Relevant bibliographies by topics / Hyper-Fusion

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Hyper-Fusion'

Author: Grafiati
Published: 7 June 2025
Last updated: 17 July 2025

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Journal articles on the topic "Hyper-Fusion"

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Chen, Zhi, Yongzhao Du, Jianhua Deng, Jiafu Zhuang, and Peizhong Liu. "Adaptive Hyper-Feature Fusion for Visual Tracking." IEEE Access 8 (2020): 68711–24. http://dx.doi.org/10.1109/access.2020.2986157.

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Rubinder, K., B. S. Chadha, S. Singh, and H. S. Saini. "Amylase hyper-producing haploid recombinant strains of Thermomyces lanuginosus obtained by intraspecific protoplast fusion." Canadian Journal of Microbiology 46, no. 7 (2000): 669–73. http://dx.doi.org/10.1139/w00-042.

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Amylase hyper-producing, catabolite-repression-resistant, recombinant strains were produced by intraspecific protoplast fusion of thermophilic fungus Thermomyces lanuginosus strains, using well-characterized, morphological, and 2-deoxy-D-glucose resistant markers. The fusant heterokaryons exhibited enhanced amylase activities as compared to the amylase hyper-producing parental strain (T2). Diploids derived from heterokaryons segregated to stable haploid recombinant strains. In the haploid strain (Tlh 4q), approximately 5-fold higher specific activities of α-amylase and glucoamylase in the culture filtrate were observed as compared to the wild-type strain (W0).Key words: Thermomyces lanuginosus, protoplast fusion, amylase hyper-producing strain, catabolite repression.
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Alshmrani, Goram Mufarah, Qiang Ni, Richard Jiang, and Nada Muhammed. "Hyper-Dense_Lung_Seg: Multimodal-Fusion-Based Modified U-Net for Lung Tumour Segmentation Using Multimodality of CT-PET Scans." Diagnostics 13, no. 22 (2023): 3481. http://dx.doi.org/10.3390/diagnostics13223481.

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The majority of cancer-related deaths globally are due to lung cancer, which also has the second-highest mortality rate. The segmentation of lung tumours, treatment evaluation, and tumour stage classification have become significantly more accessible with the advent of PET/CT scans. With the advent of PET/CT scans, it is possible to obtain both functioning and anatomic data during a single examination. However, integrating images from different modalities can indeed be time-consuming for medical professionals and remains a challenging task. This challenge arises from several factors, including differences in image acquisition techniques, image resolutions, and the inherent variations in the spectral and temporal data captured by different imaging modalities. Artificial Intelligence (AI) methodologies have shown potential in the automation of image integration and segmentation. To address these challenges, multimodal fusion approach-based U-Net architecture (early fusion, late fusion, dense fusion, hyper-dense fusion, and hyper-dense VGG16 U-Net) are proposed for lung tumour segmentation. Dice scores of 73% show that hyper-dense VGG16 U-Net is superior to the other four proposed models. The proposed method can potentially aid medical professionals in detecting lung cancer at an early stage.
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Peters, Malte, Guido Blinn, Fian Solem, Martina Fischer, Karl-Hermann Meyer zum Büschenfelde, and Stefan Rose-John. "In Vivo and In Vitro Activities of the gp130-Stimulating Designer Cytokine Hyper-IL-6." Journal of Immunology 161, no. 7 (1998): 3575–81. http://dx.doi.org/10.4049/jimmunol.161.7.3575.

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Abstract IL-6 is a multifactorial cytokine mediating acute inflammatory responses in the liver. When IL-6 binds to a specific receptor (IL-6R), the IL-6/IL-6R complex associates with the signal transducer gp130, initiating intracellular signaling. A soluble form of the IL-6R (sIL-6R) renders target cells sensitive to IL-6 that do not express the IL-6R on their surfaces. A designer cytokine, termed Hyper-IL-6, consisting of IL-6 covalently linked to the sIL-6R was fully active on gp130-expressing cells at 100- to 1000-fold lower concentrations than unlinked IL-6 and IL-6R. Mice were injected i.p. with Hyper-IL-6 or IL-6. Upon injection of Hyper-IL-6 into mice, the acute phase response, as measured by haptoglobin mRNA expression in the liver, was markedly increased and lasted significantly longer compared with that in mice injected with a 10-fold higher dose of IL-6 alone. On human hepatoma cells, Hyper-IL-6 caused similar effects, indicating that the longer lasting response to the fusion protein could not only be explained by the longer plasma half-life of the fusion protein. Experiments using iodinated IL-6 and Hyper-IL-6 revealed that Hyper-IL-6 bound with high affinity to gp130 and was less efficiently internalized. This effect might explain the longer lasting activity of this protein on cells. The highly active IL-6/sIL-6R designer protein might be of significant clinical importance for the stimulation of cells that are more responsive to the IL-6/sIL-6R complex than to IL-6 alone. Such cells include hemopoietic progenitor cells and hepatocytes.
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Rizwan, Muhammad, Sana Shahab, Akhlaq Ahmad Bhatti, Muhammad Javaid, and Mohd Anjum. "On the Hyper Zagreb Index of Trees with a Specified Degree of Vertices." Symmetry 15, no. 7 (2023): 1295. http://dx.doi.org/10.3390/sym15071295.

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Topological indices are the numerical descriptors that correspond to some certain physicochemical properties of a chemical compound such as the boiling point, acentric factor, enthalpy of vaporisation, heat of fusion, etc. Among these topological indices, the Hyper Zagreb index, is the most effectively used topological index to predict the acentric factor of some octane isomers. In the current work, we investigate the extremal values of the Hyper Zagreb index for some classes of trees.
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Bi, Lei, Michael Fulham, and Jinman Kim. "Hyper-fusion network for semi-automatic segmentation of skin lesions." Medical Image Analysis 76 (February 2022): 102334. http://dx.doi.org/10.1016/j.media.2021.102334.

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Shu, Shibin, Tianyuan Zhang, Wenting Jing, et al. "Hyper-Selective Posterior Fusion in Lenke 5C Adolescent Idiopathic Scoliosis." Spine 45, no. 18 (2020): 1269–76. http://dx.doi.org/10.1097/brs.0000000000003513.

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Narasimha Raju, Akella S., Kayalvizhi Jayavel, and Thulasi Rajalakshmi. "Dexterous Identification of Carcinoma through ColoRectalCADx with Dichotomous Fusion CNN and UNet Semantic Segmentation." Computational Intelligence and Neuroscience 2022 (October 10, 2022): 1–29. http://dx.doi.org/10.1155/2022/4325412.

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Human colorectal disorders in the digestive tract are recognized by reference colonoscopy. The current system recognizes cancer through a three-stage system that utilizes two sets of colonoscopy data. However, identifying polyps by visualization has not been addressed. The proposed system is a five-stage system called ColoRectalCADx, which provides three publicly accessible datasets as input data for cancer detection. The three main datasets are CVC Clinic DB, Kvasir2, and Hyper Kvasir. After the image preprocessing stages, system experiments were performed with the seven prominent convolutional neural networks (CNNs) (end-to-end) and nine fusion CNN models to extract the spatial features. Afterwards, the end-to-end CNN and fusion features are executed. These features are derived from Discrete Wavelet Transform (DWT) and Vector Support Machine (SVM) classification, that was used to retrieve time and spatial frequency features. Experimentally, the results were obtained for five stages. For each of the three datasets, from stage 1 to stage 3, end-to-end CNN, DenseNet-201 obtained the best testing accuracy (98%, 87%, 84%), ((98%, 97%), (87%, 87%), (84%, 84%)), ((99.03%, 99%), (88.45%, 88%), (83.61%, 84%)). For each of the three datasets, from stage 2, CNN DaRD-22 fusion obtained the optimal test accuracy ((93%, 97%) (82%, 84%), (69%, 57%)). And for stage 4, ADaRDEV2-22 fusion achieved the best test accuracy ((95.73%, 94%), (81.20%, 81%), (72.56%, 58%)). For the input image segmentation datasets CVC Clinc-Seg, KvasirSeg, and Hyper Kvasir, malignant polyps were identified with the UNet CNN model. Here, the loss score datasets (CVC clinic DB was 0.7842, Kvasir2 was 0.6977, and Hyper Kvasir was 0.6910) were obtained.
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Gaiyun, Zhou, Zhang Guoping, Chang Cunhong, and Ma Li. "Multi Feature Information Fusion Target Image Recognition Based on Hyper Plane Fusion of Learning Prototype." International Journal of Signal Processing, Image Processing and Pattern Recognition 9, no. 12 (2016): 103–12. http://dx.doi.org/10.14257/ijsip.2016.9.12.11.

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Aguilar, Hector C., Vanessa Aspericueta, Lindsey R. Robinson, Karen E. Aanensen, and Benhur Lee. "A Quantitative and Kinetic Fusion Protein-Triggering Assay Can Discern Distinct Steps in the Nipah Virus Membrane Fusion Cascade." Journal of Virology 84, no. 16 (2010): 8033–41. http://dx.doi.org/10.1128/jvi.00469-10.

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ABSTRACT The deadly paramyxovirus Nipah virus (NiV) contains a fusion glycoprotein (F) with canonical structural and functional features common to its class. Receptor binding to the NiV attachment glycoprotein (G) triggers F to undergo a two-phase conformational cascade: the first phase progresses from a metastable prefusion state to a prehairpin intermediate (PHI), while the second phase is marked by transition from the PHI to the six-helix-bundle hairpin. The PHI can be captured with peptides that mimic F's heptad repeat regions, and here we utilized a NiV heptad repeat peptide to quantify PHI formation and the half-lives (t 1/2) of the first and second fusion cascade phases. We found that ephrinB2 receptor binding to G triggered ∼2-fold more F than that triggered by ephrinB3, consistent with the increased rate and extent of fusion observed with ephrinB2- versus ephrinB3-expressing cells. In addition, for a series of hyper- and hypofusogenic F mutants, we quantified F-triggering capacities and measured the kinetics of their fusion cascade phases. Hyper- and hypofusogenicity can each be manifested through distinct stages of the fusion cascade, giving rise to vastly different half-lives for the first (t 1/2, 1.9 to 7.5 min) or second (t 1/2, 1.5 to 15.6 min) phase. While three mutants had a shorter first phase and a longer second phase than the wild-type protein, one mutant had the opposite phenotype. Thus, our results reveal multiple critical parameters that govern the paramyxovirus fusion cascade, and our assays should help efforts to elucidate other class I membrane fusion processes.
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Dissertations / Theses on the topic "Hyper-Fusion"

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Apatean, Anca Ioana. "Contributions à la fusion des informations : application à la reconnaissance des obstacles dans les images visible et infrarouge." Phd thesis, INSA de Rouen, 2010. http://tel.archives-ouvertes.fr/tel-00621202.

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Afin de poursuivre et d'améliorer la tâche de détection qui est en cours à l'INSA, nous nous sommes concentrés sur la fusion des informations visibles et infrarouges du point de vue de reconnaissance des obstacles, ainsi distinguer entre les véhicules, les piétons, les cyclistes et les obstacles de fond. Les systèmes bimodaux ont été proposées pour fusionner l'information à différents niveaux: des caractéristiques, des noyaux SVM, ou de scores SVM. Ils ont été pondérés selon l'importance relative des capteurs modalité pour assurer l'adaptation (fixe ou dynamique) du système aux conditions environnementales. Pour évaluer la pertinence des caractéristiques, différentes méthodes de sélection ont été testés par un PPV, qui fut plus tard remplacée par un SVM. Une opération de recherche de modèle, réalisée par 10 fois validation croisée, fournit le noyau optimisé pour SVM. Les résultats ont prouvé que tous les systèmes bimodaux VIS-IR sont meilleurs que leurs correspondants monomodaux.
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Benhalouche, Fatima Zohra. "Méthodes de démélange et de fusion des images multispectrales et hyperspectrales de télédétection spatiale." Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30083/document.

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Au cours de cette thèse, nous nous sommes intéressés à deux principales problématiques de la télédétection spatiale de milieux urbains qui sont : le "démélange spectral " et la "fusion". Dans la première partie de la thèse, nous avons étudié le démélange spectral d'images hyperspectrales de scènes de milieux urbains. Les méthodes développées ont pour objectif d'extraire, d'une manière non-supervisée, les spectres des matériaux présents dans la scène imagée. Le plus souvent, les méthodes de démélange spectral (méthodes dites de séparation aveugle de sources) sont basées sur le modèle de mélange linéaire. Cependant, lorsque nous sommes en présence de paysage non-plat, comme c'est le cas en milieu urbain, le modèle de mélange linéaire n'est plus valide et doit être remplacé par un modèle de mélange non-linéaire. Ce modèle non-linéaire peut être réduit à un modèle de mélange linéaire-quadratique/bilinéaire. Les méthodes de démélange spectral proposées sont basées sur la factorisation matricielle avec contrainte de non-négativité, et elles sont conçues pour le cas particulier de scènes urbaines. Les méthodes proposées donnent généralement de meilleures performances que les méthodes testées de la littérature. La seconde partie de cette thèse à été consacrée à la mise en place de méthodes qui permettent la fusion des images multispectrale et hyperspectrale, afin d'améliorer la résolution spatiale de l'image hyperspectrale. Cette fusion consiste à combiner la résolution spatiale élevée des images multispectrales et la haute résolution spectrale des images hyperspectrales. Les méthodes mises en place sont des méthodes conçues pour le cas particulier de fusion de données de télédétection de milieux urbains. Ces méthodes sont basées sur des techniques de démélange spectral linéaire-quadratique et utilisent la factorisation en matrices non-négatives. Les résultats obtenus montrent que les méthodes développées donnent globalement des performances satisfaisantes pour la fusion des données hyperspectrale et multispectrale. Ils prouvent également que ces méthodes surpassent significativement les approches testées de la littérature<br>In this thesis, we focused on two main problems of the spatial remote sensing of urban environments which are: "spectral unmixing" and "fusion". In the first part of the thesis, we are interested in the spectral unmixing of hyperspectral images of urban scenes. The developed methods are designed to unsupervisely extract the spectra of materials contained in an imaged scene. Most often, spectral unmixing methods (methods known as blind source separation) are based on the linear mixing model. However, when facing non-flat landscape, as in the case of urban areas, the linear mixing model is not valid any more, and must be replaced by a nonlinear mixing model. This nonlinear model can be reduced to a linear-quadratic/bilinear mixing model. The proposed spectral unmixing methods are based on matrix factorization with non-negativity constraint, and are designed for urban scenes. The proposed methods generally give better performance than the tested literature methods. The second part of this thesis is devoted to the implementation of methods that allow the fusion of multispectral and hyperspectral images, in order to improve the spatial resolution of the hyperspectral image. This fusion consists in combining the high spatial resolution of multispectral images and high spectral resolution of hyperspectral images. The implemented methods are designed for urban remote sensing data. These methods are based on linear-quadratic spectral unmixing techniques and use the non-negative matrix factorization. The obtained results show that the developed methods give good performance for hyperspectral and multispectral data fusion. They also show that these methods significantly outperform the tested literature approaches
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Book chapters on the topic "Hyper-Fusion"

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Engels, Ralf, G. Farren, K. Grigoryev, et al. "Hyper-Polarized Deuterium Molecules: An Option to Produce and Store Polarized Fuel for Nuclear Fusion?" In Springer Proceedings in Physics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39471-8_4.

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Birkmann, Josef, Stefan Rose-John, Manfred Smetak, Thomas Sievert, and Walter M. Gallmeier. "Hyper-IL-6 (H-IL-6), a Fusion Protein of Soluble IL-6 Receptor (Sil-6R), and Interleukin-6 (IL-6), Acts Synergistic with Thrombopoietin (TPO) and Stem Cell Factor (SCF) in Expanding Megakaryocyte Progenitors from Human Cd34++/Cd90+ Cell." In Molecular Biology of Hematopoiesis 6. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4797-6_21.

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Passera, Katia Marina, and Luca Tommaso Mainardi. "Image Registration Algorithms for Applications in Oncology." In Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-314-2.ch009.

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Image registration is the process of determining the correspondence of features between images collected at different times or using different imaging modalities. A wide range of registration algorithms was proposed in literature for solving this task. In this chapter the focus will be on oncology applications, where registration is the prior step of: i) subtraction imaging (to emphasize hyper (or hypo) enhanced structures), ii) fusion imaging (to integrate anatomical and functional information about lesions) and iii) serial imaging comparison (to monitor the progression/regression of a disease). These applications are of great relevance in tumors diagnosis, staging and treatment planning. The goal of this chapter is to provide an overview of registration algorithms considering these different applications in oncology. We discuss the advantages/disadvantages of each algorithm, the results gained and the possible future developments to comply with new requirements.
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Chaudhry, Meeta, and Nandini Goswami. "Shaping Tomorrow's Minds." In Integrating Generative AI in Education to Achieve Sustainable Development Goals. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2440-0.ch022.

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The chapter explores the evolution of AI, distinguishing it from generative AI, and examines its current applications in education, spanning from primary to higher education levels. Unveiling the multifaceted landscape of AI technologies, such as machine learning, deep learning, and natural language processing, the narrative delves into personalized learning, smart tutoring systems, and administrative efficiency. The chapter anticipates a future where AI collaborates seamlessly with educators, offering hyper-personalized learning journeys, immersive experiences through augmented and virtual reality, and fostering collaborative and project-based learning. Challenges, including ethical concerns, digital inclusion, and teacher training, are acknowledged, emphasizing the importance of addressing these issues for an inclusive and equitable AI-augmented educational future. As we stand at the brink of this transformative era, the chapter envisions a harmonious fusion of technology and human ingenuity shaping the minds of tomorrow.
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Pamisetty, Avinash. "Agentic Intelligence and Cloud-Powered Supply Chains: Transforming Wholesale, Banking, and Insurance with Big Data and Artificial Intelligence." In Deep Science Publishing. Deep Science Publishing, 2025. https://doi.org/10.70593/978-93-49307-44-5.

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In an era defined by exponential technological advancement, the convergence of artificial intelligence, big data, and cloud computing is reshaping the global economic landscape. Industries long regarded as traditional—wholesale, banking, and insurance—are undergoing rapid transformation as they adapt to the demands of a hyper-connected, data-driven world. This book explores the transformative potential of agentic intelligence—AI systems capable of autonomous decision-making—and the pivotal role of cloud-powered supply chains in driving efficiency, resilience, and innovation across sectors. As businesses face increasingly complex market dynamics, the ability to harness real-time data and deploy intelligent systems is no longer optional; it is essential for survival and growth. From the optimization of procurement and logistics in wholesale markets to risk modeling and fraud detection in banking, and from personalized policy offerings to automated claims processing in insurance, the fusion of advanced analytics and AI is unlocking unprecedented opportunities. Cloud infrastructure, meanwhile, enables scalability, security, and global accessibility, empowering organizations to reimagine traditional operations and deliver value at a new scale. This book serves as a comprehensive guide for leaders, practitioners, and scholars seeking to understand the strategic and operational implications of these emerging technologies. Through case studies, conceptual frameworks, and forward-looking analysis, we offer insights into how agentic intelligence and cloud ecosystems are not only enhancing business performance but also redefining the future of enterprise. As we stand on the brink of the next digital revolution, the pages that follow will illuminate the pathways through which technology can be harnessed not just to keep pace with change, but to lead it.
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Conference papers on the topic "Hyper-Fusion"

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Huang, Shouzhi, Xiaoyu Li, Zhuqing Jiang, Xiaoqiang Guo, and Aidong Men. "Hyper Feature Fusion Pyramid Network for Object Detection." In 2018 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). IEEE, 2018. http://dx.doi.org/10.1109/icmew.2018.8551547.

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Huang, Zuowei, and Shixiong Xie. "Classification Method for Crop by fusion Hyper Spectral and LiDAR Data." In 2022 14th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). IEEE, 2022. http://dx.doi.org/10.1109/icmtma54903.2022.00205.

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Pu, Zaiyi, Chaobin Wang, and Dawei Luo. "Cloud Computing Based Packet Intrusion Detection for Hyper Fusion Network Storage." In 2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC). IEEE, 2022. http://dx.doi.org/10.1109/ipec54454.2022.9777482.

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Zhang, Jianlong, Tianhong Wang, Bin Wang, and Chen Chen. "A Subspace Fusion of Hyper-parameter Optimization Method Based on Mean Regression." In 2022 IEEE International Conference on Smart Internet of Things (SmartIoT). IEEE, 2022. http://dx.doi.org/10.1109/smartiot55134.2022.00035.

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Riese, Felix M., and Sina Keller. "Fusion of Hyper Spectral and Ground Penetrating Radar Data to Estimate Soil Moisture." In 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2018. http://dx.doi.org/10.1109/whispers.2018.8747076.

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Kannan, Aswin, Anamitra Roy Choudhury, Vaibhav Saxena, et al. "HyperASPO: Fusion of Model and Hyper Parameter Optimization for Multi-objective Machine Learning." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671604.

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Steiner, Matthias, Nicolas Baumann, Luzian Lebovitz, and Michele Magno. "A Robust and Real-Time Hyper-Spectral Sensor-Fusion Model for Concrete Crack Segmentation." In 2023 IEEE Sensors Applications Symposium (SAS). IEEE, 2023. http://dx.doi.org/10.1109/sas58821.2023.10254080.

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Pan, Qiao, Mingjie Zheng, Shuai Wang, and Yueqi Song. "HHFS: A Hyper-Heterogeneous Fusion System Solution for Product Quality Inspection based on Cloud- Edge- End Architecture." In 2024 9th International Conference on Electronic Technology and Information Science (ICETIS). IEEE, 2024. http://dx.doi.org/10.1109/icetis61828.2024.10593673.

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"A Multi-Network Fusion Prediction Method for Patient Blood Glucose Concentration Prediction and Hyper/Hypoglycemia Blood Glucose Warning." In 2022 the 12th International Workshop on Computer Science and Engineering. WCSE, 2022. http://dx.doi.org/10.18178/wcse.2022.06.021.

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Ali, Mr Shadab, Dr Tasneem Ahmed, Dr Santosh Kumar Dwivedi, Mr Ajeet Singh, and Mr Ashwin Kumar Shrivastava. "IMPACT OF URBAN VEGETATION IN SUSTAINABLE SMART CITIES DEVELOPMENT: A COMPREHENSIVE APPRAISAL." In Computing for Sustainable Innovation: Shaping Tomorrow’s World. Innovative Research Publication, 2024. http://dx.doi.org/10.55524/csistw.2024.12.1.33.

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Urban vegetation is the crucial need for the sustainable development of green smart cities. In the current green smart cities, absence of urban vegetation causes unusual high temperature and adverse effect of human health like heat stroke, headache, dehydration, cardiovascular, respiratory etc. This article offers an extensive examination of image processing methods employed in the assessment of urban vegetation. Urban environments often suffer from a scarcity of green areas, underscoring the importance of comprehending and tracking vegetation's condition and distribution for both environmental and human welfare. Notably, high-resolution aerial and satellite imagery, especially satellite images, serve as invaluable resources for evaluating urban vegetation. Within this paper, we delve into cutting-edge image processing techniques used in urban vegetation research, with a primary focus on classification, segmentation, and change detection algorithms. The study scrutinizes a range of approaches for feature extraction and classification, encompassing methodologies like texture analysis, spectral indices, and object-based analysis. Additionally, we explore machine learning and deep learning integration, multi-sensor data fusion, and the adoption of emerging technologies such as LiDAR and hyper spectral imaging as limitations and future avenues in urban vegetation analysis. The insights derived from this review will prove beneficial to researchers, practitioners, and policymakers involved in endeavors aimed at monitoring and enhancing green spaces within urban areas.
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Reports on the topic "Hyper-Fusion"

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Cohen, Yafit, Carl Rosen, Victor Alchanatis, David Mulla, Bruria Heuer, and Zion Dar. Fusion of Hyper-Spectral and Thermal Images for Evaluating Nitrogen and Water Status in Potato Fields for Variable Rate Application. United States Department of Agriculture, 2013. http://dx.doi.org/10.32747/2013.7594385.bard.

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Potato yield and quality are highly dependent on an adequate supply of nitrogen and water. Opportunities exist to use airborne hyperspectral (HS) remote sensing for the detection of spatial variation in N status of the crop to allow more targeted N applications. Thermal remote sensing has the potential to identify spatial variations in crop water status to allow better irrigation management and eventually precision irrigation. The overall objective of this study was to examine the ability of HS imagery in the visible and near infrared spectrum (VIS-NIR) and thermal imagery to distinguish between water and N status in potato fields. To lay the basis for achieving the research objectives, experiments in the US and in Israel were conducted in potato with different irrigation and N-application amounts. Thermal indices based merely on thermal images were found sensitive to water status in both Israel and the US in three potato varieties. Spectral indices based on HS images were found suitable to detect N stress accurately and reliably while partial least squares (PLS) analysis of spectral data was more sensitive to N levels. Initial fusion of HS and thermal images showed the potential of detecting both N stress and water stress and even to differentiate between them. This study is one of the first attempts at fusing HS and thermal imagery to detect N and water stress and to estimate N and water levels. Future research is needed to refine these techniques for use in precision agriculture applications.
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Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, spectral characteristics in the VIS/NIR (0.4-1.0 micron) will be used in conjunction with thermal data to provide accurate and robust detection of fruit in the tree canopy. Hyper-spectral image pairs will be combined to provide automatic stereo matching for accurate 3D position. Secondly, VIS/NIR/FIR (0.4-15.0 micron) spectral sensor technology will be evaluated for potential in-field on-the-tree grading of surface defect, maturity and size for selective fruit harvest. Thirdly, new adaptive Lyapunov-basedHBVS (homography-based visual servo) methods to compensate for camera uncertainty, distortion effects, and provide range to target from a single camera will be developed, simulated, and implemented on a camera testbed to prove concept. HBVS methods coupled with imagespace navigation will be implemented to provide robust target tracking. And finally, harvesting test will be conducted on the developed technologies using the University of Florida harvesting manipulator test bed. During the course of the project it was determined that the second objective was overly ambitious for the project period and effort was directed toward the other objectives. The results reflect the synergistic efforts of the three principals. The USA team has focused on citrus based approaches while the Israeli counterpart has focused on apples. The USA team has improved visual servo control through the use of a statistical-based range estimate and homography. The results have been promising as long as the target is visible. In addition, the USA team has developed improved fruit detection algorithms that are robust under light variation and can localize fruit centers for partially occluded fruit. Additionally, algorithms have been developed to fuse thermal and visible spectrum image prior to segmentation in order to evaluate the potential improvements in fruit detection. Lastly, the USA team has developed a multispectral detection approach which demonstrated fruit detection levels above 90% of non-occluded fruit. The Israel team has focused on image registration and statistical based fruit detection with post-segmentation fusion. The results of all programs have shown significant progress with increased levels of fruit detection over prior art.
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