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Remote sensing is being actively researched in the fields of environment, military and urban planning through technologies such as monitoring of natural climate phenomena on the earth, land cover classification, and object detection. Recently, satellites equipped with observation cameras of various resolutions were launched, and remote sensing images are acquired by various observation methods including cluster satellites. However, the atmospheric and environmental conditions present in the observed scene degrade the quality of images or interrupt the capture of the Earth's surface information. One method to overcome this is by generating synthetic images through image simulation. Synthetic images can be generated by using statistical or knowledge-based models or by using spectral and optic-based models to create a simulated image in place of the unobtained image at a required time. Various proposed methodologies will provide economical utility in the generation of image learning materials and time series data through image simulation. The 6 published articles cover various topics and applications central to Remote sensing image simulation. Although submission to this Special Issue is now closed, the need for further in-depth research and development related to image simulation of High-spatial and spectral resolution, sensor fusion and colorization remains.I would like to take this opportunity to express my most profound appreciation to the MDPI Book staff, the editorial team of Applied Sciences journal, especially Ms. Nimo Lang, the assistant editor of this Special Issue, talented authors, and professional reviewers.

image fusion --- random forest regression --- SAR image --- panchromatic image --- high-resolution --- multi-beam LiDAR --- in situ self-calibration --- mobile mapping system --- 3D point cloud --- backpack-based mapping --- aerial orthoimage --- Sentinel-2 --- super-resolution --- image simulation --- residual U-Net --- interferometry --- remote sensing --- computational simulation --- denoising --- detection --- SAR imagery --- fusing region proposals --- KOMPSAT-3A --- strip --- sensor modeling --- RPCs --- mosaic --- matching --- discrepancy --- n/a

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Biomedical image processing is an interdisciplinary field involving a variety of disciplines, e.g., electronics, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the human body. Biomedical image processing is finding an increasing number of important applications in, for example, the study of the internal structure or function of an organ and the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, which could help medical doctors in refining their clinical picture.

Technology: general issues --- MR brain segmentation --- fuzzy clustering --- object extraction --- silhouette analysis --- DICOM processing --- 3D modeling --- semantic segmentation --- convolutional neural networks --- kidney biopsy --- kidney transplantation --- glomerulus detection --- glomerulosclerosis --- pattern recognition --- hemoglobin --- anemia --- human tissues --- conjunctiva --- non-invasive medical device --- training size --- deep learning --- convolutional neural network --- U-Net --- segmentation --- artificial intelligence --- digital pathology --- kidney fibrosis --- blood vessel segmentation --- inferior vena cava --- ultrasound imaging --- binary tree model --- pulsatility --- fluid volume assessment --- MR brain segmentation --- fuzzy clustering --- object extraction --- silhouette analysis --- DICOM processing --- 3D modeling --- semantic segmentation --- convolutional neural networks --- kidney biopsy --- kidney transplantation --- glomerulus detection --- glomerulosclerosis --- pattern recognition --- hemoglobin --- anemia --- human tissues --- conjunctiva --- non-invasive medical device --- training size --- deep learning --- convolutional neural network --- U-Net --- segmentation --- artificial intelligence --- digital pathology --- kidney fibrosis --- blood vessel segmentation --- inferior vena cava --- ultrasound imaging --- binary tree model --- pulsatility --- fluid volume assessment

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Remote sensing is being actively researched in the fields of environment, military and urban planning through technologies such as monitoring of natural climate phenomena on the earth, land cover classification, and object detection. Recently, satellites equipped with observation cameras of various resolutions were launched, and remote sensing images are acquired by various observation methods including cluster satellites. However, the atmospheric and environmental conditions present in the observed scene degrade the quality of images or interrupt the capture of the Earth's surface information. One method to overcome this is by generating synthetic images through image simulation. Synthetic images can be generated by using statistical or knowledge-based models or by using spectral and optic-based models to create a simulated image in place of the unobtained image at a required time. Various proposed methodologies will provide economical utility in the generation of image learning materials and time series data through image simulation. The 6 published articles cover various topics and applications central to Remote sensing image simulation. Although submission to this Special Issue is now closed, the need for further in-depth research and development related to image simulation of High-spatial and spectral resolution, sensor fusion and colorization remains.I would like to take this opportunity to express my most profound appreciation to the MDPI Book staff, the editorial team of Applied Sciences journal, especially Ms. Nimo Lang, the assistant editor of this Special Issue, talented authors, and professional reviewers.

Technology: general issues --- History of engineering & technology --- image fusion --- random forest regression --- SAR image --- panchromatic image --- high-resolution --- multi-beam LiDAR --- in situ self-calibration --- mobile mapping system --- 3D point cloud --- backpack-based mapping --- aerial orthoimage --- Sentinel-2 --- super-resolution --- image simulation --- residual U-Net --- interferometry --- remote sensing --- computational simulation --- denoising --- detection --- SAR imagery --- fusing region proposals --- KOMPSAT-3A --- strip --- sensor modeling --- RPCs --- mosaic --- matching --- discrepancy --- n/a

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This book focuses on a variety of interdisciplinary perspectives concerning the theory and application of artificial intelligence (AI) in medicine, medically oriented human biology, and healthcare. The list of topics includes the application of AI in biomedicine and clinical medicine, machine learning-based decision support, robotic surgery, data analytics and mining, laboratory information systems, and usage of AI in medical education. Special attention is given to the practical aspect of a study. Hence, the inclusion of a clinical assessment of the usefulness and potential impact of the submitted work is strongly highlighted.

computational intelligence --- medical assistance --- instance-based learning --- healthcare --- clinical decision support systems --- deep neural networks --- medical imaging --- backdoor attacks --- security and privacy --- COVID-19 --- gastric cancer --- endoscopy --- deep learning --- convolutional neural network --- brain --- pituitary adenoma --- dysembryoplastic neuroepithelial tumor --- DNET --- ganglioglioma --- digital pathology --- computer vision --- machine learning --- CNN --- ATLAS --- HarDNet --- Swin transformer --- segmentation --- U-Net --- cerebral infarction --- CycleGAN --- advanced statistics --- schizophrenia --- aggression --- forensic psychiatry --- medical image segmentation --- CT image segmentation --- kernel density --- semi-automated labeling tool --- Bayesian learning --- neuroimaging --- feature selection --- kernel formulation --- mental disorders --- MRI --- visual acuity --- fundus images --- ophthalmology --- SVM --- n/a

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Photoacoustic (or optoacoustic) imaging, including photoacoustic tomography (PAT) and photoacoustic microscopy (PAM), is an emerging imaging modality with great clinical potential. PAI’s deep tissue penetration and fine spatial resolution also hold great promise for visualizing physiology and pathology at the molecular level. PAI combines optical contrast with ultrasonic resolution, and is capable of imaging at depths of up to 7 cm with a real-time scalable spatial resolution of 10 to 500 µm. PAI has demonstrated applications in brain imaging and cancer imaging, such as breast cancer, prostate cancer, ovarian cancer etc. This Special Issue focuses on the novel technological developments and pre-clinical and clinical biomedical applications of PAI. Topics include but are not limited to: brain imaging; cancer imaging; image reconstruction; quantitative imaging; light source and delivery for PAI; photoacoustic detectors; nanoparticles designed for PAI; photoacoustic molecular imaging; photoacoustic spectroscopy.

History of engineering & technology --- photoacoustic imaging --- tomography --- thermoacoustic --- radio frequency --- image quality assessment --- image formation theory --- image reconstruction techniques --- sparsity --- signal processing --- deconvolution --- empirical mode decomposition --- signal deconvolution --- photoacoustics --- tissue characterization --- absorption --- Photoacoustic Computed Tomography (PACT) --- ring array --- fast imaging --- low cost --- photoacoustic tomography --- full-field detection --- wave equation --- final time inversion --- uniqueness --- stability --- iterative reconstruction --- 3D photoacoustic tomography --- full-view illumination and ultrasound detection --- photoacoustic coplanar --- quartz bowl --- correlation matrix filter --- time reversal operator --- photo-acoustic tomography --- reflection artifacts --- deep learning --- convolutional neural network --- time reversal --- Landweber algorithm --- U-net --- optoacoustic imaging --- respiratory gating --- motion artifacts --- full-ring illumination --- diffused-beam illumination --- point source illumination --- ultrasound tomography (UST) --- photoacoustic tomography (PAT) --- photoacoustic imaging --- tomography --- thermoacoustic --- radio frequency --- image quality assessment --- image formation theory --- image reconstruction techniques --- sparsity --- signal processing --- deconvolution --- empirical mode decomposition --- signal deconvolution --- photoacoustics --- tissue characterization --- absorption --- Photoacoustic Computed Tomography (PACT) --- ring array --- fast imaging --- low cost --- photoacoustic tomography --- full-field detection --- wave equation --- final time inversion --- uniqueness --- stability --- iterative reconstruction --- 3D photoacoustic tomography --- full-view illumination and ultrasound detection --- photoacoustic coplanar --- quartz bowl --- correlation matrix filter --- time reversal operator --- photo-acoustic tomography --- reflection artifacts --- deep learning --- convolutional neural network --- time reversal --- Landweber algorithm --- U-net --- optoacoustic imaging --- respiratory gating --- motion artifacts --- full-ring illumination --- diffused-beam illumination --- point source illumination --- ultrasound tomography (UST) --- photoacoustic tomography (PAT)