9,063 research outputs found
Machine learning in solar physics
The application of machine learning in solar physics has the potential to
greatly enhance our understanding of the complex processes that take place in
the atmosphere of the Sun. By using techniques such as deep learning, we are
now in the position to analyze large amounts of data from solar observations
and identify patterns and trends that may not have been apparent using
traditional methods. This can help us improve our understanding of explosive
events like solar flares, which can have a strong effect on the Earth
environment. Predicting hazardous events on Earth becomes crucial for our
technological society. Machine learning can also improve our understanding of
the inner workings of the sun itself by allowing us to go deeper into the data
and to propose more complex models to explain them. Additionally, the use of
machine learning can help to automate the analysis of solar data, reducing the
need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a
Living Review in Solar Physics (LRSP
An advanced deep learning models-based plant disease detection: A review of recent research
Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation
The State of the Art in Deep Learning Applications, Challenges, and Future Prospects::A Comprehensive Review of Flood Forecasting and Management
Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts and control are essential to lessen these effects and safeguard populations. By utilizing its capacity to handle massive amounts of data and provide accurate forecasts, deep learning has emerged as a potent tool for improving flood prediction and control. The current state of deep learning applications in flood forecasting and management is thoroughly reviewed in this work. The review discusses a variety of subjects, such as the data sources utilized, the deep learning models used, and the assessment measures adopted to judge their efficacy. It assesses current approaches critically and points out their advantages and disadvantages. The article also examines challenges with data accessibility, the interpretability of deep learning models, and ethical considerations in flood prediction. The report also describes potential directions for deep-learning research to enhance flood predictions and control. Incorporating uncertainty estimates into forecasts, integrating many data sources, developing hybrid models that mix deep learning with other methodologies, and enhancing the interpretability of deep learning models are a few of these. These research goals can help deep learning models become more precise and effective, which will result in better flood control plans and forecasts. Overall, this review is a useful resource for academics and professionals working on the topic of flood forecasting and management. By reviewing the current state of the art, emphasizing difficulties, and outlining potential areas for future study, it lays a solid basis. Communities may better prepare for and lessen the destructive effects of floods by implementing cutting-edge deep learning algorithms, thereby protecting people and infrastructure
Knowledge Distillation and Continual Learning for Optimized Deep Neural Networks
Over the past few years, deep learning (DL) has been achieving state-of-theart performance on various human tasks such as speech generation, language translation, image segmentation, and object detection. While traditional machine learning models require hand-crafted features, deep learning algorithms can automatically extract discriminative features and learn complex knowledge from large datasets. This powerful learning ability makes deep learning models attractive to both academia and big corporations.
Despite their popularity, deep learning methods still have two main limitations: large memory consumption and catastrophic knowledge forgetting. First, DL algorithms use very deep neural networks (DNNs) with many billion parameters, which have a big model size and a slow inference speed. This restricts the application of DNNs in resource-constraint devices such as mobile phones and autonomous vehicles. Second, DNNs are known to suffer from catastrophic forgetting. When incrementally learning new tasks, the model performance on old tasks significantly drops. The ability to accommodate new knowledge while retaining previously learned knowledge is called continual learning. Since the realworld environments in which the model operates are always evolving, a robust neural network needs to have this continual learning ability for adapting to new changes
Weekly high-resolution multi-spectral and thermal uncrewed-aerial-system mapping of an alpine catchment during summer snowmelt, Niwot Ridge, Colorado
Alpine ecosystems are experiencing rapid change as a result of warming temperatures and changes in the quantity, timing and phase of precipitation. This in turn impacts patterns and processes of ecohydrologic connectivity,
vegetation productivity and water provision to downstream regions. The fine-scale heterogeneous nature of these environments makes them challenging
areas to measure with traditional instrumentation and spatiotemporally coarse satellite imagery. This paper describes the data collection,
processing, accuracy assessment and availability of a series of approximately weekly-interval uncrewed-aerial-system (UAS) surveys flown over the Niwot Ridge Long Term Ecological Research site during the 2017 summer-snowmelt season. Visible, near-infrared and thermal-infrared imagery was collected. This unique series of 5–25 cm resolution multi-spectral and thermal orthomosaics provides a unique snapshot of seasonal transitions in a high alpine catchment. Weekly radiometrically calibrated normalised
difference vegetation index maps can be used to track vegetation health at the pixel scale through time. Thermal imagery can be used to map the
movement of snowmelt across and within the near sub-surface as well as identify locations where groundwater is discharging to the surface. A 10 cm resolution digital surface model and dense point cloud (146 points m−2) are also provided
for topographic analysis of the snow-free surface. These datasets augment ongoing data collection within this heavily studied and important
alpine site; they are made publicly available to facilitate wider use by the research community. Datasets and related metadata can be accessed through the Environmental Data Initiative Data Portal, https://doi.org/10.6073/pasta/dadd5c2e4a65c781c2371643f7ff9dc4 (Wigmore, 2022a), https://doi.org/10.6073/pasta/073a5a67ddba08ba3a24fe85c5154da7 (Wigmore, 2022c), https://doi.org/10.6073/pasta/a4f57c82ad274aa2640e0a79649290ca
(Wigmore and Niwot Ridge LTER, 2021a), https://doi.org/10.6073/pasta/444a7923deebc4b660436e76ffa3130c (Wigmore and Niwot Ridge LTER, 2021b), https://doi.org/10.6073/pasta/1289b3b41a46284d2a1c42f1b08b3807 (Wigmore and Niwot Ridge LTER, 2022a), https://doi.org/10.6073/pasta/70518d55a8d6ec95f04f2d8a0920b7b8 (Wigmore and Niwot Ridge LTER, 2022b). A summary of the available datasets can be found in the data availability section below.</p
GlobalMind: Global Multi-head Interactive Self-attention Network for Hyperspectral Change Detection
High spectral resolution imagery of the Earth's surface enables users to
monitor changes over time in fine-grained scale, playing an increasingly
important role in agriculture, defense, and emergency response. However, most
current algorithms are still confined to describing local features and fail to
incorporate a global perspective, which limits their ability to capture
interactions between global features, thus usually resulting in incomplete
change regions. In this paper, we propose a Global Multi-head INteractive
self-attention change Detection network (GlobalMind) to explore the implicit
correlation between different surface objects and variant land cover
transformations, acquiring a comprehensive understanding of the data and
accurate change detection result. Firstly, a simple but effective Global Axial
Segmentation (GAS) strategy is designed to expand the self-attention
computation along the row space or column space of hyperspectral images,
allowing the global connection with high efficiency. Secondly, with GAS, the
global spatial multi-head interactive self-attention (Global-M) module is
crafted to mine the abundant spatial-spectral feature involving potential
correlations between the ground objects from the entire rich and complex
hyperspectral space. Moreover, to acquire the accurate and complete
cross-temporal changes, we devise a global temporal interactive multi-head
self-attention (GlobalD) module which incorporates the relevance and variation
of bi-temporal spatial-spectral features, deriving the integrate potential same
kind of changes in the local and global range with the combination of GAS. We
perform extensive experiments on five mostly used hyperspectral datasets, and
our method outperforms the state-of-the-art algorithms with high accuracy and
efficiency.Comment: 14 page, 18 figure
KYT2022 Finnish Research Programme on Nuclear Waste Management 2019–2022 : Final Report
KYT2022 (Finnish Research Programme on Nuclear Waste Management 2019–2022), organised by the Ministry of Economic Affairs and Employment, was a national research programme with the objective to ensure that the authorities have sufficient levels of nuclear expertise and preparedness that are needed for safety of nuclear waste management.
The starting point for public research programs on nuclear safety is that they create the conditions for maintaining the knowledge required for the continued safe and economic use of nuclear energy, developing new know-how and participating in international collaboration.
The content of the KYT2022 research programme was composed of nationally important research topics, which are the safety, feasibility and acceptability of nuclear waste management.
KYT2022 research programme also functioned as a discussion and information-sharing forum for the authorities, those responsible for nuclear waste management and the research organizations, which helped to make use of the limited research resources. The programme aimed to develop national research infrastructure, ensure the continuing availability of expertise, produce high-level scientific research and increase general knowledge of nuclear waste management
Antenna Development in Brain-Implantable Biotelemetric Systems for Next-Generation of Human Healthcare
In the growing efforts of promoting patients’ life quality through health technology solutions, implantable wireless medical devices (IMDs) have been identified as one of the frontrunners. They are bringing compelling wireless solutions for medical diagnosis and treatment through bio-telemetric systems that deliver real-time transmission of in-body physiological data to an external monitoring/control unit. To set up this bidirectional wireless biomedical communication link for the long- term, the IMDs need small and efficient antennas. Designing antenna-enabled biomedical telemetry is a challenging aim, which must fulfill demanding issues and criteria including miniaturization, appropriate radiation performance, bandwidth enhancement, good impedance matching, and biocompatibility.
Overcoming the size restriction mainly depends on the resonant frequency of the required applications. Defined frequency bands for biomedical telemetry systems contain the Medical Implant Communication Service (MICS) operating at the frequency band of 402– 405 MHz, Medical Device Radiocommunication Service (MedRadio) resonating at the frequency ranges of 401– 406 MHz, 413 – 419 MHz, 426 – 432 MHz, 438 – 444 MHz, and 451 – 457 MHz, Wireless Medical Telemetry Service (WMTS) operating at frequency specturms of 1395 to 1400 MHz and 1427 to 1432 MHz, and Industrial, Scientific, and Medical (ISM) bands of 433.1–434.8 MHz, 868–868.6 MHz, 902.8–928.0 MHz, and 2.4–2.48 GHz. On the other hand, a single band antenna may not fulfill all requirements of a bio-telemetry system in either MedRadio, WMTS, or ISM bands. As a result, analyzing dual/multi-band implantable antenna supporting wireless power, data transmission, and control signaling can meet the demand for multitasking biotelemetry systems. In addition, among different antenna structures, PIFA has been found a promising type in terms of size-performance balance in lossy human tissues.
To overcome the above-mentioned challenges, this thesis, first, starts with a discussion of antenna radiation in a lossy medium, the requirements of implantable antenna development, and numerical modeling of the human head tissues. In the following discussion, we concentrate on approaching a new design for far-field small antennas integrated into brain-implantable biotelemetric systems that provide attractive features for versatile functions in modern medical applications. To this end, we introduce three different implantable antenna structures including a compact dual-band PIFA, a miniature triple-band PIFA and a small quad-band PIFA for brain care applications. The compelling performance of the proposed antennas is analyzed and discussed with simulation results and the triple-band PIFA is evaluated using simulation outcomes compared with the measurement results of the fabricated prototype. Finally, the first concept and platform of in-body and off-body units are proposed for wireless dopamine monitoring as a brain care application.
In addition to the main focus of this thesis, in the second stage, we focus on introducing an equivalent circuit model to the electrical connector-line transition. We present a data fitting technique for two transmission lines characterization independent of the dielectric properties of the substrate materials at the ultra-high frequency band (UHF). This approach is a promising solution for the development of wearable and off-body antennas employing textile materials in biomedical telemetry systems. The approach method is assessed with measurement results of several fabricated transmission lines on different substrate materials
Identification of Body Contouring Surgery Complications by Multispectral RGB/Infrared Thermography Imaging
Infrared thermography can assist in the documentation of inflammatory vascular healing reactions and tissue perfusion resulting from esthetic surgical procedures in different parts of the body and face. Both in preoperative planning and in its postoperative evolutionary behavior. Infrared thermography is a functional imaging technique of cutaneous vascular activity using long-infrared electromagnetic radiation emitted by tissue cells. It can accurately identify terminal cutaneous perforating vessels related to greater or lesser skin perfusion, non-invasively, quickly, painlessly, safely and without emission of ionizing radiation by scanning a segment or entire body in a single image. This facilitates the evaluation of patients undergoing lipoabdominoplasty and their postoperative follow-up. Monitoring of new techniques and traditional procedures with infrared scanning technology helps in the early diagnostic elucidation of complications (edema, seromas, epidermolysis, hematoma, dehiscence, infection, necrosis), evolutionary studies of healing and local effects of thermoguided procedures (such as manual therapy, laser photobiomodulation, ultrasound, radiofrequency, hyperbaric oxygen therapy) direct the treatment with more objectivity, better results, and safety
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