Satellite selection with an end-to-end deep learning network

GPS Solutions ◽  
2018 ◽  
Vol 22 (4) ◽  
Author(s):  
Panpan Huang ◽  
Chris Rizos ◽  
Craig Roberts
Keyword(s):  
Deep Learning ◽  
Learning Network ◽  
Satellite Selection ◽  
End To End ◽  
Deep Learning Network

A deep learning network based end-to-end image composition

2021 ◽  
pp. 116570
Author(s):  
Xiaoyu Zhu ◽  
Haodi Wang ◽  
Zhiyi Zhang ◽  
Xiuping Wu ◽  
Junqi Guo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Composition ◽  
Learning Network ◽  
End To End ◽  
Deep Learning Network

scholarly journals End to end system for hazy image classification and reconstruction based on mean channel prior using deep learning network

2021 ◽  
Author(s):  
Sivaji Satrasupalli ◽  
Ebenezer Daniel ◽  
Sitaramanjaneya Reddy Guntur ◽  
Shaik Shehanaz
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Learning Network ◽  
End To End ◽  
Deep Learning Network

scholarly journals End-to-End Multimodal 16-Day Hatching Eggs Classification

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 759
Author(s):  
Lei Geng ◽  
Zhen Peng ◽  
Zhitao Xiao ◽  
Jiangtao Xi
Keyword(s):  
Deep Learning ◽  
Blood Vessel ◽  
Network Structure ◽  
Short Term Memory ◽  
Classification Problem ◽  
Feature Maps ◽  
Learning Network ◽  
End To End ◽  
Deep Learning Network ◽  
Hatching Eggs

Sixteen-day hatching eggs are divided into fertile eggs, waste eggs, and recovered eggs. Because different categories may have the same characteristics, they are difficult to classify. Few existing algorithms can successfully solve this problem. To this end, we propose an end-to-end deep learning network structure that uses multiple forms of signals. First, we collect the photoplethysmography (PPG) signal of the hatching eggs to obtain heartbeat information and photograph hatching eggs with a camera to obtain blood vessel pictures. Second, we use two different network structures to process the two kinds of signals: Temporal convolutional networks are used to process heartbeat information, and convolutional neural networks (CNNs) are used to process blood vessel pictures. Then, we combine the two feature maps and use the long short-term memory (LSTM) network to model the context and recognize the type of hatching eggs. The system is then trained with our dataset. The experimental results demonstrate that the proposed end-to-end multimodal deep learning network structure is significantly more accurate than using a single modal network. Additionally, the method successfully solves the 16-day hatching egg classification problem.

scholarly journals An End-to-End Deep Learning Network for 3D Object Detection From RGB-D Data Based on Hough Voting

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 138810-138822 ◽  
Author(s):  
Ming Yan ◽  
Zhongtong Li ◽  
Xinyan Yu ◽  
Cong Jin
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
3D Object ◽  
Learning Network ◽  
Hough Voting ◽  
End To End ◽  
Deep Learning Network ◽  
3D Object Detection

scholarly journals UAVs in rail damage image diagnostics supported by deep-learning networks

2021 ◽  
Vol 11 (1) ◽  
pp. 339-348
Author(s):  
Piotr Bojarczak ◽  
Piotr Lesiak
Keyword(s):  
Deep Learning ◽  
Head Width ◽  
Learning Networks ◽  
Image Brightness ◽  
Learning Network ◽  
Rail Head ◽  
Image Recording ◽  
Python Language ◽  
Deep Learning Network ◽  
Efficiency Rate

Abstract The article uses images from Unmanned Aerial Vehicles (UAVs) for rail diagnostics. The main advantage of such a solution compared to traditional surveys performed with measuring vehicles is the elimination of decreased train traffic. The authors, in the study, limited themselves to the diagnosis of hazardous split defects in rails. An algorithm has been proposed to detect them with an efficiency rate of about 81% for defects not less than 6.9% of the rail head width. It uses the FCN-8 deep-learning network, implemented in the Tensorflow environment, to extract the rail head by image segmentation. Using this type of network for segmentation increases the resistance of the algorithm to changes in the recorded rail image brightness. This is of fundamental importance in the case of variable conditions for image recording by UAVs. The detection of these defects in the rail head is performed using an algorithm in the Python language and the OpenCV library. To locate the defect, it uses the contour of a separate rail head together with a rectangle circumscribed around it. The use of UAVs together with artificial intelligence to detect split defects is an important element of novelty presented in this work.

scholarly journals BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets

2021 ◽  
Vol 11 (13) ◽  
pp. 5880
Author(s):  
Paloma Tirado-Martin ◽  
Raul Sanchez-Reillo
Keyword(s):  
Deep Learning ◽  
Temporal Variations ◽  
Physical Activities ◽  
Learning Tools ◽  
Biometric Recognition ◽  
Learning Network ◽  
Deep Learning Network ◽  
Electrocardiogram Ecg

Nowadays, Deep Learning tools have been widely applied in biometrics. Electrocardiogram (ECG) biometrics is not the exception. However, the algorithm performances rely heavily on a representative dataset for training. ECGs suffer constant temporal variations, and it is even more relevant to collect databases that can represent these conditions. Nonetheless, the restriction in database publications obstructs further research on this topic. This work was developed with the help of a database that represents potential scenarios in biometric recognition as data was acquired in different days, physical activities and positions. The classification was implemented with a Deep Learning network, BioECG, avoiding complex and time-consuming signal transformations. An exhaustive tuning was completed including variations in enrollment length, improving ECG verification for more complex and realistic biometric conditions. Finally, this work studied one-day and two-days enrollments and their effects. Two-days enrollments resulted in huge general improvements even when verification was accomplished with more unstable signals. EER was improved in 63% when including a change of position, up to almost 99% when visits were in a different day and up to 91% if the user experienced a heartbeat increase after exercise.

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