相关论文: A deep learning approach for determining the chira…

Chiral carbon nanotubes (CNTs) are direct-gap semiconductors with optical properties governed by one-dimensional excitons with enormous oscillator strengths. Each species of chiral CNTs has an enantiomeric pair of left- and right-handed…

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and the revival of deep CNN. CNNs enable learning data-driven, highly representative, layered hierarchical image…

Controlling crystalline material defects is crucial, as they affect properties of the material that may be detrimental or beneficial for the final performance of a device. Defect analysis on the sub-nanometer scale is enabled by…

The variation of the optical absorption of carbon nanotubes with their geometry has been a long standing question at the heart of both metrological and applicative issues, in particular because optical spectroscopy is one of the primary…

Carbon nanotube (CNT) is expected for much more important and broader applications in the future, because of its amazing electrical and mechanical properties. However, today, the prospect is detained by the fact that the growth of CNTs…

Image segmentation is considered to be one of the critical tasks in hyperspectral remote sensing image processing. Recently, convolutional neural network (CNN) has established itself as a powerful model in segmentation and classification by…

Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are…

Deep learning is an effective approach to solving image recognition problems. People draw intuitive conclusions from trading charts; this study uses the characteristics of deep learning to train computers in imitating this kind of intuition…

This paper presents an innovative deep learning pipeline which estimates the relative pose of a spacecraft by incorporating the temporal information from a rendezvous sequence. It leverages the performance of long short-term memory (LSTM)…

Single wall carbon nanotubes (SWCNT) exhibit remarkable optical and electrical properties making them one of the most promising materials for next generation electronic and optoelectronic devices. Their electronic properties strongly depend…

The linear polarizability absorption spectra of the double-walled carbon nanotubes (DWNTs) have been calculated by using the tight-binding (TB) model and sum-over-state (SOS) method, supplemented by the first principles CASTEP calculations.…

In image classification, Convolutional Neural Network(CNN) models have achieved high performance with the rapid development in deep learning. However, some categories in the image datasets are more difficult to distinguished than others.…

Classification of ceramic thin sections is fundamental for understanding ancient pottery production techniques, provenance, and trade networks. Although effective, traditional petrographic analysis is time-consuming. This study explores the…

Single-walled carbon nanotubes (SWNTs) are potential materials for future nanoelectronics. Since the electronic and optical properties of SWNTs strongly depend on tube diameter and chirality, obtaining SWNTs with narrow (n,m) chirality…

Multiple instance (MI) learning with a convolutional neural network enables end-to-end training in the presence of weak image-level labels. We propose a new method for aggregating predictions from smaller regions of the image into an…

An important part of breast cancer staging is the assessment of the sentinel axillary node for early signs of tumor spreading. However, this assessment by pathologists is not always easy and retrospective surveys often requalify the status…

Carbon nanotubes (CNTs) are critical building blocks in nanotechnology, yet the characterization of their dynamic growth is limited by the experimental challenges in nanoscale motion measurement using scanning electron microscopy (SEM)…

As the aerospace industry becomes increasingly demanding for stronger lightweight materials, the ultra-strong carbon nanotube (CNT) composites with highly aligned CNT network structures could be the answer. In this work, a novel methodology…

Semantic image segmentation is one of the most important tasks in medical image analysis. Most state-of-the-art deep learning methods require a large number of accurately annotated examples for model training. However, accurate annotation…

Computer Tomography (CT) images have become quite important to diagnose diseases. CT scan slice contains a vast amount of data that may not be properly examined with the requisite precision and speed using normal visual inspection. A…