Dispersion analysis of two-dimensional unstructured transmission line modelling (UTLM)

Numerical simulation techniques play an important role due to their flexibility in dealing with a broad range of complex geometries and material responses. This flexibility requires substantial computational time and memory. Most numerical methods use structured grid for graphical discretization, although this approach is straightforward it is not ideal for smoothly curved boundaries. In this thesis the two-dimensional Transmission Line Modelling (TLM) method based on unstructured meshes is adopted. TLM is an established numerical simulation technique that has been employed in a variety of applications area. Using unstructured meshes to discretize the problem domain permits smooth boundary presentation which provides significant enhancement in the flexibility and accuracy of the TLM simulations. An algorithm is developed to implement Unstructured Transmission Line Modelling (UTLM) which is carefully designed for simplicity and scalability of model size. Several examples are employed to test the accuracy and efficiency of the UTLM simulations. Delaunay meshes, as a type of unstructured meshes, provide good quality triangles but have the disadvantage of providing close to zero transmission line length which has impact on the maximum permissible time step for stable operation. In this thesis, a simple perturbation method for relaxing the minimum link length and clustering triangles in pairs is presented, which permits substantial increase in time step and hence computational runtime to be made without compromising the simulation stability or accuracy. Also, a new model for relaxing the short link lines that fall on the boundaries is presented. UTLM method is based on temporal and spatial sampling of electromagnetic fields which results in dispersion. In this thesis, dispersion characteristics of the unstructured TLM mesh are investigated and compared against structured TLM results for different mesh sizes and shapes. Unlike the structured TLM mesh, the unstructured mesh gives rise to spatial mode coupling. Intermodal coupling behaviour is investigated in a statistical manner upon the change of the mesh local characteristics

Dispersion analysis of two-dimensional unstructured transmission line modelling (UTLM)

Numerical simulation techniques play an important role due to their flexibility in dealing with a broad range of complex geometries and material responses. This flexibility requires substantial computational time and memory. Most numerical methods use structured grid for graphical discretization, although this approach is straightforward it is not ideal for smoothly curved boundaries. In this thesis the two-dimensional Transmission Line Modelling (TLM) method based on unstructured meshes is adopted. TLM is an established numerical simulation technique that has been employed in a variety of applications area. Using unstructured meshes to discretize the problem domain permits smooth boundary presentation which provides significant enhancement in the flexibility and accuracy of the TLM simulations. An algorithm is developed to implement Unstructured Transmission Line Modelling (UTLM) which is carefully designed for simplicity and scalability of model size. Several examples are employed to test the accuracy and efficiency of the UTLM simulations. Delaunay meshes, as a type of unstructured meshes, provide good quality triangles but have the disadvantage of providing close to zero transmission line length which has impact on the maximum permissible time step for stable operation. In this thesis, a simple perturbation method for relaxing the minimum link length and clustering triangles in pairs is presented, which permits substantial increase in time step and hence computational runtime to be made without compromising the simulation stability or accuracy. Also, a new model for relaxing the short link lines that fall on the boundaries is presented. UTLM method is based on temporal and spatial sampling of electromagnetic fields which results in dispersion. In this thesis, dispersion characteristics of the unstructured TLM mesh are investigated and compared against structured TLM results for different mesh sizes and shapes. Unlike the structured TLM mesh, the unstructured mesh gives rise to spatial mode coupling. Intermodal coupling behaviour is investigated in a statistical manner upon the change of the mesh local characteristics

Location inaccuracies in WSAN placement algorithms

The random deployment of Wireless Sensor and Actuator Network (WSAN) nodes in areas often inaccessible, results in so-called coverage holes – i.e. areas in the network that are not adequately covered by nodes to suit the requirements of the network. Various coverage protocol algorithms have been designed to reduce or eliminate coverage holes within WSANs by indicating how to move the nodes. The effectiveness of such coverage protocols could be jeopardised by inaccuracy in the initial node location data that is broadcast by the respective nodes. This study examines the effects of location inaccuracies on five sensor deployment and reconfiguration algorithms – They include two algorithms which assume that mobile nodes are deployed (referred to as the VEC and VOR algorithms); two that assume static nodes are deployed (referred to as the CNPSS and OGDC algorithms); and a single algorithm (based on a bidding protocol) that assumes a hybrid scenario in which both static and mobile nodes are deployed. Two variations of this latter algorithm are studied. A location simulation tool was built using the GE Smallworld GIS application and the Magik programming language. The simulation results are based on three above-mentioned deployment scenarios; mobile, hybrid and static. The simulation results suggest the VOR algorithm is reasonably robust if the location inaccuracies are somewhat lower than the sensing distance and also if a high degree of inaccuracy is limited to a relatively small percentage of the nodes. The VEC algorithm is considerably less robust, but prevents nodes from drifting beyond the boundaries in the case of large inaccuracies. The bidding protocol used by the hybrid algorithm appears to be robust only when the static nodes are accurate and there is a low degree of inaccuracy within the mobile nodes. Finally the static algorithms are shown to be the most robust; the CPNSS algorithm appears to be immune to location inaccuracies whilst the OGDC algorithm was shown to reduce the number of active nodes in the network to a better extent than that of the CPNSS algorithm. CopyrightDissertation (MSc)--University of Pretoria, 2010.Computer Scienceunrestricte

Reduced-Complexity Algorithms for Indoor Map-Aware Localization Systems

The knowledge of environmental maps (i.e., map-awareness) can appreciably improve the accuracy of optimal methods for position estimation in indoor scenarios. This improvement, however, is achieved at the price of a significant complexity increase with respect to the case of map-unawareness, specially for large maps. This is mainly due to the fact that optimal map-aware estimation algorithms require integrating highly nonlinear functions or solving nonlinear and nonconvex constrained optimization problems. In this paper, various techniques for reducing the complexity of such estimators are developed. In particular, two novel strategies for restricting the search domain of map-aware position estimators are developed and the exploitation of state-of-the-art numerical integration and optimization methods is investigated; this leads to the development of a new family of suboptimal map-aware localization algorithms. Our numerical and experimental results evidence that the accuracy of these algorithms is very close to that offered by their optimal counterparts, despite their significantly lower computational complexity

A practical search with Voronoi distributed autonomous marine swarms

Submitted in partial fulfillment of the requirements for the degree of Master of Science in Mechanical Engineering at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2022.The search for underwater threats in littoral regions is a problem that has been researched for nearly a century. However, recent developments in autonomy and robotics have made this issue more complex. The advent of capable autonomous underwater vehicles presents a 21st century flare to this traditional problem. These vehicles can be smaller, quieter, and expendable. Therefore, new methods and tactics used to detect and track these vehicles are needed. The use of a swarm of marine robots can increase the likelihood of uncovering these threats. This thesis provides various Voronoi partition-based methods to autonomously control a swarm of identically capable autonomous surface vessels in a limited coverage and tracking problem. These methods increase the probability of interdiction of an adversary vehicle crossing a defined region. The results achieved from Monte Carlo simulations demonstrate how different protocols of swarm movement can improve detection probability as compared to a stationary swarm provided the detection capability does not change. The swarm control algorithms are employed on Clearpath Heron USVs to validate the autonomy algorithms

Pedestrian Mobility Mining with Movement Patterns

In street-based mobility mining, pedestrian volume estimation receives increasing attention, as it provides important applications such as billboard evaluation, attraction ranking and emergency support systems. In practice, empirical measurements are sparse due to budget limitations and constrained mounting options. Therefore, estimation of pedestrian quantity is required to perform pedestrian mobility analysis at unobserved locations. Accurate pedestrian mobility analysis is difficult to achieve due to the non-random path selection of individual pedestrians (resulting from motivated movement behaviour), causing the pedestrian volumes to distribute non-uniformly among the traffic network. Existing approaches (pedestrian simulations and data mining methods) are hard to adjust to sensor measurements or require more expensive input data (e.g. high fidelity floor plans or total number of pedestrians in the site) and are thus unfeasible. In order to achieve a mobility model that encodes pedestrian volumes accurately, we propose two methods under the regression framework which overcome the limitations of existing methods. Namely, these two methods incorporate not just topological information and episodic sensor readings, but also prior knowledge on movement preferences and movement patterns. The first one is based on Least Squares Regression (LSR). The advantage of this method is the easy inclusion of route choice heuristics and robustness towards contradicting measurements. The second method is Gaussian Process Regression (GPR). The advantages of this method are the possibilities to include expert knowledge on pedestrian movement and to estimate the uncertainty in predicting the unknown frequencies. Furthermore the kernel matrix of the pedestrian frequencies returned by the method supports sensor placement decisions. Major benefits of the regression approach are (1) seamless integration of expert data and (2) simple reproduction of sensor measurements. Further advantages are (3) invariance of the results against traffic network homeomorphism and (4) the computational complexity depends not on the number of modeled pedestrians but on the traffic network complexity. We compare our novel approaches to state-of-the-art pedestrian simulation (Generalized Centrifugal Force Model) as well as existing Data Mining methods for traffic volume estimation (Spatial k-Nearest Neighbour) and commonly used graph kernels for the Gaussian Process Regression (Squared Exponential, Regularized Laplacian and Diffusion Kernel) in terms of prediction performance (measured with mean absolute error). Our methods showed significantly lower error rates. Since pattern knowledge is not easy to obtain, we present algorithms for pattern acquisition and analysis from Episodic Movement Data. The proposed analysis of Episodic Movement Data involve spatio-temporal aggregation of visits and flows, cluster analyses and dependency models. For pedestrian mobility data collection we further developed and successfully applied the recently evolved Bluetooth tracking technology. The introduced methods are combined to a system for pedestrian mobility analysis which comprises three layers. The Sensor Layer (1) monitors geo-coded sensor recordings on people’s presence and hands this episodic movement data in as input to the next layer. By use of standardized Open Geographic Consortium (OGC) compliant interfaces for data collection, we support seamless integration of various sensor technologies depending on the application requirements. The Query Layer (2) interacts with the user, who could ask for analyses within a given region and a certain time interval. Results are returned to the user in OGC conform Geography Markup Language (GML) format. The user query triggers the (3) Analysis Layer which utilizes the mobility model for pedestrian volume estimation. The proposed approach is promising for location performance evaluation and attractor identification. Thus, it was successfully applied to numerous industrial applications: Zurich central train station, the zoo of Duisburg (Germany) and a football stadium (Stade des Costières Nîmes, France)

Advances in Trans-dimensional Geophysical Inference

This research presents a series of novel Bayesian trans-dimensional methods for geophysical inversion. A first example illustrates how Bayesian prior information obtained from theory and numerical experiments can be used to better inform a difficult multi-modal inversion of dispersion information from empirical Greens functions obtained from ambient noise cross-correlation. This approach is an extension of existing partition modeling schemes. An entirely new class of trans-dimensional algorithm, called the trans-dimensional tree method is introduced. This new method is shown to be more efficient at coupling to a forward model, more efficient at convergence, and more adaptable to different dimensions and geometries than existing approaches. The efficiency and flexibility of the trans-dimensional tree method is demonstrated in two different examples: (1) airborne electromagnetic tomography (AEM) in a 2D transect inversion, and (2) a fully non-linear inversion of ambient noise tomography. In this latter example the resolution at depth has been significantly improved by inverting a contiguous band of frequencies jointly rather than as independent phase velocity maps, allowing new insights into crustal architecture beneath Iceland. In a first test case for even larger scale problems, an application of the trans-dimensional tree approach to large global data set is presented. A global database of nearly 5 million multi-model path average Rayleigh wave phase velocity observations has been used to construct global phase velocity maps. Results are comparable to existing published phase velocity maps, however, as the trans-dimensional approach adapts the resolution appropriate to the data, rather than imposing damping or smoothing constraints to stabilize the inversion, the recovered anomaly magnitudes are generally higher with low uncertainties. While further investigation is needed, this early test case shows that trans-dimensional sampling can be applied to global scale seismology problems and that previous analyses may, in some locales, under estimate the heterogeneity of the Earth. Finally, in a further advancement of partition modelling with variable order polynomials, a new method has been developed called trans-dimensional spectral elements. Previous applications involving variable order polynomials have used polynomials that are both difficult to work with in a Bayesian framework and unstable at higher orders. By using the orthogonal polynomials typically used in modern full-waveform solvers, the useful properties of this type of polynomial and its application in trans-dimensional inversion are demonstrated. Additionally, these polynomials can be directly used in complex differential solvers and an example of this for 1D inversion of surface wave dispersion curves is given