Dr. Emmanuel Ndashimye
|Project Investigator||Dr. Emmanuel Ndashimye|
|PI Profile||Emmanuel Ndashimye (email@example.com) holds a Ph.D. in Computer Science from Auckland University of Technology (AUT), Auckland, New Zealand. Dr Emmanuel received master’s degree in Communication and information systems from Huazhong University of Science and Technology (China, 2009) and BSc degree in Electronics and Telecommunication Engineering from University of Rwanda (2005). He is currently lecturer at University of Rwanda and Adjunct instructor at Carnegie Mellon University-Africa (CMU-Africa). Emmanuel’s current research interests are in 5G networks, IoT, LTE and WiFi, mobility and QoS management in vehicular network communications and advanced handoff techniques for heterogeneous wireless networks.||Title of the project:||Agriculture Data from Acoustic Monitoring|
|Type of the project: Postdoc/research award , cooperability, academia-industry, etc||Research award|
|Partners:||Prof. Vodacek Anthony, Rochester institute of technology (RIT), USA|
|Team members:||Prof. Anthony Vodacek , Dr. Damien Hanyurwimfura, Peace Bamurigire, Fidele Maniraguha, Musanase Christine|
|Project amount:||84000 USD|
|Funder:||PASET||Project period||2 years||Short description of the project:||One-way farmers can understand the status of a crop or herd is by listening. However, just as with visual observation, a farmer cannot always listen everywhere on their farm. This project proposes to build passive acoustic listening networks for use on farms to give farmers “ears” throughout their lands and in their buildings, 24 hours per day. The focus of this project then is to build low-cost acoustic sensing networks and to create machine learning techniques for processing acoustic data into information.
The project seeks to deploy AudioMoths microphone devices for collecting acoustic data from farms. In terms of hardware, the key capabilities to develop are powering the device, networking devices so data can be transmitted rather than stored on microSD cards and decreasing cost. Networking can be accomplished with Internet of Things communications protocols and hardware added to the system (LoRA, for example).
There are three main outcomes for the project that will support the objectives. The first is the capture and delivery of useful information to farmers about the status of their farms. The second outcome is advancement in machine learning techniques for processing the acoustic data into useful information. The third outcome is advancement in adapting machine learning algorithms for embedded processing.
|Project status:||The project started in March 2022. The project team is investigating appropriate components for the implementation of the project.|
|Type of the Project: National or International :||International|