Feature-based model selection for object detection from point cloud data

no code implementations • 26 Sep 2022 • Kairi Tokuda, Ryoichi Shinkuma, Takehiro Sato, Eiji Oki

In smart monitoring, object detection from point cloud data acquired by 3D image sensors is implemented for detecting moving objects such as vehicles and pedestrians to ensure safety on the road.

Model Selection Object +2

Watch from sky: machine-learning-based multi-UAV network for predictive police surveillance

no code implementations • 6 Mar 2022 • Ryusei Sugano, Ryoichi Shinkuma, Takayuki Nishio, Sohei Itahara, Narayan B. Mandayam

This paper presents the watch-from-sky framework, where multiple unmanned aerial vehicles (UAVs) play four roles, i. e., sensing, data forwarding, computing, and patrolling, for predictive police surveillance.

BIG-bench Machine Learning reinforcement-learning +1

Estimation of Individual Device Contributions for Incentivizing Federated Learning

no code implementations • 20 Sep 2020 • Takayuki Nishio, Ryoichi Shinkuma, Narayan B. Mandayam

Federated learning (FL) is an emerging technique used to train a machine-learning model collaboratively using the data and computation resource of the mobile devices without exposing privacy-sensitive user data.

Federated Learning