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Over the past decade, there has been a global increase in the incidence of skin cancers. Skin cancer has serious consequences if left untreated, potentially leading to more advanced cancer stages. In recent years, deep learning based convolutional neural network have emerged as powerful tools for skin cancer detection.
View Article and Find Full Text PDFOptical diffraction tomography using a self-reference module.
Biomed Opt Express
January 2025
Department of Electronic Engineering, Maynooth University, Maynooth, Co. Kildare, Ireland.
Optical diffraction tomography enables label-free, 3D refractive index (RI) imaging of biological samples. We present a novel, cost-effective approach to ODT that employs a modular design incorporating a self-reference holographic capture module. This two-part system consists of an illumination module and a capture module that can be seamlessly integrated with any life-science microscope using an automated alignment protocol.
View Article and Find Full Text PDFMulti-culture label-free quantitative cell migration sensing with single-cell precision.
Biomed Opt Express
January 2025
Warsaw University of Technology, Institute of Micromechanics and Photonics, 8 Sw. A. Boboli St., 02-525 Warsaw, Poland.
A fair comparison of multiple live cell cultures requires examining them under identical environmental conditions, which can only be done accurately if all cells are prepared simultaneously and studied at the same time and place. This contribution introduces a multiplexed lensless digital holographic microscopy system (MLS), enabling synchronous, label-free, quantitative observation of multiple live cell cultures with single-cell precision. The innovation of this setup lies in its ability to robustly compare the behaviour, i.
View Article and Find Full Text PDFIMPACT: In-Memory ComPuting Architecture based on Y-FlAsh Technology for Coalesced Tsetlin machine inference.
Philos Trans A Math Phys Eng Sci
January 2025
Microsystems Group, School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.
The increasing demand for processing large volumes of data for machine learning (ML) models has pushed data bandwidth requirements beyond the capability of traditional von Neumann architecture. In-memory computing (IMC) has recently emerged as a promising solution to address this gap by enabling distributed data storage and processing at the micro-architectural level, significantly reducing both latency and energy. In this article, we present In-Memory comPuting architecture based on Y-FlAsh technology for Coalesced Tsetlin machine inference (IMPACT), underpinned on a cutting-edge memory device, Y-Flash, fabricated on a 180 nm complementary metal oxide semiconductor (CMOS) process.
View Article and Find Full Text PDFIn this Letter, we present a novel, to the best of our knowledge, approach for recovering objects directly from the Fraunhofer diffraction integral, where the diffraction field of an object is approximated by the Fourier transform of this object augmented by an additional phase factor. This phase factor at the observation plane is universal for the diffraction fields generated by objects located at the same plane and illuminated by the same monochromatic plane wave. It can be first extracted from dividing the Fraunhofer diffraction field by the Fourier transform of an object reference.
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