Body image and self-concept in adolescent girls [R]

Anorexia Nervosa has been recently recognized as one of the most common chronic illnesses that affects the female adolescent population today. Although there has been an abundance of research into eating disorders in a variety of fields, significant limitations within the research still exist. Since very early descriptions of the disorder, self-concept and body image have been identified as core components of the anorexia nervosa. However, research has been somewhat limited in that there have not been any consistent theoretical underpinnings for self-concept and body image within the eating disorders field. Furthermore, researchers have tended to adopt traditional inferential statistics and multivariate methods to assess the role of self-concept and body image. As a result there has been very little consistency in research results. The current paper summarizes the significant findings from a doctoral thesis that attempted to address current limitations in self-concept and body image literature within the field of eating disorders.

Improved image recovery from compressed data contaminated with impulsive noise

Compressed sensing (CS) is a new information sampling theory for acquiring sparse or compressible data with much fewer measurements than those otherwise required by the Nyquist/Shannon counterpart. This is particularly important for some imaging applications such as magnetic resonance imaging or in astronomy. However, in the existing CS formulation, the use of the â„“ 2 norm on the residuals is not particularly efficient when the noise is impulsive. This could lead to an increase in the upper bound of the recovery error. To address this problem, we consider a robust formulation for CS to suppress outliers in the residuals. We propose an iterative algorithm for solving the robust CS problem that exploits the power of existing CS solvers. We also show that the upper bound on the recovery error in the case of non-Gaussian noise is reduced and then demonstrate the efficacy of the method through numerical studies.