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norskBrowsing Artikler, rapporter og annet (fysikk og teknologi) by Author "Prasad, Dilip K."
Now showing items 1-11 of 11
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Artefact removal in ground truth deficient fluctuations-based nanoscopy images using deep learning
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020-12-08)Image denoising or artefact removal using deep learning is possible in the availability of supervised training dataset acquired in real experiments or synthesized using known noise models. Neither of the conditions can be fulfilled for nanoscopy (super-resolution optical microscopy) images that are generated from microscopy videos through statistical analysis techniques. Due to several physical ... -
Classification of Micro-Damage in Piezoelectric Ceramics Using Machine Learning of Ultrasound Signals
(Journal article; Peer reviewed, 2019-09-28)Ultrasound based structural health monitoring of piezoelectric material is challenging if a damage changes at a microscale over time. Classifying geometrically similar damages with a difference in diameter as small as 100 m is difficult using conventional sensing and signal analysis approaches. Here, we use an unconventional ultrasound sensing approach that collects information of the entire ... -
ELM-HTM guided bio-inspired unsupervised learning for anomalous trajectory classification
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020-05-23)Artificial intelligent systems often model the solutions of typical machine learning problems, inspired by biological processes, because of the biological system is faster and much adaptive than deep learning. The utility of bio-inspired learning methods lie in its ability to discover unknown patterns, and its less dependence on mathematical modeling or exhaustive training. In this paper, we propose ... -
High space-bandwidth in quantitative phase imaging using partially spatially coherent digital holographic microscopy and a deep neural network
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020-11-16)Quantitative phase microscopy (QPM) is a label-free technique that enables monitoring of morphological changes at the subcellular level. The performance of the QPM system in terms of spatial sensitivity and resolution depends on the coherence properties of the light source and the numerical aperture (NA) of objective lenses. Here, we propose high space-bandwidth quantitative phase imaging using ... -
Learning Nanoscale Motion Patterns of Vesicles in Living Cells
(Conference object; Konferansebidrag, 2020-08-05)Detecting and analyzing nanoscale motion patterns of vesicles, smaller than the microscope resolution (~250 nm), inside living biological cells is a challenging problem. State-of-the-art CV approaches based on detection, tracking, optical flow or deep learning perform poorly for this problem. We propose an integrative approach, built upon physics based simulations, nanoscopy algorithms, and shallow ... -
Learning nanoscale motion patterns of vesicles in living cells
(Chapter; Bokkapittel, 2020)Detecting and analyzing nanoscale motion patterns of vesicles, smaller than the microscope resolution (~250 nm), inside living biological cells is a challenging problem. State-of-the-art CV approaches based on detection, tracking, optical flow or deep learning perform poorly for this problem. We propose an integrative approach, built upon physics based simulations, nanoscopy algorithms, and shallow ... -
Object detection neural network improves Fourier ptychography reconstruction
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020-11-23)High resolution microscopy is heavily dependent on superb optical elements and superresolution microscopy even more so. Correcting unavoidable optical aberrations during post-processing is an elegant method to reduce the optical system’s complexity. A prime method that promises superresolution, aberration correction, and quantitative phase imaging is Fourier ptychography. This microscopy technique ... -
Physics-based machine learning for subcellular segmentation in living cells
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-12-15)Segmenting subcellular structures in living cells from fluorescence microscope images is a ground truth (GT)-deficient problem. The microscopes’ three-dimensional blurring function, finite optical resolution due to light diffraction, finite pixel resolution and the complex morphological manifestations of the structures all contribute to GT-hardness. Unsupervised segmentation approaches are quite ... -
RS-HeRR: a rough set-based Hebbian rule reduction neuro-fuzzy system
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020-06-01)Interpretabilty is one of the desired characteristics in various classification task. Rule-based system and fuzzy logic can be used for interpretation in classification. The main drawback of rule-based system is that it may contain large complex rules for classification and sometimes it becomes very difficult in interpretation. Rule reduction is also difficult for various reasons. Removing important ... -
seMLP: Self-Evolving Multi-Layer Perceptron in Stock Trading Decision Making
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-02-24)There is a growing interest in automatic crafting of neural network architectures as opposed to expert tuning to fnd the best architecture. On the other hand, the problem of stock trading is considered one of the most dynamic systems that heavily depends on complex trends of the individual company. This paper proposes a novel self-evolving neural network system called self-evolving Multi-Layer ... -
Single-shot multispectral quantitative phase imaging of biological samples using deep learning
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-05-16)Multispectral quantitative phase imaging (MS-QPI) is a high-contrast label-free technique for morphological imaging of the specimens. The aim of the present study is to extract spectral dependent quantitative information in single-shot using a highly spatially sensitive digital holographic microscope assisted by a deep neural network. There are three different wavelengths used in our method: 𝜆=532 , ...
