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The promise of chemical computation lies in controlling systems incompatible with traditional electronic micro-controllers, with applications in synthetic biology and nano-scale manufacturing. Computation is typically embedded in…
The hotspots, which are typically found in nanogaps between metal structures, are critical for the enhancement of the electromagnetic field. Surface-enhanced Raman scattering (SERS), a technique known for its exceptional sensitivity and…
Laser interstitial thermal therapy (LITT) is a novel minimally invasive treatment that is used to ablate intracranial structures to treat mesial temporal lobe epilepsy (MTLE). Region of interest (ROI) segmentation before and after LITT…
Deep learning techniques have shown their success in medical image segmentation since they are easy to manipulate and robust to various types of datasets. The commonly used loss functions in the deep segmentation task are pixel-wise loss…
Blind inpainting algorithms based on deep learning architectures have shown a remarkable performance in recent years, typically outperforming model-based methods both in terms of image quality and run time. However, neural network…
We propose a conservative energy method based on neural networks with subdomains for solving variational problems (CENN), where the admissible function satisfying the essential boundary condition without boundary penalty is constructed by…
Deep neural networks (NNs) encounter scalability limitations when confronted with a vast array of neurons, thereby constraining their achievable network depth. To address this challenge, we propose an integration of tensor networks (TN)…
Proton therapy systems without a gantry can be more compact and less expensive in terms of capital cost, and therefore more available to a larger patient population. Would the advances in pencil beam scanning and robotics make gantry-less…
In this paper we outline the development of a scalable PBF thermal history simulation built on CAPL and based on melt pool physics and dynamics. The new approach inherits linear scalability from CAPL and has three novel ingredients.…
Computational experiments are exploited in finding a well-designed processing path to optimize material structures for desired properties. This requires understanding the interplay between the processing-(micro)structure-property linkages…
We present a method to infer temperature fields from stereo particle-image velocimetry (PIV) data in turbulent Rayleigh-B\'enard convection (RBC) using Physics-informed neural networks (PINNs). The physical setup is a cubic RBC cell with…
While deep neural networks (NN) significantly advance image compressed sensing (CS) by improving reconstruction quality, the necessity of training current CS NNs from scratch constrains their effectiveness and hampers rapid deployment.…
Minimally invasive interventions performed inside brain vessels with the synergistic use of microcatheters pushed over guidewires have revolutionized the way aneurysms, stroke, arteriovenous malformations, brain tumors and other…
Scanning Transmission Electron Microscopy (STEM) has become the main stay for materials characterization on atomic level, with applications ranging from visualization of localized and extended defects to mapping order parameter fields. In…
This work presents a neural network model capable of recognizing small and tiny objects in thermal images collected by unmanned aerial vehicles. Our model consists of three parts, the backbone, the neck, and the prediction head. The…
To date, the simulation of organ deformations for applications like therapy planning or image-guided interventions is calculated by solving the elastodynamics equations. While efficient solvers have been proposed for fast simulations,…
Transfer learning using pre-trained Convolutional Neural Networks (CNNs) has been successfully applied to images for different classification tasks. In this paper, we propose a new pipeline for pain expression recognition in neonates using…
Deep learning methods based on Convolutional Neural Networks (CNNs) have shown great potential to improve early and accurate diagnosis of Alzheimer's disease (AD) dementia based on imaging data. However, these methods have yet to be widely…
Programmable microheater arrays are essential for a variety of applications including gas sensing, microfluidic lab on a chip devices, 3D printers, and biosensors that rely on DNA amplification. Increasing the density and number of heating…
Autonomy in robotic surgery is very challenging in unstructured environments, especially when interacting with deformable soft tissues. The main difficulty is to generate model-based control methods that account for deformation dynamics…