Proposal of a Wireless Power Transfer Technique for Low-Power Multireceiver Applications

In this paper, we proposed and verified the feasibility of a unique wireless power transfer structure called a rail transformer to drive multiple low-power devices such as electronic shelf label (ESL) devices. The rail transformer is composed of a rectangular, circular-shaped transmitting yoke and two transmitting coils to provide wireless power. Multiple receiving yokes coupled with receiving coils are installed across the elongated edge of the transmitting yoke. It can be driven by low-frequency ac power at 50/60 Hz. In our prototype, the transmitting yoke is 900 mm long and 15 mm wide. We obtained the minimal induced wireless power, and the voltage was similar to 61 mW and 3.5 V, which is sufficient to drive a typical ESL device. By designing a nonuniform gap thickness between the transmitting and the receiving yokes at the specific locations, we improved the uniformity of the induced power for multiple ESL devices.ArticleIEEE TRANSACTIONS ON MAGNETICS. 51(11):8402904 (2015)journal articl

Networks, Hierarchies, and Markets: Aggregating Collective Problem Solving in Social Systems

How do decentralized systems collectively solve problems? Here we explore the interplay among three canonical forms of collective organization--markets, networks, and hierarchies--in aggregating decentralized problem solving. We examine these constructs in the context of how the offices of members of Congress individually and collectively wrestle with the Internet, and, in particular, their use of official websites. Each office is simultaneously making decisions about how to utilize their website. These decisions are only partially independent, where offices are looking at each other for lessons, following the same directives from above about what to do with the websites, and confront the same array of potential vendors to produce their website. Here we present the initial results from interviews with 99 Congressional offices and related survey of 100 offices about their decisions regarding how to use official Member websites. Strikingly, we find that there are relatively few efforts by offices to evaluate what constituents want or like on their websites. Further, we find that diffusion occurs at the "tip of the iceberg": offices often look at each others' websites (which are publicly visible), but rarely talk to each other about their experiences or how they manage what is on their websites (which are not publicly visible). We also find that there are important market drivers of what is on websites, with the emergence of a small industry of companies seeking to serve the 440 Members. Hierarchical influences--through the House and through the party conferences--also constrain and subsidize certain practices.

Interactive tabletops in education

Interactive tabletops are gaining increased attention from CSCL researchers. This paper analyses the relation between this technology and teaching and learning processes. At a global level, one could argue that tabletops convey a socio-constructivist flavor: they support small teams that solve problems by exploring multiple solutions. The development of tabletop applications also witnesses the growing importance of face-to-face collaboration in CSCL and acknowledges the physicality of learning. However, this global analysis is insufficient. To analyze the educational potential of tabletops in education, we present 33 points that should be taken into consideration. These points are structured on four levels: individual user-system interaction, teamwork, classroom orchestration, and socio-cultural contexts. God lies in the detail

Enhance Representation Learning of Clinical Narrative with Neural Networks for Clinical Predictive Modeling

Medicine is undergoing a technological revolution. Understanding human health from clinical data has major challenges from technical and practical perspectives, thus prompting methods that understand large, complex, and noisy data. These methods are particularly necessary for natural language data from clinical narratives/notes, which contain some of the richest information on a patient. Meanwhile, deep neural networks have achieved superior performance in a wide variety of natural language processing (NLP) tasks because of their capacity to encode meaningful but abstract representations and learn the entire task end-to-end. In this thesis, I investigate representation learning of clinical narratives with deep neural networks through a number of tasks ranging from clinical concept extraction, clinical note modeling, and patient-level language representation. I present methods utilizing representation learning with neural networks to support understanding of clinical text documents. I first introduce the notion of representation learning from natural language processing and patient data modeling. Then, I investigate word-level representation learning to improve clinical concept extraction from clinical notes. I present two works on learning word representations and evaluate them to extract important concepts from clinical notes. The first study focuses on cancer-related information, and the second study evaluates shared-task data. The aims of these two studies are to automatically extract important entities from clinical notes. Next, I present a series of deep neural networks to encode hierarchical, longitudinal, and contextual information for modeling a series of clinical notes. I also evaluate the models by predicting clinical outcomes of interest, including mortality, length of stay, and phenotype predictions. Finally, I propose a novel representation learning architecture to develop a generalized and transferable language representation at the patient level. I also identify pre-training tasks appropriate for constructing a generalizable language representation. The main focus is to improve predictive performance of phenotypes with limited data, a challenging task due to a lack of data. Overall, this dissertation addresses issues in natural language processing for medicine, including clinical text classification and modeling. These studies show major barriers to understanding large-scale clinical notes. It is believed that developing deep representation learning methods for distilling enormous amounts of heterogeneous data into patient-level language representations will improve evidence-based clinical understanding. The approach to solving these issues by learning representations could be used across clinical applications despite noisy data. I conclude that considering different linguistic components in natural language and sequential information between clinical events is important. Such results have implications beyond the immediate context of predictions and further suggest future directions for clinical machine learning research to improve clinical outcomes. This could be a starting point for future phenotyping methods based on natural language processing that construct patient-level language representations to improve clinical predictions. While significant progress has been made, many open questions remain, so I will highlight a few works to demonstrate promising directions

Space benefits: The secondary application of aerospace technology in other sectors of the economy

Benefit cases of aerospace technology utilization are presented for manufacturing, transportation, utilities, and health. General, organization, geographic, and field center indexes are included

Special Libraries, October 1961

Volume 52, Issue 8https://scholarworks.sjsu.edu/sla_sl_1961/1007/thumbnail.jp

Special Libraries, October 1961

Volume 52, Issue 8https://scholarworks.sjsu.edu/sla_sl_1961/1007/thumbnail.jp

Integration of UTM and U-Space on Norwegian continental shelf

In this master thesis, we present an overview of the U-Space and Regulations in Europe, while also taking into consideration the progression of the integration of both parts in Norwegian airspace over the Norwegian continental shelf. This thesis is mainly separated into three parts. The first part is taking a look into the European Union's roadmap/plan for establishing an Unmanned Aircraft System Traffic Management (UTM) and how they plan to develop their system into a single European sky. The end goal is that essentially every operator of a drone can do so all over Europe without having any issues with crossing borders or different regulations. The second part of the thesis is dedicated to a detailed insight into the technical side of a UTM, the different layers, examples of which systems are the most relevant to be utilized on the Norwegian continental shelf. The third part of this thesis is dedicated to looking at the regulatory side of things, in regards of the UTM system in itself, different factors of drone operations, requirements for every part of an operation. In addition, discussing and concluding about everything we have been though in the thesis. Additionally, there are uses cases where everything comes together to see how it would work in practise and in certain scenarios. In the final part of the thesis the previous parts of the project will be discussed, as well as drawing final conclusions to the project