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Artificial Intelligence and Sensing Technologies Based Astrophysics and Applications
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Artificial Intelligence and Sensing Technologies Based Astrophysics and Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 12480

Special Issue Editors

Prof. Juan Ruiz-Alzola

E-Mail Website
Guest Editor
Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
Interests: medical image processing
Dr. Alejandro Oscoz Abad

E-Mail Website
Guest Editor
1. Instituto de Astrofísica de Canarias, La Laguna, Spain
2. Departamento de Astrofísica, Universidad de La Laguna, La Laguna, Spain
Interests: telescopes; optical and infrared instrumentation; gravitational lensing; small satellites; remote sensing; high spatial resolution
Prof. Dr. Fernando Aguado Agelet

E-Mail Website
Guest Editor
1. Department of Signal Theory and Communications, University Vigo, Spain
2. Department of Electronics, Norwagian University of Science and Technology, Norway
Interests: Small Satellites; Systems Engineering; Constellations; Communications Systems; Remote Sensing; Model Based Systems Engineering
Dr. Marc Huertas-Company

E-Mail Website
Guest Editor
1. Instituto de Astrofísica de Canarias, Spain
2. LERMA – Observatoire de Paris, France
3. Université de Paris, France
Interests: galaxy evolution; deep learning; galaxy structure

Special Issue Information

Dear Colleagues,

Astrophysics is a major branch of science that relies on advanced and innovative technology. In particular, imaging and image data processing are cornerstones for the development of the field. More recently artificial intelligence and, in particular, machine and deep learning have become key to facilitate the understanding of such complex big data as that provided by modern observation instruments. Technology innovations in Astrophysics have traditionally sparked further innovations in many other fields and application domains. The main goal of this special issue is to promote cross-fertilization between astrophysics technology and other fields, such as remote sensing, aerospace, unmanned vehicles, and medical imaging by leveraging recent advances in artificial intelligence and sensing technologies.

We solicit manuscripts of both review and original research articles in all the aspects pertinent to this Special Issue on “Artificial Intelligence and Sensing Technologies Based Astrophysics and Applications”. Topics of interest include, but are not limited to, the following:

  • Imaging. Light is the main tool, and in most of the occasions the only one, for astrophysicists. One of the main objectives of observational astronomy is to detect faint targets, as weak as possible, and to be able to discriminate their details. However, desirable resolution is not always obtained and, therefore, for many years, astronomers have struggled to offer diffraction-limited imaging using techniques such as adaptive optics, lucky imaging or image postprocessing. The developments in astronomy involve some cutting-edge technologies and knowledge that can be exported to other different fields; mainly, but not limited, to the medical and biomedical sectors. In this issue, we invite researchers to present techniques used in and from astrophysics to improve the quality of images, including the cross-fusion of remote sensing images, hyperspectral images, medical images and other images with astrophysical technologies. Additionally, due to the unprecedented situation that the unfolding COVID-19 pandemic is causing World-wide, we make a special call to solicit research articles where deep learning (or, in general, machine learning) enabled biomedical sensing might help to monitor and control the spread of the disease and to mitigate its effects. For example:
  •   Camera-based (thermal, NIR, visible, etc) distant monitoring of biomedical parameters
  •   Machine-learning based biomarker extraction for triage, screening and diagnosis
  • Aerospace, unmanned vehicles and remote sensing: In this special issue, we make a special call to present research contributions based on astrophysics technologies and algorithms that have been or potentially apply to space applications as well unmanned vehicles, especially in the area of remote sensing. Also are welcome contributions presenting technologies and artificial algorithms used in remote sensing and aerospace areas that can apply to astrophysics research problems.
  • Artificial intelligence, machine learning, deep learning, as related to imaging and sensing applications: Artificial Intelligence is rapidly becoming a standard approach to process images in many fields of fundamental and applied research including astrophysics. It is used for a variety of purposes going from simple classification tasks to more complex segmentation, regression, tracking or even unsupervised generation and anomaly detection. In recent years, astrophysics research has widely benefited from the artificial intelligence revolution. On the other side, because of the specific properties of astrophysical data (e.g. low S/N, high dynamic range), applications to astrophysics can also induce new progress in the fields of machine learning and computer science by better probing the limits of the different techniques. We are precisely interested in this issue in exploring this cross-fertilization between the two fields.

Promoting science and technology cross-fertilization between Astrophysics and other fields is the main goal of this special issue, with a particular focus on imaging, sensing and artificial intelligence. Therefore, authors must clearly show how their research leverages synergies among astrophysics technology and other fields or what the potential cross-impact may be.

Prof. Juan Ruiz-Alzola
Dr. Alejandro Oscoz Abad
Prof. Dr. Fernando Aguado Agelet
Dr. Marc Huertas-Company
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Imaging
  • Artificial intelligence
  • Deep learning
  • Astrophysics
  • Imaging in Astrophysics
  • Adaptive optics
  • Signal and Image Processing Techniques
  • Remote Sensing
  • Satellite Applications
  • Constellations
  • Unmanned Vehicles applications
  • Medical Imaging
  • Medical Image Computing
  • COVID-19

Published Papers (4 papers)

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Research

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16 pages, 2365 KiB  
Article
Assessment of Registration Methods for Thermal Infrared and Visible Images for Diabetic Foot Monitoring
by Sara González-Pérez, Daniel Perea Ström, Natalia Arteaga-Marrero, Carlos Luque, Ignacio Sidrach-Cardona, Enrique Villa and Juan Ruiz-Alzola
Sensors 2021, 21(7), 2264; https://doi.org/10.3390/s21072264 - 24 Mar 2021
Cited by 8 | Viewed by 2412
Abstract
This work presents a revision of four different registration methods for thermal infrared and visible images captured by a camera-based prototype for the remote monitoring of diabetic foot. This prototype uses low cost and off-the-shelf available sensors in thermal infrared and visible spectra. [...] Read more.
This work presents a revision of four different registration methods for thermal infrared and visible images captured by a camera-based prototype for the remote monitoring of diabetic foot. This prototype uses low cost and off-the-shelf available sensors in thermal infrared and visible spectra. Four different methods (Geometric Optical Translation, Homography, Iterative Closest Point, and Affine transform with Gradient Descent) have been implemented and analyzed for the registration of images obtained from both sensors. All four algorithms’ performances were evaluated using the Simultaneous Truth and Performance Level Estimation (STAPLE) together with several overlap benchmarks as the Dice coefficient and the Jaccard index. The performance of the four methods has been analyzed with the subject at a fixed focal plane and also in the vicinity of this plane. The four registration algorithms provide suitable results both at the focal plane as well as outside of it within 50 mm margin. The obtained Dice coefficients are greater than 0.950 in all scenarios, well within the margins required for the application at hand. A discussion of the obtained results under different distances is presented along with an evaluation of its robustness under changing conditions. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensing Technologies Based Astrophysics and Applications)
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15 pages, 2771 KiB  
Article
Supernovae Detection with Fully Convolutional One-Stage Framework
by Kai Yin, Juncheng Jia, Xing Gao, Tianrui Sun and Zhengyin Zhou
Sensors 2021, 21(5), 1926; https://doi.org/10.3390/s21051926 - 09 Mar 2021
Cited by 3 | Viewed by 2976
Abstract
A series of sky surveys were launched in search of supernovae and generated a tremendous amount of data, which pushed astronomy into a new era of big data. However, it can be a disastrous burden to manually identify and report supernovae, because such [...] Read more.
A series of sky surveys were launched in search of supernovae and generated a tremendous amount of data, which pushed astronomy into a new era of big data. However, it can be a disastrous burden to manually identify and report supernovae, because such data have huge quantity and sparse positives. While the traditional machine learning methods can be used to deal with such data, deep learning methods such as Convolutional Neural Networks demonstrate more powerful adaptability in this area. However, most data in the existing works are either simulated or without generality. How do the state-of-the-art object detection algorithms work on real supernova data is largely unknown, which greatly hinders the development of this field. Furthermore, the existing works of supernovae classification usually assume the input images are properly cropped with a single candidate located in the center, which is not true for our dataset. Besides, the performance of existing detection algorithms can still be improved for the supernovae detection task. To address these problems, we collected and organized all the known objectives of the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) and the Popular Supernova Project (PSP), resulting in two datasets, and then compared several detection algorithms on them. After that, the selected Fully Convolutional One-Stage (FCOS) method is used as the baseline and further improved with data augmentation, attention mechanism, and small object detection technique. Extensive experiments demonstrate the great performance enhancement of our detection algorithm with the new datasets. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensing Technologies Based Astrophysics and Applications)
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16 pages, 8150 KiB  
Article
Segmentation Approaches for Diabetic Foot Disorders
by Natalia Arteaga-Marrero, Abián Hernández, Enrique Villa, Sara González-Pérez, Carlos Luque and Juan Ruiz-Alzola
Sensors 2021, 21(3), 934; https://doi.org/10.3390/s21030934 - 30 Jan 2021
Cited by 12 | Viewed by 3287
Abstract
Thermography enables non-invasive, accessible, and easily repeated foot temperature measurements for diabetic patients, promoting early detection and regular monitoring protocols, that limit the incidence of disabling conditions associated with diabetic foot disorders. The establishment of this application into standard diabetic care protocols requires [...] Read more.
Thermography enables non-invasive, accessible, and easily repeated foot temperature measurements for diabetic patients, promoting early detection and regular monitoring protocols, that limit the incidence of disabling conditions associated with diabetic foot disorders. The establishment of this application into standard diabetic care protocols requires to overcome technical issues, particularly the foot sole segmentation. In this work we implemented and evaluated several segmentation approaches which include conventional and Deep Learning methods. Multimodal images, constituted by registered visual-light, infrared and depth images, were acquired for 37 healthy subjects. The segmentation methods explored were based on both visual-light as well as infrared images, and optimization was achieved using the spatial information provided by the depth images. Furthermore, a ground truth was established from the manual segmentation performed by two independent researchers. Overall, the performance level of all the implemented approaches was satisfactory. Although the best performance, in terms of spatial overlap, accuracy, and precision, was found for the Skin and U-Net approaches optimized by the spatial information. However, the robustness of the U-Net approach is preferred. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensing Technologies Based Astrophysics and Applications)
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Other

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13 pages, 1832 KiB  
Letter
Sub-Millisecond Phase Retrieval for Phase-Diversity Wavefront Sensor
by Yu Wu, Youming Guo, Hua Bao and Changhui Rao
Sensors 2020, 20(17), 4877; https://doi.org/10.3390/s20174877 - 28 Aug 2020
Cited by 24 | Viewed by 2932
Abstract
We propose a convolutional neural network (CNN) based method, namely phase diversity convolutional neural network (PD-CNN) for the speed acceleration of phase-diversity wavefront sensing. The PD-CNN has achieved a state-of-the-art result, with the inference speed about 0.5 ms, while fusing the information of [...] Read more.
We propose a convolutional neural network (CNN) based method, namely phase diversity convolutional neural network (PD-CNN) for the speed acceleration of phase-diversity wavefront sensing. The PD-CNN has achieved a state-of-the-art result, with the inference speed about 0.5 ms, while fusing the information of the focal and defocused intensity images. When compared to the traditional phase diversity (PD) algorithms, the PD-CNN is a light-weight model without complicated iterative transformation and optimization process. Experiments have been done to demonstrate the accuracy and speed of the proposed approach. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensing Technologies Based Astrophysics and Applications)
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