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Daniel Bonilla Licea

Researcher at University of Leeds

Publications -  24
Citations -  135

Daniel Bonilla Licea is an academic researcher from University of Leeds. The author has contributed to research in topics: Computer science & Wireless. The author has an hindex of 5, co-authored 13 publications receiving 62 citations. Previous affiliations of Daniel Bonilla Licea include Czech Technical University in Prague.

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Journal ArticleDOI

Improving Radio Energy Harvesting in Robots Using Mobility Diversity

TL;DR: This paper is the first use of the mobility diversity principle to optimize energy harvesting from an RF signal and demonstrates that mobility, if intelligently controlled, is actually not a foe but is indeed a friend that can provide significant benefits under wireless fading channels.
Journal ArticleDOI

Mobility Diversity-Assisted Wireless Communication for Mobile Robots

TL;DR: The theoretical framework for a generalized version of the mobility diversity with multithreshold algorithm (MDMTA) is established, which allows improved wireless communications in fading channels for mobile robots via intelligent robotic motion with low mechanical energy expenditure.
Proceedings ArticleDOI

MRS Modular UAV Hardware Platforms for Supporting Research in Real-World Outdoor and Indoor Environments

TL;DR: This paper presents a family of autonomous Unmanned Aerial Vehicles (UAVs) platforms designed for a diverse range of indoor and outdoor applications and presents mechanical designs, electric configurations, and dynamic models of the UAVs, followed by numerous recommendations and technical details required for building such a fully autonomous UAV system for experimental verification of scientific achievements.
Journal ArticleDOI

MORED: A Moroccan Buildings’ Electricity Consumption Dataset

TL;DR: The Moroccan Buildings' Electricity Consumption Dataset (MORED) as discussed by the authors provides three main data components: whole premises (WP) electricity consumption, individual load (IL) ground-truth consumption, and fully labeled IL signatures, from affluent and disadvantaged neighborhoods.
Journal ArticleDOI

Mobile Robot Path Planners With Memory for Mobility Diversity Algorithms

TL;DR: The results show that MDAs, which adapt the location of those points, can in fact outperform the MDAs that use predetermined locations for those points and allow MRs to mitigate poor wireless channel conditions in an energy-efficient manner.