Millions of ionograms are acquired annually to monitor the ionosphere. The accumulated data contain untapped information from a range of locations, multiple solar cycles, and various geomagnetic conditions. In this study, we propose the application of deep convolutional neural networks to automatically classify and scale high-latitude ionograms. A supervised approach is implemented and the networks ...

Programmable nanoscale carriers, such as liposomes and DNA, are readily being explored for personalized medicine or disease prediction and diagnostics. The characterization of these nanocarriers is limited and challenging due to their complex chemical composition. Here, we demonstrate the utilization of surface-enhanced Raman spectroscopy (SERS), which provides a unique molecular fingerprint of ...

EISCAT_3D is a project to build a multi-site phased-array incoherent scatter radar system in northern Fenno-Scandinavia. We demonstrate via numerical simulation how useful monochromatic images taken by a multi-point imager network are for auroral research in the EISCAT_3D project. We apply the generalized aurora computed tomography (G-ACT) method to modelled observational data from real instruments, ...

Microfossil classification is an important discipline in subsurface exploration, for both oil & gas and Carbon Capture and Storage (CCS). The abundance and distribution of species found in sedimentary rocks provide valuable information about the age and depositional environment. However, the analysis is difficult and time-consuming, as it is based on manual work by human experts. Attempts to automate ...

With applications ranging from metabolomics to histopathology, quantitative phase microscopy (QPM) is a powerful label-free imaging modality. Despite significant advances in fast multiplexed imaging sensors and deep-learning-based inverse solvers, the throughput of QPM is currently limited by the pixel-rate of the image sensors. Complementarily, to improve throughput further, here we propose to ...

The objective of this paper is to take a closer look at the theory of damage stability, i.e., origin, construction, organization and human developments, regulations, and in this context pinpoint a possible causal relationship between two specific ship losses: the losses of RMS Titanic and KNM Helge Ingstad. The paper does not discuss direct causes but rather tries to discuss possible causal links ...

Deepfake is a type of face manipulation technique using deep learning that allows for the replacement of faces in videos in a very realistic way. While this technology has many practical uses, if used maliciously, it can have a significant number of bad impacts on society, such as spreading fake news or cyberbullying. Therefore, the ability to detect deepfake has become a pressing need. This ...

Extracellular vesicles (EVs) released from cells attract interest for their possible role in health and diseases. The detection and characterization of EVs is challenging due to the lack of specialized methodologies. Raman spectroscopy, however, has been suggested as a novel approach for biochemical analysis of EVs. To extract information from the spectra, a novel deep learning architecture is ...

Pneumonia remains a critical health concern worldwide, necessitating efficient diagnostic tools to enhance patient care. This research proposes a concatenated modified LeNet classifier to classify pneumonia images accurately. The model leverages deep learning techniques to improve the diagnosis of Pneumonia, leading to more effective and timely treatment. Our modified LeNet architecture incorporates ...

The introduction of maritime autonomous surface ships (MASSs) has prompted a significant shift in the role of navigators from traditional navigation to supervising artificial intelligence (AI) collision avoidance systems or managing operations from remote operation centers (ROCs). This may be problematic because the integration of AI technologies into collision avoidance systems may jeopardize safety ...