Self-Organizing Hierarchical Knowledge Discovery by an ARTMAP Image Fusion System

Classifying novel terrain or objects front sparse, complex data may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods described here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among objects are assumed to be unknown to the automated system or the human user. The ARTMAP information fusion system used distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchical knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships.

Adaptive self-calibrating image rejection receiver

An adaptive self-calibrating image rejection receiver is described, containing a modified Weaver image rejection mixer and a Digital Image Rejection Processor (DIRP). The blind source-separation-based DIRP eliminates the I/Q errors improving the Image Rejection Ratio (IRR) without the need for trimming or use of power-hungry discrete components. Hardware complexity is minimal, requiring only two complex coefficients; hence it can be easily integrated into the signal processing path of any receiver. Simulation results show that the proposed approach achieves 75-97 dB of IRR.

The Relationship between Participation in an Exercise Program and Body Image in Post-Menopausal Women Self-Reporting Osteoporosis

The current study investigated body image differences in post-menopausal women who self-reported having (SRO) or not having (SRN) osteoporosis and the impact of a 16-week exercise program on body image in these groups. Participants completed a measure of body image, and were randomly assigned to a 16-week exercise program or control group, stratified by self-reported osteoporosis status. After 16 weeks, they completed the same body image measure. There were no differences in body image between the two osteoporosis groups. The exercise intervention had a positive impact on body image for both the SRO and SRN groups. The exercise groups showed increases in fitness and health orientation and body areas satisfaction from baseline to 16-weeks, while the non-exercise group showed decreases in appearance and health evaluation, health orientation and body areas satisfaction. The results suggest an exercise program for post-menopausal women can lead to improvements in body image, regardless of osteoporosis status.

The Effect of Trainer Muscularity and Expertise on Self-Presentational Concerns, Body Image, and Performance in College Men during One-Repetition Maximum Testing

This study attempted to manipulate self-presentational efficacy to examine the effect on social anxiety, social physique anxiety, drive for muscularity, and maximal strength performance during a one-repetition maximum (1-RM) chest press and leg press test. Ninety-nine college men with a minimum of six months of previous weight training experience were randomly assigned to complete a 1-RM protocol with either a muscular male trainer described as an expert or a lean male trainer described as a novice. Participants completed measures of self-presentation and body image prior to meeting their respective trainer, and following the completion of the 1-RM tests. Although the self-presentational efficacy manipulation was not successful, the trainers were perceived significantly differently on musculature and expertise. The group with the muscular, expert trainer reported higher social anxiety and attained higher 1-RM scores for the chest and leg press. Thus, trainer characteristics can affect strength performance and self-presentational concerns in this population.

Formation of triple helical nanofibers using self-assembling chiral benzene-1,3,5-tricarboxamides and reversal of the nanostructure's handedness using mirror image building blocks

Intertwining triple helical nanofibers with an overall handedness have been formed from self-assembling chiral benzene-1,3,5-tricarboxamides 1, 2 and 3, whereas the achiralbenzene-1,3,5-tricarboxamide 4 upon self-association gives rise to straight nanofibers without any twist and transmission electron microscopy images of chiral compounds clearly demonstrate that the handedness of the triple helical nanofibers can be reversed by using the enantiomeric benzene-1,3,5-tricarboxamide building blocks.

Bio-inspired FPGA Architecture for Self-Calibration of an Image Compression Core based on Wavelet Transforms in Embedded Systems

A generic bio-inspired adaptive architecture for image compression suitable to be implemented in embedded systems is presented. The architecture allows the system to be tuned during its calibration phase. An evolutionary algorithm is responsible of making the system evolve towards the required performance. A prototype has been implemented in a Xilinx Virtex-5 FPGA featuring an adaptive wavelet transform core directed at improving image compression for specific types of images. An Evolution Strategy has been chosen as the search algorithm and its typical genetic operators adapted to allow for a hardware friendly implementation. HW/SW partitioning issues are also considered after a high level description of the algorithm is profiled which validates the proposed resource allocation in the device fabric. To check the robustness of the system and its adaptation capabilities, different types of images have been selected as validation patterns. A direct application of such a system is its deployment in an unknown environment during design time, letting the calibration phase adjust the system parameters so that it performs efcient image compression. Also, this prototype implementation may serve as an accelerator for the automatic design of evolved transform coefficients which are later on synthesized and implemented in a non-adaptive system in the final implementation device, whether it is a HW or SW based computing device. The architecture has been built in a modular way so that it can be easily extended to adapt other types of image processing cores. Details on this pluggable component point of view are also given in the paper.

A self-adaptive image processing application based on evolvable and scalable hardware

Evolvable Hardware (EH) is a technique that consists of using reconfigurable hardware devices whose configuration is controlled by an Evolutionary Algorithm (EA). Our system consists of a fully-FPGA implemented scalable EH platform, where the Reconfigurable processing Core (RC) can adaptively increase or decrease in size. Figure 1 shows the architecture of the proposed System-on-Programmable-Chip (SoPC), consisting of a MicroBlaze processor responsible of controlling the whole system operation, a Reconfiguration Engine (RE), and a Reconfigurable processing Core which is able to change its size in both height and width. This system is used to implement image filters, which are generated autonomously thanks to the evolutionary process. The system is complemented with a camera that enables the usage of the platform for real time applications.

Self-organizing cultured neural networks: Image analysis techniques for longitudinal tracking and modeling of the underlying network structure

Esta tesis estudia la evolución estructural de conjuntos de neuronas como la capacidad de auto-organización desde conjuntos de neuronas separadas hasta que forman una red (clusterizada) compleja. Esta tesis contribuye con el diseño e implementación de un algoritmo no supervisado de segmentación basado en grafos con un coste computacional muy bajo. Este algoritmo proporciona de forma automática la estructura completa de la red a partir de imágenes de cultivos neuronales tomadas con microscopios de fase con una resolución muy alta. La estructura de la red es representada mediante un objeto matemático (matriz) cuyos nodos representan a las neuronas o grupos de neuronas y los enlaces son las conexiones reconstruidas entre ellos. Este algoritmo extrae también otras medidas morfológicas importantes que caracterizan a las neuronas y a las neuritas. A diferencia de otros algoritmos hasta el momento, que necesitan de fluorescencia y técnicas inmunocitoquímicas, el algoritmo propuesto permite el estudio longitudinal de forma no invasiva posibilitando el estudio durante la formación de un cultivo. Además, esta tesis, estudia de forma sistemática un grupo de variables topológicas que garantizan la posibilidad de cuantificar e investigar la progresión de las características principales durante el proceso de auto-organización del cultivo. Nuestros resultados muestran la existencia de un estado concreto correspondiente a redes con configuracin small-world y la emergencia de propiedades a micro- y meso-escala de la estructura de la red. Finalmente, identificamos los procesos físicos principales que guían las transformaciones morfológicas de los cultivos y proponemos un modelo de crecimiento de red que reproduce el comportamiento cuantitativamente de las observaciones experimentales. ABSTRACT The thesis analyzes the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. In particular, it contributes with the design and implementation of a graph-based unsupervised segmentation algorithm, having an associated very low computational cost. The processing automatically retrieves the whole network structure from large scale phase-contrast images taken at high resolution throughout the entire life of a cultured neuronal network. The network structure is represented by a mathematical object (a matrix) in which nodes are identified neurons or neurons clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocyto- chemistry techniques, our measures are non invasive and entitle us to carry out a fully longitudinal analysis during the maturation of a single culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main networks characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graphs micro- and meso-scale properties emerge. Finally, we identify the main physical processes taking place during the cultures morphological transformations, and embed them into a simplified growth model that quantitatively reproduces the overall set of experimental observations.

Self-organizing cultured neural networks: Image analysis techniques for longitudinal tracking and modeling of the underlying network structure (versión actualizada)

Esta tesis estudia la evolución estructural de conjuntos de neuronas como la capacidad de auto-organización desde conjuntos de neuronas separadas hasta que forman una red (clusterizada) compleja. Esta tesis contribuye con el diseño e implementación de un algoritmo no supervisado de segmentación basado en grafos con un coste computacional muy bajo. Este algoritmo proporciona de forma automática la estructura completa de la red a partir de imágenes de cultivos neuronales tomadas con microscopios de fase con una resolución muy alta. La estructura de la red es representada mediante un objeto matemático (matriz) cuyos nodos representan a las neuronas o grupos de neuronas y los enlaces son las conexiones reconstruidas entre ellos. Este algoritmo extrae también otras medidas morfológicas importantes que caracterizan a las neuronas y a las neuritas. A diferencia de otros algoritmos hasta el momento, que necesitan de fluorescencia y técnicas inmunocitoquímicas, el algoritmo propuesto permite el estudio longitudinal de forma no invasiva posibilitando el estudio durante la formación de un cultivo. Además, esta tesis, estudia de forma sistemática un grupo de variables topológicas que garantizan la posibilidad de cuantificar e investigar la progresión de las características principales durante el proceso de auto-organización del cultivo. Nuestros resultados muestran la existencia de un estado concreto correspondiente a redes con configuracin small-world y la emergencia de propiedades a micro- y meso-escala de la estructura de la red. Finalmente, identificamos los procesos físicos principales que guían las transformaciones morfológicas de los cultivos y proponemos un modelo de crecimiento de red que reproduce el comportamiento cuantitativamente de las observaciones experimentales. ABSTRACT The thesis analyzes the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. In particular, it contributes with the design and implementation of a graph-based unsupervised segmentation algorithm, having an associated very low computational cost. The processing automatically retrieves the whole network structure from large scale phase-contrast images taken at high resolution throughout the entire life of a cultured neuronal network. The network structure is represented by a mathematical object (a matrix) in which nodes are identified neurons or neurons clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocyto- chemistry techniques, our measures are non invasive and entitle us to carry out a fully longitudinal analysis during the maturation of a single culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main networks characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graphs micro- and meso-scale properties emerge. Finally, we identify the main physical processes taking place during the cultures morphological transformations, and embed them into a simplified growth model that quantitatively reproduces the overall set of experimental observations.

Self-contained encrypted image folding

The recently introduced approach for Encrypted Image Folding is generalized to make it self-contained. The goal is achieved by enlarging the folded image so as to embed all the necessary information for the image recovery. The need for extra size is somewhat compensated by considering a transformation with higher folding capacity. Numerical examples show that the size of the resulting cipher image may be significantly smaller than the plain text one. The implementation of the approach is further extended to deal also with color images.

Perceptions of family environment, body image and self-esteem of eating disordered and non-eating disordered women

This study explored the perceptions of family environment, body image and self esteem of women who suffer from anorexia nervosa, bulimia nervosa, and depression. Using a nonequivalent control group design, one hundred and fifty women with anorexia nervosa (n = 50), bulimia nervosa (n = 50), and depression (n = 50) were given the Family Environment Scale (FES) and the Eating Disorders Inventory-2 (EDI-2). The objectives of this study were to: (1) study how women with anorexia nervosa and bulimia nervosa perceive their family environment as measured by the FES; (2) compare and contrast perceptions of family environment of women with anorexia nervosa and bulimia nervosa with the control group; (3) compare and contrast perceived levels of self esteem and body image as measured by the EDI-2 of women with anorexia nervosa and bulimia nervosa with the control group; and (4) examine the perceived family environments of eating disordered and non-eating disordered women with regard to body image and self esteem. Results suggested, women who suffered from anorexia nervosa or bulimia nervosa scored significantly lower (p $<$.021) on the Expressiveness, Intellectual-Cultural Orientation, and Active-Recreational subscales of the FES. The results also indicated that women who suffered from bulimia nervosa scored significantly higher (p $<$.015) than women who suffered from anorexia nervosa on the Conflict and Independence subscales of the FES. The results of studying these three populations reflected that women who suffered from anorexia nervosa scored significantly different (p $<$.000) than women who suffered from bulimia nervosa on many of the subscales of the EDI-2. The findings of the study confirmed that women who suffered from anorexia nervosa or bulimia nervosa scored significantly different (p $<$.000) on the subscales of the EDI-2 compared to women who suffered from depression. It was also confirmed that a relationship does exist between perceptions of body image and self esteem and perceptions of family environment amongst women with anorexia nervosa and bulimia nervosa as compared to depressed women. The findings of the study indicated that women who suffered from anorexia nervosa tended to: be less expressive and independent; handle conflict less openly; have a greater drive for thinness; have greater body dissatisfaction; be more perfectionistic; and struggle more intensely with fears around maturity and social insecurity than did women who suffered from bulimia nervosa or depression. In addition, the findings of the study also suggested that women who suffered from bulimia nervosa tended to: be raised in homes where openly expressed anger is permitted amongst family members; have a lesser drive for thinness; have less body dissatisfaction; be less perfectionistic; and not struggle as intensely with fears around maturity and social insecurity as do women who suffered from anorexia nervosa, but more than women who suffer from depression. Treatment implications that may assist community college professors and counselors in meeting the special needs of this special group of women were also discussed. (Abstract shortened by UMI.) ^

Art and the Body Image: about Self and Stereotypes

Today's man is socially absorbed by problematic body issues and everything that this means and involves. Literature, publicity, science, technology and medicine compound these issues in a form of this theme that has never been seen before. In the artistic framework, body image is constantly suffering modifications. Body image in sculpture unfolds itself, assuming different messages and different forms. The body is a synonym of subject, an infinite metaphorical history of our looks, desires, that leads one to interrogate his/her image and social and sexual relations. These are understood as a manifestation of individual desires freed from a moral and social imposition. It attempts a return to profound human nature before we are turned into a cloning industry. In thisstudy it isimportant for usto understand in which form doessculpture reflect body image as a sociocultural and psychological phenomenon within the coordinates of our time. To understand how they represent and what artists represent in sculpture as a multiple and complex structure of human sexuality. Today, the sculptural body, expanding its representation, no longer as a reproduction of the corporal characteristics, presents the body in what it possesses of most intimate, unique, human and real, that moves, reacts, feels, suffers and pulsates, a mirror of us all.