The Science Studio – A Workshop Approach to Introductory Physical Science

This paper describes the Science Studio, an innovative workshop approach for instruction in a physical science course that combines aspects of traditional lecture and laboratory. The target audience for this introductory course is non-science majors, including prospective teachers. An inquiry-based, technology-rich learning environment has been created to allow students hands-on, in-depth exploration of topics in physics, and earth and space science. Course philosophy, course development, and sample activities are described in this paper, along with outcomes from a project-wide evaluation of the Virginia Collaborative for Excellence in the Preparation of Teachers (VCEPT), an investigation of change in student attitudes and the lasting impact of the studio model at Norfolk State University

Fast and Accurate Bilateral Filtering using Gauss-Polynomial Decomposition

The bilateral filter is a versatile non-linear filter that has found diverse applications in image processing, computer vision, computer graphics, and computational photography. A widely-used form of the filter is the Gaussian bilateral filter in which both the spatial and range kernels are Gaussian. A direct implementation of this filter requires $O(\sigma^2)$ operations per pixel, where $\sigma$ is the standard deviation of the spatial Gaussian. In this paper, we propose an accurate approximation algorithm that can cut down the computational complexity to $O(1)$ per pixel for any arbitrary $\sigma$ (constant-time implementation). This is based on the observation that the range kernel operates via the translations of a fixed Gaussian over the range space, and that these translated Gaussians can be accurately approximated using the so-called Gauss-polynomials. The overall algorithm emerging from this approximation involves a series of spatial Gaussian filtering, which can be implemented in constant-time using separability and recursion. We present some preliminary results to demonstrate that the proposed algorithm compares favorably with some of the existing fast algorithms in terms of speed and accuracy.Comment: To appear in the IEEE International Conference on Image Processing (ICIP 2015

Tailoring the curriculum to fit students’ needs: Designing and improving a language course at Dhaka University - Bangladesh

The assessment of students’ language needs is a crucial pre-requisite in EAP (English for Academic purposes) curriculum development. Effective needs analysis leads to the specification of objectives for a course at the same time considering the available resources and existing constraints. This leads to curriculum design and choice of methodology, which is implemented through appropriately selected teaching materials. This paper presents the findings of a research study undertaken at the Business Studies Faculty of Dhaka University. A needs analysis was conducted on ninety students of three departments of the Business Studies Faculty to assess their English language needs. A corresponding needs analysis was conducted on faculty members to find out their perceptions of their students’ English language needs. Several procedures, namely questionnaires, interviews and classroom observations, were used to gather information about the objective needs of the students and teaching staff. Analysis of the findings of this needs analysis revealed that some perceptions of the two groups converged to some extent but there was also some incongruency that needed to be addressed. The EAP course that was being used was evaluated in order to negotiate a more effective curriculum that would address the needs of all the stakeholders involved

Constant-time filtering using shiftable kernels

It was recently demonstrated in [5] that the non-linear bilateral filter [14] can be efficiently implemented using a constant-time or O(1) algorithm. At the heart of this algorithm was the idea of approximating the Gaussian range kernel of the bilateral filter using trigonometric functions. In this letter, we explain how the idea in [5] can be extended to few other linear and non-linear filters [14, 17, 2]. While some of these filters have received a lot of attention in recent years, they are known to be computationally intensive. To extend the idea in [5], we identify a central property of trigonometric functions, called shiftability, that allows us to exploit the redundancy inherent in the filtering operations. In particular, using shiftable kernels, we show how certain complex filtering can be reduced to simply that of computing the moving sum of a stack of images. Each image in the stack is obtained through an elementary pointwise transform of the input image. This has a two-fold advantage. First, we can use fast recursive algorithms for computing the moving sum [15, 6], and, secondly, we can use parallel computation to further speed up the computation. We also show how shiftable kernels can also be used to approximate the (non-shiftable) Gaussian kernel that is ubiquitously used in image filtering.Comment: Accepted in IEEE Signal Processing Letter