47 research outputs found

    Artificial Neural Network for LiDAL Systems

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    In this paper, we introduce an intelligent light detection and localization (LiDAL) system that uses artificial neural networks (ANN). The LiDAL systems of interest are MIMO LiDAL and MISO IMG LiDAL systems. A trained ANN with the LiDAL system of interest is used to distinguish a human (target) from the background obstacles (furniture) in a realistic indoor environment. In the LiDAL systems, the received reflected signals in the time domain have different patterns corresponding to the number of targets and their locations in an indoor environment. The indoor environment with background obstacles (furniture) appears as a set of patterns in the time domain when the transmitted optical signals are reflected from objects in LiDAL systems. Hence, a trained neural network that has the ability to classify and recognize the received signal patterns can distinguish the targets from the background obstacles in a realistic environment, especially given the mobility of targets (humans) which distinguishes them from static obstacles (furniture). The LiDAL systems with ANN are evaluated in a realistic indoor environment through computer simulation

    Detection and Localisation Using Light

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    Visible light communication (VLC) systems have become promising candidates to complement conventional radio frequency (RF) systems due to the increasingly saturated RF spectrum and the potentially high data rates that can be achieved by VLC systems. Furthermore, people detection and counting in an indoor environment has become an emerging and attractive area in the past decade. Many techniques and systems have been developed for counting in public places such as subways, bus stations and supermarkets. The outcome of these techniques can be used for public security, resource allocation and marketing decisions. This thesis presents the first indoor light-based detection and localisation system that builds on concepts from radio detection and ranging (radar) making use of the expected growth in the use and adoption of visible light communication (VLC), which can provide the infrastructure for our light detection and localisation (LiDAL) system. Our system enables active detection, counting and localisation of people, in addition to being fully compatible with existing VLC systems. In order to detect human (targets), LiDAL uses the visible light spectrum. It sends pulses using a VLC transmitter and analyses the reflected signal collected by an optical receiver. Although we examine the use of the visible spectrum here, LiDAL can be used in the infrared spectrum and other parts of the light spectrum. We introduce LiDAL with different transmitter-receiver configurations and optimum detectors considering the fluctuation of the received reflected signal from the target in the presence of Gaussian noise. We design an efficient multiple input multiple output (MIMO) LiDAL system with wide field of view (FOV) single photodetector receiver, and also design a multiple input single output (MISO) LiDAL system with an imaging receiver to eliminate ambiguity in target detection and localisation. We develop models for the human body and its reflections and consider the impact of the colour and texture of the cloth used as well as the impact of target mobility. A number of detection and localisation methods are developed iii for our LiDAL system including cross correlation, a background subtraction method and a background estimation method. These methods are considered to distinguish a mobile target from the ambient reflections due to background obstacles (furniture) in a realistic indoor environment

    Loss of Specificity in Basal Ganglia Related Movement Disorders

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    The basal ganglia (BG) are a group of interconnected nuclei which play a pivotal part in limbic, associative, and motor functions. This role is mirrored by the wide range of motor and behavioral abnormalities directly resulting from dysfunction of the BG. Studies of normal behavior have found that BG neurons tend to phasically modulate their activity in relation to different behavioral events. In the normal BG, this modulation is highly specific, with each neuron related only to a small subset of behavioral events depending on specific combinations of movement parameters and context. In many pathological conditions involving BG dysfunction and motor abnormalities, this neuronal specificity is lost. Loss of specificity (LOS) manifests in neuronal activity related to a larger spectrum of events and consequently a large overlap of movement-related activation patterns between different neurons. We review the existing evidence for LOS in BG-related movement disorders, the possible neural mechanisms underlying LOS, its effects on frequently used measures of neuronal activity and its relation to theoretical models of the BG. The prevalence of LOS in a many BG-related disorders suggests that neuronal specificity may represent a key feature of normal information processing in the BG system. Thus, the concept of neuronal specificity may underlie a unifying conceptual framework for the BG role in normal and abnormal motor control

    Optical Channel Impulse Response-Based Localization Using An Artificial Neural Network

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    Visible light positioning has the potential to yield sub-centimeter accuracy in indoor environments, yet conventional received signal strength (RSS)-based localization algorithms cannot achieve this because their performance degrades from optical multipath reflection. However, this part of the optical received signal is deterministic due to the often static and predictable nature of the optical wireless channel. In this paper, the performance of optical channel impulse response (OCIR)-based localization is studied using an artificial neural network (ANN) to map embedded features of the OCIR to the user equipment's location. Numerical results show that OCIR-based localization outperforms conventional RSS techniques by two orders of magnitude using only two photodetectors as anchor points. The ANN technique can take advantage of multipath features in a wide range of scenarios, from using only the DC value to relying on high-resolution time sampling that can result in sub-centimeter accuracy

    Visual Techniques for Geological Fieldwork Using Mobile Devices

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    Visual techniques in general and 3D visualisation in particular have seen considerable adoption within the last 30 years in the geosciences and geology. Techniques such as volume visualisation, for analysing subsurface processes, and photo-coloured LiDAR point-based rendering, to digitally explore rock exposures at the earth’s surface, were applied within geology as one of the first adopting branches of science. A large amount of digital, geological surface- and volume data is nowadays available to desktop-based workflows for geological applications such as hydrocarbon reservoir exploration, groundwater modelling, CO2 sequestration and, in the future, geothermal energy planning. On the other hand, the analysis and data collection during fieldwork has yet to embrace this ”digital revolution”: sedimentary logs, geological maps and stratigraphic sketches are still captured in each geologist’s individual fieldbook, and physical rocks samples are still transported to the lab for subsequent analysis. Is this still necessary, or are there extended digital means of data collection and exploration in the field ? Are modern digital interpretation techniques accurate and intuitive enough to relevantly support fieldwork in geology and other geoscience disciplines ? This dissertation aims to address these questions and, by doing so, close the technological gap between geological fieldwork and office workflows in geology. The emergence of mobile devices and their vast array of physical sensors, combined with touch-based user interfaces, high-resolution screens and digital cameras provide a possible digital platform that can be used by field geologists. Their ubiquitous availability increases the chances to adopt digital workflows in the field without additional, expensive equipment. The use of 3D data on mobile devices in the field is furthered by the availability of 3D digital outcrop models and the increasing ease of their acquisition. This dissertation assesses the prospects of adopting 3D visual techniques and mobile devices within field geology. The research of this dissertation uses previously acquired and processed digital outcrop models in the form of textured surfaces from optical remote sensing and photogrammetry. The scientific papers in this thesis present visual techniques and algorithms to map outcrop photographs in the field directly onto the surface models. Automatic mapping allows the projection of photo interpretations of stratigraphy and sedimentary facies on the 3D textured surface while providing the domain expert with simple-touse, intuitive tools for the photo interpretation itself. The developed visual approach, combining insight from all across the computer sciences dealing with visual information, merits into the mobile device Geological Registration and Interpretation Toolset (GRIT) app, which is assessed on an outcrop analogue study of the Saltwick Formation exposed at Whitby, North Yorkshire, UK. Although being applicable to a diversity of study scenarios within petroleum geology and the geosciences, the particular target application of the visual techniques is to easily provide field-based outcrop interpretations for subsequent construction of training images for multiple point statistics reservoir modelling, as envisaged within the VOM2MPS project. Despite the success and applicability of the visual approach, numerous drawbacks and probable future extensions are discussed in the thesis based on the conducted studies. Apart from elaborating on more obvious limitations originating from the use of mobile devices and their limited computing capabilities and sensor accuracies, a major contribution of this thesis is the careful analysis of conceptual drawbacks of established procedures in modelling, representing, constructing and disseminating the available surface geometry. A more mathematically-accurate geometric description of the underlying algebraic surfaces yields improvements and future applications unaddressed within the literature of geology and the computational geosciences to this date. Also, future extensions to the visual techniques proposed in this thesis allow for expanded analysis, 3D exploration and improved geological subsurface modelling in general.publishedVersio

    Functional Properties of Striatal Fast-Spiking Interneurons

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    Striatal fast-spiking interneurons (FSIs) have a major influence over behavioral output, and a deficit in these cells has been observed in dystonia and Tourette syndrome. FSIs receive cortical input, are coupled together by gap junctions, and make perisomatic GABAergic synapses onto many nearby projection neurons. Despite being critical components of striatal microcircuits, until recently little was known about FSI activity in behaving animals. Striatal FSIs are near-continuously active in awake rodents, but even neighboring FSIs show uncorrelated activity most of the time. A coordinated “pulse” of increased FSI firing occurs throughout striatum when rats initiate one chosen action while suppressing a highly trained alternative. This pulse coincides with a drop in globus pallidus population activity, suggesting that pallidostriatal disinhibition may have a important role in timing or coordinating action execution. In addition to changes in firing rate, FSIs show behavior-linked modulation of spike timing. The variability of inter-spike intervals decreases markedly following instruction cues, and FSIs also participate in fast striatal oscillations that are linked to rewarding events and dopaminergic drugs. These studies have revealed novel and unexpected properties of FSIs, that should help inform new models of striatal information processing in both normal and aberrant conditions

    LiDAL: Light Detection and Localization

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    In this paper, we present the first indoor light-based detection and localization system that builds on concepts from radio detection and ranging (radar) making use of the expected growth in the use and adoption of visible light communication (VLC), which can provide the infrastructure for our Light Detection and Localization (LiDAL) system. Our system enables active detection, counting, and localization of people, in addition to being fully compatible with the existing VLC systems. In order to detect human (targets), LiDAL uses the visible light spectrum. It sends pulses using a VLC transmitter and analyses the reflected signal collected by a photodetector receiver. Although we examine the use of the visible spectrum here, LiDAL can be used in the infrared spectrum and other parts of the light spectrum. We introduce LiDAL with different transmitter-receiver configurations and optimum and sub-optimum detectors considering the fluctuation of the received reflected signal from the target in the presence of Gaussian noise. We design an efficient multiple input multiple output (MIMO) LiDAL system with a wide field of view (FOV) single photodetector receiver, and also design a multiple input single output (MISO) LiDAL system with an imaging receiver to eliminate the ambiguity in target detection and localization. We develop models for the human body and its reflections and consider the impact of the color and texture of the cloth used as well as the impact of target mobility. A number of detection and localization methods are developed for our LiDAL system, including cross correlation and a background subtraction method. These methods are considered to distinguish a mobile target from the ambient reflections due to background obstacles (furniture) in a realistic indoor environment

    Neighboring Pallidal Neurons Do Not Exhibit more Synchronous Oscillations than Remote Ones in the MPTP Primate Model of Parkinson's Disease

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    In the healthy primate, neurons of the external and internal segments of the globus pallidus (GP) present a primarily irregular firing pattern, and a negligible level of synchrony is observed between pairs of neurons. This holds even for neighboring cells, despite their higher probability to receive common inputs and to innervate each other via lateral connectivity. In the Parkinsonian primate, this changes drastically, and many pairs of GP cells show synchronous oscillations. To address the relation between distance and synchrony in the Parkinsonian state, we compared the synchrony of discharge of close pairs of neurons, recorded by the same electrode, with remote pairs, recorded by different ones. However, spike trains of neighboring cells recorded by the same extracellular electrode exhibit the shadowing effect; i.e., lack of detection of spikes that occur within a few milliseconds of each other. Here, we demonstrate that the shadowing artifact can both induce apparent correlations between non-correlated neurons, as well as conceal existing correlations between neighboring ones. We therefore introduced artificial shadowing in the remote pairs, similar to the effect we observed in the close ones. After the artificial shadowing, neighboring cells did not show a higher tendency to oscillate synchronously than remote ones. On the contrary, the average percentage (over all sessions) of artificially shadowed remote pairs exhibiting synchronous oscillations was 35.4% compared to 17.2% in the close ones. Similar trend was found when the unshadowed remote pairs were separated according to the estimated distance between electrode tips: 29.9% of pairs at approximate distance of less than 750 Όm were significantly synchronized, in comparison with 28.5% of the pairs whose distance was more than 750 Όm. We conclude that the synchronous oscillations in the GP of MPTP treated primates are homogenously distributed
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