## Inspiration

Everyone in the group knows the experience of having a bad landlord during a study term, and mental health can sometimes take a toll from the negligence of one. RateMyLandlord is a streamlined way for past tenants to share experiences with landlords and their respective properties

## What it does

The user has an option to either search for a landlord’s name that exists or a property. If they enter an address, a marker is added on to the location and they can see if there already is a landlord. If not they can add a new landlord and their review. Alternatively, the user can search for a landlord's name and all their properties and reviews is displayed in a chart.

## How we built it

The data for landlords and address was stored using MongoDB, the backend was made with Node.js and the frontend was made using Javascript, Bootstrap, EJS, HTML. Google Maps API was used to display each location on the map.

## Challenges we ran into

Google Maps API implementation, NodeJS, EJS and MongoDB usage

## Accomplishments that I'm proud of

Completing the hackathon

## What we learned

Javscript, Front-end and Back-end development, MongoDB, Google Maps API

## What's next for RateMyLandlord

Implement automatic verification, display data in a better way, better front-end
## Inspiration

When attending crowded lectures or tutorials, it's fairly difficult to discern content from other ambient noise. What if a streamlined pipeline existed to isolate and amplify vocal audio while transcribing text from audio, and providing general context in the form of images? Is there any way to use this technology to minimize access to education? These were the questions we asked ourselves when deciding the scope of our project.

## The Stack

**Front-end :** react-native

**Back-end :** python, flask, sqlalchemy, sqlite

**AI + Pipelining Tech :** OpenAI, google-speech-recognition, scipy, asteroid, RNNs, NLP

## What it does

We built a mobile app which allows users to record video and applies an AI-powered audio processing pipeline.

**Primary use case:** Hard of hearing aid which:

1. Isolates + amplifies sound from a recorded video (pre-trained RNN model)
2. Transcribes text from isolated audio (google-speech-recognition)
3. Generates NLP context from transcription (NLP model)
4. Generates an associated image to topic being discussed (OpenAI API)

## How we built it

* Frameworked UI on Figma
* Started building UI using react-native
* Researched models for implementation
* Implemented neural networks and APIs
* Testing, Testing, Testing

## Challenges we ran into

Choosing the optimal model for each processing step required careful planning. Algorithim design was also important as responses had to be sent back to the mobile device as fast as possible to improve app usability.

## Accomplishments that we're proud of + What we learned

* Very high accuracy achieved for transcription, NLP context, and .wav isolation
* Efficient UI development
* Effective use of each tem member's strengths

## What's next for murmr

* Improve AI pipeline processing, modifying algorithms to decreasing computation time
* Include multi-processing to return content faster
* Integrate user-interviews to improve usability + generally focus more on usability
## \*\* Internet of Things 4 Diabetic Patient Care \*\*

## The Story Behind Our Device

One team member, from his foot doctor, heard of a story of a diabetic patient who almost lost his foot due to an untreated foot infection after stepping on a foreign object. Another team member came across a competitive shooter who had his lower leg amputated after an untreated foot ulcer resulted in gangrene.

A symptom in diabetic patients is diabetic nephropathy which results in loss of sensation in the extremities. This means a cut or a blister on a foot often goes unnoticed and untreated.
Occasionally, these small cuts or blisters don't heal properly due to poor blood circulation, which exacerbates the problem and leads to further complications. These further complications can result in serious infection and possibly amputation.

We decided to make a device that helped combat this problem. We invented IoT4DPC, a device that detects abnormal muscle activity caused by either stepping on potentially dangerous objects or caused by inflammation due to swelling.

## The technology behind it

A muscle sensor attaches to the Nucleo-L496ZG board that feeds data to a Azure IoT Hub. The IoT Hub, through Trillo, can notify the patient (or a physician, depending on the situation) via SMS that a problem has occurred, and the patient needs to get their feet checked or come in to see the doctor.

## Challenges

While the team was successful in prototyping data aquisition with an Arduino, we were unable to build a working prototype with the Nucleo board. We also came across serious hurdles with uploading any sensible data to the Azure IoTHub.

## What we did accomplish

We were able to set up an Azure IoT Hub and connect the Nucleo board to send JSON packages. We were also able to aquire test data in an excel file via the Arduino