using a low cost Camera and a DE2 board. This device was tested on building facades showing building moisture that can be related to the inhabitant health. In a different project we have studied around 17000 buildings in Beirut to build and energy model, the simulation was run on Azure. In this proposal we try to merge the two concepts/techniques.
Smart City
Waste Management Monitoring System
AP103 »
This project is about a smart bin that can detect whether the bin is full or not. The status of each bin will be informed to the collector so that he can collect the bins which are full according to the sensor data.
Smart City
IoT Pollution Box System with ML Processing
AP109 »
With climate change showing real and measurable effect on our daily lives with increase in extreme natural calamities, planning for a sustainable future has become a nessasity. To solve a problem of this scale, one needs to understand it fully first. To understand the extent of our effect on a climate a easy to use and distribute measuring units will be crucial. We will be tacking this problem.
A end-to-end Pollution Detection system with Machine Learning Predictions will be designed. It will consists of two major part. Client-Side Pollution Box which will be compact and fully integrated with various sensors and modules to detect Air, Water, Light, Noise Pollution Parameters and Server-Side Cloud Processing Center which will use ML based systems to find patterns and correlations between various pollution parameter and how they are affected with weather conditions. The project will provide a easy to replicate system which can be used by concerned authorities in the Metropolitan areas to monitor and curb pollution on a real-time basis.
Pollution Box will contain a Camera, Microphone, Air Quality and Gas Sensor, Water TDS and Turbidity sensor, which will be used to create a Holistic Pollution Parameter which will be used to build a fully contained pollution index.
Camera will be used to scan the night sky and provide a light pollution metric which can be used to further plan the areas street light consumption. Microphone will be used to provide a all-day look at the noise pollution and can issue mental health warning if exceeding the researched paramter. Air Quality and Gas Sensors can be used to provide a accurate Air Quality Index. Whereas resistivity based TDS calculation can identify increase in toxicity and salinty of water on a real-time basis.
Making the Pollution box compact and easy to use will make it easier for the authorities to make a mesh of these IoT enabled boxes that can give a better resolution in detecting the problem areas and solving the issue at a larger scale and thus ensuring the sustainble future.
At the server end the data will be processed and Machine Learning based program will find patterns and correlation between different parameters to further our understanding on the effects of pollution.
Smart City
Dumpyard Gas Monitoring System (DuGaMoS)
AP123 »
Existence of Toxic gases in huge dump yards and landfills has become a major concern in urban pockets. It leads to a lot of health issues, environment pollution and overall ecosystem damage. In order to address this problem we have come up with a solution of identifying or detecting the prevalence of toxic gases in these landfills. We propose to identify poisonous gases like Methane, Hydrogen Sulphide, Carbon Monoxide, Ammonia etc through an array of gas sensors integrated with Intel FPGA as the processor. Our target segment will be huge landfills and dump yards. We prefer to use Intel FPGA due to its parallel processing capabilities, extendable interfaces with several I/O ports, and high performance computing facility even with complex algorithms.
Smart City
Efficient method for automatic detection of door through sobel edge detector using fpga
AP126 »
Image Processing in its general form pertains to the alteration and analysis of pictorial information. The objective of image processing is to visually enhance or statistically evaluate some aspect of an Image not readily apparent in its original form. This processing is used for convenience in order to reduce the complexity faced during the operations performed on an image. Edge detection is one such branch of image processing used to detect the edges of the objects in a picture by calculation the difference in brightness of that edge pixel with its surrounding pixels using gradient method. In this project, Sobel operator is used as a filter for detection of edges of projection of a door without further increasing the already complex process of image processing. This is done using MATLAB, Sobel filter and FPGA.
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.
Smart City
FPGA Catalyst
AS006 »
Not yet determined
Smart City
(Panotti's Ears) Real time Voice Analytics Co-processor
AP007 »
Speech recognition models have been used extensively on various platforms to provide ease of use, digital smart assistance, and hands-free control. At the cutting edge of this technology, the use of hidden Markov models is common. To improve the computational efficiency of hidden Markov model-based speech recognition systems, various techniques are used, amongst which the Viterbi beam search algorithm is one of the best. However, for large vocabulary speech recognition models with larger beam widths, the beam search algorithm’s sparse matrix operations create a highly constrictive bottleneck. Traditionally GPUs have been used to accelerate such models but with the algorithm's not so parallel nature, GPUs don’t provide an efficient solution and power constraints of IOT devices completely rule them out for Edge level.In this project, we research and formulate an FPGA based Co-processor (RTL level abstraction) to accelerate sparse matrix operation of the beam search algorithm so it can be used on edge devices to revolutionize how we interact with IOT edge level devices.
Smart City
Model-Predictive-Traffic-Mangement
AP010 »
We are proposing to build a hardware platform that's able to identify individual vehicles in real-time. The device will be connected to a real-time traffic model that's run on the cloud. Then by placing this device at a strategic location in a city, over time, we would be able to build a very accurate predictive traffic model.
Overall, we expect our project (hardware platform together with the back-end cloud-based software) will provide much better tools for city authorities to manage traffic than what's available today.
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.