The Art of Knowledge Exchange: A Results-Focused Planning Guide for Development Practitioners

Designing and implementing knowledge exchange initiatives can be a big undertaking. This guide takes the guesswork out of the process by breaking it down into simple steps and providing tools to help you play a more effective role as knowledge connector and learning facilitator

Spectrum is periodic for n-Intervals

In this paper we study spectral sets which are unions of finitely many intervals in R. We show that any spectrum associated with such a spectral set is periodic, with the period an integral multiple of the measure of the set. As a consequence we get a structure theorem for such spectral sets and observe that the generic case is that of the equal interval case.Comment: 19 page

Boost converter fed high performance BLDC drive for solar PV array powered air cooling system

This paper proposes the utilization of a DC-DC boost converter as a mediator between a Solar Photovoltaic (SPV) array and the Voltage Source Inverters (VSI) in an SPV array powered air cooling system to attain maximum efficiency. The boost converter, over the various common DC-DC converters, offers many advantages in SPV based applications. Further, two Brushless DC (BLDC) motors are employed in the proposed air cooling system: one to run the centrifugal water pump and the other to run a fan-blower. Employing a BLDC motor is found to be the best option because of its top efficiency, supreme reliability and better performance over a wide range of speeds. The air cooling system is developed and simulated using the MATLAB/Simulink environment considering the steady state variation in the solar irradiance. Further, the efficiency of BLDC drive system is compared with a conventional Permanent Magnet DC (PMDC) motor drive system and from the simulated results it is found that the proposed system performs better

Mitigation of Through-Wall Distortions of Frontal Radar Images using Denoising Autoencoders

Radar images of humans and other concealed objects are considerably distorted by attenuation, refraction and multipath clutter in indoor through-wall environments. While several methods have been proposed for removing target independent static and dynamic clutter, there still remain considerable challenges in mitigating target dependent clutter especially when the knowledge of the exact propagation characteristics or analytical framework is unavailable. In this work we focus on mitigating wall effects using a machine learning based solution -- denoising autoencoders -- that does not require prior information of the wall parameters or room geometry. Instead, the method relies on the availability of a large volume of training radar images gathered in through-wall conditions and the corresponding clean images captured in line-of-sight conditions. During the training phase, the autoencoder learns how to denoise the corrupted through-wall images in order to resemble the free space images. We have validated the performance of the proposed solution for both static and dynamic human subjects. The frontal radar images of static targets are obtained by processing wideband planar array measurement data with two-dimensional array and range processing. The frontal radar images of dynamic targets are simulated using narrowband planar array data processed with two-dimensional array and Doppler processing. In both simulation and measurement processes, we incorporate considerable diversity in the target and propagation conditions. Our experimental results, from both simulation and measurement data, show that the denoised images are considerably more similar to the free-space images when compared to the original through-wall images

School Poverty Concentration and Kindergarten Students' Numerical Skills

Schools that enroll disproportionately high percentages of pupils from low-income families are widely believed to have negative consequences for student performance. Prior research has investigated the relationship of school poverty and outcomes in numerous ways, but the basic proposition is that school composition affects student learning, such that otherwise similar students realize different levels of achievement in schools with different proportions of low-income. This paper updates and extends the research on compositional effects in several respects. First, we extend the research to the early elementary level of schooling. Second, the data needed to assess the relative importance of individual and school factors have not been available and the mechanisms that mediate the school-level effect independent of student background factors are thus not clear. This paper draws upon nationally representative data on kindergarten pupils and the schools they attend to estimate both the overall impact of school poverty on mathematics achievement and its impact on a variety of other school and schooling-experience variables that may in turn affect student learning. Finally, this paper analyzes the effects of high poverty schools on several schooling variables hypothesized to affect student achievement.