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Digital Elevation Model enhancement using Deep Learning
[article]
2021
arXiv
pre-print
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We demonstrate high fidelity enhancement of planetary digital elevation models (DEMs) using optical images and deep learning with convolutional neural networks. ...
Deep learning-based photoclinometry robustly recovers features obscured by non-ideal lighting conditions. Method can be automated at global scale. ...
This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. ...
arXiv:2101.04812v1
fatcat:ndedyqelunhq7iexk6hhm6anbm
Preprocessing for Unintended Conducted Emissions Classification with ResNet
2021
Applied Sciences
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As a side-benefit, much was learned regarding the best preprocessing to use with the selected deep network for the UCE collected from these low power consumer devices obtained via current transformers. ...
progression of preprocessing and a deep neural network (ResNet architecture) to classify UCE data obtained via current transformers. ...
Acknowledgments: Thanks to Olivia Shafer of ORNL for help with her technical writing edits.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app11198808
fatcat:2qi2oxyfnfdulkrw7nrvqfqxj4
A Spatiotemporal Fusion Method Based on Multiscale Feature Extraction and Spatial Channel Attention Mechanism
2022
Remote Sensing
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In recent years, spatiotemporal image fusion based on deep learning has received wide attention. ...
Remote sensing satellite images with a high spatial and temporal resolution play a crucial role in Earth science applications. ...
Deeper networks can learn richer feature information and correlation mappings, while deep residual learning for image recognition (ResNet) [36] makes it easier to train deep networks. ...
doi:10.3390/rs14030461
fatcat:5s36qvzxireibmufythw5ysazm
Quantifying the Chaos Level of Infants' Environment via Unsupervised Learning
[article]
2019
arXiv
pre-print
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These unsupervised techniques include hierarchical clustering using K-Means, clustering using self-organizing map (SOM) and deep learning. ...
In this work, we use three unsupervised machine learning techniques to quantify household chaos in infants' homes. ...
With the deep learning approach, a common challenge is the interpretability of the extracted features. ...
arXiv:1912.04844v1
fatcat:ouzxxr23knh5tdx3vdgafwihxy
Music Genre Classification using Deep Learning
2021
International Journal for Research in Applied Science and Engineering Technology
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In this paper, we propose two methods for boosting music genre classification with convolutional neural networks: 1) using a process inspired by residual learning to combine peak- and average pooling to ...
Machine learning techniques were used to classify music genres in this research. Deep neural networks (DNN) have recently been demonstrated to be effective in a variety of classification tasks. ...
is fed into a classification deep neural network. ...
doi:10.22214/ijraset.2021.36087
fatcat:2tkneqaehrb6nd6atng3ifgziq
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
[article]
2016
arXiv
pre-print
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Next, we train a deep recurrent-convolutional network inspired by state-of-the-art video classification to learn robust representations from the sequence of images. ...
The proposed approach is designed to preserve the spatial, spectral, and temporal structure of EEG which leads to finding features that are less sensitive to variations and distortions within each dimension ...
Our approach is fundamentally different from the previous attempts to learn high-level representations from EEG using deep neural networks. ...
arXiv:1511.06448v3
fatcat:7izufqjvpzhalnvus42owkyobm
Seismic features and automatic discrimination of deep and shallow induced-microearthquakes using neural network and logistic regression
2016
Geophysical Journal International
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The used machine-learning techniques have application for efficient automatic classification of low energy signals recorded at one or more seismic stations. ...
using logistic regression and artificial neural network models, respectively. ...
To take a closer look at the seismic features and their correlations, we applied the X-means method on the first 17 features with highest merits presented in Table 5 . ...
doi:10.1093/gji/ggw258
fatcat:bmfodjkjz5hmtchzbu57mcawhi
Deep Learning Techniques for Speech Emotion Recognition, from Databases to Models
2021
Sensors
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The current study reviews deep learning approaches for SER with available datasets, followed by conventional machine learning techniques for speech emotion recognition. ...
Ultimately, we present a multi-aspect comparison between practical neural network approaches in speech emotion recognition. ...
convolutional and recurrent neural networks as a deep learning method. ...
doi:10.3390/s21041249
pmid:33578714
pmcid:PMC7916477
fatcat:nj5ihjhvnfcxtk7hu3n4zx4bka
LC-RNN: A Deep Learning Model for Traffic Speed Prediction
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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Furthermore, since traffic evolution is restricted by the underlying road network, a network embedded convolution structure is proposed to capture topology aware features. ...
The fusion with other information, including periodicity and context factors, is also considered to further improve accuracy. ...
., 2016] is a spectral approach that ensures strictly localized filter and low computational complexity as well. ...
doi:10.24963/ijcai.2018/482
dblp:conf/ijcai/LvX0YZZ18
fatcat:ufu6ezwjkzhjlcdcegqbtaesiq
CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection
[article]
2018
arXiv
pre-print
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Here, we introduce the Cnn-Rnn Earthquake Detector (CRED), a detector based on deep neural networks. ...
The robustness of the trained model with respect to the noise level and non-earthquake signals is shown by applying it to a set of semi-synthetic signals. ...
A closer look at a standard RNN unit in the previous figure. ...
arXiv:1810.01965v1
fatcat:tm33346ewrgyppgtmrco4e6ppi
Deep neural networks for the evaluation and design of photonic devices
[article]
2020
arXiv
pre-print
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We will also examine how deep generative networks can learn geometric features in device distributions and even be configured to serve as robust global optimizers. ...
In this Review, we will show how deep neural networks, configured as discriminative networks, can learn from training sets and operate as high-speed surrogate electromagnetic solvers. ...
With layer-by-layer processing of data inputted to the network, data features with higher levels of abstraction are captured from lower level features, and complex network input-output relations can be ...
arXiv:2007.00084v1
fatcat:s76z7d6ghfdd7c3pe6f5guhksa
Deep convolutional embedding for digitized painting clustering
[article]
2020
arXiv
pre-print
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optimized with the task of finding a set of cluster centroids in this latent feature space. ...
To address these issues, we propose to use a deep convolutional embedding model for digitized painting clustering, in which the task of mapping the raw input data to an abstract, latent space is jointly ...
the low-level colour and texture features. ...
arXiv:2003.08597v2
fatcat:mhxomrao65g5vmeuwx3ehp3lsu
Bottom-up Attention, Models of
[article]
2019
arXiv
pre-print
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In spite of tremendous efforts and huge progress, there is still room for improvement in terms finer-grained analysis of deep saliency models, evaluation measures, datasets, annotation methods, cognitive ...
Sometimes deep models neglect low-level image features (local contrast) and overweight the contribution of high-level features (e.g. faces or text). ...
Early computational saliency models were primarily concerned with identifying conspicuous regions due to low-level feature contrast (e.g. pop-out search). ...
arXiv:1810.05680v3
fatcat:yurrgypswnbt5kyxnxugivdwiq
InverseNet: Solving Inverse Problems with Splitting Networks
[article]
2017
arXiv
pre-print
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We propose a new method that uses deep learning techniques to solve the inverse problems. ...
The two networks are trained jointly to learn the end-to-end mapping, getting rid of a two-step training. ...
Similar to the strategy in [17, 40] , we design a deep convolutional neural network to learn the inversion. ...
arXiv:1712.00202v1
fatcat:p3aootffcjgevmcqp27vlgxf3y
Research on super-resolution reconstruction of remote sensing images: a comprehensive review
2021
Optical Engineering: The Journal of SPIE
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, and deep-learning-based methods. ...
The experimental results indicate the advantages and limitations of single-and multi-frame reconstruction, with the latter showing a higher performance. ...
The network uses residuals and skip connections in a very deep architecture to transmit information processed at different abstraction levels and alleviate data degradation. ...
doi:10.1117/1.oe.60.10.100901
fatcat:44ssaq55ebfkrghyatvo5lbr2m
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