scholarly journals An Efficient Indoor Localization Method Based on the Long Short-Term Memory Recurrent Neuron Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 123912-123921 ◽  
Author(s):  
Bo Xu ◽  
Xiaorong Zhu ◽  
Hongbo Zhu
Keyword(s):  
Short Term Memory ◽  
Indoor Localization ◽  
Neuron Network ◽  
Short Term ◽  
Term Memory ◽  
Localization Method ◽  
Long Short Term Memory

scholarly journals Spatial Structure-Related Sensory Landmarks Recognition Based on Long Short-Term Memory Algorithm

Micromachines ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 781
Author(s):  
Yikang Wang ◽  
Jiangnan Zhang ◽  
Hairui Zhao ◽  
Mengjie Liu ◽  
Shiyi Chen ◽  
...  
Keyword(s):  
Spatial Structure ◽  
Short Term Memory ◽  
Indoor Localization ◽  
Location Based Services ◽  
Short Term ◽  
Term Memory ◽  
Motion Patterns ◽  
Interpolation Filter ◽  
Long Short Term Memory ◽  
Architectural Structures

Indoor localization is the basis for most Location-Based Services (LBS), including consumptions, health care, public security, and augmented reality. Sensory landmarks related to the indoor spatial structures (such as escalators, stairs, and corners) do not rely on active signal transmitting devices and have fixed positions, which can be used as the absolute positioning information to improve the performance of indoor localization effectively without extra cost. Specific motion patterns are presented when users pass these architectural structures, which can be captured by mobile built-in sensors, including accelerometers, gyroscopes, and magnetometers, to achieve the recognition of structure-related sensory landmarks. Therefore, the recognition of these landmarks can draw on the mature methods of Human Activity Recognition (HAR) with improvements. To this end, we improved a Long Short-Term Memory (LSTM) neural network to recognize different kinds of spatial structure-related sensory landmarks. Labels of structural sensory landmarks were proposed, and data processing methods (including interpolation, filter, and window length) were used and compared to achieve the highest recognition accuracy of 99.6%.

Incorporating Financial News for Forecasting Bitcoin Prices Based on Long Short-Term Memory Networks

2020 ◽  
Author(s):  
Abdolreza Nazemi ◽  
Johannes Jakubik ◽  
Andreas Geyer-Schulz ◽  
Frank J. Fabozzi
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Financial News ◽  
Long Short Term Memory
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