32 research outputs found

    Design of a Robust Radio-Frequency Fingerprint Identification Scheme for Multimode LFM Radar

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    International audienceRadar is an indispensable part of the Internet of Things (IoT). Specific emitter identification is essential to identify the legitimate radars and, more importantly, to reject the malicious radars. Conventional methods rely on pulse parameters that are not capable to identify the specific emitter as two radars may have the same configuration or a malicious radar can perform spoofing attacks. Radio frequency fingerprint (RFF) is the unique and intrinsic hardware characteristic of devices resulted from hardware imperfection, which can be used as the device identity. This paper proposes a robust and reliable radar identification scheme based on the RFF, taking linear frequency modulation (LFM) radar as a case study. This scheme first classifies the operation mode of the pulses, then eliminates the noise effect, and finally identifies the radar emitters based on the transient and modulation-based RFF features. Experimental results verify the effectiveness of our radar identification scheme among three real LFM radars (same model) operating at four modes, each mode with 2,000 pulses from each radar. The identification rates of the four modes are all higher than 90% when the signal-tonoise ratio (SNR) is about 5 dB. In addition, mode 3 achieves almost 100% identification accuracy even when the SNR is as low as-10 dB

    Proceedings, MSVSCC 2014

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    Proceedings of the 8th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 17, 2014 at VMASC in Suffolk, Virginia

    Deep Model for Improved Operator Function State Assessment

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    A deep learning framework is presented for engagement assessment using EEG signals. Deep learning is a recently developed machine learning technique and has been applied to many applications. In this paper, we proposed a deep learning strategy for operator function state (OFS) assessment. Fifteen pilots participated in a flight simulation from Seattle to Chicago. During the four-hour simulation, EEG signals were recorded for each pilot. We labeled 20- minute data as engaged and disengaged to fine-tune the deep network and utilized the remaining vast amount of unlabeled data to initialize the network. The trained deep network was then used to assess if a pilot was engaged during the four-hour simulation

    Abstracts on Radio Direction Finding (1899 - 1995)

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    The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion

    3D reconstruction and object recognition from 2D SONAR data

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    Accurate and meaningful representations of the environment are required for autonomy in underwater applications. Thanks to favourable propagation properties in water, acoustic sensors are commonly preferred to video cameras and lasers but do not provide direct 3D information. This thesis addresses the 3D reconstruction of underwater scenes from 2D imaging SONAR data as well as the recognition of objects of interest in the reconstructed scene. We present two 3D reconstruction methods and two model-based object recognition methods. We evaluate our algorithms on multiple scenarios including data gathered by an AUV. We show the ability to reconstruct underwater environments at centimetre-level accuracy using 2D SONARs of any aperture. We demonstrate the recognition of structures of interest on a medium-sized oil-ïŹeld type environment providing accurate yet low memory footprint semantic world models. We conclude that accurate 3D semantic representations of partially-structured marine environments can be obtained from commonly embedded 2D SONARs, enabling online world modelling, relocalisation and model-based applications

    AI for time-resolved imaging: from fluorescence lifetime to single-pixel time of flight

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    Time-resolved imaging is a field of optics which measures the arrival time of light on the camera. This thesis looks at two time-resolved imaging modalities: fluorescence lifetime imaging and time-of-flight measurement for depth imaging and ranging. Both of these applications require temporal accuracy on the order of pico- or nanosecond (10−12 − 10−9s) scales. This demands special camera technology and optics that can sample light-intensity extremely quickly, much faster than an ordinary video camera. However, such detectors can be very expensive compared to regular cameras while offering lower image quality. Further, information of interest is often hidden (encoded) in the raw temporal data. Therefore, computational imaging algorithms are used to enhance, analyse and extract information from time-resolved images. "A picture is worth a thousand words". This describes a fundamental blessing and curse of image analysis: images contain extreme amounts of data. Consequently, it is very difficult to design algorithms that encompass all the possible pixel permutations and combinations that can encode this information. Fortunately, the rise of AI and machine learning (ML) allow us to instead create algorithms in a data-driven way. This thesis demonstrates the application of ML to time-resolved imaging tasks, ranging from parameter estimation in noisy data and decoding of overlapping information, through super-resolution, to inferring 3D information from 1D (temporal) data

    Doctor of Philosophy

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    dissertationMicrowave/millimeter-wave imaging systems have become ubiquitous and have found applications in areas like astronomy, bio-medical diagnostics, remote sensing, and security surveillance. These areas have so far relied on conventional imaging devices (empl

    Reconstruction de l'activité corticale à partir de données MEG à l'aide de réseaux cérébraux et de délais de transmission estimés à partir d'IRMd

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    White matter fibers transfer information between brain regions with delays that are observable with magnetoencephalography and electroencephalography (M/EEG) due to their millisecond temporal resolution. We can represent the brain as a graph where nodes are the cortical sources or areas and edges are the physical connections between them: either local (between adjacent vertices on the cortical mesh) or non-local (long-range white matter fibers). Long-range anatomical connections can be obtained with diffusion MRI (dMRI) tractography which yields a set of streamlines representing white matter fiber bundles. Given the streamlines’ lengths and the information conduction speed, transmission delays can be estimated for each connection. dMRI can thus give an insight into interaction delays of the macroscopicbrain network.Localizing and recovering electrical activity of the brain from M/EEG measurements is known as the M/EEG inverse problem. Generally, there are more unknowns (brain sources) than the number of sensors, so the solution is non-unique and the problem ill-posed. To obtain a unique solution, prior constraints on the characteristics of source distributions are needed. Traditional linear inverse methods deploy different constraints which can favour solutions with minimum norm, impose smoothness constraints in space and/or time along the cortical surface, etc. Yet, structural connectivity is rarely considered and transmission delays almost always neglected.The first contribution of this thesis consists of a multimodal preprocessing pipeline used to integrate structural MRI, dMRI and MEG data into a same framework, and of a simulation procedure of source-level brain activity that was used as a synthetic dataset to validate the proposed reconstruction approaches.In the second contribution, we proposed a new framework to solve the M/EEG inverse problem called Connectivity-Informed M/EEG Inverse Problem (CIMIP), where prior transmission delays supported by dMRI were included to enforce temporal smoothness between time courses of connected sources. This was done by incorporating a Laplacian operator into the regularization, that operates on a time-dependent connectivity graph. Nonetheless, some limitations of the CIMIP approach arised, mainly due to the nature of the Laplacian, which acts on the whole graph, favours smooth solutions across all connections, for all delays, and it is agnostic to directionality.In this thesis, we aimed to investigate patterns of brain activity during visuomotor tasks, during which only a few regions typically get significantly activated, as shown by previous studies. This led us to our third contribution, an extension of the CIMIP approach that addresses the aforementioned limitations, named CIMIP_OML (“Optimal Masked Laplacian”). We restricted the full source space network (the whole cortical mesh) to a network of regions of interest and tried to find how the information is transferred between its nodes. To describe the interactions between nodes in a directed graph, we used the concept of network motifs. We proposed an algorithm that (1) searches for an optimal network motif – an optimal pattern of interaction between different regions and (2) reconstructs source activity given the found motif. Promising results are shown for both simulated and real MEG data for a visuomotor task and compared with 3 different state-of-the-art reconstruction methods.To conclude, we tackled a difficult problem of exploiting delays supported by dMRI for the reconstruction of brain activity, while also considering the directionality in the information transfer, and provided new insights into the complex patterns of brain activity.Les fibres de la matiĂšre blanche permettent le transfert d’information dans le cerveau avec des dĂ©lais observables en MagnĂ©toencĂ©phalographie et ÉlectroencĂ©phalographie (M/EEG) grĂące Ă  leur haute rĂ©solution temporelle. Le cerveau peut ĂȘtre reprĂ©sentĂ© comme un graphe oĂč les nƓuds sont les rĂ©gions corticales et les liens sont les connexions physiques entre celles-ci: soit locales (entre sommets adjacents sur le maillage cortical), soit non locales (fibres de la matiĂšre blanche). Les connexions non-locales peuvent ĂȘtre reconstruites avec la tractographie de l’IRM de diffusion (IRMd) qui gĂ©nĂšre un ensemble de courbes («streamlines») reprĂ©sentant des fibres de la matiĂšre blanche. Sachant les longueurs des fibres et la vitesse de conduction de l’information, les dĂ©lais de transmission peuvent ĂȘtre estimĂ©s. L’IRMd peut donc donner un aperçu des dĂ©lais d’interaction du rĂ©seau cĂ©rĂ©bral macroscopique.La localisation et la reconstruction de l’activitĂ© Ă©lectrique cĂ©rĂ©brale Ă  partir des mesures M/EEG est un problĂšme inverse. En gĂ©nĂ©ral, il y a plus d’inconnues (sources cĂ©rĂ©brales) que de capteurs. La solution n’est donc pas unique et le problĂšme est dit mal posĂ©. Pour obtenir une solution unique, des hypothĂšses sur les caractĂ©ristiques des distributions de sources sont requises. Les mĂ©thodes inverses linĂ©aires traditionnelles utilisent diffĂ©rentes hypothĂšses qui peuvent favoriser des solutions de norme minimale, imposer des contraintes de lissage dans l’espace et/ou dans le temps, etc. Pourtant, la connectivitĂ© structurelle est rarement prise en compte et les dĂ©lais de transmission sont presque toujours nĂ©gligĂ©s.La premiĂšre contribution de cette thĂšse est un pipeline de prĂ©traitement multimodal utilisĂ© pour l’intĂ©gration des donnĂ©es d’IRM, IRMd et MEG dans un mĂȘme cadre, et d’une mĂ©thode de simulation de l’activitĂ© corticale qui a Ă©tĂ© utilisĂ©e comme jeu de donnĂ©es synthĂ©tiques pour valider les approches de reconstruction proposĂ©es. Nous proposons Ă©galement une nouvelle approche pour rĂ©soudre le problĂšme inverse M/EEG appelĂ©e «ProblĂšme Inverse M/EEG InformĂ© par la Connectivité» (CIMIP pour Connectivity-Informed M/EEG Inverse Problem), oĂč des dĂ©lais de transmission provenant de l’IRMd sont inclus pour renforcer le lissage temporel entre les dĂ©cours des sources connectĂ©es. Pour cela, un opĂ©rateur Laplacien, basĂ© sur un graphe de connectivitĂ© en fonction du temps, a Ă©tĂ© intĂ©grĂ© dans la rĂ©gularisation. Cependant, certaines limites de l’approche CIMIP sont apparues en raison de la nature du Laplacien qui agit sur le graphe entier et favorise les solutions lisses sur toutes les connexions, pour tous les dĂ©lais, et indĂ©pendamment de la directionnalitĂ©.Lors de tĂąches visuo-motrices, seules quelques rĂ©gions sont gĂ©nĂ©ralement activĂ©es significativement. Notre troisiĂšme contribution est une extension de CIMIP pour ce type de tĂąches qui rĂ©pond aux limitations susmentionnĂ©es, nommĂ©e CIMIP_OML («Optimal Masked Laplacian») ou Laplacien MasquĂ© Optimal. Nous essayons de trouver comment l’information est transfĂ©rĂ©e entre les nƓuds d’un sous-rĂ©seau de rĂ©gions d’intĂ©rĂȘt du rĂ©seau complet de l’espace des sources. Pour dĂ©crire les interactions entre nƓuds dans un graphe orientĂ©, nous utilisons le concept de motifs de rĂ©seau. Nous proposons un algorithme qui 1) cherche un motif de rĂ©seau optimal- un modĂšle optimal d’interaction entre rĂ©gions et 2) reconstruit l’activitĂ© corticale avec le motif trouvĂ©. Des rĂ©sultats prometteurs sont prĂ©sentĂ©s pour des donnĂ©es MEG simulĂ©es et rĂ©elles (tĂąche visuo-motrice) et comparĂ©s avec 3 mĂ©thodes de l’état de l’art. Pour conclure, nous avons abordĂ© un problĂšme difficile d’exploitation des dĂ©lais de l’IRMd lors l’estimation de l’activitĂ© corticale en tenant compte de la directionalitĂ© du transfert d’information, fournissant ainsi de nouvelles perspectives sur les patterns complexes de l’activitĂ© cĂ©rĂ©brale

    Recognition of radar emitter signals based on SVD and AF main ridge slice

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    Temporal integration of loudness as a function of level

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