Results 91 to 100 of about 849,090 (345)
Advanced Functional Materials, EarlyView.This study presents a novel method using laser‐induced graphene (LIG) to enable high‐yield transfer of silver nanowire (AgNW) networks onto ultra‐low modulus, breathable silicone substrates. This approach creates ultra‐conformal epidermal electrodes (≈50 µm) for long‐term, high‐fidelity electrophysiological monitoring, even in challenging conditions ...
Jiuqiang Li +10 more
wiley +1 more source
Understanding of a convolutional neural network [PDF]
2017 International Conference on Engineering and Technology (ICET), 2017 The term Deep Learning or Deep Neural Network refers to Artificial Neural Networks (ANN) with multi layers. Over the last few decades, it has been considered to be one of the most powerful tools, and has become very popular in the literature as it is able to handle a huge amount of data. The interest in having deeper hidden layers has recently begun to
Albawi, Saad +2 more
openaire +2 more sources
Advances in Multimedia, 2022 In order to improve the classification effect of the 3D CAD model, this paper combines the knowledge recognition algorithm of convolutional neural network to construct the 3D CAD model classification model.
Weiwei Wang, Dandan Sun
doaj +1 more source
One weird trick for parallelizing convolutional neural networks [PDF]
arXiv, 2014 I present a new way to parallelize the training of convolutional neural networks across multiple GPUs. The method scales significantly better than all alternatives when applied to modern convolutional neural networks.
arxiv
Recurrent Models of Visual Attention [PDF]
, 2014 Applying convolutional neural networks to large images is computationally expensive because the amount of computation scales linearly with the number of image pixels.
Alex Graves +4 more
core
Optoelectronic Devices for In‐Sensor Computing
Advanced Materials, EarlyView.The raw data obtained directly from sensors in the noisy analogue domain is often unstructured, which lacks a predefined format or organization and does not conform to a specific data model. Optoelectronic devices for in‐sensor visual processing can integrate perception, memory, and processing functions in the same physical units, which can compress ...
Qinqi Ren +7 more
wiley +1 more source
Powerset Convolutional Neural Networks
, 2019 We present a novel class of convolutional neural networks (CNNs) for set functions, i.e., data indexed with the powerset of a finite set. The convolutions are derived as linear, shift-equivariant functions for various notions of shifts on set functions.
Wendler, Chris +2 more
openaire +3 more sources
Blind Interleaver Recognition Using Deep Learning Techniques
IEEE AccessIn digital communication systems, channel encoders and interleavers play a crucial role in mitigating the random and burst errors introduced by noisy channels.
Nayim Ahamed, Swaminathan R., B. Naveen
doaj +1 more source
Occupational Therapy International, 2022 In this paper, we construct a model of convolutional neural network speech emotion algorithm, analyze the classroom identified by the neural network with a certain degree of confidence together with the school used in the dataset, find the ...
Qinying Yuan
doaj +1 more source
A Lexicon and Depth-wise Separable Convolution Based Handwritten Text Recognition System [PDF]
arXiv, 2022 Cursive handwritten text recognition is a challenging research problem in the domain of pattern recognition. The current state-of-the-art approaches include models based on convolutional recurrent neural networks and multi-dimensional long short-term memory recurrent neural networks techniques. These methods are highly computationally extensive as well
arxiv