World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
6938
World Ranking
8440
National Ranking
514

Overview

Lyudmila Mihaylova is affiliated with the University of Sheffield in the United Kingdom. Their research spans several intersecting fields within computer science and engineering, reflecting an extensive publication record and collaborations across diverse technical domains.

Their main fields of study include:

  • Computer Science
  • Engineering

Within these fields, Mihaylova's work focuses on subfields such as:

  • Artificial Intelligence
  • Computer Networks and Communications
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Computer Vision and Pattern Recognition

The primary topics of research encompass:

  • Gaussian Processes and Bayesian Inference
  • Target Tracking and Data Fusion in Sensor Networks
  • Robotics and Sensor-Based Localization
  • Indoor and Outdoor Localization Technologies
  • Air Quality Monitoring and Forecasting
  • Distributed Sensor Networks and Detection Algorithms
  • Fault Detection and Control Systems

The scientist has contributed significant work in journals and conferences, with frequent publications in these venues:

  • IEEE Transactions on Aerospace and Electronic Systems
  • arXiv (Cornell University)
  • 2022 25th International Conference on Information Fusion (FUSION)
  • Neurocomputing
  • Frontiers in Robotics and AI

Notable recent papers include:

  • A deep learning-enhanced Digital Twin framework for improving safety and reliability in human-robot collaborative manufacturing (2023), Robotics and Computer-Integrated Manufacturing
  • Simultaneous Localization and Mapping for Inspection Robots in Water and Sewer Pipe Networks: A Review (2021), IEEE Access
  • A Sliding Window Variational Outlier-Robust Kalman Filter Based on Student's t-Noise Modeling (2022), IEEE Transactions on Aerospace and Electronic Systems
  • A Learning Gaussian Process Approach for Maneuvering Target Tracking and Smoothing (2020), IEEE Transactions on Aerospace and Electronic Systems
  • Analysis of Air Pollution in Urban Areas with Airviro Dispersion Model-A Case Study in the City of Sheffield, United Kingdom (2020), Atmosphere

Mihaylova collaborates frequently with several researchers, including:

  • Lance Kaplan
  • Gökhan İnalhan
  • Michael Rice
  • Daniel O'hagan
  • Editor-In-Chief Systems

Best Publications

  • Overview of Environment Perception for Intelligent Vehicles

    Hao Zhu;Ka-Veng Yuen;Lyudmila Mihaylova;Henry Leung

  • Brief paper: Freeway traffic estimation within particle filtering framework

    Lyudmila Mihaylova;René Boel;Andreas Hegyi

  • Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking

    Lyudmila Mihaylova;Avishy Y. Carmi;François Septier;Amadou Gning

  • Sequential Monte Carlo tracking by fusing multiple cues in video sequences

    Paul Brasnett;Lyudmila Mihaylova;David Bull;Nishan Canagarajah

  • A comparison of decision making criteria and optimization methods for active robotic sensing

    Lyudmila Mihaylova;Tine Lefebvre;Herman Bruyninckx;Klaas Gadeyne

  • Weighted Transfer Learning for Improving Motor Imagery-Based Brain–Computer Interface

    Ahmed M. Azab;Lyudmila Mihaylova;Kai Keng Ang;Mahnaz Arvaneh

  • A compositional stochastic model for real time freeway traffic simulation

    René Boel;Lyudmila Mihaylova

  • Mobility Tracking in Cellular Networks Using Particle Filtering

    L. Mihaylova;D. Angelova;S. Honary;D.R. Bull

  • Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks

    Oussama Laib;Mohamed Tarek Khadir;Lyudmila Mihaylova

  • Extended Object Tracking Using Monte Carlo Methods

    D. Angelova;L. Mihaylova

  • A particle filter for freeway traffic estimation

    L. Mihaylova;R. Boel

  • Bernoulli Particle/Box-Particle Filters for Detection and Tracking in the Presence of Triple Measurement Uncertainty

    A. Gning;B. Ristic;L. Mihaylova

  • Sequential Monte Carlo Methods for State and Parameter Estimation in Abruptly Changing Environments

    Christopher Nemeth;Paul Fearnhead;Lyudmila Mihaylova

  • Multiple object tracking using particle filters

    M. Jaward;L. Mihaylova;N. Canagarajah;D. Bull

  • Joint target tracking and classification with particle filtering and mixture Kalman filtering using kinematic radar information

    Donka Angelova;Lyudmila Mihaylova

  • Group Object Structure and State Estimation With Evolving Networks and Monte Carlo Methods

    Amadou Gning;Lyudmila Mihaylova;Simon Maskell;Sze Kim Pang

  • Structural Similarity-Based Object Tracking in Video Sequences

    A. Loza;L. Mihaylova;N. Canagarajah;D. Bull

  • A Review of Point Set Registration: From Pairwise Registration to Groupwise Registration.

    Hao Zhu;Bin Guo;Ke Zou;Yongfu Li

  • Wildlife surveillance using deep learning methods

    Ruilong Chen;Ruth Little;Lyudmila Mihaylova;Richard Delahay

  • Localization of Mobile Nodes in Wireless Networks with Correlated in Time Measurement Noise

    Lyudmila Mihaylova;Donka Angelova;David Bull;Nishan Canagarajah

  • Particle filtering with multiple cues for object tracking in video sequences

    Paul A. Brasnett;Lyudmila Mihaylova;Nishan Canagarajah;David R. Bull

Frequent Co-Authors

David Bull
David Bull University of Bristol
Simon J. Godsill
Simon J. Godsill University of Cambridge
Nishan Canagarajah
Nishan Canagarajah University of Bristol
Branko Ristic
Branko Ristic RMIT University
Jonathon A. Chambers
Jonathon A. Chambers Harbin Engineering University
Paul Fearnhead
Paul Fearnhead Lancaster University
Lakhmi C. Jain
Lakhmi C. Jain University of South Australia
Kai Keng Ang
Kai Keng Ang A*STAR - Agency for Science, Technology and Research

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