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Machine Learning Performance at the Edge: When to Offload an Inference Task
Machine Learning (ML) techniques play a crucial role in extracting valuable insights from the large amounts of data massively collected through networked sensing systems. Given the increased capabilities of user devices and the growing demand for ...
EmbHD: A Library for Hyperdimensional Computing Research on MCU-Class Devices
This paper presents EmbHD, a library for embedded Hyperdimensional Computing research on severely resource-constrained computing devices. The increasing demand for power-efficient and low-latency machine learning in mobile applications has driven the ...
AgriAdapt: Towards Resource-Efficient UAV Weed Detection using Adaptable Deep Learning
- Octavian M. Machidon,
- Andraž Krašovec,
- Alina L. Machidon,
- Veljko Pejović,
- Daniele Latini,
- Sarathchandrakumar T. Sasidharan,
- Fabio Del Frate
The 2022--2023 food crises and the ongoing human population growth make the efficient use of the available agricultural land a pressing matter. However, weeds present a major obstacle towards efficient land use, and cause up to 40% yield loss in all ...
Evaluating the use of machine learning algorithms in environmental sensing for energy saving
Coastal lagoons are complex ecosystems characterized by the interaction of several actors, that can have a significant impact on them. The SMARTLAGOON project has the primary aim of integrating novel artificial intelligence-based technologies with an ...
Energy-Efficient Cooperative Caching Scheme for Green ICWSN: Preliminary Analysis and Testbed Development
This paper presents a wireless sensor network technology for supporting the deployment of sustainable IoT applications essential to future zero-carbon smart cities. The proposed cooperative caching scheme includes data forwarding and retrieval for ...
Index Terms
- Proceedings of the 2nd Workshop on Networked Sensing Systems for a Sustainable Society
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Acceptance Rates
Year | Submitted | Accepted | Rate |
---|---|---|---|
NET4us '23 | 8 | 5 | 63% |
Overall | 8 | 5 | 63% |