Results 61 to 70 of about 1,429,068 (340)

Analysis and training of a traffic sign recognition neural network model

Вестник Дагестанского государственного технического университета: Технические науки, 2023
Objective. The purpose of the research is to develop and train a neural network model based on convolutional neural networks for effective recognition of road signs in images.Method.
A. U. Mentsiev, T. G. Aigumov, E. M. Abdulmukminova   +2 more
doaj   +1 more source

CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes [PDF]

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018
We propose a network for Congested Scene Recognition called CSRNet to provide a data-driven and deep learning method that can understand highly congested scenes and perform accurate count estimation as well as present high-quality density maps.
Yuhong Li, Xiaofan Zhang, Deming Chen
semanticscholar   +1 more source

Cervical Spinal Cord Magnetization Transfer Ratio and Its Relationship With Clinical Outcomes in Multiple Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective The cervical spinal cord (cSC) is highly relevant to clinical dysfunction in multiple sclerosis (MS) but remains understudied using quantitative magnetic resonance imaging (MRI). We assessed magnetization transfer ratio (MTR), a semi‐quantitative MRI measure sensitive to MS‐related tissue microstructural changes, in the cSC and its ...
Lisa Eunyoung Lee, Julien Cohen‐Adad, Irene M. Vavasour, Melanie Guenette, Katherine Sawicka, Neda Rashidi‐Ranjbar, Nathan Churchill, Akash Chopra, Adelia Adelia, Pierre‐Louis Benveniste, Anthony Traboulsee, Nathalie Arbour, Fabrizio Giuliani, Larry D. Lynd, Scott B. Patten, Alexandre Prat, Alice Schabas, Penelope Smyth, Roger Tam, Yunyan Zhang, Simon J. Graham, Mojgan Hodaie, Anthony Feinstein, Shannon Kolind, Tom A. Schweizer, Jiwon Oh, CanProCo Study Group   +26 more
wiley   +1 more source

An Optimized Convolutional Neural Network for the 3D Point-Cloud Compression

Sensors, 2023
Due to the tremendous volume taken by the 3D point-cloud models, knowing how to achieve the balance between a high compression ratio, a low distortion rate, and computing cost in point-cloud compression is a significant issue in the field of virtual ...
Guoliang Luo, Bingqin He, Yanbo Xiong, Luqi Wang, Hui Wang, Zhiliang Zhu, Xiangren Shi   +6 more
doaj   +1 more source

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

Advanced Engineering Materials, EarlyView.
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani, Surabhi Nadigotti, Erfan Fatehi, Derek Aranguren van Egmond, Behnam Ashrafi   +4 more
wiley   +1 more source

Interpretable Convolutional Neural Networks [PDF]

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018
This paper proposes a method to modify traditional convolutional neural networks (CNNs) into interpretable CNNs, in order to clarify knowledge representations in high conv-layers of CNNs. In an interpretable CNN, each filter in a high conv-layer represents a certain object part.
Ying Nian Wu, Quanshi Zhang, Song-Chun Zhu   +2 more
openaire   +3 more sources

Structurally Colored Physically Unclonable Functions with Ultra‐Rich and Stable Encoding Capacity

Advanced Functional Materials, Volume 35, Issue 12, March 18, 2025.
This study reports a design strategy for generating bright‐field resolvable physically unclonable functions with extremely rich encoding capacity coupled with outstanding thermal and chemical stability. The optical response emerges from thickness‐dependent structural color formation in ZnO features, which are fabricated by physical vapor deposition ...
Abidin Esidir, Miaoning Ren, Sami Pekdemir, Mustafa Kalay, Nilgun Kayaci, Nail Gunaltay, Hakan Usta, Xian Huang, Mustafa Serdar Onses   +8 more
wiley   +1 more source

Modeling and Predictive Analysis of Small Internal Leakage of Hydraulic Cylinder Based on Neural Network

Energies, 2021
The internal leakage of a hydraulic cylinder is an inevitable hydraulic system failure that seriously affects the working efficiency of the hydraulic system. Therefore, it is very important to accurately identify and predict leakage data in the hydraulic
Yuan Guo, Ge Xiong, Liangcai Zeng, Qingfeng Li   +3 more
doaj   +1 more source

Carbon Nanotube 3D Integrated Circuits: From Design to Applications

Advanced Functional Materials, EarlyView.
As Moore's law approaches its physical limits, carbon nanotube (CNT) 3D integrated circuits (ICs) emerge as a promising alternative due to the miniaturization, high mobility, and low power consumption. CNT 3D ICs in optoelectronics, memory, and monolithic ICs are reviewed while addressing challenges in fabrication, design, and integration.
Han‐Yang Liu, Yun Sun, Dong‐Ming Sun, Hui‐Ming Cheng   +3 more
wiley   +1 more source

Quantum Dilated Convolutional Neural Networks

IEEE Access, 2022
In recent years, with rapid progress in the development of quantum technologies, quantum machine learning has attracted a lot of interest. In particular, a family of hybrid quantum-classical neural networks, consisting of classical and quantum elements ...
Yixiong Chen
doaj   +1 more source