Mississippi State University

About: Mississippi State University is a education organization based out in Starkville, Mississippi, United States. It is known for research contribution in the topics: Population & Catfish. The organization has 14115 authors who have published 28594 publications receiving 700030 citations. The organization is also known as: The Mississippi State University of Agriculture and Applied Science & Mississippi State University of Agriculture and Applied Science.

Block Compressed Sensing of Images Using Directional Transforms

Abstract: Recent years have seen significant interest in the paradigm of compressed sensing (CS) which permits, under certain conditions, signals to be sampled at sub-Nyquist rates via linear projection onto a random basis while still enabling exact reconstruction of the original signal. As applied to 2D images, however, CS faces several challenges including a computationally expensive reconstruction process and huge memory required to store the random sampling operator. Recently, several fast algorithms have been developed for CS reconstruction, while the latter challenge was addressed by Gan using a block-based sampling operation as well as projection-based Landweber iterations to accomplish fast CS reconstruction while simultaneously imposing smoothing with the goal of improving the reconstructed-image quality by eliminating blocking artifacts. In this technique, smoothing is achieved by interleaving Wiener filtering with the Landweber iterations, a process facilitated by the relative simple implementation of the Landweber algorithm. In this work, we adopt Gan's basic framework of block-based CS sampling of images coupled with iterative projection-based reconstruction with smoothing. Our contribution lies in that we cast the reconstruction in the domain of recent transforms that feature a highly directional decomposition. These transforms---specifically, contourlets and complex-valued dual-tree wavelets---have shown promise to overcome deficiencies of widely-used wavelet transforms in several application areas. In their application to iterative projection-based CS recovery, we adapt bivariate shrinkage to their directional decomposition structure to provide sparsity-enforcing thresholding, while a Wiener-filter step encourages smoothness of the result. In experimental simulations, we find that the proposed CS reconstruction based on directional transforms outperforms equivalent reconstruction using common wavelet and cosine transforms. Additionally, the proposed technique usually matches or exceeds the quality of total-variation (TV) reconstruction, a popular approach to CS recovery for images whose gradient-based operation also promotes smoothing but runs several orders of magnitude slower than our proposed algorithm.

A Geometrically Exact Model for Externally Loaded Concentric-Tube Continuum Robots

Abstract: Continuum robots, which are composed of multiple concentric, precurved elastic tubes, can provide dexterity at diameters equivalent to standard surgical needles. Recent mechanics-based models of these “active cannulas” are able to accurately describe the curve of the robot in free space, given the preformed tube curves and the linear and angular positions of the tube bases. However, in practical applications, where the active cannula must interact with its environment or apply controlled forces, a model that accounts for deformation under external loading is required. In this paper, we apply geometrically exact rod theory to produce a forward kinematic model that accurately describes large deflections due to a general collection of externally applied point and/or distributed wrench loads. This model accommodates arbitrarily many tubes, with each having a general preshaped curve. It also describes the independent torsional deformation of the individual tubes. Experimental results are provided for both point and distributed loads. Average tip error under load was 2.91 mm (1.5% - 3% of total robot length), which is similar to the accuracy of existing free-space models.

Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm

Abstract: This paper presents the inception and subsequent revisions of an immune-inspired supervised learning algorithm, Artificial Immune Recognition System (AIRS). It presents the immunological components that inspired the algorithm and describes the initial algorithm in detail. The discussion then moves to revisions of the basic algorithm that remove certain unnecessary complications of the original version. Experimental results for both versions of the algorithm are discussed and these results indicate that the revisions to the algorithm do not sacrifice accuracy while increasing the data reduction capabilities of AIRS.

Constructivism: The Career and Technical Education Perspective

Abstract: For over three-quarters of a century, the implicit learning theory underlying the curriculum and pedagogy of career and technical education has been behaviorism, but the emerging theory of constructivism may have implications for career and technical education practice in the future. Preparation of workers for entry into and advancement in the workplace of the next decade requires an educational program that provides not only job skills, as career and technical education did throughout the 1900s, but also higher order thinking, problem solving, and collaborative work skills. Classical behaviorist theory does not adequately address the latter kinds of learning, but constructivist theory may. Constructivist principles are examined in light of the fundamental requirements of career and technical education as we move into the new century with a new name for a redesigned profession. Of the three basic types of constructivism discussed, cognitive constructivism is most compatible with career and technical education. The authors recommend a more thorough examination of the relative efficacy of behaviorism and cognitive constructivism to serve as the learning theory on which to base career and technical education in the future. To embrace such a foundational change, leaders in the profession must re-think many of the fundamental assumptions underlying the mission, curriculum, and pedagogy of career and technical education. Perhaps such a rethinking is due.