Results 41 to 50 of about 102,874 (306)
Remote Sensing, 2017 Deep convolutional neural networks (CNNs) have been widely used to obtain high-level representation in various computer vision tasks. However, in the field of remote sensing, there are not sufficient images to train a useful deep CNN. Instead, we tend to
Jie Wang +4 more
doaj +1 more source
A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy
Advanced Engineering Materials, EarlyView.A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle +5 more
wiley +1 more source
A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys
Advanced Engineering Materials, EarlyView.High‐entropy alloys offer vast potential for various applications, including electrocatalysis; however, their compositional complexity challenges conventional screening. We introduce an inverse‐design framework combining two neural networks to determine optimal compositions and reconstruct nanoparticle geometry from targeted properties and conventional
Mikael Takoutsin +14 more
wiley +1 more source
Ensemble of CNNs for Steganalysis
, 2016 There has been growing interest in using convolutional neural networks (CNNs) in the fields of image forensics and steganalysis, and some promising results have been reported recently. These works mainly focus on the architectural design of CNNs, usually,
Xu, Guanshuo +5 more
core +1 more source
Structured Receptive Fields in CNNs [PDF]
, 2016 Learning powerful feature representations with CNNs is hard when training data are limited. Pre-training is one way to overcome this, but it requires large datasets sufficiently similar to the target domain.
van Gemert, J. +7 more
core +2 more sources
Detecting Anomalous Cell Behavior in Electrochemical Battery Testing Using Machine Learning
Advanced Engineering Materials, EarlyView.Machine‐learning‐based screening enables automated identification of anomalous battery cells from complementary electrochemical tests. A curated battery database supports configuration‐aware comparison of rate‐capability and impedance data. Supervised classification of rate‐test data achieves 90% accuracy, while CNN‐VAE‐based impedance analysis reaches
Minu Rose +7 more
wiley +1 more source
INTELLIGENT SURVEILLANCE SYSTEM FOR FIRE DETECTION USING YOLOV8
Iraqi Journal for Computers and InformaticsThis study describes a lightweight deep learning model trained on a self-made image dataset taken inside farms and open areas of the Holy Shrine of Al-Hussainiya in the City of Karbala, Iraq.
Muthanna S. Mohammed +2 more
doaj +1 more source
P-CNN: Percept-CNN for semantic segmentation
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & VisualizationThe task of image segmentation remains a fundamental challenge, in the field of computer vision. Convolutional Neural Networks (CNNs) have achieved significant success in this field, yet there are some limitations in the conventional approach. The process of accurate, pixel-wise image annotation is time-consuming, as well as requires more human effort.
Deepak Hegde, G. N. Balaji
openaire +3 more sources
Generalized CNN: Potentials of a CNN with non-uniform weights [PDF]
CNNA '92 Proceedings Second International Workshop on Cellular Neural Networks and Their Applications, 2003 A generalization of the cellular neural network (CNN) paradigm is obtained by removing the uniformly constraint on weight values. Such generalized CNNs are capable of new tasks, such as function approximation or associative memory. A stability analysis of these networks is presented.
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Advanced Functional Materials, EarlyView.Low‐frequency noise spectroscopy defines the resolvable conductance states of synaptic FeFETs by coupling read‐current fluctuation with usable dynamic range. The resulting noise‐limited bit precision establishes a universal, device‐agnostic reliability metric beyond the memory window, enabling quantitative benchmarking and rational design of high ...
Jaehong Park +12 more
wiley +1 more source