This research focuses on developing a cognitive radio that could operate reliably in unforeseen communications environments like those faced by the disaster and emergency response communities. Cognitive radios may also offer the potential to open up secondary or complimentary spectrum markets, effectively easing the perceived spectrum crunch while providing new competitive wireless services to the consumer. A structure and process for embedding cognition in a radio is presented, including discussion of how the mechanism was derived from the human learning process and mapped to a mathematical formalism called the BioCR. Results from the implementation and testing of the model in a hardware test bed and simulation test bench are presented, with a focus on rapidly deployable disaster communications. Research contributions include developing a biologically inspired model of cognition in a radio architecture, proposing that genetic algorithm operations could be used to realize this model, developing an algorithmic framework to realize the cognition mechanism, developing a cognitive radio simulation toolset for evaluating the behavior the cognitive engine, and using this toolset to analyze the cognitive engineà ­s performance in different operational scenarios. Specifically, this research proposes and details how the chaotic meta-knowledge search, optimization, and machine learning properties of distributed genetic algorithm operations could be used to map this model to a computable mathematical framework in conjunction with dynamic multi-stage distributed memories. The system formalism is contrasted with existing cognitive radio approaches, including traditionally brittle artificial intelligence approaches. The cognitive engine architecture and algorithmic framework is developed and introduced, including the Wireless Channel Genetic Algorithm (WCGA), Wireless System Genetic Algorithm (WSGA), and Cognitive System Monitor (CSM). Experimental results show that the cognitive engine finds the best tradeoff between a host radio's operational parameters in changing wireless conditions, while the baseline adaptive controller only increases or decreases its data rate based on a threshold, often wasting usable bandwidth or excess power when it is not needed due its inability to learn. Limitations of this approach include some situations where the engine did not respond properly due to sensitivity in algorithm parameters, exhibiting ghosting of answers, bouncing back and forth between solutions. Future research could be pursued to probe the limits of the engineà ­s operation and investigate opportunities for improvement, including how best to configure the genetic algorithms and engine mathematics to avoid engine solution errors. Future research also could include extending the cognitive engine to a cognitive radio network and investigating implications for secure communications. / Ph. D.

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The essence of smart pervasive Cyber-Physical Environments (CPEs) is to enhance the dependability, security and efficiency of their encompassing systems and infrastructures and their services. In CPEs, interactive information resources are integrated and coordinated with physical resources to better serve human users. To bridge the interaction gap between users and the physical environment, a CPE is instrumented with a large number of small devices, called sensors, that are capable of sensing, computing and communicating. Sensors with heterogeneous capabilities should autonomously organize on-demand and interact to furnish real-time, high fidelity information serving a wide variety of user applications with dynamic and evolving requirements. CPEs with their associated networked sensors promise aware services for smart systems and infrastructures with the potential to improve the quality of numerous application domains, in particular mission-critical infrastructure domains. Examples include healthcare, environment protection, transportation, energy, homeland security, and national defense. To build smart CPEs, Networked Sensor Environments (NSEs) are needed to manage demand-driven sharing of large-scale federated heterogeneous resources among multiple applications and users. We informally define NSE as a tailorable, application agnostic, distributed platform with the purpose of managing a massive number of federated resources with heterogeneous computing, communication, and monitoring capabilities. We perceive the need to develop scalable, trustworthy, cost-effective NSEs. A NSE should be endowed with dynamic and adaptable computing and communication services capable of efficiently running diverse applications with evolving QoS requirements on top of federated distributed resources. NSEs should also enable the development of applications independent of the underlying system and device concerns. To our knowledge, a NSE with the aforementioned capabilities does not currently exist. The large scale of NSEs, the heterogeneous node capabilities, the highly dynamic topology, and the likelihood of being deployed in inhospitable environments pose formidable challenges for the construction of resilient shared NSE platforms. Additionally, nodes in NSE are often resource challenged and therefore trustworthy node cooperation is required to provide useful services. Furthermore, the failure of NSE nodes due to malicious or non-malicious conditions represents a major threat to the trustworthiness of NSEs. Applications should be able to survive failure of nodes and change their runtime structure while preserving their operational integrity. It is also worth noting that the decoupling of application programming concerns from system and device concerns has not received the appropriate attention in most existing wireless sensor network platforms. In this dissertation, we present a Biologically-inspired Secure Elastic Networked Sensor Environment (BioSENSE) that synergistically integrates: (1) a novel bio-inspired construction of adaptable system building components, (2) associative routing framework with extensible adaptable criteria-based addressing of resources, and (3) management of multi-dimensional software diversity and trust-based variant hot shuffling. The outcome is that an application using BioSENSE is able to allocate, at runtime, a dynamic taskforce, running over a federated resource pool that would satisfy its evolving mission requirements. BioSENSE perceives both applications and the NSE itself to be elastic, and allows them to grow or shrink based upon needs and conditions. BioSENSE adopts Cell-Oriented-Architecture (COA), a novel architecture that supports the development, deployment, execution, maintenance, and evolution of NSE software. COA employs mission-oriented application design and inline code distribution to enable adaptability, dynamic re-tasking, and re-programmability. The cell, the basic building block in COA, is the abstraction of a mission-oriented autonomously active resource. Generic cells are spontaneously created by the middleware, then participate in emerging tasks through a process called specialization. Once specialized, cells exhibit application specific behavior. Specialized cells have mission objectives that are being continuously sought, and sensors that are used to monitor performance parameters, mission objectives, and other phenomena of interest. Due to the inherent anonymous nature of sensor nodes, associative routing enables dynamic semantically-rich descriptive identification of NSE resources. As such, associative routing presents a clear departure from most current network addressing schemes. Associative routing combines resource discovery and path discovery into a single coherent role, leading to significant reduction in traffic load and communication latency without any loss of generality. We also propose Adaptive Multi-Criteria Routing (AMCR) protocol as a realization of associative routing for NSEs. AMCR exploits application-specific message semantics, represented as generic criteria, and adapts its operation according to observed traffic patterns. BioSENSE intrinsically exploits software diversity, runtime implementation shuffling, and fault recovery to achieve security and resilience required for mission-critical NSEs. BioSENSE makes NSE software a resilient moving target that : 1) confuses the attacker by non-determinism through shuffling of software component implementations; 2) improves the availability of NSE by providing means to gracefully recover from implementation flaws at runtime; and 3) enhances the software system by survival of the fittest through trust-based component selection in an online software component marketplace. In summary, BioSENSE touts the following advantages: (1) on-demand, online distribution and adaptive allocation of services and physical resources shared among multiple long-lived applications with dynamic missions and quality of service requirements, (2) structural, functional, and performance adaptation to dynamic network scales, contexts and topologies, (3) moving target defense of system software, and (4) autonomic failure recovery. / Ph. D.

The human vision system is generally recognized as being superior to all known artificial vision systems. Visual attention, among many processes that are related to human vision, is responsible for identifying relevant regions in a scene for further processing. In most cases, analyzing an entire scene is unnecessary and inevitably time consuming. Hence considering visual attention might be advantageous. A subfield of computer vision where this particular functionality is computationally emulated has been shown to retain high potential in solving real world vision problems effectively. In this monograph, elements of visual attention are explored and algorithms are proposed that exploit such elements in order to enhance image understanding capabilities. Satellite images are given special attention due to their practical relevance, inherent complexity in terms of image contents, and their resolution. Processing such large-size images using visual attention can be very helpful since one can first identify relevant regions and deploy further detailed analysis in those regions only. Bottom-up features, which are directly derived from the scene contents, are at the core of visual attention and help identify salient image regions. In the literature, the use of intensity, orientation and color as dominant features to compute bottom-up attention is ubiquitous. The effects of incorporating an entropy feature on top of the above mentioned ones are also studied. This investigation demonstrates that such integration makes visual attention more sensitive to fine details and hence retains the potential to be exploited in a suitable context. One interesting application of bottom-up attention, which is also examined in this work, is that of image segmentation. Since low salient regions generally correspond to homogenously textured regions in the input image; a model can therefore be learned from a homogenous region and used to group similar textures existing in other image regions. Experimentation demonstrates that the proposed method produces realistic segmentation on satellite images. Top-down attention, on the other hand, is influenced by the observer’s current states such as knowledge, goal, and expectation. It can be exploited to locate target objects depending on various features, and increases search or recognition efficiency by concentrating on the relevant image regions only. This technique is very helpful in processing large images such as satellite images. A novel algorithm for computing top-down attention is proposed which is able to learn and quantify important bottom-up features from a set of training images and enhances such features in a test image in order to localize objects having similar features. An object recognition technique is then deployed that extracts potential target objects from the computed top-down attention map and attempts to recognize them. An object descriptor is formed based on physical appearance and uses both texture and shape information. This combination is shown to be especially useful in the object recognition phase. The proposed texture descriptor is based on Legendre moments computed on local binary patterns, while shape is described using Hu moment invariants. Several tools and techniques such as different types of moments of functions, and combinations of different measures have been applied for the purpose of experimentations. The developed algorithms are generalized, efficient and effective, and have the potential to be deployed for real world problems. A dedicated software testing platform has been designed to facilitate the manipulation of satellite images and support a modular and flexible implementation of computational methods, including various components of visual attention models.

Congestionamentos no trânsito urbano são uma preocupação principal em grandes cidades pelo mundo, devido a seus impactos negativos multifacetados na saúde humana, no meio ambiente e na economia. A urbanização crescente, e seu consequente aumento no volume do trânsito, causam ainda mais congestionamentos por causa do ritmo lento - e, em alguns casos, inexistente - das melhoras na infraestrutura urbana. Uma solução com bom custo-benefício para reduzir o tempo médio de viagem dos veículos e prevenir os congestionamentos é o controle do trânsito urbano. No entanto, a maior parte das abordagens de controle do trânsito urbano adota um ciclo de controle fixo, o qual limita o desempenho de controle devido à consequente inabilidade de agir quando necessário. Ao contrário dessas abordagens, esse trabalho propõe uma rede neural bioinspirada que monitora o estado do sistema de forma contínua e é capaz de agir em qualquer momento. A rede neural bioinspirada proposta adota plasticidade intrínseca e inibição lateral para gerar uma competição natural entre os neurônios, a qual determina quais semáforos devem ser ativados em cada momento. Além disso, interneurônios inibitórios são adotados para coordenar intersecções vizinhas e melhorar os fluxos de veículos. Devido à grande quantidade de possíveis combinações dos parâmetros, um método para determinar o comportamento do modelo de acordo com as características intrínsecas da rede neural bioinspirada também é proposto. A convergência e a estabilidade do modelo proposto são avaliadas por seus pontos-fixos e autovalores, respectivamente. Ademais, o tempo de processamento e a complexidade computacional da rede neural bioinspirada também são avaliados. Por fim, o desempenho do modelo para diferentes demandas de veículos e situações do trânsito é avaliado com um simulador de mobilidade urbana e comparado a um método de controle adaptativo. / Traffic congestions are a major concern for big cities around the world due to its multifaceted negative impacts on human health, the environment and the economy. Growing urbanization, and the consequent increase in traffic volume, causes even more traffic congestions due to the slow-paced - and, in some cases, non-existing - improvements in the urban traffic infrastructure. A cost-effective solution to reduce vehicle travel times and prevent traffic congestions is traffic signal control. However, most approaches to traffic signal control adopt a fixed control cycle, which limits control performance due to the consequent inability to act when necessary. Contrary to these approaches, this work proposes a biologically-inspired neural network that monitors the system state continuously and can act upon it at any moment. The biologically-inspired neural network proposed adopts intrinsic plasticity and lateral inhibition to generate natural competition among neurons, determining which semaphores should be active at each moment. Furthermore, inhibitory interneurons are also adopted to coordinate neighboring intersections and to improve vehicle flows. Due to the large number of parameter combinations, a method to determine the model behavior according to the intrinsic characteristics of the biologically-inspired neural network is also proposed. Model convergence and stability are evaluated by its fixed-points and eigenvalues, respectively. Moreover, the computation time and computational complexity of the biologically-inspired neural network are also evaluated. Finally, the model performance for different vehicle demands and traffic situations is evaluated with a simulator of urban mobility and compared to an adaptive control method.

Congestionamentos no trânsito urbano são uma preocupação principal em grandes cidades pelo mundo, devido a seus impactos negativos multifacetados na saúde humana, no meio ambiente e na economia. A urbanização crescente, e seu consequente aumento no volume do trânsito, causam ainda mais congestionamentos por causa do ritmo lento - e, em alguns casos, inexistente - das melhoras na infraestrutura urbana. Uma solução com bom custo-benefício para reduzir o tempo médio de viagem dos veículos e prevenir os congestionamentos é o controle do trânsito urbano. No entanto, a maior parte das abordagens de controle do trânsito urbano adota um ciclo de controle fixo, o qual limita o desempenho de controle devido à consequente inabilidade de agir quando necessário. Ao contrário dessas abordagens, esse trabalho propõe uma rede neural bioinspirada que monitora o estado do sistema de forma contínua e é capaz de agir em qualquer momento. A rede neural bioinspirada proposta adota plasticidade intrínseca e inibição lateral para gerar uma competição natural entre os neurônios, a qual determina quais semáforos devem ser ativados em cada momento. Além disso, interneurônios inibitórios são adotados para coordenar intersecções vizinhas e melhorar os fluxos de veículos. Devido à grande quantidade de possíveis combinações dos parâmetros, um método para determinar o comportamento do modelo de acordo com as características intrínsecas da rede neural bioinspirada também é proposto. A convergência e a estabilidade do modelo proposto são avaliadas por seus pontos-fixos e autovalores, respectivamente. Ademais, o tempo de processamento e a complexidade computacional da rede neural bioinspirada também são avaliados. Por fim, o desempenho do modelo para diferentes demandas de veículos e situações do trânsito é avaliado com um simulador de mobilidade urbana e comparado a um método de controle adaptativo. / Traffic congestions are a major concern for big cities around the world due to its multifaceted negative impacts on human health, the environment and the economy. Growing urbanization, and the consequent increase in traffic volume, causes even more traffic congestions due to the slow-paced - and, in some cases, non-existing - improvements in the urban traffic infrastructure. A cost-effective solution to reduce vehicle travel times and prevent traffic congestions is traffic signal control. However, most approaches to traffic signal control adopt a fixed control cycle, which limits control performance due to the consequent inability to act when necessary. Contrary to these approaches, this work proposes a biologically-inspired neural network that monitors the system state continuously and can act upon it at any moment. The biologically-inspired neural network proposed adopts intrinsic plasticity and lateral inhibition to generate natural competition among neurons, determining which semaphores should be active at each moment. Furthermore, inhibitory interneurons are also adopted to coordinate neighboring intersections and to improve vehicle flows. Due to the large number of parameter combinations, a method to determine the model behavior according to the intrinsic characteristics of the biologically-inspired neural network is also proposed. Model convergence and stability are evaluated by its fixed-points and eigenvalues, respectively. Moreover, the computation time and computational complexity of the biologically-inspired neural network are also evaluated. Finally, the model performance for different vehicle demands and traffic situations is evaluated with a simulator of urban mobility and compared to an adaptive control method.