Zobrazeno 1 - 10
of 165
pro vyhledávání: '"Yamaguchi, Hirozumi"'
This paper presents a novel system for reconstructing high-resolution GPS trajectory data from truncated or synthetic low-resolution inputs, addressing the critical challenge of balancing data utility with privacy preservation in mobility application
Externí odkaz:
http://arxiv.org/abs/2410.12818
Accurate taxi-demand prediction is essential for optimizing taxi operations and enhancing urban transportation services. However, using customers' data in these systems raises significant privacy and security concerns. Traditional federated learning
Externí odkaz:
http://arxiv.org/abs/2408.04931
The growing demand for ride-hailing services has led to an increasing need for accurate taxi demand prediction. Existing systems are limited to specific regions, lacking generalizability to unseen areas. This paper presents a novel taxi demand foreca
Externí odkaz:
http://arxiv.org/abs/2310.18215
With the increasing number of IoT devices, there is a growing demand for energy-free sensors. Human activity recognition holds immense value in numerous daily healthcare applications. However, the majority of current sensing modalities consume energy
Externí odkaz:
http://arxiv.org/abs/2307.16162
Taxi-demand prediction is an important application of machine learning that enables taxi-providing facilities to optimize their operations and city planners to improve transportation infrastructure and services. However, the use of sensitive data in
Externí odkaz:
http://arxiv.org/abs/2305.08107
The growing demand for intelligent environments unleashes an extraordinary cycle of privacy-aware applications that makes individuals' life more comfortable and safe. Examples of these applications include pedestrian tracking systems in large areas.
Externí odkaz:
http://arxiv.org/abs/2303.09915
Autor:
Lala, Betty, Kala, Srikant Manas, Rastogi, Anmol, Dahiya, Kunal, Yamaguchi, Hirozumi, Hagishima, Aya
Thermal comfort in indoor environments has an enormous impact on the health, well-being, and performance of occupants. Given the focus on energy efficiency and Internet-of-Things enabled smart buildings, machine learning (ML) is being increasingly us
Externí odkaz:
http://arxiv.org/abs/2206.14202
Unlicensed LTE-WiFi coexistence networks are undergoing consistent densification to meet the rising mobile data demands. With the increase in coexistence network complexity, it is important to study network feature relationships (NFRs) and utilize th
Externí odkaz:
http://arxiv.org/abs/2111.07583
Publikováno v:
In Pervasive and Mobile Computing May 2024 100
Autor:
Ohori, Fumiko1,2 (AUTHOR) fumiko@nict.go.jp, Yamaguchi, Hirozumi2 (AUTHOR), Itaya, Satoko1 (AUTHOR), Matsumura, Takeshi1 (AUTHOR)
Publikováno v:
Sensors (14248220). Oct2023, Vol. 23 Issue 20, p8588. 16p.