Different signal processing techniques for synthetic aperture acoustic (SAA) and highresolution voxel radar (HRVR) sensing modalities for side-attack explosive ballistic (SAEB) detection are proposed in this thesis. The sensing modalities were vehicle mounted and the data used was collected at an army test site. More specifically, the use of a frequency azimuthal (fraz) feature for SAA and the fusion of a matched filter (MF) and size contrast filter (SCF) for HRVR was explored. For SAA, the focus was to find a signature in the target’s response that would vary as the vehicle’s view on the target changed. For the HRVR, the focus was put on finding objects that were both anomalous (SCF) and target-like (MF). The results in both cases are obtained using receiver operating characteristic (ROC) curves and both are very encouraging.

The development of a computer vision algorithm for use with 3D voxel space radar imagery is observed in this thesis. The goal is to detect explosive hazards present in 3D synthetic aperture radar (SAR) image data. The algorithm consists of three primary stages; a precreener to find areas of interest, clustering for labeling distinct areas, and a classifier. The performance between multiple prescreener methods are compared when using a heuristic classifier. Finally, a convolutional neural network (CNN) is used as a classifier stage and a comparison between a deep network, a shallow network, and human experts is conducted.

Despite the fact ultra-wideband (UWB) technology has been around for over 30 years, there is a newfound excitement about its potential for communications. With the advantageous qualities of multipath immunity and low power spectral density, researchers are examining fundamental questions about UWB communication systems. In this work, we examine UWB communication systems paying particular attention to transmitter and receiver design. This thesis is specifically focused on a software radio transceiver design for impulse-based UWB with the ability to transmit a raw data rate of 100 Mbps yet encompasses the adaptability of a reconfigurable digital receiver. A 500 ps wide Gaussian pulse is generated at the transmitter utilizing the fast-switching characteristics of a step recovery diode. Pulse modulation is accomplished via several stages of RF switches, filters, and amplifiers on a fully designed printed circuit board specifically manufactured for this project. Critical hardware components at the receiver consist of a bank of ADCs performing parallel sampling and an FPGA employed for data processing. Using a software radio design, various modulation schemes and digital receiver topologies are accommodated along with a vast number of algorithms for acquisition, synchronization, and data demodulation methods. Verification for the design is accomplished through transmitter hardware testing and receiver design simulation. The latter includes bit error rate testing for a variety of modulation schemes and wireless channels using a pilot-based matched filter estimation technique. Ultimately, the transceiver design demonstrates the advantages and challenges of UWB technology while boasting high data rate communication capability and providing the flexibility of a research testbed. / Master of Science

No description available.

This thesis presents a simulation of the control system for Laser Guide Star (LGS) wavefront sensing of the Narrow Field Infrared Adaptive Optics System (NFIRAOS) which will be the Adaptive Optics (AO) system on the Thirty Meter Telescope. The control system is multirate and combines data from multiple sources, both natural and artificial, to provide wavefront correction. Artificial guide stars are generated by exciting atoms in the mesospheric sodium (Na) layer. The characteristics of the Na layer have been examined; its variability, altitude and thickness will lead to false atmospheric turbulence measurements by AO systems integrated with Extremely Large Telescopes. A periodically updated constrained matched filter algorithm has been implemented in the control system simulation in order to gauge its ability to mitigate these effects. The control system has also been implemented on the University of Victoria LGS Test Bench which reproduces wavefront measurements as they will be made by several of the wavefront sensors of NFIRAOS. The simulation has provided insight into the stability of the proposed control system and allowed necessary improvements to be made. It has been shown to meet the requirements of stability over long term with fast convergence. The matched filter algorithm has been shown to effectively reject the Na layer fluctuations both in simulation and on the test bench.

One of the most significant goals in coal mining technology research is the automation of underground coal mining machinery. A current challenge with automating underground coal mining machinery is measuring and maintaining a coal mining horizon. The coal mining horizon is the horizontal path the machinery follows through the undulating coal seam during the mining operation. A typical mining practice is to leave a thin remnant of coal unmined in order to maintain geological stability of the cutting face. If the remnant layer is too thick, resources are wasted as the unmined coal is permanently unrecoverable. If the remnant layer is too thin, the product is diluted by mining into the overburden and there is an increased risk of premature roof fall which increases danger. The main challenge therefore is to develop a robust sensing method to estimate the thickness of thin remant coal layers. This dissertation addresses this challenge by presenting a pattern recognition methodology to estimate thin remnant coal layer thickness using ground penetrating radar (GPR). The approach is based upon a novel feature vector, derived from the bispectrum, that is used to characterise the early-time segment of 1D GPR data. The early-time segment is dominated by clutter inherent in GPR systems such as antenna crosstalk, ringdown and ground-bounce. It is common practice to either time-gate the signal, disregard the clutter by rendering the early-time segment unusable, or configure the GPR equipment to minimise the clutter effects which in turn reduces probing range. Disregarding the early-time signal essentially imposes a lower thickness limit on traditional GPR layer thickness estimators. The challenges of estimating thin layer thickness is primarily due to these inherent clutter components. Traditional processing strategies attempt to minimise the clutter using pre-processing techniques such as the subtraction of a calibration signal. The proposed method, however, treats the clutter as a deterministic but unknown signal with additive noise. Hence the proposed approach utilises the energy from the clutter and monitors change in media from subtle changes in the signal shape. Two complementary processing methods important to horizon sensing have been also proposed. These methods, near-surface interface detection and antenna height estimation, may be used as pre-validation tools to increase the robustness of the thickness estimation technique. The proposed methods have been tested with synthetic data and validated with real data obtained using a low power 1.4 GHz GPR system and a testbed with known conditions. With the given test system, it is shown that the proposed thin layer thickness estimator and near-surface interface detector outperform the traditional matched filter based processing methods for layers less than 5 cm in thickness. It is also shown that the proposed antenna height estimator outperforms the traditional height estimator for heights less than 7 cm. These new methods provide a means for reliably extending layer thickness estimation to the thin layer case where traditional approaches are known to fail.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-07-04T20:35:11Z No. of bitstreams: 1 dayaneoliveiragoncalves.pdf: 5780108 bytes, checksum: 66585bbc9ff0f98a7f32499de344449c (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-08-08T14:48:18Z (GMT) No. of bitstreams: 1 dayaneoliveiragoncalves.pdf: 5780108 bytes, checksum: 66585bbc9ff0f98a7f32499de344449c (MD5) / Made available in DSpace on 2017-08-08T14:48:18Z (GMT). No. of bitstreams: 1 dayaneoliveiragoncalves.pdf: 5780108 bytes, checksum: 66585bbc9ff0f98a7f32499de344449c (MD5) Previous issue date: 2017-04-11 / O calorímetro de Telhas (TileCal) é o calorímetro hadrônico central de um dos experimentos do Grande Colisor de Hádrons (LHC), o ATLAS. O TileCal fornece medidas de energia finamente segmentadas (10.000 canais de leitura) para as partículas incidentes no detector. Análises realizadas nos dados resultantes de colisões de partículas constataram que utilizar as informações da camada radial externa do TileCal, em coincidência com as câmaras de múons (MS) do ATLAS, pode proporcionar uma redução de falsos sinais de trigger (filtragem online) de múons gerados pelas iteração de prótons de baixo momento, na blindagem do feixe do LHC, com o MS. O projeto TileMuon foi desenvolvido para este propósito e sua principal atividade, no programa de atualização ATLAS, é habilitar o TileCal para fornecer as informações de trigger para a primeira etapa de filtragem online para a identificação de múons no ATLAS. Esta dissertação apresenta o estudo, o desenvolvimento e a implementação de uma técnica para a identificação de múons no contexto TileMuon. Técnicas de estimação encontradas na literatura foram aplicadas no contexto do projeto e comparadas. Os resultados para dados experimentais mostraram que o método para a identificação de múons, baseado no filtro casado para ruído gaussiano, obteve o melhor desempenho, em termos de erro de detecção, bem como viabilidade de implementação online, e foi a técnica escolhida para a aplicação. / The Tile Calorimeter (TileCal) is the central hadronic calorimeter of the ATLAS experiment at the Large Hadron Collider (LHC). TileCal provides highly-segmented energy measurements for incident particles. Information from TileCal’s outermost radial layer in coincidence with the ATLAS muon chambers can provide a reduction of the fake muon triggers due to slow charged particles (typically protons). The TileMuon project was development aims this purpose and its main activity of the Tile-Muon Trigger in the ATLAS upgrade program is to install and to activate the TileCal signal processor module for providing trigger inputs to the Level-1 Muon Trigger. This dissertation presents the study, the development and the implementation of the Muon identification techniqueinthe TileMuon context. Amplitude estimation techniques found in the literature were applied to the problem and confronted against each other. The results for experimental data shown that the Muon identification based on the maximum likelihood for Gaussian noise achieved the best performance in terms of detection error as well as online implementation feasibility, and it has been the chosen technique for the application.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-09-28T18:09:06Z No. of bitstreams: 1 davispereirabarbosa.pdf: 10683458 bytes, checksum: 8cd37a50126b8e958532ac4b151e99d4 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-10-09T19:24:00Z (GMT) No. of bitstreams: 1 davispereirabarbosa.pdf: 10683458 bytes, checksum: 8cd37a50126b8e958532ac4b151e99d4 (MD5) / Made available in DSpace on 2017-10-09T19:24:00Z (GMT). No. of bitstreams: 1 davispereirabarbosa.pdf: 10683458 bytes, checksum: 8cd37a50126b8e958532ac4b151e99d4 (MD5) Previous issue date: 2017-03-17 / Esta tese propõe uma nova abordagem baseada em representação esparsa para o problema de estimação de energia em calorimetria de altas energias em cenários com empilhamento de sinais. Inserida dentro do programa de atualização do experimento ATLAS, no LHC, ela teve como principal motivação o aumento progressivo da luminosidade no colisionador e suas consequências relativas ao problema da estimação da energia nos canais do calorímetro eletromagnético do ATLAS, o LArg. Dois métodos de estimação foram propostos e denominados de SPARSE e SPARSE-COF, ambos utilizando programação linear na busca pela esparsidade. Esses métodos tiveram os seus desempenhos avaliados em diversas simulações e foram comparados com o método clássico utilizado nos calorímetros do ATLAS, denominado OF, e com o DM-COF, método recentemente desenvolvido para o calorímetro hadrônico do ATLAS que trata o problema de empilhamento de sinais em sua formulação. Nas diversas simulações realizadas, os métodos SPARSE e SPARSE-COF apresentaram desempenho superior aos demais, principalmente quando a janela de observação utilizada para a estimação da energia não contém todas as amostras do pulso típico do calorímetro, operando em cenários de empilhamento de sinais. Adicionalmente, através dados de simulações Monte Carlo do LArg, os métodos baseados em representação esparsa foram avaliados utilizando programação linear e também métodos esparsos de menor complexidade computacional,como o IRLS,o OMP e o LS-OMP. Os resultados mostraram que o método LS-OMP apresentou desempenho equivalente aos métodos e SPARSE e SPARSE-COF, qualificando-o como candidato a ser utilizado para estimação on-line de energia no LArg. / This thesis proposes a new approach based on sparse representation for the energy estimation problem in high energy calorimetry operating in pile-up scenarios. This work was mainly motivated by the progressive increase of the LHC luminosity and its consequences on the energy estimation problem for channels of the electromagnetic calorimeter of ATLAS (LArg), in the context of the ATLAS experiment upgrade program at the LHC. Two estimation methods were proposed and named SPARSE and SPARSE-COF, both using linear programming in the search for sparsity. These methods were evaluated in several simulations and compared with the classical method used in ATLAS calorimeters, called OF, and with DM-COF, a recently developed method for the ATLAS hadronic calorimeter that addresses pileup problem in its formulation. In the various simulations performed, SPARSE and SPARSE-COF methods performed better than others, especially when the observation window used for energy estimation does not contain all samples of the typical calorimeter pulse, operating in pile-up scenarios. In addition, through LArg Monte Carlo simulations, the methods based on sparse representation were evaluated using linear programming and also sparse methods with less computational complexity, such as IRLS, OMP and LS-OMP. The results showed that the LS-OMP method presented performance equivalent to the SPARSE and SPARSE-COF methods,qualifying it as a candidate to be used for online energy estimation in LArg.

Le positionnement d'objets sous-marins représente un enjeu stratégique pour des applications militaires, industrielles et scientifiques. Les systèmes de positionnement reposent sur des signaux de type SONAR « Sound Navigation and Ranging ». Plusieurs émetteurs synchrones avec des temps d'émission connus sont alors considérés, l'objectif étant que la position d'un récepteur se fasse en fonction des positions des émetteurs. Nous avons la main mise sur la détection des signaux en réception d'une part, et sur le choix des formes d'ondes à l'émission d'autre part. La méthode de détection, basée sur le filtrage adapté, se veut robuste aux différentes perturbations engendrées par le canal de propagation (pertes par transmission, multi-trajets) et par le système lui-même (environnement multi-émetteurs). De plus, la détection restreinte à une somme de tests d'hypothèses binaires, nécessite un fonctionnement en temps réel. A l'émission, les formes d'ondes doivent permettre d'identifier indépendamment les émetteurs les uns des autres. Ainsi les travaux portent essentiellement sur les modulations FHSS, les paramètres de construction de ces signaux étant alors choisis de sorte à optimiser la méthode de détection étudiée. Enfin, l'implémentation des algorithmes issus de ces travaux sur des systèmes embarqués a permis leur validation sur des données enregistrées, puis en conditions réelles. Ces essais ont été réalisés avec l'entreprise ALSEAMAR, dans le cadre de la thèse CIFRE-DGA. / There is an increasing interest in underwater positioning system in industry (off-shore, military, and biology). In order to localize a receiver relative to a grid of transmitters, thanks to the knowledge of positions and transmission time, it needs to detect each signal and estimate the TOA (Time Of Arrival). Thus, a range between a transmitter and receiver can be deduced by estimation of TOA. When receiver knows three ranges at least, it can deduce its position by triangulation. This work takes into account signal detection, and waveform choice. Detection method, based on matched filter, needs to be robust face to propagation channel (transmission loss, multi-paths) and to the system (multi-users environment). Moreover, the detection structure, being a combination of binary hypothesis testing, must work in real time. In a CDMA context which requires to distinguish each transmitter, the FHSS (Frequency Hopped Spread Spectrum) modulation, allocating one code per user, is adapted. FHSS signals performance, depending of the number of frequency shifts N and the time-bandwidth product, are analyzed from detection criterion point of view. Moreover, detection method and adapted signal is tested in a shallow water environment.The research was supported by ALSEAMAR and DGA-MRIS scholarship.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2015-12-16T13:33:15Z No. of bitstreams: 1 bernardosottomaiorperalva.pdf: 8631621 bytes, checksum: e4e7f3d592c91e719474b259727bab6c (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2015-12-16T15:15:06Z (GMT) No. of bitstreams: 1 bernardosottomaiorperalva.pdf: 8631621 bytes, checksum: e4e7f3d592c91e719474b259727bab6c (MD5) / Made available in DSpace on 2015-12-16T15:15:06Z (GMT). No. of bitstreams: 1 bernardosottomaiorperalva.pdf: 8631621 bytes, checksum: e4e7f3d592c91e719474b259727bab6c (MD5) Previous issue date: 2015-09-11 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Esta tese apresenta técnicas de processamento de dados para a detecção de sinais e estimação de energia usando calorimetria de altas energias. Os calorímetros modernos possuem milhares de canais de leitura e operam sob alta taxa de eventos. Tipicamente, a reconstrução da energia envolve etapas de detecção e estimação, e é baseada na medida da amplitude do sinal (digitalizado) recebido. Os métodos empregados, atualmente, em experimentos de altas energias são baseados em técnicas de minimização da variância e selecionam os sinais de interesse a partir da estimação da energia. Este trabalho explora o uso de filtros casados para a detecção de sinais e faz uso de uma calibração para a estimação da energia dos sinais. Na abordagem proposta, os parâmetros aleatórios do pulso processado (fase e deformação) e a estatística do ruído de fundo são considerados no projeto do filtro digital, aumentando seu desempenho. No caso particular de experimentos onde a probabilidade de empilhamento de sinais é alta, uma outra solução, baseada na desconvolução linear de sinais para estimação de energia, é discutida. As técnicas propostas nesta tese foram implementadas offline e aplicadas no calorímetro de telhas (TileCal) do ATLAS no LHC. Foram utilizados sinais simulados, assim como dados reais adquiridos durante a operação nominal do LHC. Os estimadores propostos apresentaram menor erro quando comparados aos métodos empregados em calorímetros modernos e estão, atualmente, sendo validados para serem utilizados no TileCal. / This thesis presents data processing techniques of signal detection and energy estimation for high energy calorimetry. Modern calorimeters have thousands of readout channels and operate at high event rate conditions. Typically, the energy reconstruction involves both detection and estimation tasks, and it is based on the amplitude estimation of the received digitized signal. The current methods employed by high energy experiments are based on variance minimization techniques, and the valid signals are selected based on the energy estimation. This work explores the use of a technique based on Matched Filter for signal detection, and it makes use of a calibration factor to estimate the energy. In the proposed approach, the stochastic parameters of the pulse (phase and deformation) and the statistics from the background are considered for the filter design in order to increase performance. In particular cases, where the signal pile-up is likely to occur, another promising technique, based on linear signal deconvolution is discussed. The techniques proposed in this thesis were implemented offline and applied on the ATLAS Tile Calorimeter (TileCal) at LHC. Both simulated signals and real data acquired during nominal LHC operation were used. The proposed estimators presented smaller error with respect to the methods currently used in modern calorimeter systems, and they have been extensively tested to be used in TileCal.