34 research outputs found
Investigating Temporal Convolutional Neural Networks for Satellite Image Time Series Classification
Satellite Image Time Series (SITS) of the Earth's surface provide detailed
land cover maps, with their quality in the spatial and temporal dimensions
consistently improving. These image time series are integral for developing
systems that aim to produce accurate, up-to-date land cover maps of the Earth's
surface. Applications are wide-ranging, with notable examples including
ecosystem mapping, vegetation process monitoring and anthropogenic land-use
change tracking. Recently proposed methods for SITS classification have
demonstrated respectable merit, but these methods tend to lack native
mechanisms that exploit the temporal dimension of the data; commonly resulting
in extensive data pre-processing prohibitively long training times. To overcome
these shortcomings, this paper seeks to study and enhance the newly proposed
method for SITS classification from literature; namely Temporal CNNs.
Comprehensive experiments are carried out on two benchmark SITS datasets with
the results demonstrating that Temporal CNNs display a superior or competitive
performance to the benchmark algorithms for both datasets. Investigations into
the Temporal CNNs architecture also highlighted the non-trivial task of
optimising the model for a new dataset.Comment: 20 pages, Submitted for publishin
RED CoMETS: An ensemble classifier for symbolically represented multivariate time series
Multivariate time series classification is a rapidly growing research field
with practical applications in finance, healthcare, engineering, and more. The
complexity of classifying multivariate time series data arises from its high
dimensionality, temporal dependencies, and varying lengths. This paper
introduces a novel ensemble classifier called RED CoMETS (Random Enhanced
Co-eye for Multivariate Time Series), which addresses these challenges. RED
CoMETS builds upon the success of Co-eye, an ensemble classifier specifically
designed for symbolically represented univariate time series, and extends its
capabilities to handle multivariate data. The performance of RED CoMETS is
evaluated on benchmark datasets from the UCR archive, where it demonstrates
competitive accuracy when compared to state-of-the-art techniques in
multivariate settings. Notably, it achieves the highest reported accuracy in
the literature for the 'HandMovementDirection' dataset. Moreover, the proposed
method significantly reduces computation time compared to Co-eye, making it an
efficient and effective choice for multivariate time series classification.Comment: Accepted by AALTD 2023; fixed typos and minor error in Table
Understandable Controller Extraction from Video Observations of Swarms
Swarm behavior emerges from the local interaction of agents and their
environment often encoded as simple rules. Extracting the rules by watching a
video of the overall swarm behavior could help us study and control swarm
behavior in nature, or artificial swarms that have been designed by external
actors. It could also serve as a new source of inspiration for swarm robotics.
Yet extracting such rules is challenging as there is often no visible link
between the emergent properties of the swarm and their local interactions. To
this end, we develop a method to automatically extract understandable swarm
controllers from video demonstrations. The method uses evolutionary algorithms
driven by a fitness function that compares eight high-level swarm metrics. The
method is able to extract many controllers (behavior trees) in a simple
collective movement task. We then provide a qualitative analysis of behaviors
that resulted in different trees, but similar behaviors. This provides the
first steps toward automatic extraction of swarm controllers based on
observations
Activity Recognition with Evolving Data Streams: A Review
Activity recognition aims to provide accurate and opportune information on people’s activities by leveraging
sensory data available in today’s sensory rich environments. Nowadays, activity recognition has become an
emerging field in the areas of pervasive and ubiquitous computing. A typical activity recognition technique
processes data streams that evolve from sensing platforms such as mobile sensors, on body sensors, and/or
ambient sensors. This paper surveys the two overlapped areas of research of activity recognition and data
stream mining. The perspective of this paper is to review the adaptation capabilities of activity recognition
techniques in streaming environment. Categories of techniques are identified based on different features
in both data streams and activity recognition. The pros and cons of the algorithms in each category are
analysed and the possible directions of future research are indicated
A Time Series Approach to Parkinson's Disease Classification from EEG
Firstly, we present a novel representation for EEG data, a 7-variate series
of band power coefficients, which enables the use of (previously inaccessible)
time series classification methods. Specifically, we implement the
multi-resolution representation-based time series classification method MrSQL.
This is deployed on a challenging early-stage Parkinson's dataset that includes
wakeful and sleep EEG. Initial results are promising with over 90% accuracy
achieved on all EEG data types used. Secondly, we present a framework that
enables high-importance data types and brain regions for classification to be
identified. Using our framework, we find that, across different EEG data types,
it is the Prefrontal brain region that has the most predictive power for the
presence of Parkinson's Disease. This outperformance was statistically
significant versus ten of the twelve other brain regions (not significant
versus adjacent Left Frontal and Right Frontal regions). The Prefrontal region
of the brain is important for higher-order cognitive processes and our results
align with studies that have shown neural dysfunction in the prefrontal cortex
in Parkinson's Disease