21 research outputs found
Segment Parameter Labelling in MCMC Mean-Shift Change Detection
This work addresses the problem of segmentation in time series data with
respect to a statistical parameter of interest in Bayesian models. It is common
to assume that the parameters are distinct within each segment. As such, many
Bayesian change point detection models do not exploit the segment parameter
patterns, which can improve performance. This work proposes a Bayesian
mean-shift change point detection algorithm that makes use of repetition in
segment parameters, by introducing segment class labels that utilise a
Dirichlet process prior. The performance of the proposed approach was assessed
on both synthetic and real world data, highlighting the enhanced performance
when using parameter labelling
Multivariate time-frequency analysis
Recent advances in time-frequency theory have led to the development of high resolution time-frequency algorithms, such as the empirical mode decomposition (EMD) and
the synchrosqueezing transform (SST). These algorithms provide enhanced localization in representing time varying oscillatory components over conventional linear and quadratic time-frequency algorithms. However, with the emergence of low cost multichannel sensor technology, multivariate extensions of time-frequency algorithms are needed in order to
exploit the inter-channel dependencies that may arise for multivariate data. Applications
of this framework range from filtering to the analysis of oscillatory components.
To this end, this thesis first seeks to introduce a multivariate extension of the synchrosqueezing transform, so as to identify a set of oscillations common to the multivariate
data. Furthermore, a new framework for multivariate time-frequency representations is developed using the proposed multivariate extension of the SST. The performance of the proposed algorithms are demonstrated on a wide variety of both simulated and real world
data sets, such as in phase synchrony spectrograms and multivariate signal denoising.
Finally, multivariate extensions of the EMD have been developed that capture the
inter-channel dependencies in multivariate data. This is achieved by processing such data directly in higher dimensional spaces where they reside, and by accounting for the power
imbalance across multivariate data channels that are recorded from real world sensors, thereby preserving the multivariate structure of the data. These optimized performance
of such data driven algorithms when processing multivariate data with power imbalances and inter-channel correlations, and is demonstrated on the real world examples of Doppler radar processing.Open Acces
RADIATE: A Radar Dataset for Automotive Perception in Bad Weather
Datasets for autonomous cars are essential for the development and
benchmarking of perception systems. However, most existing datasets are
captured with camera and LiDAR sensors in good weather conditions. In this
paper, we present the RAdar Dataset In Adverse weaThEr (RADIATE), aiming to
facilitate research on object detection, tracking and scene understanding using
radar sensing for safe autonomous driving. RADIATE includes 3 hours of
annotated radar images with more than 200K labelled road actors in total, on
average about 4.6 instances per radar image. It covers 8 different categories
of actors in a variety of weather conditions (e.g., sun, night, rain, fog and
snow) and driving scenarios (e.g., parked, urban, motorway and suburban),
representing different levels of challenge. To the best of our knowledge, this
is the first public radar dataset which provides high-resolution radar images
on public roads with a large amount of road actors labelled. The data collected
in adverse weather, e.g., fog and snowfall, is unique. Some baseline results of
radar based object detection and recognition are given to show that the use of
radar data is promising for automotive applications in bad weather, where
vision and LiDAR can fail. RADIATE also has stereo images, 32-channel LiDAR and
GPS data, directed at other applications such as sensor fusion, localisation
and mapping. The public dataset can be accessed at
http://pro.hw.ac.uk/radiate/.Comment: Accepted at IEEE International Conference on Robotics and Automation
2021 (ICRA 2021
Detecting Changes in the Variance of Multi-Sensory Accelerometer Data Using MCMC
An important field in exploratory sensory data analysis is the segmentation of time-series data to identify activities of interest. In this work, we analyse the performance of univariate and multi-sensor Bayesian change detection algorithms in segmenting accelerometer data. In particular, we provide theoretical analysis and also performance evaluation on synthetic data and real-world data. The results illustrate the advantages of using multi-sensory variance change detection in the segmentation of dynamic data (e.g. accelerometer data)
Pattern Identification for State Prediction in Dynamic Data Streams
This work proposes a pattern identification and online prediction algorithm for processing Internet of Things (IoT) time-series data. This is achieved by first proposing a new data aggregation and datadriven discretisation method that does not require data segment normalisation. We apply a dictionary based algorithm in order to identify patterns of interest along with prediction of the next pattern. The performance of the proposed method is evaluated using synthetic and real-world datasets. The evaluations results shows that our system is able to identify the patterns by up to 85% accuracy which is 16.5% higher than a baseline using the Symbolic Aggregation Approximation (SAX) method
Combining automotive radar and LiDAR for surface detection in adverse conditions
Abstract Automotive radar and light detection and ranging (LiDAR) sensors have complementary strengths and weaknesses for 3D surface mapping. We present a method using Markov chain Monte Carlo sampling to recover surface returns from full‐wave longitudinal signals that takes advantage of the high spatial resolution of the LiDAR in range, azimuth and elevation together with the ability of the radar to penetrate obscuring media. The approach is demonstrated using both simulated and real data from an automotive system
Stream Data Analysis as a web service: A Case Study Using IoT Sensor Data
The advent of Internet of Things, has resulted in the development of infrastructure for capturing and storing data from domains ranging from smart devices (e.g. smartphones) to smart cities. This data is often available publicly and has enabled a wider range of data consumers to utilise such data sets for applications ranging from scientific experimentation to enhancing commercial activity for businesses. Accordingly this has resulted in the need for the development data analysis tools that are both simple to use and provide the most effective tools for a given data set. To this end, we introduce data analysis tools as web service, that enables the data consumer to make a simple HTTP request for processing data over the internet. By providing such tools as a web service, we demonstrate the potential of such a system to aid both the advanced and novice data consumer. Furthermore, this work provides an use case example of the proposed tool on publicly available data extracted from the smart city CityPulse IoT project