Antennas Embedded in the Front Frame of Smart Glasses

Ergonomic head-up display is enabled by reliable, low latency, radio frequency (RF) communication between smart glasses and smartphones. The form factor of antennas on head-worn frames is impacted by the proximity of the frame to the human head and by cross-body interference. In addition, the large bandwidth required on the glass-to-phone link entails the use of multiple antennas, limiting the space available to route signals. The presence of antennas also restricts the choice of material for the front frames. This disclosure describes a multi-element, front-frame structure for smart glasses that enables metallic front-frame materials that enable superior cosmetic, structural, and communication design options. The described structure reduces the space occupied by antennas and reduces the complexity of assembly of multiple discrete antennas. The propagation loss presented by the human head is nullified, enabling lower radiated power, lower power consumption, and smaller and/or lighter smart glasses

Wildcats never trail, Clobber Morehead State 79-54

Article published in the Lexington Herald-Leader on November 22, 2019 on Morehead State vs. University of Kentucky women\u27s basketball game.https://scholarworks.moreheadstate.edu/college_histories/1259/thumbnail.jp

Session 5: \u3cem\u3eEquipment Finance Credit Risk Modeling - A Case Study in Creative Model Development & Nimble Data Engineering\u3c/em\u3e

This presentation will focus first on providing an overview of Channel and the Risk Analytics team that performed this case study. Given that context, we’ll then dive into our approach for building the modeling development data set, techniques and tools used to develop and implement the model into a production environment, and some of the challenges faced upon launch. Then, the presentation will pivot to the data engineering pipeline. During this portion, we will explore the application process and what happens to the data we collect. This will include how we extract & store the data along with how it is integrated into our other systems for decision purposes. We will also talk about how the data is transformed from a raw, sometimes unstructured state, to something more usable by a data science team – and demonstrate how this data was harnessed to help guide model enhancements as key opportunity areas have been identified

Systems Engineering Lessons Learned for Class D Missions

One of NASA's goals within human exploration is to determine how to get humans to Mars safely and to live and work on the Martian surface. To accomplish this goal, several smaller missions act as stepping-stones to the larger end goal. NASA uses these smaller missions to develop new technologies and learn about how to survive outside of Low Earth Orbit for long periods. Additionally, keeping a cadence of these missions allows the team to maintain proficiency in the complex art of bringing spacecraft to fruition. Many of these smaller missions are robotic in nature and have smaller timescales, whereas there are others that involve crew and have longer mission timelines. Given the timelines associated with these various missions, different levels of risk and rigor need to be implemented to be more in line with what is appropriate for the mission. Thus, NASA has four different classifications that range from Class A to Class D based on the mission details. One of these projects is the Resource Prospector (RP) Mission, which is a multi-center and multi-institution collaborative project to search for volatiles in the polar regions of the Moon. The RP mission is classified as a Class D mission and as such, has the opportunity to more tightly manage, and therefore accept, greater levels of risk. The requirements for Class D missions were at the forefront of the design and thus presented unique challenges in vehicle development and systems engineering processes. This paper will discuss the systems engineering process at NASA and how that process is tailored for Class D missions, specifically the RP mission

Second Year Results from Razor C.O.A.C.H.

In this paper, we present the results of a random assignment evaluation on the Razor C.O.A.C.H. (Creating Opportunities for Arkansan’s Career Hopes) program. As the program started in the 2012-13 school year, this evaluation analyzes the impact of the program in its second year of operation and includes two cohorts of students. The focus of the comprehensive evaluation is the impacts of the program on students’ academic outcomes, short-term noncognitive outcomes, short-term college and career readiness outcomes, and post-secondary outcomes

Digital Phenotyping and Sensitive Health Data: Implications for Data Governance

Mobile and wearable devices, such as smartwatches and fitness trackers, increasingly enable the continuous collection of physiological and behavioural data that permit inferences about users’ physical and mental health. Growing consumer adoption of these technologies has reduced the cost of generating clinically meaningful data. This can help reduce medical research costs and aid large-scale studies. However, the collection, processing, and storage of data comes with significant ethical, security, and data governance considerations. A complex ecosystem is developing, with the need for collaboration among researchers, healthcare providers, and a broad range of entities across public and private sectors, some of which are not traditionally associated with healthcare. This has raised important questions in the literature regarding the role of the individual as a patient, customer, research participant, researcher, and user when consenting to data processing in this ecosystem. Here, we use the emerging concept of “digital phenotyping” to highlight key lessons for data governance which draw on parallels with the history of genomics research, while highlighting areas where digital phenotyping will require novel governance frameworks.I.P.P. work is supported by GlaxoSmithKline and EPSRC through an iCase fellowship (17100053); D.S. work is supported by the Embiricos Trust Scholarship of Jesus College Cambridge, and EPSRC through Grant DTP (EP/N509620/1); J.C. is the recipient of a doctoral scholarship from The Alan Turing Institute and J.M. is supported by the Wellcome Trust

A search for rapidly pulsating hot subdwarf stars in the GALEX survey

NASA's Galaxy Evolution Explorer (GALEX) provided near- and far-UV observations for approximately 77 percent of the sky over a ten-year period; however, the data reduction pipeline initially only released single NUV and FUV images to the community. The recently released Python module gPhoton changes this, allowing calibrated time-series aperture photometry to be extracted easily from the raw GALEX data set. Here we use gPhoton to generate light curves for all hot subdwarf B (sdB) stars that were observed by GALEX, with the intention of identifying short-period, p-mode pulsations. We find that the spacecraft's short visit durations, uneven gaps between visits, and dither pattern make the detection of hot subdwarf pulsations difficult. Nonetheless, we detect UV variations in four previously known pulsating targets and report their UV pulsation amplitudes and frequencies. Additionally, we find that several other sdB targets not previously known to vary show promising signals in their periodograms. Using optical follow-up photometry with the Skynet Robotic Telescope Network, we confirm p-mode pulsations in one of these targets, LAMOST J082517.99+113106.3, and report it as the most recent addition to the sdBVr class of variable stars.Comment: 11 Pages, 8 Figures, Accepted for publication in the Astrophysical Journa

Senior Recital: Zach Wilson, jazz piano

This recital is presented in partial fulfillment of requirements for the degree Bachelor of Music in Performance. Mr. Wilson studies jazz piano with Tyrone Jackson.https://digitalcommons.kennesaw.edu/musicprograms/2214/thumbnail.jp