This study investigated the effects of Dynamic Assessment (DA) training on the mediational strategies of experienced teachers of French as a foreign language. Moreover the strategies that mediators used for students at different levels of language experience were investigated. Last the ways in which mediators manifested mediational sensitivity, reciprocity and management was examined. Four mediators underwent DA training that exposed them to the theoretical underpinnings of DA as well as sound DA procedures. To determine the effect of this training, the way in which the mediators conducted their mediation was compared from pre-DA training to post-DA training. Three of these four mediators worked with 12 students of French as a foreign language at different levels of language learning experience. Their interactions were recorded, transcribed and analyzed. The results of this study show that the DA training did indeed have an affect on the way in which mediators conducted their mediation with students. Also there seems to be a difference, however minute, in the way that mediators mediate students possessing different levels of language experience. The implications of this study suggest that mediators would have benefitted from more robust DA training as well as an increased field experience with DA. Second students should also be trained in DA procedures so that they may be able to better participate in the dialogic activity that occurs during mediation. Third more foreign language practitioner focused definitions of DA and cognition, within a Sociocultural Theory framework, are offered. It is believed that more accessible definitions will facilitate DA's use in the foreign language classroom.

This thesis proposes a general and unified methodological framework suitable for studying the locomotion of a wide range of robots, especially bio-inspired. The objective of this thesis is twofold. First, it contributes to the classification of locomotion robots by adopting the mathematical tools developed by the American school of geometric mechanics.Secondly, by taking advantage of the recursive nature of the Newton-Euler formulation, it proposes numerous efficient tools in the form of computational algorithms capable of solving the external direct dynamics and the internal inverse dynamics of any locomotion robot considered as a mobile multi-body system. These generic tools can help the engineers or researchers in the design, control and motion planning of manipulators as well as locomotion robots with a large number of internal degrees of freedom. The efficient algorithms are proposed for discrete and continuous robots. These methodological tools are applied to numerous illustrative examples taken from the bio-inspired robotics such as snake-like robots, caterpillars, and others like snake-board, etc.

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.

This thesis surveys the response of the ideology of 20th century Islamic revival to the West and the Western-inspired regimes at home. Based on J.S. Mill&rsquo / s &ldquo / method of difference, it compares the ideological perspectives of the prominent figures of Islamic revival: Mawlana Abu&rsquo / l-A&lsquo / la Mawdudi in Indio-Pakistan, Sayyid Qutb in Egypt and Ruhollah Khomeini in Iran. In this context, it analyzes the appraisal of Islam as a total way of life in order to refute the Western tradition and refuse its interference into the socio-politics of the Islamic world in the ideal order proposed by those ideologues. The question of the West is the dependent variable of the comparison and is searched through two independent variables: (1) ontology/epistemology of the ideal Islamic order which necessitates a socio-political transformation from &ldquo / jahiliyya&rdquo / to &ldquo / hakimiyya&rdquo / and (2) the methodology to attain the ideal order which is based on activism. In this perspective, it questions the revivalist proposal of &ldquo / Occidentalism&rdquo / as a reaction to the Western-originated system of governance. To this end, the thesis firstly explores the characteristics of the independent variables &ndash / the epistemology/ontology and methodology- of Islamic revival from a historical point of view. Then, it continues with comparing how Mawdudi, Qutb and Khomeini respectively regard the West in the theory of ideal Islamic order which reads a process from &ldquo / jahiliyya&rdquo / to &ldquo / hakimiyya&rdquo / through activism.

This work explores the representations and mathematical modeling of biologically-inspired robotic muscles called Cellular Actuator Arrays. These actuator arrays are made of many small interconnected actuation units which work together to provide force, displacement, robustness and other properties beyond the original actuator's capability. The arrays can also exhibit properties generally associated with biological muscle and can thus provide test bed for research into the interrelated nature of the nervous system and muscles, kinematics/dynamics experiments to understand balance and synergies, and building full-strength, safe muscles for prosthesis, rehabilitation, human force amplification, and humanoid robotics. This thesis focuses on the mathematical tools needed bridge the gap between the conceptual idea of the cellular actuator array and the engineering design processes needed to build physical robotic muscles. The work explores the representation and notation needed to express complex actuator array typologies, the mathematical modeling needed to represent the complex dynamics of the arrays, and properties to guide the selection of arrays for engineering purposes. The approach is designed to aid automation and simulation of actuator arrays and provide an intuitive base for future controls and physiology work. The work is validated through numerical results using MatLab's SimMechanics dynamic modeling system and with three physical actuator arrays built using solenoids and shape memory alloy actuators.

Biologically inspired design (BID) is a widespread and growing movement in modern design, pulled in part by the need for environmentally sustainable design and pushed partly by rapid advances in biology and the desire for creativity and innovation in design. Yet, our current understanding of cognition in BID is limited and at present there are few computational methods or tools available for supporting its practice. In this dissertation, I develop a cognitive model of BID, build computational methods and tools for supporting its practice, and describe results from deploying the methods and the tools in a Georgia Tech BID class. One key and novel finding in my cognitive study of BID is the surprisingly large degree to which biological analogues influence problem formulation and understanding in addition to generation of design solutions. I call the process by which a biological analogue influences the evolution of the problem formulation analogical problem evolution. I use the method of grounded theory to develop a knowledge schema called SR.BID (for structured representations for biologically inspired design) for representing design problem formulations. I show through case study analysis that SR.BID provides a useful analytic framework for understanding the two-way interaction between problems and solutions. I then develop two tools based on the SR.BID schema to scaffold the processes of problem formulation and analogue evaluation in BID. I deployed the two tools, the four-box method of problem specification and the T-chart method of analogical evaluation, in a Georgia Tech BID class. I show that with minimal training, the four-box method was used by students to complete design problem specifications in 2011 and 2012 with 75% of students achieving better than 80% accuracy. Finally I describe a web-based application for interactively supporting BID practice including problem formulation and analogue evaluation. Thus, my dissertation develops a cognitive model of analogical problem evolution in BID, a knowledge schema for representing problem formulations, a computational technique for evaluating biological analogues, and an interactive web-based tool for supporting BID practice. Through a better cognitive understanding of BID and computational methods and tools for supporting its practice, it also contributes to computational creativity.

The thesis explores the technical feasibility of an alternative method of decoupling air-conditioning systems function within the context of ecological issues. The system is a variant of dedicated outdoor air systems to separate dehumidification and cooling in air conditioning equipment. The project specifically investigates locating these components within the building envelope. Placement in the envelope moves the systems closer to fresh air and offers architectural expression for components that are normally out of sight. Designers, engineers, building science, mechanical, structural, biologist, and architectural engineers ideally as agents offer beneficial improvement to the system. The reduction in size of components into the building envelope offers risk. The thesis design space uses historical works, biological analogues, and past work to ground the technical understanding of the topic. Specific use of biological inspired design realizes translation from other systems to improve the alternative decoupled air conditioning system. The thesis develops prototype models for lighting analysis and for sensible and latent heat calculations. Psychrometric charts serve as tools to understand the thermodynamic air-conditioning process in conventional direct expansion vapor compression and solar liquid desiccant air conditioning systems. Data, models, and sketches provide tools for improvements to the 'thick' building envelope. Finally, the diagrams translate into functional decompositions for modifications to improve the system. The thesis probes the constraints in the areas of cost, fabrication, and technology that may not yet exist for selective improvement rather than a barrier to development of the thesis.