High-Power Microwaves (HPM) can be used to intentionally disturb or destroy electronic equipment at a distance by inducing high voltages and currents. This thesis presents results from experiments with a narrow band HPM source, the vircator. The high voltages needed to generate HPM puts the vircator under great stress, especially the electrode materials. Several electrode materials have been tested for endurance and their influence on the characteristics of the microwave pulse. With the proper materials the shot-to-shot variations are small and the geometry can be optimized in terms of e.g. output power or frequency content. Experiments with a resonant cavity added to the vircator geometry showed that with proper tuning of the cavity, the frequency content of the microwave radiation is very narrow banded and the highest radiated fields are registred. Since HPM pulses are very short and have high field strengths, special field probes are needed. An HPM pulse may shift in frequency during the pulse so it is very important to be able to compensate for the frequency dependence of the entire measurement system. The development and use of a far-field measurement system is described. / QC 20110616

No description available.

The main idea of this thesis is to de ne and formulate the role of bias estimation in multitarget{multisensor scenarios as a general framework for various measurement types. After a brief introduction of the work that has been done in this thesis, three main contributions are explained in detail, which exercise the novel ideas. Starting with radar measurements, a new bias estimation method that can estimate o set and scaling biases in large network of radars is proposed. Further, Cram er{Rao Lower Bound is calculated for the bias estimation algorithm to show the theoretical accuracy that can be achieved by the proposed method. In practice, communication loss is also part of the distributed systems, which sometimes can not be avoided. A novel technique is also developed to accompany the proposed bias estimation method in this thesis to compensate for communication loss at di erent rates by the use of tracklets. Next, bearing{only measurements are considered. Biases in this type of measurement can be di cult to tackle because the measurement noise and systematic biases are normally larger than in radar measurements. In addition, target observability is sensitive to sensor{target alignment and can vary over time. In a multitarget{ multisensor bearing{only scenario with biases, a new model is proposed for the biases that is decoupled form the bearing{only measurements. These decoupled bias measurements then are used in a maximum likelihood batch estimator to estimate the biases and then be used for compensation. The thesis is then expanded by applying bias estimation algorithms into video sensor measurements. Video sensor measurements are increasingly implemented in distributed systems because of their economical bene ts. However, geo{location and geo{registration of the targets must be considered in such systems. In last part of the thesis, a new approach proposed for modeling and estimation of biases in a two video sensor platform which can be used as a standalone algorithm. The proposed algorithm can estimate the gimbal elevation and azimuth biases e ectively. It is worth noting that in all parts of the thesis, simulation results of various scenarios with di erent parameter settings are presented to support the ideas, the accuracy, mathematical modelings and proposed algorithms. These results show that the bias estimation methods that have been conducted in this thesis are viable and can handle larger biases and measurement errors than previously proposed methods. Finally, the thesis conclude with suggestions for future research in three main directions. / Thesis / Doctor of Philosophy (PhD)

No description available.

Deep Neural Networks (DNNs) have been proposed to solve many difficult problems within the context of wireless sensing. Indoor localization and human activity recognition (HAR) are two major applications of wireless sensing. However, current fingerprint-based localization methods require massive amounts of labeled data and suffer severe performance degradation in NLOS environments. To address this challenge, we first apply DNNs to multi-modal wireless signals, including Wi-Fi, an inertial measurement unit (IMU), and ultra-wideband (UWB). By formulating localization as a multi-modal sequence regression problem, a multi- stream recurrent fusion method is developed to combine the current hidden state of each modality. This is done in the context of recurrent neural networks while accounting for the modality uncertainty directly learned from its immediate past states. The proposed method was evaluated on a large-scale open dataset and compared with a wide range of baseline methods. It is shown that the proposed approach has an average error below 20 centimeters, which is nearly three times better than classic methods. Second, in the context of activity recognition, we propose a multi-band WiFi fusion frame- work that hierarchically combines the features of sub-6 GHz channel state information (CSI) and the beam signal-to-noise ratio (SNR) at 60 GHz at different granularity levels. Specifically, we introduce three fusion methods: simple input fusion, feature fusion, and a more customized feature permutation that accounts for the granularity correspondence between the CSI and beam SNR measurements for task-specific sensing. To mitigate the problem of limited labeled training data, we further propose an autoencoder-based unsupervised fusion network consisting of separate encoders and decoders for the CSI and beam SNR. The effectiveness of the framework is thoroughly validated using an in-house experimental platform which includes indoor localization, pose recognition, and occupancy sensing. Finally, in the context of array processing, we solve the Model order estimation (MOE) problem, a prerequisite for Direction of Arrival (DoA) estimation in the presence of correlated multipath, a well-known difficult problem. Due to the limits imposed by array geometry, it is not possible to estimate spatial parameters for an arbitrary number of sources; an estimate of the signal model is required. While classic methods fail at MOE in the presence of correlated multi-path interference, we show that data-driven supervised learning models can meet this challenge. In particular, we propose the application of Residual Neural Net- works (ResNets), with grouped symmetric kernel filters to provide an accuracy over 95%, and a weighted loss function to eliminate the underestimation error of model order. The improved MOE is shown improve subsequent array processing tasks such as reducing the overhead needed for temporal smoothing, reducing the search space for signal association, and improving DoA estimation. / Doctor of Philosophy / Radio Frequency (RF) signals are used not only for wireless communication (its most well-known application), but is also commonly used to sense the environment. One specific application, localization and navigation, can require accuracy of 0.5 meters or below, which is a significant challenge indoors. To address this problem, we apply deep learning (a technique that has gains significant attention in recent years) to fuse types of RF signals, including signals and devices commonly used in smart phones (e.g., UWB, WiFi and IMUs). The result is a technique that can achieve 20cm accuracy in indoor location applications. In addition to localization, commercial WiFi signals can also be used to sense/determine human activity. The received signals from a WiFi transmitter contain sensing information about the environment, including geometric information (angles, distance and velocity) about objects. We specifically show that our proposed approach can successfully recognize human pose, whether or not a specific seat is occupied, and a person's location. Moreover, we show that this can be done with relatively little labelled data using a technique known as transfer learning. Finally, we apply the another neural network structure to solve a particular problem in multi-antenna processing, model order estimation in the presence of coherent multipath. The resulting system can deliver a 95% accuracy in complex environments greatly improving overall array processing.

The growing Landsat data archive represents more than four decades of continuous Earth observation. Landsat's role in scientific analysis has increased dramatically in recent years as a result of the open-access policy of the U.S. Geological Survey (USGS). However, this rich data record suffers from relatively low temporal resolution due to the 16-day revisit period of each Landsat satellite. To estimate Landsat images at other points in time, researchers have proposed data-fusion approaches that combine existing Landsat data with images from other sensors, such as MODIS (Moderate Resolution Imaging Spectroradiometer) from the Terra and Aqua satellites. MODIS provides daily revisits, however, with a spatial resolution that is significantly lower than that of Landsat. Fusion of Landsat and MODIS is challenging because of differences in their spatial resolution, band designations, swath width, viewing angle and the noise level. Fusion is even more challenging for heterogeneous landscapes. In the first part of our work, the multiresolution analysis offered by the wavelet transform was explored as a suitable environment for Landsat and MODIS fusion. Our proposed Wavelet-based Spatiotemporal Adaptive Reflectance Fusion Model (WSTARFM) is the first model to merge Landsat and MODIS successfully. It handles the heterogeneity of the landscapes more effectively than the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) does. The system has been tested on simulated data and on actual data of two study areas in North Carolina. For a challenging heterogeneous study area near Greensboro, North Carolina, WSTARFM produced results with median R-squared values of 0.98 and 0.95 for the near-infrared band over deciduous forests and developed areas, respectively. Those results were obtained by withholding an actual Landsat image, and comparing it with a predicted version of the same image. These values represent an improvement over results obtained using the well-known STARFM technique. Similar improvements were obtained for the red band. For the second (homogeneous) study area, WSTARFM produced comparable prediction results to STARFM. In the second part of our work, Landsat-MODIS fusion has been explored from the temporal perspective. The fusion is performed on the Landsat and MODIS per-pixel time series. A new Multisensor Adaptive Time Series Fitting Model (MATSFM) is proposed. MATSFM is the first model to use mapped MODIS values to guide the fitting applied to the sparse Landsat time series. MATSFM produced results with median R-squared of 0.98 over the NDVI images of the first heterogeneous study area compared to 0.97 produced by STARFM. For the second study area, MATSFM also produced better prediction accuracy than STARFM. / Ph. D. / The growing Landsat data archive represents more than four decades of continuous Earth observation. Landsat’s role in scientific analysis has increased dramatically in recent years as a result of the open-access policy of the U.S. Geological Survey (USGS). With its spatial resolution of 30 m, Landsat facilitates analysis at a local scale. However, this rich data record suffers from relatively low temporal resolution due to the 16-day revisit period of each Landsat satellite. This long revisit cycle limits the utility of Landsat data for such tasks as tracking rapid changes or investigating intra-seasonal variations. To estimate Landsat images at other points in time, researchers have proposed data-fusion approaches that combine existing Landsat data with images from other sensors, such as MODIS (Moderate Resolution Imaging Spectroradiometer) from the Terra and Aqua satellites. MODIS provides daily revisits, however, with a spatial resolution that is significantly lower than that of Landsat. Landsat-MODIS fusion is a challenging problem due to differences between the two sensors that greatly affect the prediction accuracy over areas having various land cover types. This work presents two Landsat-MODIS fusion models to estimate the unavailable Landsat images from the spatial and temporal perspectives as an attempt to achieve better prediction accuracy especially over heterogeneous areas.

Les leucémies aigues sont la conséquence d’une prolifération clonale et maligne des cellules hématopoïétiques. Elles surviennent suite à un évènement oncogénique qui se produit dans une cellule souche hématopoïétique (CSH) ou progénitrice. Cela lui confère une certaine instabilité qui engendre l’accumulation d’autres évènements génétiques et/ou épigénétiques responsables du développement clinique de la maladie. Les leucémies MLL représentent environ 10% des leucémies aigues et aujourd’hui, plus de 70 gènes de fusion ont été caractérisés. Les sangs de cordon sont une source importante de CSH et progénitrices. La purification de ces cellules et leur transformation en cellules leucémiques à l’aide de gènes de fusion MLL nous permettent de générer des leucémies aigues humaines dans des souris immunodéficientes NSG et ainsi étudier le potentiel leucémique de différents gènes de fusion MLL. Dans un premier temps, 4 gènes de fusion MLL ont été étudiés : MLL-AF9, MLL-AF4, MLL-ENL et MLL-ELL. In vitro, nous sommes capables de transformer des CSH en cellules leucémiques capables de proliférer rapidement. Les résultats in vivo nous montrent qu’il est possible de générer des leucémies avec les oncogènes MLL-AF9 et MLL-ENL. Pour les fusions MLL-ELL et MLL-AF4, bien que quelques leucémies ont pu être obtenues, plusieurs problèmes techniques nous empêchent aujourd’hui de disposer d’un modèle adéquat permettant l’étude complète de ces oncogènes. Dans un second temps, les leucémies aigues MLL-AF9 ont été étudiées dans un modèle contrôlé où les cellules souches proviennent d’un donneur unique. Grâce à ce modèle, nous avons pu démontrer que l’oncogène MLL-AF9 est suffisant pour induire le développement de la maladie. En effet aucune nouvelle mutation n’a pu être identifiée au cours du développement de la leucémie. Parmi les leucémies myéloïdes aigues (LMA) MLL-AF9 issues de ce modèle, certains gènes non mutés, dont RET, ont été identifiés comme étant de potentiels biomarqueurs de ce sous-groupe de leucémie. / Acute leukemias result from a clonal and malignant proliferation of hematopoietic cells. They arise following an oncogenic event which occurs in a hematopoietic stem cell (HSC) or progenitor cell. This generates instability, causing the accumulation of other genetic and/or epigenetic events leading to the clinical development of the disease. MLL leukemias represent approximately 10 % of acute leukemias, and nowadays more than 70 fusion genes have been characterized. Cord blood is an important source of both HSCs and progenitor cells. Purification of these cells and subsequent transformation into leukemic cells allows us to induce human acute leukemia via MLL fusion genes into NSG immunodeficient mice and thus to study the leukemic potential of different MLL fusion genes. Firstly, four MLL fusion genes were studied: MLL-AF9, MLL-AF4, MLL-ENL and MLL-ELL. In vitro, we are able to transform HSC into leukemic cells which display rapid growth. The in vivo results showed that it is possible to induce leukemia by means of MLL-AF9 and MLL-ENL oncogenes. For the MLL- AF4 and MLL-ELL fusions, although some leukemias have been obtained, several technical difficulties prevented us from having an adequate model for the study of these oncogenes. Secondly, MLL-AF9 acute leukemias were studied in a model where stem cells originate from a single donor. Based on this model, we have determined that the single MLL-AF9 oncogene is sufficient to initiate disease. Indeed, no new mutations were identified during leukemia development. Among the different MLL-AF9 acute myeloid leukemias (AML) generated from this model, a certain number of non-mutated genes, notably the RET, have been identified as potential biomarkers for this specific subgroup of leukemia.

La fusion membranaire est une étape essentielle du cycle infectieux du virus Influenza dont la compréhension est actuellement incomplète. La fusion nécessite la protéine de surface virale hémagglutinine et, en particulier, ses vingt acides aminés N-terminaux formant le peptide de fusion. Ce peptide a été démontré capable d’initier la fusion membranaire même lorsque séparé du reste de la protéine, mais le mécanisme moléculaire par lequel il y parvient reste méconnu. Afin de mieux comprendre ce mécanisme, nous avons effectué des simulations de dynamique moléculaire du peptide de fusion, du mutant fusogène F9A et du mutant non fusogène W14A, dans des membranes modèles. Dans un premier temps, nous avons étudié la structure et la dynamique du peptide de fusion. Le peptide de fusion a adopté des conformations en hélice-a complète et en coude, et s’est positionné à l’interface membranaire presque parallèlement à la surface de la membrane. Les peptides mutants ont en plus adopté une structure en épingle. La dynamique des peptides a donc été associée à celle d’un V flexible, changeant de conformation par des mouvements de charnière. Dans un second temps, les perturbations membranaires induites par les peptides ont été étudiées par simulations. Ces perturbations incluent la protrusion des chaînes lipidiques et l’intrusion des têtes polaires. Ces deux perturbations ont été causées par des ponts hydrogène entre les phosphates lipidiques et les amides N-terminales des peptides s’insérant sous la surface de la membrane. La quantité d’intrusion des têtes polaires induite par les mutants en simulation était corrélée à leur activité fusogène expérimentale et à la profondeur d’insertion de leur extrémité N-terminale. Suivant ces résultats, nous proposons que l’intrusion des têtes polaires complémente la protrusion des chaînes lipidiques lors de la fusion membranaire en réduisant les forces répulsives entre les têtes polaires des membranes juxtaposées. Ce mécanisme modèle de fusion membranaire pourra avoir un impact sur les futures recherches d’antiviraux contre Influenza. / Membrane fusion is an essential step of the Influenza virus infectious cycle whose understanding remains incomplete. Fusion requires surface viral protein hemagglutinin and, in particular, its twenty N-terminal amino acids composing the fusion peptide. This peptide was shown to initiate fusion even when isolated from the rest of the protein, but the molecular mechanism by which it achieves membrane fusion is still misunderstood. To better understand this mechanism, we performed molecular dynamics simulations of the fusion peptide, fusogenic F9A mutant and nonfusogenic W14A mutant, in model membranes. First, we studied the structure and dynamics of the fusion peptide. The fusion peptide adopted straight a-helical and kinked conformations, and inserted at the membrane interface with an almost parallel orientation with the membrane surface. Mutant peptides additionaly adopted a hairpin structure. The dynamics of the peptides was hence compared to that of a flexible V, switching conformation by hinge movements. In a second step, fusion peptide-induced membrane perturbations were studied from simulations. Those perturbations include lipid tail protrusion and polar head intrusion. The two perturbations were caused by hydrogen bonding between lipid phosphates and membrane inserted peptide N-terminal amides. The amount of polar head intrusion induced by the mutant peptides in simulations was correlated to their experimental fusogenic activity and the insertion depth of their N-termini. Following those results, we propose that polar head intrusion would complement lipid tail protrusion during membrane fusion by reducing the repulsive forces between juxtaposed membranes polar heads. This model of membrane fusion mechanism may have an impact on future Influenza antiviral research.

Univ., Diss.--Hohenheim, 2004.

Thesis (M.Sc. (Hons.)) -- University of Western Sydney, 2002. / "A thesis submitted as part of the requirements for the degree of Master of Science (Honours)" Bibliography : leaves 175-181.