Neural Network-Based Multi-Target Detection within Correlated Heavy-Tailed Clutter

This work addresses the problem of range-Doppler multiple target detection in a radar system in the presence of slow-time correlated and heavy-tailed distributed clutter. Conventional target detection algorithms assume Gaussian-distributed clutter, but their performance is significantly degraded in the presence of correlated heavy-tailed distributed clutter. Derivation of optimal detection algorithms with heavy-tailed distributed clutter is analytically intractable. Furthermore, the clutter distribution is frequently unknown. This work proposes a deep learning-based approach for multiple target detection in the range-Doppler domain. The proposed approach is based on a unified NN model to process the time-domain radar signal for a variety of signal-to-clutter-plus-noise ratios (SCNRs) and clutter distributions, simplifying the detector architecture and the neural network training procedure. The performance of the proposed approach is evaluated in various experiments using recorded radar echoes, and via simulations, it is shown that the proposed method outperforms the conventional cell-averaging constant false-alarm rate (CA-CFAR), the ordered-statistic CFAR (OS-CFAR), and the adaptive normalized matched-filter (ANMF) detectors in terms of probability of detection in the majority of tested SCNRs and clutter scenarios.Comment: Accepted to IEEE Transactions on Aerospace and Electronic System

Reconstructing Velocities of Migrating Birds from Weather Radar – A Case Study in Computational Sustainability

Bird migration occurs at the largest of global scales, but monitoring such movements can be challenging. In the US there is an operational network of weather radars providing freely accessible data for monitoring meteorological phenomena in the atmosphere. Individual radars are sensitive enough to detect birds, and can provide insight into migratory behaviors of birds at scales that are not possible using other sensors. Archived data from the WSR-88D network of US weather radars hold valuable and detailed information about the continent-scale migratory movements of birds over the last 20 years. However, significant technical challenges must be overcome to understand this information and harness its potential for science and conservation. We describe recent work on an AI system to quantify bird migration using radar data, which is part of the larger BirdCast project to model and forecast bird migration at large scales using radar, weather, and citizen science data

Reconstructing Velocities of Migrating Birds from Weather Radar – A Case Study in Computational Sustainability

Bird migration occurs at the largest of global scales, but monitoring such movements can be challenging. In the US there is an operational network of weather radars providing freely accessible data for monitoring meteorological phenomena in the atmosphere. Individual radars are sensitive enough to detect birds, and can provide insight into migratory behaviors of birds at scales that are not possible using other sensors. Archived data from the WSR-88D network of US weather radars hold valuable and detailed information about the continent-scale migratory movements of birds over the last 20 years. However, significant technical challenges must be overcome to understand this information and harness its potential for science and conservation. We describe recent work on an AI system to quantify bird migration using radar data, which is part of the larger BirdCast project to model and forecast bird migration at large scales using radar, weather, and citizen science data

Novelty detection and context dependent processing of sky-compass cues in the brain of the desert locust Schistocerca gregaria

NERVOUS SYSTEMS facilitate purposeful interactions between animals and their environment, based on the perceptual powers, cognition and higher motor control. Through goal-directed behavior, the animal aims to increase its advantage and minimize risk. For instance, the migratory desert locust should profit from being fast in finding a fresh habitat, thus minimizing the investment of bodily resources in locomotion as well as the risk of starvation or capture by a predator en route. Efficient solutions to this and similar tasks – be it finding your way to work, the daily foraging of worker bees or the seasonal long-range migration of monarch butterflies - strongly depend on spatial orientation in local or global frames of reference. Local settings may include visual landmarks at stable positions that can be mapped onto egocentric space and learned for orientation, e.g. to remember a short route to a source of benefit (e.g. food) that is distant or visually less salient than the landmarks. Compass signals can mediate orientation to a global reference-frame (allothetic orienation), e.g. for locomotion in a particular compass direction or to merely ensure motion along a straight line. Whilst spatial orientation is a prerequisite of doing the planned in such tasks, animal survival in general depends on the ability to adequately respond to the unexpected, i.e. to unpredicted events such as the approach of a predator or mate. The process of identifying relevant events in the outside world that are not predictable from preceding events is termed novelty detection. Yet, the definition of ‘novelty’ is highly contextual: depending on the current situation and goal, some changes may be irrelevant and remain ´undetected´. The present thesis describes neuronal representations of a compass stimulus, correlates of novelty detection and interactions between the two in the minute brain of an insect, the migratory desert locust Schistocerca gregaria. Experiments were carried out in tethered locusts with legs and wings removed. More precisely, adult male subjects in the gregarious phase (see phase theory, Uvarov 1966) that migrates in swarms across territories in North Africa and the Middle East were used. The author performed electrophysiological recordings from single neurons in the locust brain, while either the compass stimulus (Chapter I) or events in the visual scenery (Chapter II) or combinations of both (Chapter III) were being presented to the animal. Injections of a tracer through the recording electrode, visualized by means of fluorescent-dye coupling, allowed the allocation of cellular morphologies to previously described types of neuron or the characterization of novel cell types, respectively. Recordings were focused on cells of the central complex, a higher integration area in the insect brain that was shown to be involved in the visually mediated control of goal-directed locomotion. Experiments delivered insights into how representations of the compass cue are modulated in a manner suited for their integration in the control of goal-directed locomotion. In particular, an interaction between compass-signaling and novelty detection was found, corresponding to a process in which input in one sensory domain (object vision) modulates the processing of concurrent input to a different exteroceptive sensory system (compass sense). In addition to deepening the understanding of the compass network in the locust brain, the results reveal fundamental parallels to higher context-dependent processing of sensory information by the vertebrate cortex, both with respect to spatial cues and novelty detection

Measuring blood flow and pro-inflammatory changes in the rabbit aorta

Atherosclerosis is a chronic inflammatory disease that develops as a consequence of progressive entrapment of low density lipoprotein, fibrous proteins and inflammatory cells in the arterial intima. Once triggered, a myriad of inflammatory and atherogenic factors mediate disease progression. However, the role of pro-inflammatory activity in the initiation of atherogenesis and its relation to altered mechanical stresses acting on the arterial wall is unclear. Estimation of wall shear stress (WSS) and the inflammatory mediator NF-κB is consequently useful. In this thesis novel ultrasound tools for accurate measurement of spatiotemporally varying 2D and 3D blood flow, with and without the use of contrast agents, have been developed. This allowed for the first time accurate, broad-view quantification of WSS around branches of the rabbit abdominal aorta. A thorough review of the evidence for a relationship between flow, NF-κB and disease was performed which highlighted discrepancies in the current literature and was used to guide the study design. Subsequently, methods for the measurement and colocalization of the spatial distribution of NF-κB, arterial permeability and nuclear morphology in the aorta of New Zealand White rabbits were developed. It was demonstrated that endothelial pro-inflammatory changes are spatially correlated with patterns of WSS, nuclear morphology and arterial permeability in vivo in the rabbit descending and abdominal aorta. The data are consistent with a causal chain between WSS, macromolecule uptake, inflammation and disease, and with the hypothesis that lipids are deposited first, through flow-mediated naturally occurring transmigration that, in excessive amounts, leads to subsequent inflammation and disease.Open Acces

Spectral remote sensing for onshore seepage characterization: A critical overview

FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOIn this article, we overview the application of spectral remote sensing data collected by multi-, and hyperspectral instruments in the visible-near infrared (VNIR), short-wave infrared (SWIR), and longwave infrared (LWIR) wavelengths for characterization of seepage systems as an exploration indicator of subsurface hydrocarbon (HC) accumulations. Two seepage systems namely macro-, and microseepage are recognized. A macroseepage is defined as visible indications of oil and gas on the surface and in the air detectable directly by a remote sensing approach. A microseepage is defined as invisible traces of light HCs in soils and sediments that are detectable by its secondary footprints in the strata, hence an indirect remote sensing target. Based on these broad categories, firstly, a comprehensive set of well-described and reliable remote sensing case studies available in the literature are thoroughly reviewed and then systematically assessed as regards the methodological shortcomings and scantiness in data gathering, processing, and interpretation. The work subsequently attempts to go through seminal papers published on microseepage concept and interrelated geochemical and geophysical techniques, exhumed HC reservoirs, lab-based spectroscopic analysis of petroleum and other related disciplines from a remote sensing standpoint. The aim is to enrich the discussion and highlight the still unexplored capabilities of this technique in accomplishing exploration objectives using the concept of seepage system. Aspects of seepage phenomenon in environmental pollution and uncertainties associated with their role in global warming are also underlined. This work benefits from illustrative products generated over two study areas located in the Ventura Basin, State of California, USA and the Tucano Basin, State of Bahia, Brazil known to host distinctive macro-, and microseepage systems, respectively. In conclusion, we recommend further research over a diverse range of seepage systems and advocate for a mature conceptual model for microseepage phenomenon1684872FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO2015/06663-

High Frame Rate Ultrasound Velocimetry of Fast Blood Flow Dynamics

In this thesis we develop and validate high frame rate ultrasound sequences for use with echo-particle image velocimetry (in 2D and 3D), with the aim of measuring the high velocity blood flow patterns in the left ventricle and abdominal aorta