Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Rizk, Hamada"'
Indoor localization has become increasingly important due to its wide-ranging applications in indoor navigation, emergency services, the Internet of Things (IoT), and accessibility for individuals with special needs. Traditional localization systems
Externí odkaz:
http://arxiv.org/abs/2410.02329
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
Locating the persons moving through an environment without the necessity of them being equipped with special devices has become vital for many applications including security, IoT, healthcare, etc. Existing device-free indoor localization systems com
Externí odkaz:
http://arxiv.org/abs/2409.00030
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
Indoor localization systems have become increasingly important in a wide range of applications, including industry, security, logistics, and emergency services. However, the growing demand for accurate localization has heightened concerns over privac
Externí odkaz:
http://arxiv.org/abs/2306.02211
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
Indoor thermal comfort immensely impacts the health and performance of occupants. Therefore, researchers and engineers have proposed numerous computational models to estimate thermal comfort (TC). Given the impetus toward energy efficiency, the curre
Externí odkaz:
http://arxiv.org/abs/2204.12380