University of North Carolina at Charlotte

About: University of North Carolina at Charlotte is a education organization based out in Charlotte, North Carolina, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 8772 authors who have published 22239 publications receiving 562529 citations. The organization is also known as: UNC Charlotte & UNCC.

A survey of quadrotor Unmanned Aerial Vehicles

Abstract: In the past decade Unmanned Aerial Vehicles (UAVs) have become a topic of interest in many research organizations. UAVs are finding applications in various areas ranging from military applications to traffic surveillance. This paper is a survey for a certain kind of UAV called quadrotor or quadcopter. Researchers are frequently choosing quadrotors for their research because a quadrotor can accurately and efficiently perform tasks that would be of high risk for a human pilot to perform. This paper encompasses the dynamic models of a quadrotor and the different model-dependent and model-independent control techniques and their comparison. Recently, focus has shifted to designing autonomous quadrotors. A summary of the various localization and navigation techniques has been given. Lastly, the paper investigates the potential applications of quadrotors and their role in multi-agent systems.

Analysis of the Reliability of the Modified Caregiver Strain Index

Abstract: Objectives The Caregiver Strain Index (CSI) is a 13-item index originally developed in the early 1980s to screen for caregiver strain after hospital discharge of an elderly family member. This study examined the reliability of a modified CSI for applications with contemporary long-term family caregivers. Methods As part of a larger family caregiver study using focus groups, telephone interviews, and mailed surveys, test-retest procedures were included to examine the reliability of the Modified CSI. The sample consisted of 158 family caregivers, with an average age of 61 years. Targeted recruitment resulted in participants from diverse racial/ethnic backgrounds. Minority caregivers represented 25% of the sample. Results The Modified CSI has slightly better internal reliability (alpha =.90) than the original Index, as reported in 1983 (alpha =.86). The two-week test-retest reliability is.88. No prior test-retest data were available for comparison. Discussion The Modified CSI can be a useful method for detecting strain levels among informal caregivers, and is easily administered and scored. The CSI continues to be a useful measure of caregiver strain for long-term care research and practice.

Recent advances in separation of roughness, waviness and form

Abstract: Engineering surfaces are comprised of a range of spatial wavelengths. Filtering techniques are commonly adopted to separate the different wavelength components into well-defined bandwidths. Filtering is done prior to numerical characterization and it is also essential for extracting information needed to provide process feedback and establish functional correlation. This paper reviews commonly used filters in surface metrology like the 2RC, Gaussian and several new ones currently under research such as the spline, morphological, wavelets, regression filters and robust regression filters. The need for these new filters and examples illustrating the features of these filters are also presented.

iPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning

Abstract: To achieve automatic recommendation of privacy settings for image sharing, a new tool called iPrivacy (image privacy) is developed for releasing the burden from users on setting the privacy preferences when they share their images for special moments. Specifically, this paper consists of the following contributions: 1) massive social images and their privacy settings are leveraged to learn the object-privacy relatedness effectively and identify a set of privacy-sensitive object classes automatically; 2) a deep multi-task learning algorithm is developed to jointly learn more representative deep convolutional neural networks and more discriminative tree classifier, so that we can achieve fast and accurate detection of large numbers of privacy-sensitive object classes; 3) automatic recommendation of privacy settings for image sharing can be achieved by detecting the underlying privacy-sensitive objects from the images being shared, recognizing their classes, and identifying their privacy settings according to the object-privacy relatedness; and 4) one simple solution for image privacy protection is provided by blurring the privacy-sensitive objects automatically. We have conducted extensive experimental studies on real-world images and the results have demonstrated both the efficiency and effectiveness of our proposed approach.