6,581 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    The terminator region of tidally locked M-dwarf exoplanets in 3-d general circulation models

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    The impressive sensitivity of the James Webb Space Telescope has made it possible to study the atmospheres of planets beyond the solar system. It will soon be followed by space missions aiming specifically at this goal, such as the Ariel mission, Twinkle, and the Habitable Worlds Observatory. One category of exoplanet has drawn interest because of its potential to harbour temperate climates with liquid surface water—and therefore potentially life. These are rocky planets orbiting cool M-class stars, or "M-Earths." Stellar population trends and observing biases lead to a high proportion of potentially habitable, terrestrial planets falling into this category. Because of the low temperatures of their host stars, however, habitable worlds of this type are found in close orbits where they are likely to be tidally locked. As the solar system has no tidally locked planets, our knowledge of their atmospheric circulation is currently limited to theoretical modelling. Past modelling work has shown that the asymmetrical irradiation of tidally locked planets results in characteristic circulation regimes which have profound consequences for observations. Atmospheric retrievals, which use statistical methods to fit 1-D atmospheric models to observational data and quantify the confidence of the fit, are not yet able to account for the 3-D nature of this circulation. For planets with large spatial variation in environmental conditions caused by tidal locking, 1-D models are not able to capture the differences and interconnections between planetary regions such as the dayside, nightside, and planetary limb or terminator. In addition, planetary atmospheres exhibit variation over time, potentially resulting in differences in retrieved properties between observing visits or even between different phases of a planet’s orbit. Accounting for 4-D circulation effects in atmospheric retrievals first requires a theoretical understanding of the impact of global-scale phenomena such as atmospheric waves and horizontal transport on conditions at the planetary limb, and then requires incorporation of this knowledge into the retrieval pipeline in the form of, for example, parameterisations. In this thesis, I address the first requirement: the theoretical understanding of the effects of fully modelled 4-D atmospheric circulation on the planetary limb, the region probed by transmission spectroscopy, on tidally locked planets. I focus in particular on effects caused by the global propagation of atmospheric waves and by horizontal transport of clouds and hazes. In Chapter 2, I show that that the atmospheric dynamics on the tidally locked Proxima Centauri b support a longitudinally asymmetric stratospheric wind oscillation (LASO), analogous to Earth’s quasi-biennial oscillation (QBO). The LASO has a vertical extent of 35–55 km, a period of 5–6.5 months, and a peak-to-peak wind speed amplitude of -70 to +130 ms−1 with a maximum at an altitude of 41 km. Unlike the QBO, the LASO displays longitudinal asymmetries related to the asymmetric thermal forcing of the planet and to interactions with the resulting stationary Rossby waves. The equatorial gravity wave sources driving the LASO are localised in the deep convection region at the substellar point and in a jet exit region near the western terminator, unlike the QBO, for which these sources are distributed uniformly around the planet. Longitudinally, the western terminator experiences the highest wind speeds and undergoes reversals earlier than other longitudes. The antistellar point only experiences a weak oscillation with a very brief, low-speed westward phase. The QBO on Earth is associated with fluctuations in the abundances of water vapour and trace gases such as ozone which are also likely to occur on exoplanets if these gases are present. Strong fluctuations in temperature and the abundance of atmospheric species at the terminators will need to be considered when interpreting atmospheric observations of tidally locked exoplanets. In Chapter 3, I investigate the presence of cloud cover at the planetary limb of water-rich Earth-like planets, which is likely to weaken chemical signatures in transmission spectra and impede attempts to characterise these atmospheres. Based on observations of Earth and solar system worlds, exoplanets with atmospheres should have both short-term weather and long-term climate variability, implying that cloud cover may be less during some observing periods. I identify and describe a mechanism driving periodic clear sky events at the terminators in simulations of tidally locked Earth-like planets. A feedback between dayside cloud radiative effects, incoming stellar radiation and heating, and the dynamical state of the atmosphere, especially the zonal wavenumber-1 Rossby wave identified in past work on tidally locked planets, leads to oscillations in Rossby wave phase speeds and in the position of Rossby gyres and results in advection of clouds to or away from the planet’s eastern terminator. I study this oscillation in simulations of Proxima Centauri b, TRAPPIST 1-e, and rapidly rotating versions of these worlds located at the inner edge of their stars’ habitable zones. I simulate time series of the transit depths of the 1.4 µm water feature and 2.7 µm carbon dioxide feature. The impact of atmospheric variability on the transmission spectra is sensitive to the structure of the dayside cloud cover and the location of the Rossby gyres, but none of my simulations have variability significant enough to be detectable with current methods. In Chapter 4, I study the interaction between the atmospheric circulation and photochemical hazes and describe the resulting haze abundances at the terminator. Transmission spectroscopy supports the presence of unknown, light-scattering aerosols in the atmospheres of many exoplanets. The complexity of factors influencing the formation, 3-D transport, radiative impact, and removal of aerosols makes it challenging to match theoretical models to the existing data. My study simplifies these factors to focus on the interaction between planetary general circulation and haze distribution at the planetary limb. I use an intermediate complexity general circulation model, ExoPlaSim, to simulate idealised organic haze particles as radiatively active tracers in the atmospheres of tidally locked terrestrial planets for a range of rotation rates. I find three distinct 3-D spatial haze distributions, corresponding to three circulation regimes, each with a different haze profile at the limb. All regimes display significant terminator asymmetry. In my parameter space, super-Earth-sized planets with rotation periods greater than 13 days have the lowest haze optical depths at the terminator, supporting the choice of slower rotators as observing targets. My thesis supports the existence of characteristic forms of temporal and spatial variability on tidally locked planets which will undoubtedly impact observations and inform our understanding of climate conditions on the surface. Overall, the effects of purely dynamical variability may be too small to be detected for Earth-like planets (but potentially detectable for larger ones). The impact of the atmospheric circulation on the distribution of clouds and hazes, on the other hand, is likely to affect even observations of terrestrial planets due to the highly scattering nature of these aerosols and will need to be accounted for in atmospheric retrievals

    Automatic Caption Generation for Aerial Images: A Survey

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    Aerial images have attracted attention from researcher community since long time. Generating a caption for an aerial image describing its content in comprehensive way is less studied but important task as it has applications in agriculture, defence, disaster management and many more areas. Though different approaches were followed for natural image caption generation, generating a caption for aerial image remains a challenging task due to its special nature. Use of emerging techniques from Artificial Intelligence (AI) and Natural Language Processing (NLP) domains have resulted in generation of accepted quality captions for aerial images. However lot needs to be done to fully utilize potential of aerial image caption generation task. This paper presents detail survey of the various approaches followed by researchers for aerial image caption generation task. The datasets available for experimentation, criteria used for performance evaluation and future directions are also discussed

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    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

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    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

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    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

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Archangel: A Hybrid UAV-based Human Detection Benchmark with Position and Pose Metadata

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    Learning to detect objects, such as humans, in imagery captured by an unmanned aerial vehicle (UAV) usually suffers from tremendous variations caused by the UAV's position towards the objects. In addition, existing UAV-based benchmark datasets do not provide adequate dataset metadata, which is essential for precise model diagnosis and learning features invariant to those variations. In this paper, we introduce Archangel, the first UAV-based object detection dataset composed of real and synthetic subsets captured with similar imagining conditions and UAV position and object pose metadata. A series of experiments are carefully designed with a state-of-the-art object detector to demonstrate the benefits of leveraging the metadata during model evaluation. Moreover, several crucial insights involving both real and synthetic data during model optimization are presented. In the end, we discuss the advantages, limitations, and future directions regarding Archangel to highlight its distinct value for the broader machine learning community.Comment: Submission to IEEE Acces

    A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

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    Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure

    SO(2) and O(2) Equivariance in Image Recognition with Bessel-Convolutional Neural Networks

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    For many years, it has been shown how much exploiting equivariances can be beneficial when solving image analysis tasks. For example, the superiority of convolutional neural networks (CNNs) compared to dense networks mainly comes from an elegant exploitation of the translation equivariance. Patterns can appear at arbitrary positions and convolutions take this into account to achieve translation invariant operations through weight sharing. Nevertheless, images often involve other symmetries that can also be exploited. It is the case of rotations and reflections that have drawn particular attention and led to the development of multiple equivariant CNN architectures. Among all these methods, Bessel-convolutional neural networks (B-CNNs) exploit a particular decomposition based on Bessel functions to modify the key operation between images and filters and make it by design equivariant to all the continuous set of planar rotations. In this work, the mathematical developments of B-CNNs are presented along with several improvements, including the incorporation of reflection and multi-scale equivariances. Extensive study is carried out to assess the performances of B-CNNs compared to other methods. Finally, we emphasize the theoretical advantages of B-CNNs by giving more insights and in-depth mathematical details
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