2,521 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
Redefining Disproportionate Arrest Rates: An Exploratory Quasi-Experiment that Reassesses the Role of Skin Tone
The New York Times reported that Black Lives Matter was the third most-read subject of 2020. These articles brought to the forefront the question of disparity in arrest rates for darker-skinned people. Questioning arrest disparity is understandable because virtually everything known about disproportionate arrest rates has been a guess, and virtually all prior research on disproportionate arrest rates is questionable because of improper benchmarking (the denominator effect). Current research has highlighted the need to switch from demographic data to skin tone data and start over on disproportionate arrest rate research; therefore, this study explored the relationship between skin tone and disproportionate arrest rates. This study also sought to determine which of the three theories surrounding disproportionate arrests is most predictive of disproportionate rates. The current theories are that disproportionate arrests increase as skin tone gets darker (stereotype threat theory), disproportionate rates are different for Black and Brown people (self-categorization theory), or disproportionate rates apply equally across all darker skin colors (social dominance theory). This study used a quantitative exploratory quasi-experimental design using linear spline regression to analyze arrest rates in Alachua County, Florida, before and after the county’s mandate to reduce arrests as much as possible during the COVID-19 pandemic to protect the prison population. The study was exploratory as no previous study has used skin tone analysis to examine arrest disparity. The findings of this study redefines the understanding of the existence and nature of disparities in arrest rates and offer a solid foundation for additional studies about the relationship between disproportionate arrest rates and skin color
Cashew dataset generation using augmentation and RaLSGAN and a transfer learning based tinyML approach towards disease detection
Cashew is one of the most extensively consumed nuts in the world, and it is
also known as a cash crop. A tree may generate a substantial yield in a few
months and has a lifetime of around 70 to 80 years. Yet, in addition to the
benefits, there are certain constraints to its cultivation. With the exception
of parasites and algae, anthracnose is the most common disease affecting trees.
When it comes to cashew, the dense structure of the tree makes it difficult to
diagnose the disease with ease compared to short crops. Hence, we present a
dataset that exclusively consists of healthy and diseased cashew leaves and
fruits. The dataset is authenticated by adding RGB color transformation to
highlight diseased regions, photometric and geometric augmentations, and
RaLSGAN to enlarge the initial collection of images and boost performance in
real-time situations when working with a constrained dataset. Further, transfer
learning is used to test the classification efficiency of the dataset using
algorithms such as MobileNet and Inception. TensorFlow lite is utilized to
develop these algorithms for disease diagnosis utilizing drones in real-time.
Several post-training optimization strategies are utilized, and their memory
size is compared. They have proven their effectiveness by delivering high
accuracy (up to 99%) and a decrease in memory and latency, making them ideal
for use in applications with limited resources
Toward forest dynamics’ systematic knowledge: concept study of a multi-sensor visually tracked rover including a new insect radar for high-accuracy robotic monitoring
Forest dynamics research is crucial in understanding the global carbon cycle and supporting various scales of forest decision-making, management, and conservation. Recent advancements in robotics and computing can be leveraged to address the need for systematic forest monitoring. We propose a common autonomous sensor box platform that enables seamless data integration from multiple sensors synchronized using a time stamp–based mechanism. The platform is designed to be open-source–oriented, ensuring interoperability and interchangeability of components. The sensor box, designed for stationary measurements, and the rover, designed for mobile mapping, are two applications of the proposed platform. The compact autonomous sensor box has a low-range radar that enables high-detail surveillance of nocturnal insects and small species. It can be extended to monitor other aspects, such as vegetation, tree phenology, and forest floor conditions. The multi-sensor visually tracked rover concept also enhances forest monitoring capabilities by enabling complex phenology monitoring. The rover has multiple sensors, including cameras, lidar, radar, and thermal sensors. These sensors operate autonomously and collect data using time stamps, ensuring synchronized data acquisition. The rover concept introduces a novel approach for achieving centimeter-accuracy data management in undercanopy forest conditions. It utilizes a prism attached to the rover, which an oriented robotic total station automatically tracks. This enables precise positioning of the rover and accurate data collection. A dense control network is deployed to ensure an accurate coordinate transfer from reference points to the rover. The demonstrated sample data highlight the effectiveness and high potential of the proposed solutions for systematic forest dynamics monitoring. These solutions offer a comprehensive approach to capturing and analyzing forest data, supporting research and management efforts in understanding and conserving forest ecosystems
Algorithms for light applications: from theoretical simulations to prototyping
[eng] Although the first LED dates to the middle of the 20th century, it has not been until the last decade that the market has been flooded with high efficiency and high durability LED solutions compared to previous technologies. In addition, luminaires that include types of LEDs differentiated in hue or color have already appeared. These luminaires offer new possibilities to reach colorimetric or non-visual capabilities not seen to date.
Due to the enormous number of LEDs on the market, with very different spectral characteristics, the use of the spectrometer as a measuring device for determining LEDs properties has become popular. Obtaining colorimetric information from a luminaire is a necessary step to commercialize it, so it is a tool commonly used by many LED manufacturers.
This doctoral thesis advances the state-of-the-art and knowledge of LED technology at the level of combined spectral emission, as well as applying innovative spectral reconstruction techniques to a commercial multichannel colorimetric sensor. On the one hand, new spectral simulation algorithms that allow obtaining a very high number of results have been developed, being able to obtain optimized values of colorimetric and non-visual parameters in multichannel light sources. MareNostrum supercomputer has been used and new relationships between colorimetric and non-visual parameters in commercial white LED datasets have been found through data analysis. Moreover, the functional improvement of a multichannel colorimetric sensor has been explored by providing it with a neural network for spectral reconstruction. A large amount of data has been generated, which has allowed simulations and statistical studies on the error committed in the spectral reconstruction process using different techniques. This improvement has led to an increase in the spectral resolution measured by the sensor, allowing better accuracy in the calculation of colorimetric parameters. Prototypes of the light sources and the colorimetric sensor have been developed in order to experimentally demonstrate the theoretical framework generated. All the prototypes have been characterized and the errors generated with respect to the theoretical models have been evaluated. The results obtained have been validated through the application of different industry standards by comparison with calibrated commercial devices.[cat] Aquesta tesi doctoral realitza un avançament en l’estat de l’art i en el coneixement sobre la tecnologia LED a nivell d’emissió espectral combinada, a més d’aplicar tècniques innovadores de reconstrucció espectral a un sensor colorimètric multicanal comercial. Per una banda, s’han desenvolupat nous algoritmes de simulació espectral que permeten obtenir un nombre molt elevat de resultats, sent capaços d’obtenir valors optimitzats de paràmetres colorimètrics i no-visuals en fonts de llum multicanal. S’ha fet ús del supercomputador MareNostrum i s’han trobat noves relacions entre paràmetres colorimètrics i no visuals en conjunts de LEDs blancs comercials a través de l’anàlisi de dades. Per altra banda, s’ha explorat la millora funcional d’un sensor colorimètric multicanal, dotant-lo d’una xarxa neuronal per a la reconstrucció espectral. S’han generat una gran quantitat de dades que han permès realitzar simulacions i estudis estadístics sobre l’error comès en el procés de reconstrucció espectral utilitzant diferents tècniques. Aquesta millora ha implicat un augment de la resolució espectral mesurada pel sensor, permetent obtenir una millor precisió en el càlcul de paràmetres colorimètrics. S’han desenvolupat prototips de les fonts de llum i del sensor colorimètric amb l’objectiu de demostrar experimentalment el marc teòric generat. Tots els prototips han estat caracteritzats i s’han avaluat els errors generats respecte els models teòrics. Els resultats obtinguts s’han validat a través de l’aplicació de diferents estàndards de la indústria o a través de la comparativa amb dispositius comercials calibrats
Funduse sinine ja lähi-infrapuna autofluorestsentsuuring autosoom-retsessiivse Stardgardti tõve, koroidereemia, PROM1-maakuli düstroofia ja okulaarse albinismi patsientidel
Väitekirja elektrooniline versioon ei sisalda publikatsiooneFunduse sinine ja lähi-infrapuna autofluorestsentsuuring autosoom-retsessiivse Stardgardti tõve, koroidereemia, PROM1-maakuli düstroofia ja okulaarse albinismi patsientidel
Pärilikud võrkkestahaigused on juhtivaks nägemiskaotuse põhjuseks tööealise elanikkonna seas arenenud riikides. Tegemist on kliiniliselt ja geneetiliselt väga heterogeense haiguste grupiga, mistõttu diagnostika ja haiguse patogeneesi uurimine on olnud vaevarikas. Võrkkesta piltdiagnostika on oluline mitte-invasiivne meetod haiguste diagnoosimiseks ja uurimiseks. Konfokaalne skanneeriv laseroftalmoskoop valgustab võrkkesta erineva lainepikkusega laserkiirega ning salvestab tagasikiirgavat valgust luues silmapõhjast pildi. Funduse autofluorestsents (AF) uuringul kasutatakse ära silmapõhja enda naturaalseid fluorofoore. Lipofustsiini ergastamiseks kasutatakse sinise spektri laserkiirt (sinine AF) ja melaniini jaoks lähipuna laserkiirt (lähipuna AF). Nende fluorofooride jaotus ja kogus silmapõhjas muutub erinevate haigusprotsesside mõjul ning need muutused on tuvastatavad AF uuringul.
Antud doktoritöös uurisime sinise ja lähipuna AF uuringu pilte autosoom-retsesiivse Stargardti tõve (STGD1), koroidereemia, PROM1-maakuli düstroofia ning okulaarse albinismi patsientidel. Töö eesmärgiks oli paremini mõista sinise ja lähipuna AF signaali allikaid erinevate haigusseisundite korral, kus võrkkesta fluorofooride jaotus ning kogused on muutunud. Lisaks kvalitatiivsele piltide hindamisele kasutamise kvantitatiivset AF signaali tugevuse mõõtmist hindamaks lipofustsiini ja melaniini taset.
Uurimustöös näitasime, et melaniin on lähipuna AF signaali peamiseks allikaks. Lisaks näitasime, et melanin võib kaudselt moduleerida lipofustsiinist tuleneva sinise AF signaali, sest okulaarse albinismi kandjate hüpopigmenteeritud võrkkesta alade sinise AF signal oli tavapärasest kõrgem. AF signaali tugevuse mõõtmisel leidsime, et lipofustsiini kuhjumine võrkkestas põhjustab lisaks sinise AF signaali tõusule ka lähipuna AF signaali tõusu STGD1 patsientidel. Kvantitatiivsel analüüsil näitasime ka, et PROM1-maakuli düstroofia patsientide sinise AF signaal oli võrreldav terve silmapõhja signaali tugevusega, eristades seda fenotüübiliselt sarnasest STGD1 haigusest ning viidates ka sellele, et lipofustsiini üleliigne kuhjumine ei ole antud haigusele omane mehhanism. Koroidereemia ja STGD1 haigete uurimisel leidsime, et pigmentepiteeli rakkude kärbumine on nähtav AF signaali hääbumisena, samas lähipuna AF uuringaitab tuvastada varasemaid muutusi kui sinine AF uuring. Lipofustsiin ja melanin on mõlemad olulised võrkkesta rakkude seisundi biomarkerid, mida on võimalik mitte-invasiivsel moel AF uuringu abil analüüsida ning hinnata haiguse progressiooni.Inherited retinal diseases are the leading cause of visual impairment among the working age-group in the developed countries. Because of genetic and phenotypical heterogeneity, diagnosis and understanding pathogenesis of inherited retinal disease has been challenging. Retinal imaging studies which are noninvasive, are an invaluable source of information. Fundus autofluorescence (FAF) utilizes natural fluorophores to create an image of the retina. Lipofuscin is the primary source for short-wavelength autofluorescence (SW-AF) and melanin for near-infrared autofluorescence (NIR-AF). The amount and distribution of these fluorophores changes in the different disease processes and is detectable in FAF images.
In this study we analyzed SW-AF and NIR-AF images in cases of genetically confirmed recessive Stargardt disease (STGD1), choroideremia, PROM1-macular disease and ocular albinism. The aim was to qualitatively describe FAF in conditions with varying levels of lipofuscin or melanin as well as to quantify FAF signal intensities. We also aimed at finding new clinical implications for autofluorescence imaging in evaluating inherited retinal disease.
We confirmed that melanin is the major source of NIR-AF signal by analyzing ocular albinism carriers and mice models with varying fundus pigmentation, but we also found that presence of melanin can modulate SW-AF signal strength. As a novel finding we confirmed that lipofuscin contributes to NIR-AF signal intensity in cases with excessive bisretinoid lipofuscin levels like seen in STGD1. The analysis of choroideremia and STGD1 patients showed that retinal pigment epithelium atrophy causes loss of signal in both SW-AF and NIR-AF, but NIR-AF could be more sensitive in detecting early cell degeneration. Quantifying the autofluorescence signal intensity helps to further understand disease processes as it is an indirect measure for levels of retinal fluorophores. We showed PROM1-macular dystrophy does not present with elevated levels of SW-AF indicating that excessive lipofuscin accumulation is likely not part of its disease mechanism. That knowledge is valuable in differentiating it from phenotypically similar STGD1 or when developing therapeutic approaches. Lipofuscin and melanin are both valuable retinal biomarkers for evaluating retinal health by using non-invasive autofluorescence imaging.https://www.ester.ee/record=b555738
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
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