Results 121 to 130 of about 86,091 (310)

Time Series Data Preparation for CNN and LSTM

<p>It a helpful code for absolute beginners to start work on time series data forecasting. It covers the following content.</p> <ul> <li>How to Prepare Time Series data for CNNs and LSTMs?</li> <li>How to Develop CNNs ...
Syed Muhammad Hasanat
core   +2 more sources

Physics‐Informed Machine Learning for Sustainable Alloy Design: Toward a Recyclable Unified Q&P Steel

Advanced Science, EarlyView.
A physics‐informed property‐bridging framework links high‐throughput hardness screening to tensile performance in quenching and partitioning steels. By transferring metallurgically guided representations across properties, a single alloy composition is designed to achieve multiple strength grades through heat‐treatment tuning alone, offering a ...
Xiaolu Wei, Yong Li, Chenchong Wang, Lingyu Wang, Xiang Song, Keming Mao, Yu Zhang, Wei Xu   +7 more
wiley   +1 more source

A Complete Neural Network-Based Representation of High-Dimension Convolutional Neural Networks

Mathematics
Convolutional Neural Networks (CNNs) are a highly used machine learning architecture in various fields. Typical descriptions of CNNs are based on low-dimension and tensor representations in the feature extraction part.
Ray-Ming Chen
doaj   +1 more source

A Portable and Dual‐Button Microneedle Device Enables Intelligent Multimodal Laser Sensing

Advanced Science, EarlyView.
A portable and dual‐button microneedle device enables rapid interstitial fluid sampling. Coupled with multimodal laser sensing and AI‐assisted data processing, the platform enables simultaneous molecular and elemental analysis for minimally invasive and multiplexed health assessment toward point‐of‐care diagnostics.
Yuanchao Liu, Xiujuan Hu, Shengqun Shi, Bowen Li, Zhixing Ge, Yunchen Long, Chaochao Sun, Annan Chen, Bingbing Gao, Lianbo Guo, Condon Lau, Wei Luo, Chwee Teck Lim   +12 more
wiley   +1 more source

Automatic Memory-Efficient Scheduling of CNNs

, 2019
Accessing large external DRAM is costly, and poses a challenge to efficiently evaluate data-intensive convolutional neural networks (CNNs) on embedded devices.
Yifan He, Peemen, Maurice, Waeijen, Luc, Maurice Peemen, Henk Corporaal, Sioutas, Savvas, Luc Waeijen, He, Yifan, Savvas Sioutas, Corporaal, Henk   +9 more
core   +2 more sources

Toward Long‐Term Reliable Human‐Machine Interaction: A Flexible, Breathable, and Self‐Powered Pressure Sensor System With Firefighting Validation

Advanced Science, EarlyView.
This work develops a breathable potentiometric self‐powered pressure sensor with good wearability, using a non‐woven fabric substrate and hydrogel electrolyte. The sensor is integrated into a five‐channel smart glove to acquire reliable hand motion signals, which are processed by a CNN for identity verification and smart vehicle control in human ...
Qilong Zhang, Zhao Yao, Jiaxu Liu, Yuxuan Hou, Zhongtao Zhang, Jingwei Xue, Leonid Chernogor, Nam Young Kim, Eun Seong Kim, Yuanyue Li, Yang Li   +10 more
wiley   +1 more source

From Label‐Free Multiphoton Imaging to Pathological Reports: A Vision‐Language Breast Cancer Margin Pathological Diagnosis System

Advanced Science, EarlyView.
MarginPath is a novel vision‐language system that automates breast cancer margin assessment using a single label‐free multiphoton microscopy image. By integrating tumor‐associated collagen signatures with virtual H&E imaging, it generates accurate margin heatmaps and comprehensive diagnostic reports.
Shu Wang, Jingze Su, Xiahui Han, Deyong Kang, Xiao Zhang, Fei Xu, Changzu Liu, Junlin Pan, Xingfu Wang, Qiaohui Zhan, Aimin Wang, Feng Huang, Heping Cheng, Wenxi Liu, Ruolan Lin, Jianxin Chen   +15 more
wiley   +1 more source

Convolutional Neural Networks for Direction of Arrival Estimation Compared to Classical Estimators and Bounds

IEEE Access
Recently, there has been a proliferation of applied machine learning (ML) research, including the use of convolutional neural networks (CNNs) for direction of arrival (DoA) estimation.
Christopher J. Bell, Kaushallya Adhikari, Andrew Brown   +2 more
doaj   +1 more source

deep-projects/camelyon-cnns-training-experiments: 0.1

, 2019
<p>Version 0.1</p ...
Christoph Jansen
core   +1 more source

Dielectric‐Confinement‐Induced in‐Plane Photoelectric Anisotropy in Isotropic Quasi‐1D γ‐GaS Nanoribbon

Advanced Science, EarlyView.
We investigate the geometry‐governed optoelectronic anisotropy arising from dielectric confinement in quasi‐1D γ‐GaS nanoribbons with intrinsically isotropic atomic structures. Dielectric mismatch between the nanoribbon and its surroundings leads to a general polarization‐dependent photoresponse during near‐field scattering.
Jiawei Jing, Jiamei Chen, Xing Xin, Peisong Peng, Zhihao Wu, Weiheng Zhong, Hanyu Zhang, Wanfu Shen, Jihua Zhang, Yuwei Shan, Yuanzheng Li, Xingang Zhao, Wei Xin, Fang Wang, Weida Hu, Haiyang Xu, Yichun Liu   +16 more
wiley   +1 more source