86 citations · 192 across the 27 of their papers we have counts for
29 papers
Gaussian Process Upper Confidence Bounds in Distributed Point Target Tracking over Wireless Sensor Networks
Xingchi Liu, Lyudmila Mihaylova, Jemin George +1
Uncertainty quantification plays a key role in the development of autonomous systems, decision-making, and tracking over wireless sensor networks (WSNs). However, there is a need o…
Out-of-distribution Object Detection through Bayesian Uncertainty Estimation
Tianhao Zhang, Shenglin Wang, Nidhal Bouaynaya +2
The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many prac…
Automated Movement Detection with Dirichlet Process Mixture Models and Electromyography
Navin Cooray, Zhenglin Li, Jinzhuo Wang +6
Numerous sleep disorders are characterised by movement during sleep, these include rapid-eye movement sleep behaviour disorder (RBD) and periodic limb movement disorder. The proces…
3, 2, 1, Drones Go! A Testbed to Take off UAV Swarm Intelligence for Distributed Sensing
Chuhao Qin, Fethi Candan, Lyudmila S. Mihaylova +1
This paper introduces a testbed to study distributed sensing problems of Unmanned Aerial Vehicles (UAVs) exhibiting swarm intelligence. Several Smart City applications, such as tra…
Robust Learning via Ensemble Density Propagation in Deep Neural Networks
Giuseppina Carannante, Dimah Dera, Ghulam Rasool +2
Learning in uncertain, noisy, or adversarial environments is a challenging task for deep neural networks (DNNs). We propose a new theoretically grounded and efficient approach for…
Scalable Learning With a Structural Recurrent Neural Network for Short-Term Traffic Prediction
Youngjoo Kim, Peng Wang, Lyudmila Mihaylova
This paper presents a scalable deep learning approach for short-term traffic prediction based on historical traffic data in a vehicular road network. Capturing the spatio-temporal…
Variational Bayesian inference of hidden stochastic processes with unknown parameters
Komlan Atitey, Pavel Loskot, Lyudmila Mihaylova
Estimating hidden processes from non-linear noisy observations is particularly difficult when the parameters of these processes are not known. This paper adopts a machine learning…
Structural Recurrent Neural Network for Traffic Speed Prediction
Youngjoo Kim, Peng Wang, Lyudmila Mihaylova
Deep neural networks have recently demonstrated the traffic prediction capability with the time series data obtained by sensors mounted on road segments. However, capturing spatio-…
Comprehensive review of models and methods for inferences in bio-chemical reaction networks
Pavel Loskot, Komlan Atitey, Lyudmila Mihaylova
Key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reac…
Uncertainty propagation in neural networks for sparse coding
Danil Kuzin, Olga Isupova, Lyudmila Mihaylova
A novel method to propagate uncertainty through the soft-thresholding nonlinearity is proposed in this paper. At every layer the current distribution of the target vector is repres…
Spatio-Temporal Structured Sparse Regression with Hierarchical Gaussian Process Priors
Danil Kuzin, Olga Isupova, Lyudmila Mihaylova
This paper introduces a new sparse spatio-temporal structured Gaussian process regression framework for online and offline Bayesian inference. This is the first framework that give…
Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning
Danil Kuzin, Le Yang, Olga Isupova +1
Gaussian process regression is a machine learning approach which has been shown its power for estimation of unknown functions. However, Gaussian processes suffer from high computat…
A Capsule Network for Traffic Speed Prediction in Complex Road Networks
Youngjoo Kim, Peng Wang, Yifei Zhu +1
This paper proposes a deep learning approach for traffic flow prediction in complex road networks. Traffic flow data from induction loop sensors are essentially a time series, whic…
Compressive Sensing Approaches for Autonomous Object Detection in Video Sequences
Danil Kuzin, Olga Isupova, Lyudmila Mihaylova
Video analytics requires operating with large amounts of data. Compressive sensing allows to reduce the number of measurements required to represent the video using the prior knowl…
Structured Sparse Modelling with Hierarchical GP
Danil Kuzin, Olga Isupova, Lyudmila Mihaylova
In this paper a new Bayesian model for sparse linear regression with a spatio-temporal structure is proposed. It incorporates the structural assumptions based on a hierarchical Gau…
Bayesian Compressive Sensing Approaches for Direction of Arrival Estimation with Mutual Coupling Effects
Matthew Hawes, Lyudmila Mihaylova, François Septier +1
The problem of estimating the dynamic direction of arrival of far field signals impinging on a uniform linear array, with mutual coupling effects, is addressed. This work proposes…
Location and Orientation Optimisation for Spatially Stretched Tripole Arrays Based on Compressive Sensing
Matthew Hawes, Lyudmila Mihaylova, Wei Liu
The design of sparse spatially stretched tripole arrays is an important but also challenging task and this paper proposes for the very first time efficient solutions to this proble…
Learning Methods for Dynamic Topic Modeling in Automated Behaviour Analysis
Olga Isupova, Danil Kuzin, Lyudmila Mihaylova
Semi-supervised and unsupervised systems provide operators with invaluable support and can tremendously reduce the operators load. In the light of the necessity to process large vo…
Dynamic Hierarchical Dirichlet Process for Abnormal Behaviour Detection in Video
Olga Isupova, Danil Kuzin, Lyudmila Mihaylova
This paper proposes a novel dynamic Hierarchical Dirichlet Process topic model that considers the dependence between successive observations. Conventional posterior inference algor…
Anomaly detection in video with Bayesian nonparametrics
Olga Isupova, Danil Kuzin, Lyudmila Mihaylova
A novel dynamic Bayesian nonparametric topic model for anomaly detection in video is proposed in this paper. Batch and online Gibbs samplers are developed for inference. The paper…
Compressive Sensing Based Design of Sparse Tripole Arrays
Matthew Hawes, Wei Liu, Lyudmila Mihaylova
This paper considers the problem of designing sparse linear tripole arrays. In such arrays at each antenna location there are three orthogonal dipoles, allowing full measurement of…
Autonomous Crowds Tracking with Box Particle Filtering and Convolution Particle Filtering
Allan De Freitas, Lyudmila Mihaylova, Amadou Gning +2
Autonomous systems such as Unmanned Aerial Vehicles (UAVs) need to be able to recognise and track crowds of people, e.g. for rescuing and surveillance purposes. Large groups genera…
Sequential Markov Chain Monte Carlo for Bayesian Filtering with Massive Data
Allan De Freitas, François Septier, Lyudmila Mihaylova
Advances in digital sensors, digital data storage and communications have resulted in systems being capable of accumulating large collections of data. In the light of dealing with…
Multi-Target Tracking and Occlusion Handling with Learned Variational Bayesian Clusters and a Social Force Model
Ata-ur-Rehman, Syed Mohsen Naqvi, Lyudmila Mihaylova +1
This paper considers the problem of multiple human target tracking in a sequence of video data. A solution is proposed which is able to deal with the challenges of a varying number…