ARM DEV SUMMIT 2021 - Environmental Data Acquisition and Processing (Session ID 506437)
I am currently a Research intern at Centre for Data Science and Artificial Intelligence (DSAIL), Dedan Kimathi University of Technology. I am also a Graduate Electrical Engineer and Data Scientist with experience in Machine Learning, IoT/Sensor systems development, IoT/Sensor systems deployment, data analysis, data visualization, and Electrical hardware (PCB) Design. In this session i'm taking the audience through a river Water level Monitoring project. Data collected can be used to diagnose the status of the river catchment.
- 🔗 Email: jason.kabi@dkut.ac.ke
I am a Research Intern at Centre for Data Science and Artificial Intelligence (DSAIL), Dedan Kimathi University of Technology. I am actively involved in design and fabrication of acoustic sensors, acoustic data collection and developing machine learning models for automatic acoustic classification of birds. This is aimed at leveraging bioacoustics and machine learning in monitoring our ecosystems. I will be taking you through the process of deploying machine learning models on the Raspberry Pi and demonstrate how automatic acoustic classification of birds is acheived during this session.
- 🔗 Email: gabriel.kiarie@dkut.ac.ke
Ecosystems around the world are suffering from degradation due to human activity. To determine ecosystems at risk, it is important to monitor these ecosystems continuously. However, conservation budgets are small and we need to leverage technology to quickly identify areas in need of conservation interventions. In this workshop we aim to demonstrate two technologies namely acoustic monitoring of bird species and anomaly detection in sensor data. We will leverage data collected in ecosystems in Kenya and demonstrate how these systems are built using low cost hardware.
The key takeaways are:
- Low cost hardware can be leveraged to build robust environmental monitoring tools.
- Open source software and datasets can be used to build useful models for environmental conservation.
For this system we will show how acoustic data can be continuously captured and processed on a Raspberry Pi to determine the presence of birds. We will use public data to train models and show attendees how models for species in their locality can be built. We will leverage python modules such as sounddevice and librosa for audio processing and tensorflow for machine learning.
This system will be built upon work we have done in water system monitoring. Here we will measure distance using an ultrasonic sensor and show how anomalies in the measurements can be automatically flagged using appropriate machine learning. Participants will have access to a real world data stream from a river in Kenya collected via LoRa and will be able to use cheap ultrasonic sensors to replicate the system locally. We will demonstrate the deployment of these models on a web application developed using dash. The models will leverage scikit-learn.
Centre for Data Science and Artificial Intelligence (DSAIL - DeKUT)
