| 1. |
- Chanussot, Jocelyn, et al.
(författare)
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Shape signaturs of fuzzy star-shaped sets based on distance from the centroid
- 2005
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Ingår i: Pattern Recognition Letters. - : Elsevier. - 0167-8655. ; 26:6, s. 735-746
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Tidskriftsartikel (refereegranskat)abstract
- We extend the shape signature based on the distance of the boundary points from the shape centroid, to the case of fuzzy sets. The analysis of the transition from crisp to fuzzy shape descriptor is first given in the continuous case. This is followed by a study of the specific issues induced by the discrete representation of the objects in a computer.We analyze two methods for calculating the signature of a fuzzy shape, derived from two ways of defining a fuzzy set: first, by its membership function, and second, as a stack of its α-cuts. The first approach is based on measuring the length of a fuzzy straight line by integration of the fuzzy membership function, while in the second one we use averaging of the shape signatures obtained for the individual α-cuts of the fuzzy set. The two methods, equivalent in the continuous case for the studied class of fuzzy shapes, produce different results when adjusted to the discrete case. A statistical study, aiming at characterizing the performances of each method in the discrete case, is done. Both methods are shown to provide more precise descriptions than their corresponding crisp versions. The second method (based on averaged Euclidean distance over the α-cuts) outperforms the others.
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| 2. |
- Einig, Lucas, et al.
(författare)
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Deep learning denoising by dimension reduction: Application to the ORION-B line cubes
- 2023
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Ingår i: Astronomy and Astrophysics. - 0004-6361 .- 1432-0746. ; 677
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Tidskriftsartikel (refereegranskat)abstract
- Context. The availability of large bandwidth receivers for millimeter radio telescopes allows for the acquisition of position-position-frequency data cubes over a wide field of view and a broad frequency coverage. These cubes contain a lot of information on the physical, chemical, and kinematical properties of the emitting gas. However, their large size coupled with an inhomogenous signal-to-noise ratio (S/N) are major challenges for consistent analysis and interpretation. Aims. We searched for a denoising method of the low S/N regions of the studied data cubes that would allow the low S/N emission to be recovered without distorting the signals with a high S/N. Methods. We performed an in-depth data analysis of the 13CO and C17O (1-0) data cubes obtained as part of the ORION-B large program performed at the IRAM 30 m telescope. We analyzed the statistical properties of the noise and the evolution of the correlation of the signal in a given frequency channel with that of the adjacent channels. This has allowed us to propose significant improvements of typical autoassociative neural networks, often used to denoise hyperspectral Earth remote sensing data. Applying this method to the 13CO (1-0) cube, we were able to compare the denoised data with those derived with the multiple Gaussian fitting algorithm ROHSA, considered as the state-of-the-art procedure for data line cubes. Results. The nature of astronomical spectral data cubes is distinct from that of the hyperspectral data usually studied in the Earth remote sensing literature because the observed intensities become statistically independent beyond a short channel separation. This lack of redundancy in data has led us to adapt the method, notably by taking into account the sparsity of the signal along the spectral axis. The application of the proposed algorithm leads to an increase in the S/N in voxels with a weak signal, while preserving the spectral shape of the data in high S/N voxels. Conclusions. The proposed algorithm that combines a detailed analysis of the noise statistics with an innovative autoencoder architecture is a promising path to denoise radio-astronomy line data cubes. In the future, exploring whether a better use of the spatial correlations of the noise may further improve the denoising performances seems to be a promising avenue. In addition, dealing with the multiplicative noise associated with the calibration uncertainty at high S/N would also be beneficial for such large data cubes.
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| 3. |
- Gaudel, Mathilde, et al.
(författare)
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Gas kinematics around filamentary structures in the Orion B cloud
- 2023
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 670
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Tidskriftsartikel (refereegranskat)abstract
- Context. Understanding the initial properties of star-forming material and how they affect the star formation process is key. From an observational point of view, the feedback from young high-mass stars on future star formation properties is still poorly constrained. Aims. In the framework of the IRAM 30m ORION-B large program, we obtained observations of the translucent (2 ≤ AV < 6 mag) and moderately dense gas (6 ≤ AV < 15 mag), which we used to analyze the kinematics over a field of 5 deg2 around the filamentary structures. Methods. We used the Regularized Optimization for Hyper-Spectral Analysis (ROHSA) algorithm to decompose and de-noise the C 18 O(1−0) and 13CO(1−0) signals by taking the spatial coherence of the emission into account. We produced gas column density and mean velocity maps to estimate the relative orientation of their spatial gradients. Results. We identified three cloud velocity layers at different systemic velocities and extracted the filaments in each velocity layer. The filaments are preferentially located in regions of low centroid velocity gradients. By comparing the relative orientation between the column density and velocity gradients of each layer from the ORION-B observations and synthetic observations from 3D kinematic toy models, we distinguish two types of behavior in the dynamics around filaments: (i) radial flows perpendicular to the filament axis that can be either inflows (increasing the filament mass) or outflows and (ii) longitudinal flows along the filament axis. The former case is seen in the Orion B data, while the latter is not identified. We have also identified asymmetrical flow patterns, usually associated with filaments located at the edge of an H II region. Conclusions. This is the first observational study to highlight feedback from H II regions on filament formation and, thus, on star formation in the Orion B cloud. This simple statistical method can be used for any molecular cloud to obtain coherent information on the kinematics.
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| 4. |
- Gratier, Pierre, et al.
(författare)
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Quantitative inference of the H2 column densities from 3mm molecular emission: case study towards Orion B
- 2021
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 645
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Tidskriftsartikel (refereegranskat)abstract
- Context. Based on the finding that molecular hydrogen is unobservable in cold molecular clouds, the column density measurements of molecular gas currently rely either on dust emission observation in the far-infrared, which requires space telescopes, or on star counting, which is limited in angular resolution by the stellar density. The (sub)millimeter observations of numerous trace molecules can be effective using ground-based telescopes, but the relationship between the emission of one molecular line and the H-2 column density is non-linear and sensitive to excitation conditions, optical depths, and abundance variations due to the underlying physico- chemistry.Aims. We aim to use multi-molecule line emission to infer the H-2 molecular column density from radio observations.Methods. We propose a data-driven approach to determine the H-2 gas column densities from radio molecular line observations. We use supervised machine-learning methods (random forest) on wide-field hyperspectral IRAM-30m observations of the Orion B molecular cloud to train a predictor of the H-2 column density, using a limited set of molecular lines between 72 and 116 GHz as input, and the Herschel-based dust-derived column densities as "ground truth" output.Results. For conditions similar to those of the Orion B molecular cloud, we obtained predictions of the H-2 column density within a typical factor of 1.2 from the Herschel-based column density estimates. A global analysis of the contributions of the different lines to the predictions show that the most important lines are (CO)-C-13(1-0), (CO)-C-12(1-0), (CO)-O-18(1-0), and HCO+(1-0). A detailed analysis distinguishing between diffuse, translucent, filamentary, and dense core conditions show that the importance of these four lines depends on the regime, and that it is recommended that the N2H+(1-0) and CH3OH(2(0)-1(0)) lines be added for the prediction of the H-2 column density in dense core conditions.Conclusions. This article opens a promising avenue for advancing direct inferencing of important physical parameters from the molecular line emission in the millimeter domain. The next step will be to attempt to infer several parameters simultaneously (e.g., the column density and far-UV illumination field) to further test the method.
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| 5. |
- Hong, Danfeng, et al.
(författare)
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Interpretable Hyperspectral Artificial Intelligence : When nonconvex modeling meets hyperspectral remote sensing
- 2021
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Ingår i: IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE. - : Institute of Electrical and Electronics Engineers (IEEE). - 2473-2397. ; 9:2, s. 52-87
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Tidskriftsartikel (refereegranskat)abstract
- Hyperspectral (HS) imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS). In the past decade, enormous efforts have been made to process and analyze these HS products, mainly by seasoned experts. However, with an ever-growing volume of data, the bulk of costs in manpower and material resources poses new challenges for reducing the burden of manual labor and improving efficiency. For this reason, it is urgent that more intelligent and automatic approaches for various HS RS applications be developed. Machine learning (ML) tools with convex optimization have successfully undertaken the tasks of numerous artificial intelligence (AI)-related applications; however, their ability to handle complex practical problems remains limited, particularly for HS data, due to the effects of various spectral variabilities in the process of HS imaging and the complexity and redundancy of higher-dimensional HS signals. Compared to convex models, nonconvex modeling, which is capable of characterizing more complex real scenes and providing model interpretability technically and theoretically, has proven to be a feasible solution that reduces the gap between challenging HS vision tasks and currently advanced intelligent data processing models.
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| 6. |
- Roueff, Antoine, et al.
(författare)
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C18O, 13CO, and 12CO abundances and excitation temperatures in the Orion B molecular cloud: Analysis of the achievable precision in modeling spectral lines within the approximation of the local thermodynamic equilibrium
- 2021
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 645
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Tidskriftsartikel (refereegranskat)abstract
- Context. CO isotopologue transitions are routinely observed in molecular clouds for the purpose of probing the column density of the gas and the elemental ratios of carbon and oxygen, in addition to tracing the kinematics of the environment. Aims. Our study is aimed at estimating the abundances, excitation temperatures, velocity field, and velocity dispersions of the three main CO isotopologues towards a subset of the Orion B molecular cloud, which includes IC 434, NGC 2023, and the Horsehead pillar. Methods. We used the Cramer Rao bound (CRB) technique to analyze and estimate the precision of the physical parameters in the framework of local-thermodynamic-equilibrium (LTE) excitation and radiative transfer with added white Gaussian noise. We propose a maximum likelihood estimator to infer the physical conditions from the 1-0 and 2-1 transitions of CO isotopologues. Simulations show that this estimator is unbiased and proves efficient for a common range of excitation temperatures and column densities (Tex > 6 K, N > 1014-1015 cm-2). Results. Contrary to general assumptions, the various CO isotopologues have distinct excitation temperatures and the line intensity ratios between different isotopologues do not accurately reflect the column density ratios. We find mean fractional abundances that are consistent with previous determinations towards other molecular clouds. However, significant local deviations are inferred, not only in regions exposed to the UV radiation field, but also in shielded regions. These deviations result from the competition between selective photodissociation, chemical fractionation, and depletion on grain surfaces. We observe that the velocity dispersion of the C18O emission is 10% smaller than that of 13CO. The substantial gain resulting from the simultaneous analysis of two different rotational transitions of the same species is rigorously quantified. Conclusions. The CRB technique is a promising avenue for analyzing the estimation of physical parameters from the fit of spectral lines. Future works will generalize its application to non-LTE excitation and radiative transfer methods.
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| 7. |
- Santa-Maria, Miriam G., et al.
(författare)
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HCN emission from translucent gas and UV-illuminated cloud edges revealed by wide-field IRAM 30 m maps of the Orion B GMC: Revisiting its role as a tracer of the dense gas reservoir for star formation
- 2023
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Ingår i: Astronomy and Astrophysics. - 0004-6361 .- 1432-0746. ; 679
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Tidskriftsartikel (refereegranskat)abstract
- Context. Massive stars form within dense clumps inside giant molecular clouds (GMCs). Finding appropriate chemical tracers of the dense gas (n(H2) > several 104 cm-3 or AV > 8 mag) and linking their line luminosity with the star formation rate is of critical importance. Aims. Our aim is to determine the origin and physical conditions of the HCN-emitting gas and study their relation to those of other molecules. Methods. In the context of the IRAM 30m ORION-B large program, we present 5 deg2 (~250 pc2) HCN, HNC, HCO+, and CO J =1-0 maps of the Orion B GMC, complemented with existing wide-field [CI] 492 GHz maps, as well as new pointed observations of rotationally excited HCN, HNC, H13CN, and HN13C lines. We compare the observed HCN line intensities with radiative transfer models including line overlap effects and electron excitation. Furthermore, we study the HCN/HNC isomeric abundance ratio with updated photochemical models. Results. We spectroscopically resolve the HCN J = 1-0 hyperfine structure (HFS) components (and partially resolved J = 2-1 and 3-2 components). We detect anomalous HFS line intensity (and line width) ratios almost everywhere in the cloud. About 70% of the total HCN J = 1-0 luminosity, L′(HCN J = 1-0) = 110 K km s-1 pc-2, arises from AV < 8 mag. The HCN/CO J = 1-0 line intensity ratio, widely used as a tracer of the dense gas fraction, shows a bimodal behavior with an inflection point at AV < 3 mag typical of translucent gas and illuminated cloud edges. We find that most of the HCN J = 1-0 emission arises from extended gas with n(H2) < 104 cm-3, and even lower density gas if the ionization fraction is χe ≥10-5 and electron excitation dominates. This result contrasts with the prevailing view of HCN J = 1-0 emission as a tracer of dense gas and explains the low-AV branch of the HCN/CO J = 1-0 intensity ratio distribution. Indeed, the highest HCN/CO ratios (~ 0.1) at AV < 3 mag correspond to regions of high [CI] 492 GHz/CO J = 1-0 intensity ratios (>1) characteristic of low-density photodissociation regions. The low surface brightness (≲ 1 K km s-1) and extended HCN and HCO+ J = 1-0 emission scale with IFIR -a proxy of the stellar far-ultraviolet (FUV) radiation field -in a similar way. Together with CO J = 1-0, these lines respond to increasing IFIR up to G0 ≅ 20. On the other hand, the bright HCN J = 1-0 emission (> 6 K km s-1) from dense gas in star-forming clumps weakly responds to IFIR once the FUV field becomes too intense (G0 > 1500). In contrast, HNC J = 1-0 and [CI] 492 GHz lines weakly respond to IFIR for all G0. The different power law scalings (produced by different chemistries, densities, and line excitation regimes) in a single but spatially resolved GMC resemble the variety of Kennicutt-Schmidt law indexes found in galaxy averages. Conclusions. Given the widespread and extended nature of the [CI] 492 GHz emission, as well as its spatial correlation with that of HCO+, HCN, and 13CO J = 1-0 lines (in this order), we argue that the edges of GMCs are porous to FUV radiation from nearby massive stars. Enhanced FUV radiation favors the formation and excitation of HCN on large scales, not only in dense star-forming clumps, and it leads to a relatively low value of the dense gas mass to total luminosity ratio, α (HCN) = 29 M⊙ /(K km s-1pc2) in Orion B. As a corollary for extragalactic studies, we conclude that high HCN/CO J = 1-0 line intensity ratios do not always imply the presence of dense gas, which may be better traced by HNC than by HCN.
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| 8. |
- Shaikh, Muhammad Saad
(författare)
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Hyperspectral imaging for in-situ applications : Methods to improve the classification of materials using hyperspectral imaging
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis addresses several research questions related to in-situ hyperspectral imaging systems, proposes measurement methods for more accurate imaging, and examines the impact of the methods on material classification.First, the thesis investigates the possibility of successfully calibrating a hyperspectral imaging system using a low-cost PTFE reference. A hyperspectral imaging system and practical calibration procedure using an inexpensive calibration reference are introduced. This reference enables accurate measurement of a material’s reflectance spectra independent of lighting and the camera’s spectral distribution of intensity and sensitivity. The study presents experiments conducted on winter roads covered with water, snow, and ice. The results show the robustness of the calibration and the suitability of the system for classifying materials.The thesis further focuses on increasing the dynamic range (DR) of line scanning hyperspectral cameras. A method that relies on the use of multiple exposures is proposed to increase DR, benefiting applications such as plastic detection and polymer sorting. Experiments show that the proposed method can increase the DR for hyperspectral SWIR imaging from 43 dB to 73 dB. Material classification experiments reveal significant accuracy improvements with multiple exposures for large dynamic ranges.The thesis also examines the effect of variations in relative humidity. It shows that even minor changes in humidity can significantly affect measurements. Frequent calibration and pruning of active wavelength bands are proposed as solutions to reduce the classification error rate for polymers from 20% to less than 1%.The thesis also investigates the classification of colored materials by combining visible and infrared imaging. The classification algorithm shows high overall accuracy, close to 99.9% for one test case, which also shows the potential of this approach.Finally, the use of infrared hyperspectral imaging combined with Convolutional Neural Networks (CNN) for the classification of black polymers is evaluated. CNN outperforms all traditional classification algorithms, further demonstrating the potential of the proposed method. Further research on larger and more diversified material samples is recommended.
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| 9. |
- Vivone, G., et al.
(författare)
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Converted measurements Bayesian extended target tracking applied to x-band marine radar data
- 2017
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Ingår i: Journal of Advances in Information Fusion. - 1557-6418. ; 12:2, s. 189-210
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Tidskriftsartikel (refereegranskat)abstract
- X-band marine radar systems are flexible and low-cost tools for monitoring multiple targets in a surveillance area. They can provide high resolution measurements both in space and time. Such features offer the opportunity to get accurate information not only about the target kinematics, as other conventional sensors, but also about the target size. In this paper we exploit the random matrix framework to track extended targets. Proper measurement models to deal with the radar’s measurement noise and its conversion into Cartesian coordinates are presented here. Benefits of the proposed extended target tracking using converted measurements can be mainly related to the problem of the targets’ size estimation, while advantages on estimation of the targets’ kinematic features can be considered negligible. The validity of the proposed approach has been demonstrated by using both simulated and real data. Gains up to 70% for the targets’ width estimation accuracy and around 65% for the length are observed on real data. The integration of the proposed model into the gamma Gaussian inverse Wishart probability hypothesis density tracker is also provided and tested on real data.
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| 10. |
- Vono, Maxime, et al.
(författare)
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A Fully Bayesian Approach for Inferring Physical Properties with Credibility Intervals from Noisy Astronomical Data
- 2019
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Ingår i: 2019 10TH WORKSHOP ON HYPERSPECTRAL IMAGING AND SIGNAL PROCESSING - EVOLUTION IN REMOTE SENSING (WHISPERS). - 2158-6268. - 9781728152943
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Konferensbidrag (refereegranskat)abstract
- The atoms and molecules of interstellar clouds emit photons when passing from an excited state to a lower energy state. The resulting emission lines can be detected by telescopes in the different wavelength domains (radio, infrared, visible, UV...). Through the excitation and chemical conditions they reveal, these lines provide key constraints on the local physical conditions reigning in giant molecular clouds (GMCs), which constitute the birthplace of stars in galaxies. Inferring these physical conditions from observed maps of GMCs using complex astrophysical models of these regions remains a complicated challenge due to potentially degenerate solutions and widely varying signal-to-noise ratios over the map. We propose a Bayesian framework to infer the probability distributions associated to each of these physical parameters, taking a spatial smoothness prior into account to tackle the challenge of low signal-to-noise ratio regions of the observed maps. A numerical astrophysical model of the cloud is involved in the likelihood within an approximate Bayesian computation (ABC) method. This enables to both infer point-wise estimators (e.g., minimum mean square or maximum a posteriori) and quantify the uncertainty associated to the estimation process. The benefits of the proposed approach are illustrated based on noisy synthetic observation maps.
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