scholarly journals Automated Discontinuity Detection and Reconstruction in Subsurface Environment of Mars Using Deep Learning: A Case Study of SHARAD Observation

2020 ◽  
Vol 10 (7) ◽  
pp. 2279
Author(s):  
Vanshika Gupta ◽  
Sharad Kumar Gupta ◽  
Jungrack Kim
Keyword(s):  
Deep Learning ◽  
Landing Site ◽  
Planetary Science ◽  
Detection Scheme ◽  
Shallow Subsurface ◽  
Discontinuity Detection ◽  
Database Construction ◽  
Subsurface Environment ◽  
Radar Echo ◽  
Subsurface Mapping

Machine learning (ML) algorithmic developments and improvements in Earth and planetary science are expected to bring enormous benefits for areas such as geospatial database construction, automated geological feature reconstruction, and surface dating. In this study, we aim to develop a deep learning (DL) approach to reconstruct the subsurface discontinuities in the subsurface environment of Mars employing the echoes of the Shallow Subsurface Radar (SHARAD), a sounding radar equipped on the Mars Reconnaissance Orbiter (MRO). Although SHARAD has produced highly valuable information about the Martian subsurface, the interpretation of the radar echo of SHARAD is a challenging task considering the vast stocks of datasets and the noisy signal. Therefore, we introduced a 3D subsurface mapping strategy consisting of radar echo pre-processors and a DL algorithm to automatically detect subsurface discontinuities. The developed components the of DL algorithm were synthesized into a subsurface mapping scheme and applied over a few target areas such as mid-latitude lobate debris aprons (LDAs), polar deposits and shallow icy bodies around the Phoenix landing site. The outcomes of the subsurface discontinuity detection scheme were rigorously validated by computing several quality metrics such as accuracy, recall, Jaccard index, etc. In the context of undergoing development and its output, we expect to automatically trace the shapes of Martian subsurface icy structures with further improvements in the DL algorithm.

scholarly journals DeepOSN: Bringing deep learning as malicious detection scheme in online social network

2020 ◽  
Vol 9 (1) ◽  
pp. 146
Author(s):  
Putra Wanda ◽  
Marselina Endah Hiswati ◽  
Huang J. Jie
Keyword(s):  
Social Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Online Social Networks ◽  
Link Prediction ◽  
Online Social Network ◽  
Detection Scheme ◽  
Malicious Activity ◽  
Manual Analysis ◽  
Learning Techniques

Manual analysis for malicious prediction in Online Social Networks (OSN) is time-consuming and costly. With growing users within the environment, it becomes one of the main obstacles. Deep learning is growing algorithm that gains a big success in computer vision problem. Currently, many research communities have proposed deep learning techniques to automate security tasks, including anomalous detection, malicious link prediction, and intrusion detection in OSN. Notably, this article describes how deep learning makes the OSN security technique more intelligent for detecting malicious activity by establishing a classifier model.

scholarly journals A Novel Signal Detection Scheme Based on Adaptive Ensemble Deep Learning Algorithm in SC-FDE Systems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 123514-123523 ◽  
Author(s):  
Yuanjian Qiao ◽  
Jun Li ◽  
Bo He ◽  
Wenxin Li ◽  
Tongliang Xin
Keyword(s):  
Deep Learning ◽  
Signal Detection ◽  
Learning Algorithm ◽  
Detection Scheme ◽  
Deep Learning Algorithm

scholarly journals Monitoring of Corroded and Loosened Bolts in Steel Structures via Deep Learning and Hough Transforms

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6888
Author(s):  
Quoc-Bao Ta ◽  
Jeong-Tae Kim
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Steel Structures ◽  
Image Processing Algorithm ◽  
Detection Scheme ◽  
Perspective Distortion ◽  
Light Intensities ◽  
Hough Transforms ◽  
Bolt Loosening ◽  
Perspective Distortions

In this study, a regional convolutional neural network (RCNN)-based deep learning and Hough line transform (HLT) algorithm are applied to monitor corroded and loosened bolts in steel structures. The monitoring goals are to detect rusted bolts distinguished from non-corroded ones and also to estimate bolt-loosening angles of the identified bolts. The following approaches are performed to achieve the goals. Firstly, a RCNN-based autonomous bolt detection scheme is designed to identify corroded and clean bolts in a captured image. Secondly, a HLT-based image processing algorithm is designed to estimate rotational angles (i.e., bolt-loosening) of cropped bolts. Finally, the accuracy of the proposed framework is experimentally evaluated under various capture distances, perspective distortions, and light intensities. The lab-scale monitoring results indicate that the suggested method accurately acquires rusted bolts for images captured under perspective distortion angles less than 15° and light intensities larger than 63 lux.

scholarly journals Radar Imager for Mars’ Subsurface Experiment—RIMFAX

2020 ◽  
Vol 216 (8) ◽  
Author(s):  
Svein-Erik Hamran ◽  
David A. Paige ◽  
Hans E. F. Amundsen ◽  
Tor Berger ◽  
Sverre Brovoll ◽  
...  
Keyword(s):  
Continuous Wave ◽  
Sand Dunes ◽  
Landing Site ◽  
Wide Band ◽  
Center Frequency ◽  
Depth Range ◽  
Fmcw Radar ◽  
Shallow Subsurface ◽  
Eventual Return ◽  
Ground Penetrating

AbstractThe Radar Imager for Mars’ Subsurface Experiment (RIMFAX) is a Ground Penetrating Radar on the Mars 2020 mission’s Perseverance rover, which is planned to land near a deltaic landform in Jezero crater. RIMFAX will add a new dimension to rover investigations of Mars by providing the capability to image the shallow subsurface beneath the rover. The principal goals of the RIMFAX investigation are to image subsurface structure, and to provide information regarding subsurface composition. Data provided by RIMFAX will aid Perseverance’s mission to explore the ancient habitability of its field area and to select a set of promising geologic samples for analysis, caching, and eventual return to Earth. RIMFAX is a Frequency Modulated Continuous Wave (FMCW) radar, which transmits a signal swept through a range of frequencies, rather than a single wide-band pulse. The operating frequency range of 150–1200 MHz covers the typical frequencies of GPR used in geology. In general, the full bandwidth (with effective center frequency of 675 MHz) will be used for shallow imaging down to several meters, and a reduced bandwidth of the lower frequencies (center frequency 375 MHz) will be used for imaging deeper structures. The majority of data will be collected at regular distance intervals whenever the rover is driving, in each of the deep, shallow, and surface modes. Stationary measurements with extended integration times will improve depth range and SNR at select locations. The RIMFAX instrument consists of an electronic unit housed inside the rover body and an antenna mounted externally at the rear of the rover. Several instrument prototypes have been field tested in different geological settings, including glaciers, permafrost sediments, bioherme mound structures in limestone, and sedimentary features in sand dunes. Numerical modelling has provided a first assessment of RIMFAX’s imaging potential using parameters simulated for the Jezero crater landing site.

Experience of DAN investigation of subsurface water on Mars onboard the rover Curiosity for the future ADRON-RM experiment on the ExoMars rover

2020 ◽  
Author(s):  
Sergei Nikiforov ◽  
Igor Mitrofanov ◽  
Maxim Litvak ◽  
Maya Djachkova ◽  
Dmitry Golovin ◽  
...  
Keyword(s):  
Cross Sections ◽  
Landing Site ◽  
Planetary Science ◽  
Subsurface Water ◽  
Energy Bands ◽  
Neutron Spectrometer ◽  
Advanced Technique ◽  
Water Abundance ◽  
Hydrogen Water ◽  
Scientific Potential

<p>     Remote neutron sensing onboard martian rovers is an advanced technique in planetary science. Such measurements provide investigations on hydrogen abundances and elements with high thermal neutron absorption cross sections down to ~60 cm of subsurface [<strong>1</strong>]. The presence of hydrogen (mostly water/ice) in subsurface significantly influences the neutron leakage spectrum due moderation and thermalization through collisions with hydrogen nuclei. As a result, the variations of neutron flux detected onboard in different energy bands correlate with subsurface hydrogen/water abundance.</p> <p><em>     Dynamic Albedo of Neutrons</em> (DAN) is the first neutron spectrometer installed on the NASA’s rover [<strong>2</strong>]. More than 7 years, NASA rover is successfully traversing across Mars surface exploring Gale crater. Adron-RM is the next generation neutron spectrometer, which is a part of the ExoMars 2022 rover payload [<strong>3</strong>].</p> <p>     This work will present scientific potential of remote neutron technique to distinguish local features in martian subsurface, based on DAN findings. In addition, we will provide Adron-RM measurement schematic and scientific potential on investigations in the area of the ExoMars 2022 landing site.</p> <p><em>     References</em></p> <p>[<strong>1</strong>] <em>Nikiforov, S. Y., et al., (2020). Assessment of water content in martian subsurface along the traverse of the Curiosity rover based on passive measurements of the DAN instrument. Icarus, 346, 113818.</em> https://doi.org/10.1016/j.icarus.2020.113818</p> <p>[<strong>2</strong>] <em>Mitrofanov, I. G., et al., (2012). Dynamic Albedo of Neutrons (DAN) experiment onboard NASA’s Mars Science Laboratory. Space Science Reviews, 170(1–4), 559–582.</em> https://doi.org/10.1007/s11214-012-9924-y</p> <p>[<strong>3</strong>] <em>Mitrofanov, I. G., et al., (2017). The ADRON-RM Instrument Onboard the ExoMars Rover. Astrobiology, 17(6–7), 585–594.</em> https://doi.org/10.1089/ast.2016.1566</p>

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