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Our research aims at contributing to the evolution of modern wireless communications and to the development of software-defined radio (SDR) and cognitive radio, in particular. It promotes a general resource management framework that facilitates the integration of computing and radio resource management. This dissertation discusses the need for computing resource management in software-defined and cognitive radios and introduces an SDR computing resource management framework with cognitive capabilities. The hard real-time computing requirements of software-defined digital signal processing chains (SDR applications), the associated radio propagation and quality of service (QoS) implications, and heterogeneous multiprocessor platforms with limited computing resources (SDR platforms) define the context of these studies. We examine heterogeneous computing techniques, multiprocessor mapping and scheduling in particular, and elaborate a flexible framework for the dynamic allocation and reallocation of computing resources for wireless communications. The framework should facilitate partial reconfigurations of SDR platforms, dynamic switches between radio access technologies (RATs), and service and QoS level adjustments as a function of the environmental conditions. It, therefore, assumes the facilities of the platform and hardware abstraction layer operating environment (P-HAL-OE). We suggest a modular framework, distinguishing between the computing system modeling and the computing resource management. Our modeling proposal is based on two computing resource management techniques, which facilitate managing the strict timing constraints of real-time systems. It is scalable and can account for many different hardware architectures and computing resource types. This work focuses on processing and interprocessor bandwidth resources and processing and data flow requirements. Our computing resource management approach consists of a general-purpose mapping algorithm and a cost function. The independence between the algorithm and the cost function facilitates implementing many different computing resource management policies. We introduce a dynamic programming based algorithm, the tw-mapping, where w controls the decision window. We present a general and parametric cost function, which guides the mapping process under the given resource constraints. An instance of it facilitates finding a mapping that meets all processing and data flow requirements of SDR applications with the available processing and bandwidth resources of SDR platforms. Several SDR reconfiguration scenarios and analyses based on simulations demonstrate the suitability and potentials of our framework for a flexible computing resource management. We extend our SDR computing resource management concepts to the cognitive radio context. The two primary objectives of cognitive radio are highly reliable communications whenever and wherever needed and the efficient use of the radio spectrum. We formulate a third objective as the efficient use of computing resources. We analyze the cognitive capabilities of our framework─the cognitive radio’s interface to SDR platforms─and indicate the potentials of our cognitive computing resource management proposal. The cognitive computing resource management needs to be coordinated with the radio resource management. We, therefore, introduce the joint resource management concept for cognitive radios. We present three cognitive cycles and discuss several interrelations between the radio, computing, and application resources, where application resources refer to the available SDR and user applications. Our approach potentiates flexibility and facilitates radio against computing resource tradeoffs. It promotes cognition at all layers of the wireless system for a cooperative or integrated resource management that may increase the performance and efficiency of wireless communications. / El objetivo de las investigaciones que se están llevando a cabo dentro del grupo de investigación es contribuir a la evolución de las radiocomunicaciones modernas y, en particular, al desarrollo de los conceptos software radio (SDR) y cognitive radio. El planteamiento general es el de extender la flexibilidad global del sistema de comunicaciones planteando la definición y desarrollo de un entorno en el que pudiesen explorarse las relaciones entre la computación y las prestaciones del sistema de comunicaciones móviles facilitando la integración de los recursos de computación con los recursos radio. Dentro de este marco, la presente tesis plantea la discusión de la necesidad de la gestión de los recursos de computación en entornos SDR y cognitive radio y define un entorno de operación que asume las características especificas del concepto SDR a la vez que incorpora capacidades cognitivas en la gestión de los recursos de computación de las plataformas que den soporte a las nuevas generaciones de sistemas móviles. Los estrictos requerimientos de procesado en tiempo real de las cadenas de procesado digital de la señal definidas por software (aplicaciones SDR), las implicaciones asociadas con la propagación radio y el concepto de calidad de servicio (QoS) y plataformas heterogéneas de múltiples procesadores con recursos de computo limitados (plataformas SDR) definen el contexto de estos estudios. Se examinan técnicas de cómputo de propósito general para definir un entorno de operación que fuese capaz de asignar de forma flexible y dinámica los recursos de cómputo necesarios para facilitar las radiocomunicaciones a los niveles de QoS deseados. Ello debería facilitar los cambios dinámicos de una tecnología de acceso radio a otra, permitiendo el ajuste del tipo de servicio o calidad de servicio en función de las preferencias de los usuarios y las condiciones del entorno. Dicho entorno de operación asume las potencialidades del platform and hardware abstraction layer operating environment (P-HAL-OE). La estructura del entorno de operación se define de forma modular y consiste en un modelado genérico y flexible de las plataformas de computación SDR y en una gestión de recursos de computación abierta y capaz de ajustarse a diferentes objetivos y políticas. En el trabajo se exponen dos técnicas de gestión que pretenden asegurar la consecución estricta de los límites temporales típicos de los sistemas en tiempo real. En cuanto al modelado, este es escalable y capaz de capturar un amplio abanico de arquitecturas hardware y recursos de computación. En el presente trabajo nos centramos en los recursos y requerimientos del procesado y transferencia de datos. Se introduce un algoritmo de mapeo genérico e independiente de la función de coste. La independencia entre el algoritmo y la función de coste facilita la implementación de diferentes políticas de gestión de recursos computacionales. El tw-mapping es un algoritmo basado en dynamic programming, donde w controla la ventana de decisión. Se presenta una función de coste genérica y parametrizable que permite guiar el proceso de gestión de los recursos. Una instancia de ella facilita encontrar una solución al proceso de asignación de recursos que cumpla todos los requerimientos de procesado y trasferencia de datos de las aplicaciones SDR con los recursos disponibles de las plataformas SDR. Diferentes escenarios y varios análisis basados en simulaciones demuestran la adecuación del entorno de trabajo definido y desarrollado, así como sus potencialidades para una gestión flexible de los recursos de cómputo. Se extienden los conceptos mencionados previamente para entornos cognitive radio. Los principales objetivos del concepto cognitive radio son la disponibilidad de comunicaciones altamente robustas en cualquier lugar y momento en que sean necesarias y el uso eficiente del espectro. Como tercer objetivo formulamos el uso eficiente de los recursos de cómputo. Analizamos las capacidades cognitivas de nuestro entorno de operación─la interfaz del sistema cognitive radio a las plataformas SDR─y resaltamos las potencialidades de nuestra propuesta de gestión cognitiva de los recursos computacionales. Dicha gestión cognitiva de los recursos computacionales plantea una integración con la gestión de los recursos radio. Para ello introducimos el concepto de gestión de recursos conjunta para entornos cognitive radio. Se presentan tres ciclos cognitivos y se discuten algunas interrelaciones entre los recursos radio, de cómputo y de aplicación, donde los recursos de aplicación se refieren a las aplicaciones SDR y de usuario disponibles. Nuestra propuesta de gestión de recursos conjunta potencia la flexibilidad y facilita los intercambios entre recursos radio y de computación

This study develops a discrete search-based optimization method for path planning in a highly nonlinear dynamical system. The method enables real-time trajectory improvement and singular configuration avoidance in satellite rotation using Control Moment Gyroscopes. By streamlining a legacy optimization method and combining it with a local singularity management scheme, this optimization method reduces the computational burden and advances the capability of satellites to make autonomous look-ahead decisions in real-time. Current optimization methods plan offline before uploading to the satellite and experience high sensitivity to disturbances. Local methods confer autonomy to the satellite but use only blind decision-making to avoid singularities. This thesis' method seeks near-optimal trajectories which balance between the optimal trajectories found using computationally intensive offline solvers and the minimal computational burden of non-optimal local solvers. The new method enables autonomous guidance capability for satellites using discretization and stage division to minimize the computational burden of real-time optimization.

In this thesis, we explore the topic of designing an architecture and processing algorithms for a multi-agent system, where agents need to address potential unexpected events in the environment, operating under soft real-time constraints. We first develop a classification of unexpected events into Opportunities, Barriers and Potential Causes of Failure, and outline the interaction required to support the allocation of tasks for these events. We then propose a hybrid architecture to provide for agent autonomy in the system, employing a central coordinating agent. Certain agents in the community operate autonomously, while others remain under the control of the coordinating agent. The coordinator is able to determine which agents should form teams to address unexpected events in a timely manner, and to oversee those agents as they perform their tasks. The proposed architecture avoids the overhead of negotiation amongst agent teams for the assignment of tasks, a benefit when operating under limited time and resource constraints. It also avoids the bottleneck of having one coordinating agent making all decisions before work can proceed in the community, by allowing some agents to work independently. We illustrate the potential usefulness of the framework by describing an implementation of a simulator loosely based on that used for the RoboCup Rescue Simulation League contest. The implementation provides a set of simulated computers, each running a simple soft real-time operating system. On top of this basic simulation we implement the model described above and test it against two different search-and-rescue scenarios. From our experiments, we observe that our architecture is able to operate in dynamic and real-time environments, and can handle, in an appropriate and timely manner, any unexpected events that occur. We also comment on the value of our proposed approach for designing adjustable autonomy multi-agent systems and for specific environments such as robotics, where reducing the overall level of communication within the system is crucial.

In order to control any system one needs to know the system's current state. In many real-world scenarios the state of the system cannot be determined with certainty due to the sensors being noisy or simply missing. In cases like these one needs to use probabilistic inference techniques to compute the likely states of the system and because such cases are common, there are lots of techniques to choose from in the field of Artificial Intelligence. Formally, we must compute a probability distribution function over all possible states. Doing this exactly is difficult because the number of states is exponential in the number of variables in the system and because the joint PDF may not have a closed form. Many approximation techniques have been developed over the years, but none ideally suited the problem we faced. Particle filtering is a popular scheme that approximates the joint PDF over the variables in the system by a set of weighted samples. It works even when the joint PDF has no closed form and the size of the sample can be adjusted to trade off accuracy for computation time. However, with many variables the size of the sample required for a good approximation can still become prohibitively large. Factored particle filtering uses the structure of variable dependencies to split the problem into many smaller subproblems and scales better if such decomposition is possible. However, our problem was unusual because some normally independent variables would become strongly correlated for short periods of time. This dynamically-changing dependency structure was not handled effectively by existing techniques. Considering variables to be always correlated meant the problem did not scale, considering them to be always independent introduced errors too large to tolerate. It was necessary to develop an approach that would utilize variables' independence whenever possible, but not introduce large errors when variables become correlated. We have developed a new technique for monitoring the state of the system for a class of systems with context-specific correlations. It is based on the idea of caching the context in which correlations arise and otherwise keeping the variables independent. Our evaluation shows that our technique outperforms existing techniques and is the first viable solution for the class of problems we consider.

The development of next generation devices for high speed switching, high efficiency energy conversion, spintronic devices require the development of advanced material systems. While conventional group IV, group II-VI and group III-V based materials systems have served as a base material in many modern device structures, they posses fundamental materials properties that limit their suitability in next generation device structures. The group III-N material system is very promising for the development of advanced device structures. GaN is currently widely used in high efficiency lighting applications. However, the development of this material system has been limited to material systems with limited indium. The growth of high indium concentration materials such as InN and GaxIn1-xN has proven difficulty due to the high thermal decomposition pressure of InN. In response to this difficulty, a high pressure chemical vapor deposition reactor system has been developed for the growth of InN which enables elevated processing temperatures as compared to conventional low-pressure growth techniques. The design criteria and implementation of this unique design is presented here. In addition, the results of in-situ real time optical characterization capabilities of this reactor system are presented as applied to thermal characterization, flow dynamics, gas phase kinetics and surface reactions. Ex-situ InN thin films grown on sapphire substrates and GaN epilayers have been analyzed by x-ray diffraction, transmission spectroscopy and raman spectroscopy. These results indicated single crystal indium nitride films with an optical absorption edge which varies between 0.7 and 1.9 eV as a function of precursor flow stoichiometry.

In the past 10 years, the bandwidths and processing capabilities of the networks have increased dramatically. The number of real-time applications using these networks has also increased. The large number of real-time packets might, in a switched multi-hop network, lead to unpredictable traffic patterns. This is not a problem when the traffic intensity is low, but if the same network is used by a large number of users simultaneously, the overall performance of the network degrades. In fact, unpredictable delays in the delivery of the message can adversely affect the execution of the tasks dependent on these messages, even if we take into account the soft real-time performance. In this paper, we investigate the effect of traffic shapers on soft real-time performance. We will consider a switched multi-hop network with FCFS queues. We will implement two versions of the network simulator. One version will be without traffic shaper and the other version will use a traffic shaper. By comparing the results (for average delay, deadline miss ratio etc.) from both the versions, we will try to conclude if it is really beneficial to use traffic shapers for soft real-time performance. Leaky bucket and token bucket algorithms are the most popular ones for traffic shaper implementation. We will consider leaky bucket algorithm for our analysis. We analyse different versions of the leaky bucket and present the trade-off’s involved.

The main scope of this thesis is to implement a new architecture of a high bandwidth phase-locked loop (PLL) with a large operating frequency range from 100~MHz to 1~GHz in a 150~$nm$ CMOS process. As PLL is the time-discrete system, the new architecture is mathematically modelled in the z-domain. The charge pump provides a proportionally damped signal, which is unlikely as a resistive or capacitive damping used in the conventional charge pump. The new damping results in a less update jitter and less peaking to achieve the lock frequency and fast locking time of the PLL. The new semi-digital PLL architecture uses $N$ storage cells. The $N$ storage cells is used to store the oscillator tuning information digitally and also enables analogue tuning of the voltage controlled oscillator (VCO). The storage cells outputs are also used for the process voltage temperature compensation. The phase-frequency detector (PFD) and VCO are implemented like a conventional PLL. The bandwidth achieved is 1/4th of the PFD update frequency for all over the operating range from 100~MHz to 1~GHz. The simulation results are also verified with the mathematical modelling. The new architecture also consumes less power and area compared to the conventional PLL.

A proof of concept study into the working of network media capture and visualization through the use of Packet Capture in real-time. An application was developed that is able to capture tcp network packets; identify and display images in raw HTTP network traffic through the use of search, sort, error detection, timeout failsafe algorithms in real time. The application was designed for network administrators to visualize raw network media content together with its relevant network source \& address identifiers. Different approaches were tried and tested such as using Perl with GTK+ and Visual Studio C\# .Net. Furthermore two different types of image identification methods were used: raw magic string identification in pure tcp network traffic and HTTP Mime type identification. The latter being more accurate and faster. C# was seen as vastly superior in both speed of prototyping and final performance evaluation. The study presents a novel new way of monitoring networks on the basis of their media content through deep packet inspection