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Droplet deposition onto a hydrophobic surface is studied experimentally and numerically. A wide range of droplet sizes can result from the same syringe, depending strongly on the needle retraction speed. Three regimes are identified…
Recent numerical analyses to optimize the design of microfluidic devices for more effective entrapment or segregation of surrogate circulating tumor cells (CTCs) from healthy cells have been reported in the literature without concurrently…
Convolutional neural network (CNN) is widely used in computer vision applications. In the networks that deal with images, CNNs are the most time-consuming layer of the networks. Usually, the solution to address the computation cost is to…
In this work a general framework for damage and fracture assessment including the effect of strain gradients is provided. Both mechanism-based and phenomenological strain gradient plasticity (SGP) theories are implemented numerically using…
We propose a spherical kernel for efficient graph convolution of 3D point clouds. Our metric-based kernels systematically quantize the local 3D space to identify distinctive geometric relationships in the data. Similar to the regular grid…
We report a scheme for very significantly accelerating growth of dendritic patterns in diverse liquids, making use of only a few hundred microwatts of laser power in the presence of an efficient absorber like carbon nanotubes (CNTs). The…
A method for measuring the altitude and particle size of noctilucent clouds, based on positioning and photometry from wide-angle three-color cameras, has been developed to determine the three velocity components, particle radius, and its…
Hardware-based spiking neural networks (SNNs) are regarded as promising candidates for the cognitive computing system due to low power consumption and highly parallel operation. In this work, we train the SNN in which the firing time…
The self-consistent field (SCF) generation of the three-dimensional (3D) electron density distribution ($\rho$) represents a fundamental aspect of density functional theory (DFT) and related first-principles calculations, and how one can…
We introduce a novel framework for Continual Learning in 3D object classification. Our approach, CL3D, is based on the selection of prototypes from each class using spectral clustering. For non-Euclidean data such as point clouds, spectral…
Graphene has shown impressive properties for nanoelectronics applications including a high mobility and a width-dependent bandgap. Use of graphene in nanoelectronics would most likey be in the form of graphene nanoribbons (GNRs) where the…
Efficiency and robustness are increasingly needed for applications on 3D point clouds, with the ubiquitous use of edge devices in scenarios like autonomous driving and robotics, which often demand real-time and reliable responses. The paper…
Accurate particle size distribution (PSD) measurement is important in industries such as mining, pharmaceuticals, and fertilizer manufacturing, significantly influencing product quality and operational efficiency. Traditional PSD methods…
The line-of-sight velocity dispersion profile of galaxy clusters exhibits a "kink" corresponding to the spatial extent of orbiting galaxies. Because the spatial extent of a cluster is correlated with the amplitude of the velocity dispersion…
Single-walled carbon nanotubes (SWNTs) are typically long (>100 nm) and have been well established as novel quasi one-dimensional systems with interesting electrical, mechanical, and optical properties. Here, quasi zero-dimensional SWNTs…
Understanding how multi-scale network structure influences circadian rhythms in the suprachiasmatic nucleus (SCN) is essential for uncovering the principles of rhythmic robustness and synchronization. Previous studies using synthetic SCN…
We present preliminary results of a detailed 3D study of supernova remnants in the nearby spiral M51 using data from the SIGNALS survey obtained with the imaging Fourier transform spectrometer SITELLE at the Canada-France-Hawaii telescope…
The mechanical stability of open single wall carbon nanotubes (SWCNT) under axial stress (compression and tension) and twist has been re-examined in a search of specific tube-length and load scaling. SWCNT with different chiralities and…
The analyses relying on 3D point clouds are an utterly complex task, often involving million of points, but also requiring computationally efficient algorithms because of many real-time applications; e.g. autonomous vehicle. However, point…
Hyperspectral Image Classification (HSIC) is a difficult task due to high inter and intra-class similarity and variability, nested regions, and overlapping. 2D Convolutional Neural Networks (CNN) emerged as a viable network whereas, 3D CNNs…