12 docs tagged with "Animal-Ecology"

The Camera Traps application is both a simulator and IoT device software for utilizing machine learning on the edge in field research. The first implementation specializes in applying computer vision (detection and classification) to wildlife images for animal ecology studies. Two operational modes are supported: "simulation" mode and "demo" mode. When executed in simulation mode, the software serves as a test bed for studying ML models, protocols and techniques that optimize storage, execution time, power and accuracy. It requires an input dataset of images to act as the images that would be generated an IoT camera device; it uses these images to drive the simulation.

This repo provides instructions for extracting workload information from AI-Driven Animal Ecology (ADAE) studies.

The ctcontroller tool can be used to manage the provisioning and releasing of edge hardware as well as running and shutting down the camera-traps application.

Figure: Workflow of AI-Driven Animal Ecology Study.png)

Architectual Overview

Architecture Overview

TapisUI provides a research oriented frontend to interact with Tapis and tenant components. In this case, the ICICLE extension extends TapisUI with custom branding

Step 1: Extract arrival rates from real ADAE Studies

Quick Start

Installation

View the main TapisUI wiki to learn how to deploy and test TapisUI extensions locally.

This tapis ui extension enables additional icicle specific branding and tabs on tapisui.