Viability of Numerical Full-Wave Techniques in Telecommunication Channel Modelling

In telecommunication channel modelling the wavelength is small compared to the physical features of interest, therefore deterministic ray tracing techniques provide solutions that are more efficient, faster and still within time constraints than current numerical full-wave techniques. Solving fundamental Maxwell's equations is at the core of computational electrodynamics and best suited for modelling electrical field interactions with physical objects where characteristic dimensions of a computing domain is on the order of a few wavelengths in size. However, extreme communication speeds, wireless access points closer to the user and smaller pico and femto cells will require increased accuracy in predicting and planning wireless signals, testing the accuracy limits of the ray tracing methods. The increased computing capabilities and the demand for better characterization of communication channels that span smaller geographical areas make numerical full-wave techniques attractive alternative even for larger problems. The paper surveys ways of overcoming excessive time requirements of numerical full-wave techniques while providing acceptable channel modelling accuracy for the smallest radio cells and possibly wider. We identify several research paths that could lead to improved channel modelling, including numerical algorithm adaptations for large-scale problems, alternative finite-difference approaches, such as meshless methods, and dedicated parallel hardware, possibly as a realization of a dataflow machine

Algorithms for the visualization and simulation of mobile ad hoc and cognitive networks

Visualization and simulation are important aspects of most advanced engineering endeavors. They may provide important insights into the functionality and perfor- mance of a system during the design and evaluation stage of the system's development. This thesis presents a number of algorithms and simulation algorithms that may be used for the design and evaluation of two types of engineered systems, mobile ad hoc and cognitive networks. The ¯rst set of algorithms provides signal radiation pattern and digital terrain visualization capabilities to OMAN, a mobile ad hoc network sim- ulator developed at Drexel University. The second set of algorithms provides a more general visualization capability for displaying complex graphs. These algorithms fo- cus on simplifying a complex graph in order to allow a user to explore its underlying basic structure. The thesis closes with a description of a GPU-based implementation of a set of spectrum-sensing algorithms. Spectrum sensing is an important function- ality needed for cognitive networks. The computational speed-ups provided by the GPU implementation o®er the possibility of real-time spectrum-sensing for adaptive, cognitive networks.M.S., Computer Science -- Drexel University, 200

Antennas and Propagation for UAV-Assisted Wireless Networks Towards Next Generation Mobile Systems

Unmanned Aerial Vehicles (UAV), also known as "drones", are attracting increasing attention as enablers for many technical applications and services, and this trend is likely to continue in the near future. UAVs are expected to be used extensively in civil and military applications where aerial surveillance and assistance in emergency situations are key factors. UAVs can be more useful and flexible in reaction to specific events, like natural disasters and terrorist attacks since they are faster to deploy, easier to reconfigure and assumed to have better communication means due to their improved position in the sky, improved visibility over ground, and reduced hindrance for propagation. In this regard, UAV enabled communications emerge as one of the most promising solutions for setting-up the next-generation mobile networks, with a special focus on the extension of coverage and capacity of mobile radio networks for 5G applications and beyond. However, air-to-ground (A2G) propagation conditions are likely to be different and more challenging than those experienced by traditional piloted aircraft. For this reason, knowledge of this specific propagation channel – together with the UAV antenna design and placement - is paramount for defining an efficient communication system and for evaluating its performance. This PhD thesis tackles this challenge, and it aims at further investigating the narrowband properties of the air-to-ground propagation channel by means of GPU accelerated ray launching simulations for 5G communications and beyond. As a conclusion, this PhD thesis might bring deep insights into the air-to-ground channel characteristics and UAV antenna design, which can be helpful for designing UAV communication networks and evaluating or optimising their performances in a fast and reliable manner, with no need for exhausting – multiple - in-field measurement campaigns

Parametric Analysis of Isolated Doubled Edged Hill Diffraction Loss Based on Rounded Edge Diffraction Loss Method and Different Radius of Curvature Methods

In this paper, parametric analysis of isolated doubled edged hill diffraction loss based on rounded edge diffraction loss method is presented. Particularly, the variation of the diffraction loss due to changes in frequency and radius of curvature of the rounded edge of isolated doubled edged hill obstruction are studied. Also, the ITU-R P.526-13 rounded edge diffraction method is used to compute the diffraction loss. However, the radius of curvature is computed using two approaches, namely, the ITU-R P.526-13 method and the occultation distance based method. The results show that the rounded edge diffraction computed based on the  ITU-R P.526-13  radius of curvature method is much higher than the one computed with the occultation distance based radius of curvature approach. At frequency of 1 GHz, the percentage difference in diffraction loss  is about 29 % and the difference increases with frequency to as high as 74.5 % at 36 GHz. Similarly, the ITU-R P.526-13 radius of curvature is extremely higher than the occultation distance based radius of curvature. At frequency of 1 GHz, the percentage difference in radius of curvature  is about 218 % and the difference increases with frequency to as high as 395 % at 36 GHz. In view of the results, the ITU-R P.526-13 radius of curvature method should be reviewed to ascertain the specific conditions it can be employed

Lightning Modeling and Its Effects on Electric Infrastructures

When it comes to dealing with high voltages or issues of high electric currents, infrastructure security and people’s safety are of paramount importance. These kinds of phenomena have dangerous consequences, therefore studies concerning the effects of lightning are crucial. The normal operation of transmission and distribution systems is greatly affected by lightning, which is one of the major causes of power interruptions: direct or nearby indirect strikes can cause flashovers in overhead transmission and distribution lines, resulting in over voltages on the line conductors. Contributions to this Special Issue have mainly focused on modelling lightning activity, investigating physical causes, and discussing and testing mathematical models for the electromagnetic fields associated with lighting phenomena. In this framework, two main topics have emerged: 1) the interaction between lightning phenomena and electrical infrastructures, such as wind turbines and overhead lines; and 2) the computation of lightning electromagnetic fields in the case of particular configuration, considering a negatively charged artificial thunderstorm or considering a complex terrain with arbitrary topograph

Novel parallel approaches to efficiently solve spatial problems on heterogeneous CPU-GPU systems

Addressing this task is difficult as (i) it requires analysing large databases in a short time, and (ii) it is commonly addressed by combining different methods with complex data dependencies, making it challenging to exploit parallelism on heterogeneous CPU-GPU systems. Moreover, most efforts in this context focus on improving the accuracy of the approaches and neglect reducing the processing time—the most accurate algorithm was designed to process the fingerprints using a single thread. We developed a new methodology to address the latent fingerprint identification problem called “Asynchronous processing for Latent Fingerprint Identification” (ALFI) that speeds up processing while maintaining high accuracy. ALFI exploits all the resources of CPU-GPU systems using asynchronous processing and fine-coarse parallelism to analyse massive fingerprint databases. We assessed the performance of ALFI on Linux and Windows operating systems using the well-known NIST/FVC databases. Experimental results revealed that ALFI is on average 22x faster than the state-of-the-art identification algorithm, reaching a speed-up of 44.7x for the best-studied case. In terrain analysis, Digital Elevation Models (DEMs) are relevant datasets used as input to those algorithms that typically sweep the terrain to analyse its main topological features such as visibility, elevation, and slope. The most challenging computation related to this topic is the total viewshed problem. It involves computing the viewshed—the visible area of the terrain—for each of the points in the DEM. The algorithms intended to solve this problem require many memory accesses to 2D arrays, which, despite being regular, lead to poor data locality in memory. We proposed a methodology called “skewed Digital Elevation Model” (sDEM) that substantially improves the locality of memory accesses and exploits the inherent parallelism of rotational sweep-based algorithms. Particularly, sDEM applies a data relocation technique before accessing the memory and computing the viewshed, thus significantly reducing the execution time. Different implementations are provided for single-core, multi-core, single-GPU, and multi-GPU platforms. We carried out two experiments to compare sDEM with (i) the most used geographic information systems (GIS) software and (ii) the state-of-the-art algorithm for solving the total viewshed problem. In the first experiment, sDEM results on average 8.8x faster than current GIS software, despite considering only a few points because of the limitations of the GIS software. In the second experiment, sDEM is 827.3x faster than the state-of-the-art algorithm considering the best case. The use of Unmanned Aerial Vehicles (UAVs) with multiple onboard sensors has grown enormously in tasks involving terrain coverage, such as environmental and civil monitoring, disaster management, and forest fire fighting. Many of these tasks require a quick and early response, which makes maximising the land covered from the flight path an essential goal, especially when the area to be monitored is irregular, large, and includes many blind spots. In this regard, state-of-the-art total viewshed algorithms can help analyse large areas and find new paths providing all-round visibility. We designed a new heuristic called “Visibility-based Path Planning” (VPP) to solve the path planning problem in large areas based on a thorough visibility analysis. VPP generates flyable paths that provide high visual coverage to monitor forest regions using the onboard camera of a single UAV. For this purpose, the hidden areas of the target territory are identified and considered when generating the path. Simulation results showed that VPP covers up to 98.7% of the Montes de Malaga Natural Park and 94.5% of the Sierra de las Nieves National Park, both located in the province of Malaga (Spain). In addition, a real flight test confirmed the high visibility achieved using VPP. Our methodology and analysis can be easily applied to enhance monitoring in other large outdoor areas.In recent years, approaches that seek to extract valuable information from large datasets have become particularly relevant in today's society. In this category, we can highlight those problems that comprise data analysis distributed across two-dimensional scenarios called spatial problems. These usually involve processing (i) a series of features distributed across a given plane or (ii) a matrix of values where each cell corresponds to a point on the plane. Therefore, we can see the open-ended and complex nature of spatial problems, but it also leaves room for imagination to be applied in the search for new solutions. One of the main complications we encounter when dealing with spatial problems is that they are very computationally intensive, typically taking a long time to produce the desired result. This drawback is also an opportunity to use heterogeneous systems to address spatial problems more efficiently. Heterogeneous systems give the developer greater freedom to speed up suitable algorithms by increasing the parallel programming options available, making it possible for different parts of a program to run on the dedicated hardware that suits them best. Several of the spatial problems that have not been optimised for heterogeneous systems cover very diverse areas that seem vastly different at first sight. However, they are closely related due to common data processing requirements, making them suitable for using dedicated hardware. In particular, this thesis provides new parallel approaches to tackle the following three crucial spatial problems: latent fingerprint identification, total viewshed computation, and path planning based on maximising visibility in large regions. Latent fingerprint identification is one of the essential identification procedures in criminal investigations. Addressing this task is difficult as (i) it requires analysing large databases in a short time, and (ii) it is commonly addressed by combining different methods with complex data dependencies, making it challenging to exploit parallelism on heterogeneous CPU-GPU systems. Moreover, most efforts in this context focus on improving the accuracy of the approaches and neglect reducing the processing time—the most accurate algorithm was designed to process the fingerprints using a single thread. We developed a new methodology to address the latent fingerprint identification problem called “Asynchronous processing for Latent Fingerprint Identification” (ALFI) that speeds up processing while maintaining high accuracy. ALFI exploits all the resources of CPU-GPU systems using asynchronous processing and fine-coarse parallelism to analyse massive fingerprint databases. We assessed the performance of ALFI on Linux and Windows operating systems using the well-known NIST/FVC databases. Experimental results revealed that ALFI is on average 22x faster than the state-of-the-art identification algorithm, reaching a speed-up of 44.7x for the best-studied case. In terrain analysis, Digital Elevation Models (DEMs) are relevant datasets used as input to those algorithms that typically sweep the terrain to analyse its main topological features such as visibility, elevation, and slope. The most challenging computation related to this topic is the total viewshed problem. It involves computing the viewshed—the visible area of the terrain—for each of the points in the DEM. The algorithms intended to solve this problem require many memory accesses to 2D arrays, which, despite being regular, lead to poor data locality in memory. We proposed a methodology called “skewed Digital Elevation Model” (sDEM) that substantially improves the locality of memory accesses and exploits the inherent parallelism of rotational sweep-based algorithms. Particularly, sDEM applies a data relocation technique before accessing the memory and computing the viewshed, thus significantly reducing the execution time. Different implementations are provided for single-core, multi-core, single-GPU, and multi-GPU platforms. We carried out two experiments to compare sDEM with (i) the most used geographic information systems (GIS) software and (ii) the state-of-the-art algorithm for solving the total viewshed problem. In the first experiment, sDEM results on average 8.8x faster than current GIS software, despite considering only a few points because of the limitations of the GIS software. In the second experiment, sDEM is 827.3x faster than the state-of-the-art algorithm considering the best case. The use of Unmanned Aerial Vehicles (UAVs) with multiple onboard sensors has grown enormously in tasks involving terrain coverage, such as environmental and civil monitoring, disaster management, and forest fire fighting. Many of these tasks require a quick and early response, which makes maximising the land covered from the flight path an essential goal, especially when the area to be monitored is irregular, large, and includes many blind spots. In this regard, state-of-the-art total viewshed algorithms can help analyse large areas and find new paths providing all-round visibility. We designed a new heuristic called “Visibility-based Path Planning” (VPP) to solve the path planning problem in large areas based on a thorough visibility analysis. VPP generates flyable paths that provide high visual coverage to monitor forest regions using the onboard camera of a single UAV. For this purpose, the hidden areas of the target territory are identified and considered when generating the path. Simulation results showed that VPP covers up to 98.7% of the Montes de Malaga Natural Park and 94.5% of the Sierra de las Nieves National Park, both located in the province of Malaga (Spain). In addition, a real flight test confirmed the high visibility achieved using VPP. Our methodology and analysis can be easily applied to enhance monitoring in other large outdoor areas

Crowd simulation and visualization

Large-scale simulation and visualization are essential topics in areas as different as sociology, physics, urbanism, training, entertainment among others. This kind of systems requires a vast computational power and memory resources commonly available in High Performance Computing HPC platforms. Currently, the most potent clusters have heterogeneous architectures with hundreds of thousands and even millions of cores. The industry trends inferred that exascale clusters would have thousands of millions. The technical challenges for simulation and visualization process in the exascale era are intertwined with difficulties in other areas of research, including storage, communication, programming models and hardware. For this reason, it is necessary prototyping, testing, and deployment a variety of approaches to address the technical challenges identified and evaluate the advantages and disadvantages of each proposed solution. The focus of this research is interactive large-scale crowd simulation and visualization. To exploit to the maximum the capacity of the current HPC infrastructure and be prepared to take advantage of the next generation. The project develops a new approach to scale crowd simulation and visualization on heterogeneous computing cluster using a task-based technique. Its main characteristic is hardware agnostic. It abstracts the difficulties that imply the use of heterogeneous architectures like memory management, scheduling, communications, and synchronization — facilitating development, maintenance, and scalability. With the goal of flexibility and take advantage of computing resources as best as possible, the project explores different configurations to connect the simulation with the visualization engine. This kind of system has an essential use in emergencies. Therefore, urban scenes were implemented as realistic as possible; in this way, users will be ready to face real events. Path planning for large-scale crowds is a challenge to solve, due to the inherent dynamism in the scenes and vast search space. A new path-finding algorithm was developed. It has a hierarchical approach which offers different advantages: it divides the search space reducing the problem complexity, it can obtain a partial path instead of wait for the complete one, which allows a character to start moving and compute the rest asynchronously. It can reprocess only a part if necessary with different levels of abstraction. A case study is presented for a crowd simulation in urban scenarios. Geolocated data are used, they were produced by mobile devices to predict individual and crowd behavior and detect abnormal situations in the presence of specific events. It was also address the challenge of combining all these individual’s location with a 3D rendering of the urban environment. The data processing and simulation approach are computationally expensive and time-critical, it relies thus on a hybrid Cloud-HPC architecture to produce an efficient solution. Within the project, new models of behavior based on data analytics were developed. It was developed the infrastructure to be able to consult various data sources such as social networks, government agencies or transport companies such as Uber. Every time there is more geolocation data available and better computation resources which allow performing analysis of greater depth, this lays the foundations to improve the simulation models of current crowds. The use of simulations and their visualization allows to observe and organize the crowds in real time. The analysis before, during and after daily mass events can reduce the risks and associated logistics costs.La simulación y visualización a gran escala son temas esenciales en áreas tan diferentes como la sociología, la física, el urbanismo, la capacitación, el entretenimiento, entre otros. Este tipo de sistemas requiere una gran capacidad de cómputo y recursos de memoria comúnmente disponibles en las plataformas de computo de alto rendimiento. Actualmente, los equipos más potentes tienen arquitecturas heterogéneas con cientos de miles e incluso millones de núcleos. Las tendencias de la industria infieren que los equipos en la era exascale tendran miles de millones. Los desafíos técnicos en el proceso de simulación y visualización en la era exascale se entrelazan con dificultades en otras áreas de investigación, incluidos almacenamiento, comunicación, modelos de programación y hardware. Por esta razón, es necesario crear prototipos, probar y desplegar una variedad de enfoques para abordar los desafíos técnicos identificados y evaluar las ventajas y desventajas de cada solución propuesta. El foco de esta investigación es la visualización y simulación interactiva de multitudes a gran escala. Aprovechar al máximo la capacidad de la infraestructura actual y estar preparado para aprovechar la próxima generación. El proyecto desarrolla un nuevo enfoque para escalar la simulación y visualización de multitudes en un clúster de computo heterogéneo utilizando una técnica basada en tareas. Su principal característica es que es hardware agnóstico. Abstrae las dificultades que implican el uso de arquitecturas heterogéneas como la administración de memoria, las comunicaciones y la sincronización, lo que facilita el desarrollo, el mantenimiento y la escalabilidad. Con el objetivo de flexibilizar y aprovechar los recursos informáticos lo mejor posible, el proyecto explora diferentes configuraciones para conectar la simulación con el motor de visualización. Este tipo de sistemas tienen un uso esencial en emergencias. Por lo tanto, se implementaron escenas urbanas lo más realistas posible, de esta manera los usuarios estarán listos para enfrentar eventos reales. La planificación de caminos para multitudes a gran escala es un desafío a resolver, debido al dinamismo inherente en las escenas y el vasto espacio de búsqueda. Se desarrolló un nuevo algoritmo de búsqueda de caminos. Tiene un enfoque jerárquico que ofrece diferentes ventajas: divide el espacio de búsqueda reduciendo la complejidad del problema, puede obtener una ruta parcial en lugar de esperar a la completa, lo que permite que un personaje comience a moverse y calcule el resto de forma asíncrona, puede reprocesar solo una parte si es necesario con diferentes niveles de abstracción. Se presenta un caso de estudio para una simulación de multitud en escenarios urbanos. Se utilizan datos geolocalizados producidos por dispositivos móviles para predecir el comportamiento individual y público y detectar situaciones anormales en presencia de eventos específicos. También se aborda el desafío de combinar la ubicación de todos estos individuos con una representación 3D del entorno urbano. Dentro del proyecto, se desarrollaron nuevos modelos de comportamiento basados ¿¿en el análisis de datos. Se creo la infraestructura para poder consultar varias fuentes de datos como redes sociales, agencias gubernamentales o empresas de transporte como Uber. Cada vez hay más datos de geolocalización disponibles y mejores recursos de cómputo que permiten realizar un análisis de mayor profundidad, esto sienta las bases para mejorar los modelos de simulación de las multitudes actuales. El uso de simulaciones y su visualización permite observar y organizar las multitudes en tiempo real. El análisis antes, durante y después de eventos multitudinarios diarios puede reducir los riesgos y los costos logísticos asociadosPostprint (published version

Real-Time GPS-Alternative Navigation Using Commodity Hardware

Modern navigation systems can use the Global Positioning System (GPS) to accurately determine position with precision in some cases bordering on millimeters. Unfortunately, GPS technology is susceptible to jamming, interception, and unavailability indoors or underground. There are several navigation techniques that can be used to navigate during times of GPS unavailability, but there are very few that result in GPS-level precision. One method of achieving high precision navigation without GPS is to fuse data obtained from multiple sensors. This thesis explores the fusion of imaging and inertial sensors and implements them in a real-time system that mimics human navigation. In addition, programmable graphics processing unit technology is leveraged to perform stream-based image processing using a computer\u27s video card. The resulting system can perform complex mathematical computations in a fraction of the time those same operations would take on a CPU-based platform. The resulting system is an adaptable, portable, inexpensive and self-contained software and hardware platform, which paves the way for advances in autonomous navigation, mobile cartography, and artificial intelligence