5,506 research outputs found
Deep Learning Techniques for Electroencephalography Analysis
In this thesis we design deep learning techniques for training deep neural networks on electroencephalography (EEG) data and in particular on two problems, namely EEG-based motor imagery decoding and EEG-based affect recognition, addressing challenges associated with them. Regarding the problem of motor imagery (MI) decoding, we first consider the various kinds of domain shifts in the EEG signals, caused by inter-individual differences (e.g. brain anatomy, personality and cognitive profile). These domain shifts render multi-subject training a challenging task and impede robust cross-subject generalization. We build a two-stage model ensemble architecture and propose two objectives to train it, combining the strengths of curriculum learning and collaborative training. Our subject-independent experiments on the large datasets of Physionet and OpenBMI, verify the effectiveness of our approach. Next, we explore the utilization of the spatial covariance of EEG signals through alignment techniques, with the goal of learning domain-invariant representations. We introduce a Riemannian framework that concurrently performs covariance-based signal alignment and data augmentation, while training a convolutional neural network (CNN) on EEG time-series. Experiments on the BCI IV-2a dataset show that our method performs superiorly over traditional alignment, by inducing regularization to the weights of the CNN. We also study the problem of EEG-based affect recognition, inspired by works suggesting that emotions can be expressed in relative terms, i.e. through ordinal comparisons between different affective state levels. We propose treating data samples in a pairwise manner to infer the ordinal relation between their corresponding affective state labels, as an auxiliary training objective. We incorporate our objective in a deep network architecture which we jointly train on the tasks of sample-wise classification and pairwise ordinal ranking. We evaluate our method on the affective datasets of DEAP and SEED and obtain performance improvements over deep networks trained without the additional ranking objective
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
ABC: Adaptive, Biomimetic, Configurable Robots for Smart Farms - From Cereal Phenotyping to Soft Fruit Harvesting
Currently, numerous factors, such as demographics, migration patterns, and economics, are leading to the critical labour shortage in low-skilled and physically demanding parts of agriculture. Thus, robotics can be developed for the agricultural sector to address these shortages. This study aims to develop an adaptive, biomimetic, and configurable modular robotics architecture that can be applied to multiple tasks (e.g., phenotyping, cutting, and picking), various crop varieties (e.g., wheat, strawberry, and tomato) and growing conditions. These robotic solutions cover the entire perception–action–decision-making loop targeting the phenotyping of cereals and harvesting fruits in a natural environment.
The primary contributions of this thesis are as follows. a) A high-throughput method for imaging field-grown wheat in three dimensions, along with an accompanying unsupervised measuring method for obtaining individual wheat spike data are presented. The unsupervised method analyses the 3D point cloud of each trial plot, containing hundreds of wheat spikes, and calculates the average size of the wheat spike and total spike volume per plot. Experimental results reveal that the proposed algorithm can effectively identify spikes from wheat crops and individual spikes. b) Unlike cereal, soft fruit is typically harvested by manual selection and picking. To enable robotic harvesting, the initial perception system uses conditional generative adversarial networks to identify ripe fruits using synthetic data. To determine whether the strawberry is surrounded by obstacles, a cluster complexity-based perception system is further developed to classify the harvesting complexity of ripe strawberries. c) Once the harvest-ready fruit is localised using point cloud data generated by a stereo camera, the platform’s action system can coordinate the arm to reach/cut the stem using the passive motion paradigm framework, as inspired by studies on neural control of movement in the brain. Results from field trials for strawberry detection, reaching/cutting the stem of the fruit with a mean error of less than 3 mm, and extension to analysing complex canopy structures/bimanual coordination (searching/picking) are presented.
Although this thesis focuses on strawberry harvesting, ongoing research is heading toward adapting the architecture to other crops. The agricultural food industry remains a labour-intensive sector with a low margin, and cost- and time-efficiency business model. The concepts presented herein can serve as a reference for future agricultural robots that are adaptive, biomimetic, and configurable
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring
Artificially intelligent perception is increasingly present in the lives of
every one of us. Vehicles are no exception, (...) In the near future, pattern
recognition will have an even stronger role in vehicles, as self-driving cars
will require automated ways to understand what is happening around (and within)
them and act accordingly. (...) This doctoral work focused on advancing
in-vehicle sensing through the research of novel computer vision and pattern
recognition methodologies for both biometrics and wellbeing monitoring. The
main focus has been on electrocardiogram (ECG) biometrics, a trait well-known
for its potential for seamless driver monitoring. Major efforts were devoted to
achieving improved performance in identification and identity verification in
off-the-person scenarios, well-known for increased noise and variability. Here,
end-to-end deep learning ECG biometric solutions were proposed and important
topics were addressed such as cross-database and long-term performance,
waveform relevance through explainability, and interlead conversion. Face
biometrics, a natural complement to the ECG in seamless unconstrained
scenarios, was also studied in this work. The open challenges of masked face
recognition and interpretability in biometrics were tackled in an effort to
evolve towards algorithms that are more transparent, trustworthy, and robust to
significant occlusions. Within the topic of wellbeing monitoring, improved
solutions to multimodal emotion recognition in groups of people and
activity/violence recognition in in-vehicle scenarios were proposed. At last,
we also proposed a novel way to learn template security within end-to-end
models, dismissing additional separate encryption processes, and a
self-supervised learning approach tailored to sequential data, in order to
ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022
to the University of Port
Tradition and Innovation in Construction Project Management
This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings
What are the performance drivers of China’s video game industry? :A multi-level modelling analysis
HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Pose Dataset with Household Objects in Realistic Scenarios
Estimating the 6D pose of objects is a major 3D computer vision problem.
Since the promising outcomes from instance-level approaches, research heads
also move towards category-level pose estimation for more practical application
scenarios. However, unlike well-established instance-level pose datasets,
available category-level datasets lack annotation quality and provided pose
quantity. We propose the new category-level 6D pose dataset HouseCat6D
featuring 1) Multi-modality of Polarimetric RGB and Depth (RGBD+P), 2) Highly
diverse 194 objects of 10 household object categories including 2
photometrically challenging categories, 3) High-quality pose annotation with an
error range of only 1.35 mm to 1.74 mm, 4) 41 large-scale scenes with extensive
viewpoint coverage and occlusions, 5) Checkerboard-free environment throughout
the entire scene, and 6) Additionally annotated dense 6D parallel-jaw grasps.
Furthermore, we also provide benchmark results of state-of-the-art
category-level pose estimation networks
VARS: Video Assistant Referee System for Automated Soccer Decision Making from Multiple Views
The Video Assistant Referee (VAR) has revolutionized association football,
enabling referees to review incidents on the pitch, make informed decisions,
and ensure fairness. However, due to the lack of referees in many countries and
the high cost of the VAR infrastructure, only professional leagues can benefit
from it. In this paper, we propose a Video Assistant Referee System (VARS) that
can automate soccer decision-making. VARS leverages the latest findings in
multi-view video analysis, to provide real-time feedback to the referee, and
help them make informed decisions that can impact the outcome of a game. To
validate VARS, we introduce SoccerNet-MVFoul, a novel video dataset of soccer
fouls from multiple camera views, annotated with extensive foul descriptions by
a professional soccer referee, and we benchmark our VARS to automatically
recognize the characteristics of these fouls. We believe that VARS has the
potential to revolutionize soccer refereeing and take the game to new heights
of fairness and accuracy across all levels of professional and amateur
federations.Comment: Accepted at CVSports'2
Cerebral Metamorphopsia: Perceived spatial distortion from lesions of the adult human central visual pathway
Metamorphopsia is the perceived visual illusion of spatial distortion. Cerebral causes of metamorphopsia are much less common than retinal or ocular causes. Cerebral metamorphopsia can be caused by lesions along the central visual pathway or as a manifestation of epileptogenic discharges. Geometric visual distortions may result from structural lesions of the central visual pathway after reorganisation of the retinotopic representation in the cortex. Very few experimental investigations have been performed regarding cerebral metamorphopsia as it is often viewed as a clinical curiousity and analysis of the perceived distortion is difficult due to its subjective nature. Investigations have been undertaken to understand cortical plasticity as an explanation for visual filling-in. There has been much interest in cortical reorganisation after injuries to the peripheral and central visual pathway. Behavioural experiments aimed at quantifying the possible visual spatial distortion surrounding homonymous paracentral scotomas may be able to demonstrate cortical reorganisation after brain-damage and provide clues regarding the neural processes of visual perception.
The aims of the thesis are:
1. To identify which cases of metamorphopsia, both published and unpublished, might be a consequence of cortical spatial reorganisation of retinotopic projections.
2. To investigate perceptual spatial distortion surrounding homonymous paracentral scotomas in adults with isolated unilateral injuries of the striate cortex.
A review of the literature describing cases of cerebral metamorphopsia was performed. Metamorphopsia caused by retinal or ocular pathology, psychiatric conditions, drugs or medications were excluded. A retrospective case series of eight patients with metamorphopsia from a cerebral cause was performed in two clinical neurology practices specialising in vision disorders. Two cases who suffered from paracentral homonymous scotomas due to isolated unilateral primary visual cortex (V1) lesions were identified from a Neuro-ophthalmology practice. Neuropsychophysical experiments to investigate visual spatial perception surrounding their scotomas were developed and tested using MATLAB and Psychtoolbox.
The use of the term 'metamorphopsia' was only in reference to cases in which contours or lines were experienced as distorted. In the published literature, few cases of cerebral metamorphopsia have been identified as being potentially due to cortical reorganisation. The main result is a statistically significant visual spatial distortion in the visual field surrounding a paracentral homonymous scotoma when compared to a normal control. There is also significant distortion of perception in the subjects' "unaffected" visual hemifield.
After lesions of V1, visual perceptual spatial distortions may occur in the visual field surrounding homonymous paracentral scotomas. The spatial distortion may also occur in the normal hemifield possibly due to long-range cortical connections crossing to the other hemisphere through the corpus callosum. A collaborative approach across disciplines within vision science is required to further investigate the mechanisms responsible for perceptual visual illusions. Behavioural testing in brain-damaged cases remains important in developing theories of normal visual processing. New neuroimaging and neuroscience techniques could then test these theories, furthering our understanding of visual perception. An understanding of normal visual perception could allow future modification of neuronal processes to harness cortical reorganisation and potentially restore functional vision in humans with lesions of the central visual pathway
Characterising the neck motor system of the blowfly
Flying insects use visual, mechanosensory, and proprioceptive information to control their
movements, both when on the ground and when airborne. Exploiting visual information for
motor control is significantly simplified if the eyes remain aligned with the external horizon.
In fast flying insects, head rotations relative to the body enable gaze stabilisation during highspeed
manoeuvres or externally caused attitude changes due to turbulent air.
Previous behavioural studies into gaze stabilisation suffered from the dynamic properties
of the supplying sensor systems and those of the neck motor system being convolved.
Specifically, stabilisation of the head in Dipteran flies responding to induced thorax roll
involves feed forward information from the mechanosensory halteres, as well as feedback
information from the visual systems. To fully understand the functional design of the blowfly
gaze stabilisation system as a whole, the neck motor system needs to be investigated
independently.
Through X-ray micro-computed tomography (ÎĽCT), high resolution 3D data has become
available, and using staining techniques developed in collaboration with the Natural History
Museum London, detailed anatomical data can be extracted. This resulted in a full 3-
dimensional anatomical representation of the 21 neck muscle pairs and neighbouring cuticula
structures which comprise the blowfly neck motor system.
Currently, on the work presented in my PhD thesis, ÎĽCT data are being used to infer
function from structure by creating a biomechanical model of the neck motor system. This
effort aims to determine the specific function of each muscle individually, and is likely to
inform the design of artificial gaze stabilisation systems. Any such design would incorporate
both sensory and motor systems as well as the control architecture converting sensor signals
into motor commands under the given physical constraints of the system as a whole.Open Acces
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