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.

O envenenamento ofídico é considerado uma condição tropical negligenciada pela OMS, e no Brasil, o gênero Bothrops está envolvido na maioria dos casos. Primeiramente, estudamos a atividade hidrolítica do veneno de B. jararaca sobre peptídeos biologicamente ativos que podem estar relacionadas com o envenenamento. A hidrólise dos peptídeos que foram substratos para as serinopeptidases não foi eficientemente bloqueada pelo soro antibotrópico produzido pelo Instituto Butantan e, portanto, as causas dessas falhas foram investigadas. Para isso, purificamos quatro serinopeptidases não bloqueadas pelo soro e, por estudos imunoquímicos, observamos que apesar deste não bloquear as atividades destas enzimas, o mesmo é capaz de reconhecê-las. Portanto, decidimos obter soros experimentais contra estas moléculas utilizando camundongos, a fim de compará-los com o soro comercial. Os soros experimentais contra as serinopeptidases mostraram capacidade de reconhecimento e alta afinidade contra elas, e mais importante, capacidade de neutralizar suas atividades in vitro. / Snakebite is considered a neglected tropical condition by WHO, and in Brazil, the Bothrops genus is involved in most of the cases. Initially, we have studied the B. jararaca venom activity over bioactive peptides which could be related with the envenomation. The hydrolysis of the peptides substrate for serinepeptidases were not efficiently blocked by the Butantan Institute bothropic antivenom, therefore, the causes of this flaw were investigated. Thereafter, we purified four serinepeptidases not blocked by the antivenom and, by immunochemistry analysis, we observed that although it could not neutralize the activity, it could well recognize these proteins. Thus, we decided to obtain experimental sera against these serinepeptidases in mice, in order to compare it with the commercial antivenom. The experimental sera against these enzymes demonstrated recognition capability and high affinity, and most important, the ability to neutralize their activity in vitro.

Simulations of various hydration levels of lamellar phase 23:2 Diyne PC were performed, and subsequent, serial docking simulations of a tyrosine monomer were replicated for each system in both hydrated and dehydrated states.<br>The goal was to evaluate how hydration impacts self-assembly and crystallization on the surface, and<br>whether or not these simulations, when run sequentially, could determine the answer. It was discovered that hydrated and dehydrated surfaces behave differently, and that<br>headgroup orientation plays a role in the initial docking and self-assembly process of the tyrosine monomer. It was also determined that potential energy as a sole metric<br>for determining whether or not a specific conformation of intermolecular orientation is not entirely useful, and docking scores are likely useful metrics in discriminating between conformations with identical potential energy values. <br>

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.

Analogy is ubiquitous in human cognition. One of the important questions related to understanding the situated nature of analogy-making is how people retrieve source analogues via their interactions with external environments. This dissertation studies interactive analogical retrieval in the context of biologically inspired design (BID). BID involves creative use of analogies to biological systems to develop solutions for complex design problems (e.g., designing a device for acquiring water in desert environments based on the analogous fog-harvesting abilities of the Namibian Beetle). Finding the right biological analogues is one of the critical first steps in BID. Designers routinely search online in order to find their biological sources of inspiration. But this task of online bio-inspiration seeking represents an instance of interactive analogical retrieval that is extremely time consuming and challenging to accomplish. This dissertation focuses on understanding and supporting the task of online bio-inspiration seeking. Through a series of field studies, this dissertation uncovered the salient characteristics and challenges of online bio-inspiration seeking. An information-processing model of interactive analogical retrieval was developed in order to explain those challenges and to identify the underlying causes. A set of measures were put forth to ameliorate those challenges by targeting the identified causes. These measures were then implemented in an online information-seeking technology designed to specifically support the task of online bio-inspiration seeking. Finally, the validity of the proposed measures was investigated through a series of experimental studies and a deployment study. The trends are encouraging and suggest that the proposed measures has the potential to change the dynamics of online bio-inspiration seeking in favor of ameliorating the identified challenges of online bio-inspiration seeking.

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.

The purpose of this work is to investigate a series of biologically inspired modifications to state-of-the-art Mel- frequency cepstral coefficients (MFCCs) that may improve automatic speech recognition results. We have provided recommendations to improve speech recognition results de- pending on signal-to-noise ratio levels of input signals. This work has been motivated by noise-robust auditory features (NRAF). In the feature extraction technique, after a signal is filtered using bandpass filters, a spatial derivative step is used to sharpen the results, followed by an envelope detector (recti- fication and smoothing) and down-sampling for each filter bank before being compressed. DCT is then applied to the results of all filter banks to produce features. The Hidden- Markov Model Toolkit (HTK) is used as the recognition back-end to perform speech recognition given the features we have extracted. In this work, we investigate the role of filter types, window size, spatial derivative, rectification types, smoothing, down- sampling and compression and compared the final results to state-of-the-art Mel-frequency cepstral coefficients (MFCC). A series of conclusions and insights are provided for each step of the process. The goal of this work has not been to outperform MFCCs; however, we have shown that by changing the compression type from log compression to 0.07 root compression we are able to outperform MFCCs for all noisy conditions.

Computational intelligence techniques, such as artificial neural networks (ANNs), have been widely used to improve the performance of power system monitoring and control. Although inspired by the neurons in the brain, ANNs are largely different from living neuron networks (LNNs) in many aspects. Due to the oversimplification, the huge computational potential of LNNs cannot be realized by ANNs. Therefore, a more brain-like artificial neural network is highly desired to bridge the gap between ANNs and LNNs. The focus of this research is to develop a biologically inspired artificial neural network (BIANN), which is not only biologically meaningful, but also computationally powerful. The BIANN can serve as a novel computational intelligence tool in monitoring, modeling and control of the power systems. A comprehensive survey of ANNs applications in power system is presented. It is shown that novel types of reservoir-computing-based ANNs, such as echo state networks (ESNs) and liquid state machines (LSMs), have stronger modeling capability than conventional ANNs. The feasibility of using ESNs as modeling and control tools is further investigated in two specific power system applications, namely, power system nonlinear load modeling for true load harmonic prediction and the closed-loop control of active filters for power quality assessment and enhancement. It is shown that in both applications, ESNs are capable of providing satisfactory performances with low computational requirements. A novel, more brain-like artificial neural network, i.e. biologically inspired artificial neural network (BIANN), is proposed in this dissertation to bridge the gap between ANNs and LNNs and provide a novel tool for monitoring and control in power systems. A comprehensive survey of the spiking models of living neurons as well as the coding approaches is presented to review the state-of-the-art in BIANN research. The proposed BIANNs are based on spiking models of living neurons with adoption of reservoir-computing approaches. It is shown that the proposed BIANNs have strong modeling capability and low computational requirements, which makes it a perfect candidate for online monitoring and control applications in power systems. BIANN-based modeling and control techniques are also proposed for power system applications. The proposed modeling and control schemes are validated for the modeling and control of a generator in a single-machine infinite-bus system under various operating conditions and disturbances. It is shown that the proposed BIANN-based technique can provide better control of the power system to enhance its reliability and tolerance to disturbances. To sum up, a novel, more brain-like artificial neural network, i.e. biologically inspired artificial neural network (BIANN), is proposed in this dissertation to bridge the gap between ANNs and LNNs and provide a novel tool for monitoring and control in power systems. It is clearly shown that the proposed BIANN-based modeling and control schemes can provide faster and more accurate control for power system applications. The conclusions, the recommendations for future research, as well as the major contributions of this research are presented at the end.

Canada fleabane is the second glyphosate-resistant (GR) weed species to be confirmed in Ontario. In 2010, GR populations were identified at eight sites in Essex County. In 2011 and 2012, 147 additional sites across eight counties were confirmed to be resistant. Twelve and seven sites were identified with multiple resistance (glyphosate and cloransulam) in 2011 and 2012, respectively, across five counties. In soybeans, preplant tankmixes of glyphosate (900 g a.e.ha-1) plus saflufenacil (25 g a.i. ha-1), saflufenacil/dimethenamid-p (245 g a.i. ha-1), metribuzin (1120 g a.i. ha-1), or flumetsulam (70 g a.i. ha-1) provided greater than 87% up to 8 weeks after application (WAA). Glyphosate rates 21 to 48X the label rate (900 g a.e. ha-1) were required for 95% control. Postemergence tankmixes did not provide acceptable control. In dicamba-tolerant soybean, dicamba applied preplant at 600 g a.e. ha-1 provided the most consistent control of GR Canada fleabane. / Monsanto Canada Inc., Grain Farmers of Ontario, Agricultural Adaptation Council