2,886 research outputs found
Flood dynamics derived from video remote sensing
Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models.
Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science
Sustainable Collaboration: Federated Learning for Environmentally Conscious Forest Fire Classification in Green Internet of Things (IoT)
Forests are an invaluable natural resource, playing a crucial role in the regulation of both local and global climate patterns. Additionally, they offer a plethora of benefits such as medicinal plants, food, and non-timber forest products. However, with the growing global population, the demand for forest resources has escalated, leading to a decline in their abundance. The reduction in forest density has detrimental impacts on global temperatures and raises the likelihood of forest fires. To address these challenges, this paper introduces a Federated Learning framework empowered by the Internet of Things (IoT). The proposed framework integrates with an Intelligent system, leveraging mounted cameras strategically positioned in highly vulnerable areas susceptible to forest fires. This integration enables the timely detection and monitoring of forest fire occurrences and plays its part in avoiding major catastrophes. The proposed framework incorporates the Federated Stochastic Gradient Descent (FedSGD) technique to aggregate the global model in the cloud. The dataset employed in this study comprises two classes: fire and non-fire images. This dataset is distributed among five nodes, allowing each node to independently train the model on their respective devices. Following the local training, the learned parameters are shared with the cloud for aggregation, ensuring a collective and comprehensive global model. The effectiveness of the proposed framework is assessed by comparing its performance metrics with the recent work. The proposed algorithm achieved an accuracy of 99.27 % and stands out by leveraging the concept of collaborative learning. This approach distributes the workload among nodes, relieving the server from excessive burden. Each node is empowered to obtain the best possible model for classification, even if it possesses limited data. This collaborative learning paradigm enhances the overall efficiency and effectiveness of the classification process, ensuring optimal results in scenarios where data availability may be constrained
Improving Cross-Lingual Transfer Learning for Event Detection
The widespread adoption of applications powered by Artificial Intelligence (AI) backbones has unquestionably changed the way we interact with the world around us. Applications such as automated personal assistants, automatic question answering, and machine-based translation systems have become mainstays of modern culture thanks to the recent considerable advances in Natural Language Processing (NLP) research. Nonetheless, with over 7000 spoken languages in the world, there still remain a considerable number of marginalized communities that are unable to benefit from these technological advancements largely due to the language they speak. Cross-Lingual Learning (CLL) looks to address this issue by transferring the knowledge acquired from a popular, high-resource source language (e.g., English, Chinese, or Spanish) to a less favored, lower-resourced target language (e.g., Urdu or Swahili). This dissertation leverages the Event Detection (ED) sub-task of Information Extraction (IE) as a testbed and presents three novel approaches that improve cross-lingual transfer learning from distinct perspectives: (1) direct knowledge transfer, (2) hybrid knowledge transfer, and (3) few-shot learning
A Benchmark Comparison of Visual Place Recognition Techniques for Resource-Constrained Embedded Platforms
Autonomous navigation has become a widely researched area of expertise over the past few years, gaining a massive following due to its necessity in creating a fully autonomous robotic system. Autonomous navigation is an exceedingly difficult task to accomplish in and of itself. Successful navigation relies heavily on the ability to self-localise oneself within a given environment. Without this awareness of one’s
own location, it is impossible to successfully navigate in an autonomous manner. Since its inception Simultaneous Localization and Mapping (SLAM) has become one of the most widely researched areas of autonomous navigation. SLAM focuses on self-localization within a mapped or un-mapped environment, and constructing or updating the map of one’s surroundings. Visual Place Recognition (VPR) is an essential part of any SLAM system. VPR relies on visual cues to determine one’s location within a mapped environment.
This thesis presents two main topics within the field of VPR. First, this thesis presents a benchmark analysis of several popular embedded platforms when performing VPR. The presented benchmark analyses six different VPR techniques
across three different datasets, and investigates accuracy, CPU usage, memory usage, processing time and power consumption. The benchmark demonstrated a clear relationship between platform architecture and the metrics measured, with platforms of the same architecture achieving comparable accuracy and algorithm efficiency.
Additionally, the Raspberry Pi platform was noted as a standout in terms of algorithm efficiency and power consumption.
Secondly, this thesis proposes an evaluation framework intended to provide information about a VPR technique’s useability within a real-time application. The approach
makes use of the incoming frame rate of an image stream and the VPR frame rate, the rate at which the technique can perform VPR, to determine how efficient VPR techniques would be in a real-time environment. This evaluation framework determined that CoHOG would be the most effective algorithm to be deployed in a real-time environment as it had the best ratio between computation time and accuracy
SFCNeXt: a simple fully convolutional network for effective brain age estimation with small sample size
Deep neural networks (DNN) have been designed to predict the chronological
age of a healthy brain from T1-weighted magnetic resonance images (T1 MRIs),
and the predicted brain age could serve as a valuable biomarker for the early
detection of development-related or aging-related disorders. Recent DNN models
for brain age estimations usually rely too much on large sample sizes and
complex network structures for multi-stage feature refinement. However, in
clinical application scenarios, researchers usually cannot obtain thousands or
tens of thousands of MRIs in each data center for thorough training of these
complex models. This paper proposes a simple fully convolutional network
(SFCNeXt) for brain age estimation in small-sized cohorts with biased age
distributions. The SFCNeXt consists of Single Pathway Encoded ConvNeXt (SPEC)
and Hybrid Ranking Loss (HRL), aiming to estimate brain ages in a lightweight
way with a sufficient exploration of MRI, age, and ranking features of each
batch of subjects. Experimental results demonstrate the superiority and
efficiency of our approach.Comment: This paper has been accepted by IEEE ISBI 202
Pre-processing training data improves accuracy and generalisability of convolutional neural network based landscape semantic segmentation
In this paper, we trialled different methods of data preparation for
Convolutional Neural Network (CNN) training and semantic segmentation of land
use land cover (LULC) features within aerial photography over the Wet Tropics
and Atherton Tablelands, Queensland, Australia. This was conducted through
trialling and ranking various training patch selection sampling strategies,
patch and batch sizes and data augmentations and scaling. We also compared
model accuracy through producing the LULC classification using a single pass of
a grid of patches and averaging multiple grid passes and three rotated version
of each patch. Our results showed: a stratified random sampling approach for
producing training patches improved the accuracy of classes with a smaller area
while having minimal effect on larger classes; a smaller number of larger
patches compared to a larger number of smaller patches improves model accuracy;
applying data augmentations and scaling are imperative in creating a
generalised model able to accurately classify LULC features in imagery from a
different date and sensor; and producing the output classification by averaging
multiple grids of patches and three rotated versions of each patch produced and
more accurate and aesthetic result. Combining the findings from the trials, we
fully trained five models on the 2018 training image and applied the model to
the 2015 test image with the output LULC classifications achieving an average
kappa of 0.84 user accuracy of 0.81 and producer accuracy of 0.87. This study
has demonstrated the importance of data pre-processing for developing a
generalised deep-learning model for LULC classification which can be applied to
a different date and sensor. Future research using CNN and earth observation
data should implement the findings of this study to increase LULC model
accuracy and transferability
Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data
Programa de Doctorado en BiotecnologĂa, IngenierĂa y TecnologĂa QuĂmicaLĂnea de InvestigaciĂłn: IngenierĂa, Ciencia de Datos y BioinformáticaClave Programa: DBICĂłdigo LĂnea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques.
Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic
Unveiling the frontiers of deep learning: innovations shaping diverse domains
Deep learning (DL) enables the development of computer models that are
capable of learning, visualizing, optimizing, refining, and predicting data. In
recent years, DL has been applied in a range of fields, including audio-visual
data processing, agriculture, transportation prediction, natural language,
biomedicine, disaster management, bioinformatics, drug design, genomics, face
recognition, and ecology. To explore the current state of deep learning, it is
necessary to investigate the latest developments and applications of deep
learning in these disciplines. However, the literature is lacking in exploring
the applications of deep learning in all potential sectors. This paper thus
extensively investigates the potential applications of deep learning across all
major fields of study as well as the associated benefits and challenges. As
evidenced in the literature, DL exhibits accuracy in prediction and analysis,
makes it a powerful computational tool, and has the ability to articulate
itself and optimize, making it effective in processing data with no prior
training. Given its independence from training data, deep learning necessitates
massive amounts of data for effective analysis and processing, much like data
volume. To handle the challenge of compiling huge amounts of medical,
scientific, healthcare, and environmental data for use in deep learning, gated
architectures like LSTMs and GRUs can be utilized. For multimodal learning,
shared neurons in the neural network for all activities and specialized neurons
for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table
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