## Inspiration

We often find downtimes where we want to do some light exercises. Throwing a ball is perfect, but it's not easy to find another person to throw the ball to. We realized that we wanted to make a machine that can substitute the role of a partner... hence the creation of *PitchPartner*!

## What it does

PitchPartner is a motorized machine designed to catch and return a tennis ball automatically. When receiving the ball, the machine keeps a counter of how many throws were made. It then feeds the ball to the bike wheel propeller to be launched back! PitchPartner is portable, making it great to use out in an open field!

## How we built it

The launcher is made with a bike tire connected to a drill that provides high rpm and torque. It's housed in a wooden frame that was nailed together. The catcher is a net (webbing) augmented with Raspberry Pi and sensors, namely the ultrasonic sensor for the speed detection and the button for the counter.

## Challenges we ran into

We initially wanted to make a frisbee thrower and catcher, but due to the complexity arising from the asymmetrical nature of the frisbee (top down), we changed the *trajectory* of our project to use the tennis ball! Because our machine has fast, moving parts, we didn't feel comfortable to potentially have the frisbee be fed into the launcher upside down and thus causing safety problems.

Some of us are relative beginners to coding for Raspberry Pi (python), so we spend a lot of time to get the counter displayed on the 7-segment display. We were able to made it work when the programmer described in detail what type of problem he is encountering. We were able to draw knowledge from another programming language, C, to fix the problem.

## Accomplishments that we're proud of

This was our first online hackathon! Our team was able to distribute work even though the hardware is built at one place. We played to our strengths and made quick decisions, enabling us to finish the PitchPartner on time!

## What we learned

Our team members come from two different cities! One thing we learned is to bounce our ideas and problems off of each other because we all have different strengths and weaknesses. Clear explanation of the problems we faced helped us move closer to finding a solution.

## What's next for PitchPartner

Installing a DC brushless motor can make the design even more compact and stable, not to mention true autonomy of the machine. We are also interested in having the machine change directions, thus capable of throwing left and right. Lastly, because we bought a used bike to extract its wheel, we think it would be very cool to incorporate the rest of the bike body to a steering system for the PitchPartner!
## **Inspiration**

The president of the US has proposed a target of achieving *80 percent* clean electricity by *2030* and in today's data-driven era of escalating energy demands, the imperative for renewable resources has never been more pressing. As global energy consumption continues to surge, the reliance on AI-powered, sustainable solutions becomes paramount. EcoNet.ai leverages AI to optimize energy utilization, reducing costs, enhancing efficiency, and mitigating environmental impact. Using predictive analytics, our platform promotes sustainable development globally, paving the way for a resilient and eco-friendly future.

## **What it does**

EcoNet.ai is a cutting-edge platform using AI-driven geospatial analysis and deep learning to optimize renewable energy deployments. By *identifying optimal locations for solar arrays and wind farms ,and allowing users to easily analyze past and future climate data. It reduces energy costs and supports data-driven decision-making for governments and corporations.* This leads to significant cost savings, improved sustainability, and economic growth. The system's neural networks and real-time data processing provide adaptive, future-proof energy solutions.

## **How we built it**

We built EcoNet.ai using a suite of AWS services to optimize renewable energy installations. **AWS Bedrock** , while **DynamoDB** managed complex geospatial data. **Amazon S3** provided storage for our vast datasets and we used PyTorch to do time series predictions. This integration of AWS technologies ensured scalability, reliability, and intelligent resource management, enabling EcoNet.ai to deliver actionable AI-driven insights for sustainable energy initiatives.

## **Challenges we ran into**

Developing EcoNet.ai involved several challenges, Efficient API querying through DynamoDB required iterative optimization and predictive caching. Hyperspectral satellite imagery was impractical due to long processing times, and incomplete government datasets hindered reliable data sourcing for our models.

## **Accomplishments that we're proud of**

Our team engineered a cutting-edge AI model using machine learning to optimize renewable energy placement by analyzing factors like terrain, local energy demands, and grid infrastructure. Our data-driven solution leads in the field, and we are proud that it can revolutionize energy accessibility and sustainability, potentially transforming communities globally through intelligent resource allocation and adaptive planning.

## What we learned

Through this project, we gained invaluable experience applying machine learning and AI to real-world sustainability challenges. We deepened our understanding of renewable energy systems and their optimal placement, enhancing our skills in big data analytics, predictive modeling, and large-scale data processing for next-generation energy solutions.

## What's next for EcoNet.ai

EcoNet.ai's future plans include:

* *Leveraging AI and ML for real-time, hyper-localized insights in renewable energy planning.*
* *Expanding globally.*
* *Improving data collection processes.*
* *Using computer vision to analyze satellite imagery for precise land-use planning.*

These solutions will help governments, corporations, and communities make informed, AI-assisted decisions, speeding up the global transition to sustainable energy sources.
## Inspiration

We were inspired to create a health-based solution (despite focusing on sustainability) due to the recent trend of healthcare digitization, spawning from the COVID-19 pandemic and progressing rapidly with increased commercial usage of AI. We did, however, want to create a meaningful solution with a large enough impact that we could go through the hackathon, motivated, and with a clear goal in mind. After a few days of research and project discussions/refinement sessions, we finally came up with a solution that we felt was not only implementable (with our current skills), but also dealt with a pressing environmental/human interest problem.

## What it does

WasteWizard is designed to be used by two types of hospital users: Custodians and Admin. At the custodian user level, alerts are sent based on timer countdowns to check on wastebin statuses in hospital rooms. When room waste bins need to be emptied, there is an option to select the type of waste and the current room to locate the nearest large bin. Wastebin status (for that room) is then updated to Empty. On the admin side, there is a dashboard to track custodian wastebin cleaning logs (by time, location, and type of waste), large bin status, and overall aggregate data to analyze their waste output. Finally, there is also an option for the admin to empty large garbage bins (once collected by partnering waste management companies) to update their status.

## How we built it

The UI/UX designers employed Figma keeping user intuitiveness in mind. Meanwhile, the backend was developed using Node.js and Express.js, employing JavaScript for server-side scripting. MongoDB served as the database, and Mongoose simplified interactions with MongoDB by defining schemas. A crucial aspect of our project was using the MappedIn SDK for indoor navigation. For authentication and authorization, the developers used Auth0 which greatly enhanced security. The development workflow followed agile principles, incorporating version control for collaboration. Thorough testing at both front-end and back-end levels ensured functionality and security. The final deployment in Azure optimized performance and scalability.

## Challenges we ran into

There were a few challenges we had to work through:

* MappedIn SDK integration/embedding: we used a front-end system that, while technically compatible, was not the best choice to use with MappedIn SDK so we ended up needing to debug some rather interesting issues
* Front End development, in general, was not any of our strong suits, so much of that phase of the project required us to switch between CSS tutorial tabs and our coding screens, which led us to taking more time than expected to finish that up
* Auth0 token issues related to redirecting users and logging out users after the end of a session + redirecting them to the correct routes
* Needing to pare down our project idea to limit the scope to an idea that we could feasibly build in 24 hours while making sure we could defend it in a project pitch as an impactful idea with potential future growth

## Accomplishments that we're proud of

In general, we're all quite proud at essentially full-stack developing a working software project in 24 hours. We're also pretty proud of our project idea, as our initial instinct was to pick broad, flashy projects that were either fairly generic or completely unbuildable in the given time frame. We managed to set realistic goals for ourselves and we feel that our project idea is niche and applicable enough to have potential outside of a hackathon environment. Finally, we're proud of our front-end build. As mentioned earlier, none of us are especially well-versed in front-end, so having our system be able to speak to its user (and have it look good) is a major success in our books.

## What we learned

We learned we suck at CSS! We also learned good project time management/task allocation and to plan for the worst as we were quite optimistic about how long it would take us to finish the project, but ended up needing much more time to troubleshoot and deal with our weak points. Furthermore, I think we all learned new skills in our development streams, as we aimed to integrate as many hackathon-featured technologies as possible. There was also an incredible amount of research that went into coming up with this project idea and defining our niche, so I think we all learned something new about biomedical waste management.

## What's next for WasteWizard

As we worked through our scope, we had to cut out a few ideas to make sure we had a reasonable project within the given time frame and set those aside for future implementation. Here are some of those ideas:

* more accurate trash empty scheduling based on data aggregation + predictive modelling
* methods of monitoring waste bin status through weight sensors
* integration into hospital inventory/ordering databases
As a note, this can be adapted to any biomedical waste-producing environment, not just hospitals (such as labs and private practice clinics).