The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

Recent Advances in Image Restoration with Applications to Real World Problems

In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included

The University Defence Research Collaboration In Signal Processing: 2013-2018

Signal processing is an enabling technology crucial to all areas of defence and security. It is called for whenever humans and autonomous systems are required to interpret data (i.e. the signal) output from sensors. This leads to the production of the intelligence on which military outcomes depend. Signal processing should be timely, accurate and suited to the decisions to be made. When performed well it is critical, battle-winning and probably the most important weapon which you’ve never heard of. With the plethora of sensors and data sources that are emerging in the future network-enabled battlespace, sensing is becoming ubiquitous. This makes signal processing more complicated but also brings great opportunities. The second phase of the University Defence Research Collaboration in Signal Processing was set up to meet these complex problems head-on while taking advantage of the opportunities. Its unique structure combines two multi-disciplinary academic consortia, in which many researchers can approach different aspects of a problem, with baked-in industrial collaboration enabling early commercial exploitation. This phase of the UDRC will have been running for 5 years by the time it completes in March 2018, with remarkable results. This book aims to present those accomplishments and advances in a style accessible to stakeholders, collaborators and exploiters

The XXL Survey: XLIV. Sunyaev-Zel’dovich mapping of a low-mass cluster at z ∼ 1: a multi-wavelength approach

High-mass clusters at low redshifts have been intensively studied at various wavelengths. However, while more distant objects at lower masses constitute the bulk population of future surveys, their physical state remain poorly explored to date. In this paper, we present resolved observations of the Sunyaev-Zel’dovich (SZ) effect, obtained with the NIKA2 camera, towards the cluster of galaxies XLSSC 102, a relatively low-mass system (M500 ∼ 2 × 1014 M⊙) at z = 0.97 detected from the XXL survey. We combine NIKA2 SZ data, XMM-Newton X-ray data, and Megacam optical data to explore, respectively, the spatial distribution of the gas electron pressure, the gas density, and the galaxies themselves. We find significant offsets between the X-ray peak, the SZ peak, the brightest cluster galaxy, and the peak of galaxy density. Additionally, the galaxy distribution and the gas present elongated morphologies. This is interpreted as the sign of a recent major merging event, which induced a local boost of the gas pressure towards the north of XLSSC 102 and stripped the gas out of the galaxy group. The NIKA2 data are also combined with XXL data to construct the thermodynamic profiles of XLSSC 102, obtaining relatively tight constraints up to about ∼r500, and revealing properties that are typical of disturbed systems. We also explore the impact of the cluster centre definition and the implication of local pressure substructure on the recovered profiles. Finally, we derive the global properties of XLSSC 102 and compare them to those of high-mass-and-low-redshift systems, finding no strong evidence for non-standard evolution. We also use scaling relations to obtain alternative mass estimates from our profiles. The variation between these different mass estimates reflects the difficulty to accurately measure the mass of low-mass clusters at z ∼ 1, especially with low signal-to-noise ratio data and for a disturbed system. However, it also highlights the strength of resolved SZ observations alone and in combination with survey-like X-ray data. This is promising for the study of high redshift clusters from the combination of eROSITA and high resolution SZ instruments and will complement the new generation of optical surveys from facilities such as LSST and Euclid

The XXL Survey: XLIV. Sunyaev-Zel'dovich mapping of a low-mass cluster at z ∼1: A multi-wavelength approach

High-mass clusters at low redshifts have been intensively studied at various wavelengths. However, while more distant objects at lower masses constitute the bulk population of future surveys, their physical state remain poorly explored to date. In this paper, we present resolved observations of the Sunyaev-Zel'dovich (SZ) effect, obtained with the NIKA2 camera, towards the cluster of galaxies XLSSC 102, a relatively low-mass system (M500 ∼ 2 × 1014 M·) at z = 0.97 detected from the XXL survey. We combine NIKA2 SZ data, XMM-Newton X-ray data, and Megacam optical data to explore, respectively, the spatial distribution of the gas electron pressure, the gas density, and the galaxies themselves. We find significant offsets between the X-ray peak, the SZ peak, the brightest cluster galaxy, and the peak of galaxy density. Additionally, the galaxy distribution and the gas present elongated morphologies. This is interpreted as the sign of a recent major merging event, which induced a local boost of the gas pressure towards the north of XLSSC 102 and stripped the gas out of the galaxy group. The NIKA2 data are also combined with XXL data to construct the thermodynamic profiles of XLSSC 102, obtaining relatively tight constraints up to about ∼r500, and revealing properties that are typical of disturbed systems. We also explore the impact of the cluster centre definition and the implication of local pressure substructure on the recovered profiles. Finally, we derive the global properties of XLSSC 102 and compare them to those of high-mass-and-low-redshift systems, finding no strong evidence for non-standard evolution. We also use scaling relations to obtain alternative mass estimates from our profiles. The variation between these different mass estimates reflects the difficulty to accurately measure the mass of low-mass clusters at z ∼ 1, especially with low signal-to-noise ratio data and for a disturbed system. However, it also highlights the strength of resolved SZ observations alone and in combination with survey-like X-ray data. This is promising for the study of high redshift clusters from the combination of eROSITA and high resolution SZ instruments and will complement the new generation of optical surveys from facilities such as LSST and Euclid

Advances and Applications of Dezert-Smarandache Theory (DSmT) for Information Fusion (Collected Works), Vol. 4

The fourth volume on Advances and Applications of Dezert-Smarandache Theory (DSmT) for information fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics. The contributions (see List of Articles published in this book, at the end of the volume) have been published or presented after disseminating the third volume (2009, http://fs.unm.edu/DSmT-book3.pdf) in international conferences, seminars, workshops and journals. First Part of this book presents the theoretical advancement of DSmT, dealing with Belief functions, conditioning and deconditioning, Analytic Hierarchy Process, Decision Making, Multi-Criteria, evidence theory, combination rule, evidence distance, conflicting belief, sources of evidences with different importance and reliabilities, importance of sources, pignistic probability transformation, Qualitative reasoning under uncertainty, Imprecise belief structures, 2-Tuple linguistic label, Electre Tri Method, hierarchical proportional redistribution, basic belief assignment, subjective probability measure, Smarandache codification, neutrosophic logic, Evidence theory, outranking methods, Dempster-Shafer Theory, Bayes fusion rule, frequentist probability, mean square error, controlling factor, optimal assignment solution, data association, Transferable Belief Model, and others. More applications of DSmT have emerged in the past years since the apparition of the third book of DSmT 2009. Subsequently, the second part of this volume is about applications of DSmT in correlation with Electronic Support Measures, belief function, sensor networks, Ground Moving Target and Multiple target tracking, Vehicle-Born Improvised Explosive Device, Belief Interacting Multiple Model filter, seismic and acoustic sensor, Support Vector Machines, Alarm classification, ability of human visual system, Uncertainty Representation and Reasoning Evaluation Framework, Threat Assessment, Handwritten Signature Verification, Automatic Aircraft Recognition, Dynamic Data-Driven Application System, adjustment of secure communication trust analysis, and so on. Finally, the third part presents a List of References related with DSmT published or presented along the years since its inception in 2004, chronologically ordered

Sparse modelling of natural images and compressive sensing

This thesis concerns the study of the statistics of natural images and compressive sensing for two main objectives: 1) to extend our understanding of the regularities exhibited by natural images of the visual world we regularly view around us, and 2) to incorporate this knowledge into image processing applications. Previous work on image statistics has uncovered remarkable behavior of the dis tributions obtained from filtering natural images. Typically we observe high kurtosis, non-Gaussian distributions with sharp central cusps, which are called sparse in the literature. These results have become an accepted fact through empirical findings us ing zero mean filters on many different databases of natural scenes. The observations have played an important role in computational and biological applications, where re searchers have sought to understand visual processes through studying the statistical properties of the objects that are being observed. Interestingly, such results on sparse distributions also share elements with the emerging field of compressive sensing. This is a novel sampling protocol where one seeks to measure a signal in already com pressed format through randomised projections, while the recovery algorithm consists of searching for a constrained solution with the sparsest transformed coefficients. In view of prior art, we extend our knowledge of image statistics from the monochrome domain into the colour domain. We study sparse response distributions of filters constructed on colour channels and observe the regularity of the distributions across diverse datasets of natural images. Several solutions to image processing problems emerge from the incorporation of colour statistics as prior information. We give a Bayesian treatment to the problem of colorizing natural gray images, and formulate image compression schemes using elements of compressive sensing and sparsity. We also propose a denoising algorithm that utilises the sparse filter responses as a regular- isation function for the effective attenuation of Gaussian and impulse noise in images. The results emanating from this body of work illustrate how the statistics of natural images, when incorporated with Bayesian inference and sparse recovery, can have deep implications for image processing applications