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* Deadline to register is October 31, 2021. Teams can still edit your proposals during judging period.
Other: SGP - Biodiversity
MEDICINAL PLANT PLUCKER

AP094 »

Plants are considered as one of the greatest assets in the field of Indian science of medicine called Ayurveda.Some plants have it's medicinal values apart from serving as the source of food.The innovation in the allopathic medicines has degraded the significance of these therapeutic plants. People fail to have their medication at their doorstep instead went behind the fastest cure unaware of its side effects.

The main reasons are extinction of medicinal plants and lack of knowledge about identifying medicinal plants among the normal one's.Plant from the basis of Ayurveda and today's modern day medicines are great source of income.Due to cutting of forests,lot of medicinal plants have almost become extinct.Because of Ecosystem is the major part of Biodiversity,Where plants plays a crucial role. So we have to replant the extincting medicinal plants to improve our Ecosystem And to do not disturb Biodiversity.

So there is an immediate need for us to identify medicinal plants and replant them for next generations.Medicinal plants identification by manually means often leads to incorrect identification.

This project aims at implementing a System which can be able to identify some medicinal plants and Plucking of plants if they are medicinal.

Health
A Portable Device for Detection of Foot-and-Mouth Disease in Livestock

AP118 »

Design and Development of Machine-Learning Based Portable IoT-Device for Clinical Detection of Foot-and-mouth disease in Livestock

Often livestock in farms become affected by the Foot-and-Mouth Disease (FMD) due to poor maintenance. This is a highly infectious and sometimes fatal viral disease and poses serious issue to animal farming. The transmission of the FMD virus is possible before an animal has apparent signs of disease, and this aspect of low-detectability at early stages increases the risk of significant spread of the virus before an outbreak is detected. Further the FMD virus can be transmitted in many ways, including close-contact, animal-to-animal spread, and the carriers can be even inanimate objects, such as fodder and motor vehicles. The incubation period for FMD is 14 days. It is reported to be 1 to 12 days in sheep, with most infections appearing in 2 to 8 days; 2 to 14 days in cattle; and usually 2 days or more in pigs, with some experiments reporting clinical signs in as little as 18 to 24 hours [1]. This disease is still prevalent and the current approach to control the spread by large-scale culling results in adverse economic and environmental effects and is traumatic for farm workers and the public. The first major FMD outbreak in the U.K. in 2001 is estimated to have cost the country at least $6 billion, and another major outbreak happened in South Korea in 2010/11, with an estimated impact of about $8 billion. Though the livestock can be protected against FMD by vaccination, non-endemic countries do not vaccinate for FMD preemptively because of the cost of vaccination. However, they must be prepared to act at the first sign of infection. Hence, early detection of clinical signs of FMD is very critical.

We propose to design and develop a portable IoT device based on machine learning (ML) algorithm using FPGA platform that can be used in the preliminary investigation of clinical signs due to FMD. The typical clinical sign is the occurrence of blisters on the nose, tongue, or lips, inside the oral cavity, between the toes, above the hooves, on the teats and at pressure points on the skin. For training the ML model, currently available images of clinical signs of FMD and the corresponding diagnosis will be used for classification. After the model is trained, the portable device can be taken to the farm, and the images of the clinical signs of the suspected animal can be captured. Then the device will classify the diagnosis with a confidence score. Based on this, further investigations, if required, can be taken up.

[1] OIE Technical Disease Card: Foot and mouth disease, World Organization for Animal Health, Sep. 2021. [online]. Available: https://www.oie.int/app/uploads/2021/09/foot-and-mouth-disease-1.pdf

Other: Wildlife and forest preservation
iFireFighter

AP006 »

Humanity is currently facing a very major problem, something that has the potential to drastically reduce our population and ruin the lives of our future generations: Climate Change. Due to increased intensity of climate change and lack of any meaningful effort to tackle it, the corresponding problems that accompany the phenomenon of climate change are worsening year by year.

One of these problems that has set the world ablaze are forest fires. The frequency, intensity, and area affected of forest fires are steadily increasing every year. It’s like every year the California wildfires or the Australian bushfires are more intense and cause more damage than the last year’s.

Once a fire has reached critical mass and spread beyond a certain limit, it’s extremely costly, time consuming, and takes a lot of manpower and effort to get it under the control. The natural remedy for tackling this issue is to nip the problem in the bud before it has a chance to blossom.

All major wildfires and forest fires start from a much smaller localized fire that once they reach a critical mass, grow out of control. Our project proposes to detect and alert the relevant authorities about these localized fires before they grow out of control.

Fighting a fire after it has grown past critical mass is extremely costly. From the equipment to the resources to the manpower and personnel, along with the potential for an immense loss of human life, a lot of money must be thrown at the problem to get the fire back under control.

Our project would reduce these costs massively since the focus would then shift from getting a raging uncontrollable fire back under control to quickly and efficiently extinguishing a much smaller fire before it spreads.

Other: Agriculture & Water Sustainability
Water Stress Detection using Aerial & Metrological Data(Agri-Bird)

AP008 »

Water is essential in agriculture. Farms use it to grow fresh produce and to sustain their livestock. Major environmental functions and human needs critically depend on water. In regions of the world affected by water scarcity economic activities can be constrained by water availability, leading to competition both among sectors and between human uses and environmental needs.

According to a 2017-18 government survey, agriculture contributes to 18.9% of the GDP and uses up 42.3% of the labor force in Pakistan. But with agriculture using up about 90% of Pakistan’s water supply, and Pakistan’s water crisis threatening to exhaust the country’s water resources by 2040, there is a dire need for solutions that help in the efficient use of water in agriculture, and farming in particular.

To combat this problem and provide a sustainable mechanism to farmers, we propose an aerial collection and soil-sampling data framework that will lead to sustainable, precise, secure, and efficient farming. Our solution will focus on the water-stress or drought-stress of plants.

Water stress refers to the water deficit in plants and has shown to be a very useful piece of information in farming. In addition to being a good predictor for the yield of the plantation, water stress also allows us to respond timely to areas that are under-watered or over-watered. Of course, water stress is most valuable as information for planning irrigation, but it can also be a very decent measure of areas that are at risk of wildfires.

Our solution proposes to mount an FPGA to the aerial unit where it will be collecting data with the help of modules, subsequently process it on the edge, and then transfer all the relevant data to the cloud for further processing and analysis. In order to give our results more credibility, we will also be collecting some soil-sampled data and combining it with the aerial data to give us our final results in the cloud. Our results will aim to give accurate predictions, useful suggestions to farmers, routing data for irrigation channels, and warnings for risks and disasters.

Industrial
LIDAR

AP013 »

Laser imaging, detection, and ranging (LIDAR), is a method for measuring the distance to a targeted object in space. This works by aiming a laser at an object then firing pulses of light and receiving these using a light sensor next to the laser. The time it takes to receive the reflected light pulses can then be utilized to determine the distance of the object.

This project will utilize a laser that can be optically steered to aim in any direction. By scanning the lasers in every direction, a 3-dimensional image can be generated that gives a complete view of all surrounding obstacles. The ideal application for such a system is within self-driving cars, to detect other vehicles and pedestrians. This is also useful for 3d mapping either on land or underwater.

The Cloud Connectivity kit is ideal for this application as it includes an FPGA that is able to rapidly process the laser measurements in real-time which is essential for an application such as autonomous vehicles. The Wi-Fi connectivity combined with the Azure IoT application makes the platform even more powerful by allowing for results to be stored and processed further and then visualized to derive useful insights.

Water Related
sustainable fishery

AP014 »

Our venture is coming up with the cutting edge End-to-End product which can help the marine species and over a 5-10 years course wild capture would be rejuvenated naturally with the ultimate solution what we offer with the existing Hardware/Software but integrating and applying it for a unique way.
Blind Fishing and overfishing has made the marine resources / wild capture as no longer a bottom less fishing.
This overfishing put a trouble to 1/3 of world population especially the under-developed and developing countries who rely ocean as their cheap protein.

Other: FPGA Based On Network On Chip
ReDeNOC :ReConfigurable Device for Network On Chip

AP016 »

Field programmable gate array (FPGA) is become one of the best way in looking the functionality of a integrated circuit. We can download any logic to an FPGA and test the logic quite easily and then if the design is correct then we can go in for an ASIC design if required. Also if the logic is going to change very frequent then the logic can be downloaded to a FPGA and used a chip.
Network on chip is a new dimension in VLSI design wherein we use a network for transfer of information rather than a bus structure which would be slow in working as the logic of implementation goes high. Many topologies like mesh, torus etc. were introduced area of network on chip in the beginning. These topologies became slow when the logic of the system grow. For this a new topology called RiCoBiT (Ring Connected Binary Tree) was introduced in this area. This topology is better in terms of the hop count by keeping the area the same as mesh or torus.
The project we are doing, will give a new dimension for FPGA based design. Here we are going to use the concept of FPGA with network on chip. We are designing a new reprogrammable device like a FPGA using RiCoBiT topology.

Food Related
Sustainable, Safe and Profitable farming using FPGA

AP019 »

A farmer’s job is quite hard. Good results depend on several factors such as the type of Soil, Water, Fertilisers, Pesticides. Excessive use of chemicals can damage a crop and can also cause harm to its consumers. To top it off, global warming has created unpredictable weather patterns and has the potential to destroy entire seasons of crops without much notice.

To solve this we will build out a system that will be able to predict the outcome of a crop season based on the various information we will collect. This system will be able to guide the farmer to use the right amount of water, fertilizer, pesticides. Predict the correct intervals to use these. Be able to detect important threats such as unexpected rodents and suggest corrective measures. The system will also be keeping account of the changing weather patterns and suggest deviations accordingly.

The system will use sensors and cameras to collect the following information from the field for real time prediction:
# Soil Properties
# Localized Weather Properties
# Collect images of the plantation and nearby areas
# Images of chemicals use, if possible quantity of chemicals used by sensors
# Water properties
# Macro weather condition

We will build out the model using the data provided by the Ministry of Agriculture and Farmer’s Welfare, Government of India and various other open data sets. The model itself will be using KNN Algorithm. It is widely used in text categorization, predictive analysis, data mining and image recognition and will be suitable for our use case.

We will use KNN algorithm on FPGA based heterogeneous computing systems using OpenCL. Based on FPGA's parallel pipeline structure. Use of FPGA will improve the efficiency of the solution compared to a conventional GPU based KNN algorithm implementation.

Marine Related
AI based Coral Reefs monitor

AP020 »

This project is an implementation of a Neural Network on the FPGA platform to monitor coral reefs and updates the details on a cloud-based dashboard. Also, ocean parameters like salinity, pH, dissolved oxygen, the temperature can also be monitored. This device would be a small battery-powered submarine that would go to selected regions in the oven and collect the data from those regions. The submarine would be autonomous but we would also provide manual control via satellite communication in case of issues. The dashboard can be used to view the route, elapsed time, available battery, and the data being collected by the submarine.

Other: Health+Biodiversity+SGP
HealthCare and Conservation of Lion Tailed Macaques

AP024 »

In order to conserve biodiversity and use the concept of FPGA and IoT, the aim is to not only protect this very critically endangered species of Karnataka but also to monitor it's health and preserve it.
We use FPGA to receive the required information and also monitor the vital statistics of the animal and even monitor the population of this species via GPS and make sure it's habitat also isnt encroached upon.

Other: Forest Conservation
FPGA based Internet Of Trees for Smart Forest

AP027 »

Forest fire has become one of the big challenges in recent days. Even with the highly evolved technology, we are struggling to avoid forest fire. Forest fire is causing plenty of loss to the vegetation and animals habitats.
Internet Of Trees is an FPGA based system, which shall be used for collect the data of environmental conditions, early fire detection, animal conservation, tree conservation in the forest.
CNN Algorithm will be running on the FPGA board, which will process the image to identify the fire.
when the fire is detected, the auxiliary circuit of the smoke detection/Co2 level sensor will be used to confirm the detection is not false detection.
This camera-based system also will be used to monitor the animal movements, image-based tree statistics, rain data. Using analog sensor boards we can detect the environmental conditions.
Microsoft Azure will be used to deploy analytics algorithm and cloud software. Using Microsoft Azure we also deploy warning systems to firefighters, Real-time video monitoring systems on the cloud.
By deploying CNN-based sensor applications using FPGA accelerates the detection speed, Accuracy of the detection, and ease of connection to the cloud.

Other: General Processor
General Purpose Neural Processing Unit

AP031 »

[The General Purpose Neural Processor Unit proposed in this project aims to simulate and cluster an SNN-based neural network that can solve at least one problem.]

There is a saying, butterfly effect. This means that the small wings of butterflies drive a big typhoon. Likewise, something happens under the influence of many things. For example, solar power generation is affected by wind, cloud, temperature, etc., and farming is affected by solar radiation, precipitation, and temperature. To solve these nonlinear problems, methods such as machine learning have emerged these days.

Machine learning is an algorithm that uses neural networks to analyze data and make decisions based on learned information. Since various types of data can be used as data for learning, it is suitable and widely used to solve nonlinear problems. However, it has only recently begun to be used because it requires a lot of computing power. And even now, big models take a lot of time.

As a way to solve this problem, a neural network model was designed and uploaded to the FPGA for use. But one neural network model had the disadvantage of being able to solve only one problem. To this end, accelerators such as NPUs equipped with many modules that perform repeated operations (mainly convolution operations) also came out.

These NPUs were created with neural networks such as CNN and RNN, which are second-generation neural networks. However, research on SNN, a third-generation neural network, is active these days, and NPU using it is being designed. A typical example is IBM's Truenorth. SNN is a neural network that mimics real neurons and has several advantages in terms of power and learning. In addition, SNN is completely bio-plausible, so it is an essential route in the future to implement artificial intelligence.

Several neurons gather to create a neural network system, and the system gathers to form a neural network network. As things happen under the influence of many things, data on many things are important. Each task is proposed as an idea to break away from not the basic way provided to the neural network as a variable and but solve it through network connection and cluster.