5 citations · 13 across the 8 of their papers we have counts for
8 papers
Radio Environment Mapping with World Models for Active Measurement Control: Should Networks Dream of Optimal Control?
Jernej Hribar, Ljupcho Milosheski, Ryoichi Shinkuma
Radio Environment Maps (REMs) have the potential to serve as an important enabler for intelligent modeling and control in emerging AI-native 6G networks. Despite significant progre…
An Indoor Radio Mapping Dataset Combining 3D Point Clouds and RSSI
Ljupcho Milosheski, Kuon Akiyama, Blaž Bertalanič +2
The growing number of smart devices supporting bandwidth-intensive and latency-sensitive applications, such as real-time video analytics, smart sensing, Extended Reality (XR), etc.…
Estimation of physical activities of people in offices from time-series point-cloud data
Koki Kizawa, Ryoichi Shinkuma, Gabriele Trovato
This paper proposes an edge computing system that enables estimating physical activities of people in offices from time-series point-cloud data, obtained by using a light-detection…
Feature-based model selection for object detection from point cloud data
Kairi Tokuda, Ryoichi Shinkuma, Takehiro Sato +1
Smart monitoring using three-dimensional (3D) image sensors has been attracting attention in the context of smart cities. In smart monitoring, object detection from point cloud dat…
Watch from sky: machine-learning-based multi-UAV network for predictive police surveillance
Ryusei Sugano, Ryoichi Shinkuma, Takayuki Nishio +2
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 pre…
Analyse or Transmit: Utilising Correlation at the Edge with Deep Reinforcement Learning
Jernej Hribar, Ryoichi Shinkuma, George Iosifidis +1
Millions of sensors, cameras, meters, and other edge devices are deployed in networks to collect and analyse data. In many cases, such devices are powered only by Energy Harvesting…
Estimation of Individual Device Contributions for Incentivizing Federated Learning
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 exposi…
Design of Ad Hoc Wireless Mesh Networks Formed by Unmanned Aerial Vehicles with Advanced Mechanical Automation
Ryoichi Shinkuma, Narayan B. Mandayam
Ad hoc wireless mesh networks formed by unmanned aerial vehicles (UAVs) equipped with wireless transceivers (access points (APs)) are increasingly being touted as being able to pro…