**Meet f.low, the intelligent audio control system that knows your surroundings.**

## Inspiration

Have you ever tried to watch a movie on the bus? Study in public? Listen to music while commuting?

We're guessing you have. And, by extension, we're guessing you've had to deal with the frustrating experience of constantly adjusting the volume to accommodate for your changing environment. Everyday distractions like crying babies and noisy neighbors hinder your productivity, your patience, and the ***sick fiya*** you're dropping in your playlist -- but they no longer need to be sources of stress.

## What it Does

Using the built in microphone on your Mac OS X device, f.low is able to detect how loud your environment is and dynamically adjust your volume on-the-fly, keeping your listening experience consistent.

Mapping microphone input power to decibel values using our fitting algorithm, as well as letting you set a maximum and minimum volume for f.low to work with, we achieve the sound you want, **all the time**.

## How We Built It

f.low is currently available on Mac OS X, and it's just a quick and easy port away from iOS. We've developed it using Swift and Xcode, making use of the hardware existing on every Mac and iPhone.

## Challenges

Achieving a natural, consistent sound is key to listening experience, so great care was put into analyzing and optimizing the data gathered from the environment and achieving the most natural volume control.

## What's Next for f.low

Of course there is much more in store and many ideas that need exploring: further optimizing user experience, improving the validity of our detecting algorithm, and re-vamping the UI are three challenges we'd love to tackle in the future.

[www.justgetflow.tech](http://www.justgetflow.tech)
## Inspiration

Want to take advantage of the AR and object detection technologies to help people to gain safer walking experiences and communicate distance information to help people with visual loss navigate.

## What it does

Augment the world with the beeping sounds that change depending on your proximity towards obstacles and identifying surrounding objects and convert to speech to alert the user.

## How we built it

ARKit; RealityKit uses Lidar sensor to detect the distance; AVFoundation, text to speech technology; CoreML with YoloV3 real time object detection machine learning model; SwiftUI

## Challenges we ran into

Computational efficiency. Going through all pixels in the LiDAR sensor in real time wasn’t feasible. We had to optimize by cropping sensor data to the center of the screen

## Accomplishments that we're proud of

It works as intended.

## What we learned

We learned how to combine AR, AI, LiDar, ARKit and SwiftUI to make an iOS app in 15 hours.

## What's next for SeerAR

Expand to Apple watch and Android devices;
Improve the accuracy of object detection and recognition;
Connect with Firebase and Google cloud APIs;
## Inspiration

Throughout high school, our team and other friends regularly called each other for many purposes. However, our parents coming into our rooms for various reasons would always lead to embarrassing moments in the call. Moving forward into university and starting our careers, the concurrent Covid-19 pandemic has amplified this problem to all workers around the world. We developed Hush to tackle concurrent and past issues that we faced in the past.

## What it does

Hush is an application that makes sure your mic listens to only what you want it to. It connects a mobile device to your desktop and mutes our computer microphone when the mobile device detects movement of the door to your room.

## How we built it

Hush was built using the Swift and integrations with Slack were deployed using autocode.

## Challenges we ran into

Our inexperience building macOS applications made it a challenge to get the application working with smooth animations. Networking between iOS and macOS with MultipeerConnectivity with auto connection was quite challenging.

## Accomplishments that we're proud of

As first time hackers, we’re really proud to have successfully made something that works and is practical.

## What we learned

This was the first time our team had used autocode but we found it to be such an amazing platform. We were able to get the Slack bot connected to our Slack channel rather quickly. Setting up the webhook was also very simple. Many of us see ourselves using autocode in the future for fast prototyping and integrations.

## What's next for Hush

This is a proof of concept that solves the problem at hand. However, many individuals will prefer to have their phones by their side. With this in mind, if we were to create a real, marketable product, it would be in the form of a small, single-button device that can be easily attached to any door. The device would be cheap to produce requiring only an accelerometer and Bluetooth pairing capabilities.