1,721,057 research outputs found
Crater-based Autonomous Position Estimation in Planetary Missions by Deep-Learning
No full textSpacecraft missions venturing beyond Earth rely upon ranging, specific payloads or supports systems, claiming the usage of facilities such as Deep Space Network or ESTRACK. This requires a significant amount of resources both on Earth and onboard, especially in monetary terms. Furthermore, satellite link is not always guaranteed, and results are not available in real-time. Therefore, to cruise independently of Earth-based operators and to achieve the requirements raised by the next planetary exploratory missions, this manuscript proposes a novel visual-based terrain relative navigation (TRN) system. TRN is promising because can be applied to a wide range of space missions, e.g. planetary exploration (rocky planets), the study of moons of gaseous planets, approach phase of comets, asteroid, and other celestial bodies. In essence, a spacecraft can retrieve its absolute position by matching a pattern of observed craters with a database. The measurements thus obtained can be integrated into a navigation filter to estimate the spacecraft state (position and velocity). The ability to detect match surface features to a map is crucial for TRN. However, craters largely vary their appearances also depending on image qualities, lighting geometry, and noises. For these reasons, realizing a crater detector able to generalize to different scenarios is complex. It is worth considering that this task must be performed in a robust way to keep high the navigation accuracy. In past, this has led to least square approaches, creating situations where corrupted navigation states render otherwise good images ineffectual, leading to unnecessary filter reinitializations, trajectory aborts (e.g. during lunar descent), or other undesirable events. Contrarily, the solution proposed is reliable, combining the strengths of a region-based convolutional neural network (Mask R-CNN) with the robustness of projective invariants theory. An extended Kalman filter completes the TRN system, further increasing the stability of the system. Despite the usage of medium resolution (118 m/px) data, results showed that the navigation accuracy lies below 400 meters in the best-case scenario for a satellite orbiting around the Moon at about 50 km altitude. This is expected to guarantee real-time autonomous onboard operations with no need for ground support
Current methods for meteorological and marine forecasting for the assistance of navigation and shipping operations
No full textA Deep Learning-based Crater Detector for Autonomous Vision-Based Spacecraft Navigation
No full textThe paper proposes the use of Cascade Mask R-CNN for the detection of craters from monocular images. Crater detection is a challenging task being the images prone to changes in lighting and noise conditions. Besides, the crater appearance is strongly modified according to the region of interest, being the shadows strongly affected by the sun vector inclination. To tackle these issues, the paper exploits the generalizability of modern deep learning architectures to create a highly reliable crater detector. The dataset used for transfer learning the model comprises more than 800 real lunar monocular images obtained from the lunar reconnaissance orbiter (LRO) cameras. Results confirm the performance reached by the multi-stage object detection architecture both in equatorial and polar regions, its robustness, and the validity of this crater detection scheme for planetary navigation tasks
Il ruolo attuale del microbiologo clinico nella gestione delle infezioni respiratorie (The role of the clinical microbiologist in the management of the respiratory infections today)
No full textRespiratory infections are widespread and are responsible for severe human infectious diseases. Their importance is related to the high economic and social costs due to health expenses for therapies and hospitalization, as well as lack of productivity Microbiological laboratory is still now a special concern, because of the possibility of a short time diagnosis, thanks also to molecular methods. Indeed, these methods offer the high advantage of the rapid identification of the microorganisms responsible for the infections, by overcoming the usually long times of the "in vitro" growth or making possible early administration of specific antibiotics and decreasing the risk for resistant strain selection. Clinical microbiologist appears, therefore, to be strategic for both diagnosis and therapeutic management of these patients
Cascade Mask R-CNN Architecture for crater detection in autonomous planetary navigation
No full textDeep Space missions require dedicated facilities to determine spacecraft's attitude and orbit, facing large operation costs, bearing the risk of signal disturbance/blockage in emergency situations, the delay caused by the roundtrip light-time, and the time required to process the data. For these reasons improving spacecraft autonomy is a crucial aspect for current and future space projects. To tackle these issues, the paper proposes a highly reliable crater detector that exploits the properties of generalizing of modern deep learning architectures. The detector is structured through a Cascade Mask R-CNN architecture, in which stages deeper into the cascade are more selective against close false positives because are trained sequentially, using the output of one stage to train the next. The backbone is ResNet-50, a 50 layers deep convolutional neural network that is able to avoid the vanishing gradient problem, which is coupled with Feature Pyramid Network, i.e. a pyramidal structure for the extraction of features at different scales to detect craters of different dimensions. In this study a database has been developed by manually labelling in COCO format using both circles and ellipsis. To train and test the Crater Detection Algorithm (CDA) 804 images have been obtained from the Wide Angle Camera global mosaic of the Lunar Reconnaissance Orbiter (LRO). Images, in orthographic projection show a resolution of 128.00 px/deg and 256.00 px/deg, respectively in latitude and longitude. From each picture a smaller squared area has been considered to avoid distortions at the edges and 134 tiles of 512x512 pixels have been obtained and manually labeled. Using grid space search to tune the parameters an encouraging mAP50 value of 77.8 has been accomplished. The CDA has been tested on a wide set of images to prove the accuracy and generalization of the detector under various challenging conditions. Namely, i) images of equatorial and polar regions of the Moon (considering a brightness variation of +50% and -50%), ii) distorted and blurred photos captured from the Narrow Angle Camera of the LRO and iii) pictures taken from missions MARS Reconnaissance Orbiter and MESSENGER observing respectively Mars and Mercury. Results prove the higher detection rates against the U-Net based architecture, adopted in recent crater detection studies. This detector is thus suitable for visual-based Terrain Relative Navigation in GNSS-denied planetary applications
The production of short-chain fatty acids by periodontopathic bacteria contributes to the impairment of local host defence
No full textIt is here hypothesized that the production of short-chain fatty acids by periodontopathic bacteria represents an important moment in the progression of the periodontitis lesion through the inhibition of mononuclear cell-mediated formation of fibrin. © 1993
A Robust Crater Matching Algorithm for Autonomous Vision-Based Spacecraft Navigation
No full textAdvancements in Computer Vision (CV) and Machine Learning (ML) of past decades have contributed to the realization of autonomous systems like self-driving cars. This manuscript explores the possibility of transferring this technology to the next planetary exploratory missions. Similarly to a star tracker, it is possible to match a pattern of observed craters with a reference, i.e. a crater catalogue, in order to perform the spacecraft state estimation with no external support (i.e. GNSS or DSN). Such kind of technology, born for missilistic applications before the advent of GPS, is known as Terrain Relative Navigation (TRN). However, unlike stars, craters largely vary their appearances also depending on image qualities, lighting geometry and noises. While these problems can nowadays be overcome with the modern approach of deep learning, the inherent limit of crater detectors, i.e. the false detections, still poses a problem for the matching phase. In response, this paper proposes a novel solution, exploiting attitude and sensor pointing knowledge to discriminate false matches. A complete TRN system, called FederNet, was finally developed implementing the matching algorithm within a processing chain including a Convolutional Neural Network and an extended Kalman filter (EKF). FederNet has been validated with a numerical anlysis on real lunar elevation images. However, the adopted methodology further extends to other airless bodies. Despite the usage of a medium resolution (118 m/px) Digital Elevation Model (DEM), results showed that the navigation accuracy lie below 400 meters in the best case scenario, guaranteeing real time autonomous on-board operations with no need for ground support. The capabilities of such TRN system can be additionally improved with higher resolution data and data fusion integration with other sensor measurements
MULTIMISSION/MULTIFREQUENCY SAR FOR IMPROVING THE MONITORING OF COASTAL AREAS
No full textThe paper shows the strong potentialities of multimission/multifrequency SAR data for improving the maritime situational awareness in coastal areas. Two main issues are analyzed: the detection of ships that are visible in SAR images and the identification of non-collaborative vessels, which are not visible in SAR images. In the first case, the multimission/multifrequency data guarantees: (a) smaller revisit time with respect to a single mission, enabling cross-check of the detection in several images and, thus, improving the detection rate, and (b) the availability of images covering large areas at low resolution as well as smaller swath observed with higher resolution. This is crucial in particular for the coastal areas where local phenomena can strongly affect the detection performance. In the second case, the multimission/multifrequency data enables innovative approaches exploiting the different appearance of ship and its wake at different frequencies
First Results of Ship Wake Detection by Deep Learning Techniques in Multispectral Spaceborne Images
No full textMaritime trade and trasport occupy a pivotal position in the current era of globalization. Thus, monitoring ships at sea represents the starting point of this paper in which a novel approach to detect ships by wake has been proposed, based on Instance Segmentation deep learning architecture Mask R-CNN. In order to train and test this network, 766 wake chips cropped from 50 multispectral images acquired from Sentinel-2 satellites were observed. In particular, B2 (blue), B3 (green), B4 (red) and B8 (Infrared) bands were considered since they are all characterized by same resolution. The results proved that Mask R-CNN is capable to detect the vast majority of ship wakes with high confidence percentage in different configurations, i.e. slanted wakes, multiple wake scenarios or wakes in dark areas not related to their features
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