## Inspiration

One of the biggest roadblocks during disaster relief is reestablishing the first line of communication between community members and emergency response personnel. Whether it is the aftermath of a hurricane devastating a community or searching for individuals in the backcountry, communication is the key to speeding up these relief efforts and ensuring a successful rescue of those at risk.

In the event of a hurricane, blizzard, earthquake, or tsunami, cell towers and other communication nodes can be knocked out leaving millions stranded and without a way of communicating with others. In other instances where skiers, hikers, or travelers get lost in the backcountry, emergency personnel have no way of communicating with those who are lost and can only rely on sweeping large areas of land in a short amount of time to be successful in rescuing those in danger.

This is where Lifeline comes in. Our project is all about leveraging communication technologies in a novel way to create a new way to establish communication in a short amount of time without the need for prior existing infrastructures such as cell towers, satellites, or wifi access point thereby speeding up natural disaster relief efforts, search and rescue missions, and helping provide real-time metrics for emergency personnel to leverage.

Lifeline uses LoRa and Wifi technologies to create an on-the-fly mesh network to allow individuals to communicate with each other across long distances even in the absence of cell towers, satellites, and wifi. Additionally, Lifeline uses an array of sensors to send vital information to emergency response personnel to assist with rescue efforts thereby creating a holistic emergency response system.

## What it does

Lifeline consists of two main portions. First is a homebrewed mesh network made up of IoT and LoRaWAN nodes built to extend communication between individuals in remote areas. The second is a control center dashboard to allow emergency personnel to view an abundance of key metrics of those at risk such as heart rate, blood oxygen levels, temperature, humidity, compass directions, acceleration, etc.

On the mesh network side, Lifeline has two main nodes. A control node and a network of secondary nodes. Each of the nodes contains a LoRa antenna capable of communication up to 3.5km. Additionally, each node consists of a wifi chip capable of acting as both a wifi access point as well as a wifi client. The intention of these nodes is to allow users to connect their cellular devices to the secondary nodes through the local wifi networks created by the wifi access point. They can then send emergency information to response personnel such as their location, their injuries, etc. Additionally, each secondary node contains an array of sensors that can be used both by those in danger in remote communities or by emergency personnel when they venture out into the field so members of the control center team can view their vitals. All of the data collected by the secondary nodes is then sent using the LoRa protocol to other secondary nodes in the area before finally reaching the control node where the data is processed and uploaded to a central server. Our dashboard then fetches the data from this central server and displays it in a beautiful and concise interface for the relevant personnel to read and utilize.

Lifeline has several main use cases:

1. Establishing communication in remote areas, especially after a natural disaster
2. Search and Rescue missions
3. Providing vitals for emergency response individuals to control center personnel when they are out in the field (such as firefighters)

## How we built it

* The hardware nodes used in Lifeline are all built on the ESP32 microcontroller platform along with a SX1276 LoRa module and IoT wifi module.
* The firmware is written in C.
* The database is a real-time Google Firebase.
* The dashboard is written in React and styled using Google's Material UI package.

## Challenges we ran into

One of the biggest challenges we ran into in this project was integrating so many different technologies together. Whether it was establishing communication between the individual modules, getting data into the right formats, working with new hardware protocols, or debugging the firmware, Lifeline provided our team with an abundance of challenges that we were proud to tackle.

## Accomplishments that we're proud of

We are most proud of being able to have successfully integrated all of our different technologies and created a working proof of concept for this novel idea. We believe that combing LoRa and wifi in the way can pave the way for a new era of fast communication that doesn't rely on heavy infrastructures such as cell towers or satellites.

## What we learned

We learned a lot about new hardware protocols such as LoRa as well as working with communication technologies and all the challenges that came along with that such as race conditions and security.

## What's next for Lifeline

We plan on integrating more sensors in the future and working on new algorithms to process our sensor data to get even more important metrics out of our nodes.
## Inspiration

Natural disasters do more than just destroy property—they disrupt lives, tear apart communities, and hinder our progress toward a sustainable future. One of our team members from Rice University experienced this firsthand during a recent hurricane in Houston. Trees were uprooted, infrastructure was destroyed, and delayed response times put countless lives at risk.

* **Emotional Impact**: The chaos and helplessness during such events are overwhelming.
* **Urgency for Change**: We recognized the need for swift damage assessment to aid authorities in locating those in need and deploying appropriate services.
* **Sustainability Concerns**: Rebuilding efforts often use non-eco-friendly methods, leading to significant carbon footprints.

Inspired by these challenges, we aim to leverage AI, computer vision, and peer networks to provide rapid, actionable damage assessments. Our AI assistant can detect people in distress and deliver crucial information swiftly, bridging the gap between disaster and recovery.

## What it Does

The Garuda Dashboard offers a comprehensive view of current, upcoming, and past disasters across the country:

* **Live Dashboard**: Displays a heatmap of affected areas updated via a peer-to-peer network.
* **Drones Damage Analysis**: Deploy drones to survey and mark damaged neighborhoods using the Llava Vision-Language Model and generate reports for the Recovery Team.
* **Detailed Reporting**: Reports have annotations to classify damage types [tree, road, roof, water], human rescue needs, site accessibility [Can response team get to the site by land], and suggest equipment dispatch [Cranes, Ambulance, Fire Control].
* **Drowning Alert**: The drone footage can detect when it identifies a drowning subject and immediately call rescue teams
* **AI-Generated Summary**: Reports on past disasters include recovery costs, carbon footprint, and total asset/life damage.

## How We Built It

* **Front End**: Developed with Next.js for an intuitive user interface tailored for emergency use.
* **Data Integration**: Utilized Google Maps API for heatmaps and energy-efficient routing.
* **Real-Time Updates**: Custom Flask API records hot zones when users upload disaster videos.
* **AI Models**: Employed MSNet for real-time damage assessment on GPUs and Llava VLM for detailed video analysis.
* **Secure Storage**: Images and videos stored on Firebase database.

## Challenges We Faced

* **Model Integration**: Adapting MSNet with outdated dependencies required deep understanding of technical papers.
* **VLM Setup**: Implementing Llava VLM was challenging due to lack of prior experience.
* **Efficiency Issues**: Running models on personal computers led to inefficiencies.

## Accomplishments We're Proud Of

* **Technical Skills**: Mastered API integration, technical paper analysis, and new technologies like VLMs.
* **Innovative Impact**: Combined emerging technologies for disaster detection and recovery measures.
* **Complex Integration**: Successfully merged backend, frontend, and GPU components under time constraints.

## What We Learned

* Expanded full-stack development skills and explored new AI models.
* Realized the potential of coding experience in tackling real-world problems with interdisciplinary solutions.
* Balanced MVP features with user needs throughout development.

## What's Next for Garuda

* **Drone Integration**: Enable drones to autonomously call EMS services and deploy life-saving equipment.
* **Collaboration with EMS**: Partner with emergency services for widespread national and global adoption.
* **Broader Impact**: Expand software capabilities to address various natural disasters beyond hurricanes.
## Inspiration

Sagnik is a student-instructor for the UC Berkeley Intro to ML class. Some students complained that they had no intuitive idea what happened when they changed model parameters, and that they'd like to see how the model's output changed interactively with change in parameters.

## What it does

Visualizes the decision boundary of various machine learning algorithms (SVM, decision trees, boosted trees, multilayer perceptrons, etc.).

This helps **democratize access to machine learning education**. There are many people who are getting started with machine learning on their own, and they rely completely on free resources available online. This visualizer will be a great tool for such people to get an in-depth understanding of decision boundaries, overfitting, generalization, etc.

Since this is a free, open-source tool available online, instructors across the world can use it in their classrooms to teach, explain, and demo machine learning algorithms, and then students can go home and use it to further their understanding.

## How we built it

We started with the code of Dash's [SVM visualizer](https://github.com/plotly/dash-sample-apps/tree/master/apps/dash-svm) and heavily modified it to add support for other machine learning algorithms.

## Challenges we ran into

* Refining the UI -- positioning the logo correctly
* Python issues with libraries

## Accomplishments that we're proud of

* Sagnik: Finishing a project at a hackathon!!!
* Completing the parameters for other ML algorithms.

## What I learned

* Sagnik: Making a web app and deploying it to Heroku
* Colin: Changing around frontend attributes using python
* Komila: Finishing designs using Adobe Illustrator

## What's next for ML Algorithm Visualizer

Sagnik will use this as a teaching tool in class, and keep adding to the web app as needed to maximize students' learning. The following are planned for the very near future:

* adding more options for the existing algorithms
* ability to import custom data
* add animated APIs
* add the ability for students to interactively click on their graph data points
* add descriptions for each ML algorithm so students can get exposed to new ones
* add a separated frontend component so that visuals can become more customized