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2017.html
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<!-- This document was automatically generated with bibtex2html 1.96
(see http://www.lri.fr/~filliatr/bibtex2html/),
with the following command:
bibtex2html -dl -nodoc -nobibsource -nokeys -nokeywords -nofooter 2017.bib -->
<p><a name="csdl2-17-01"></a>
Anthony J. Christe.
Data management for distributed sensor networks: A literature review.
Technical Report CSDL-17-01, University of Hawaii, Honolulu, HI,
March 2017.
[ <a href="http://csdl.ics.hawaii.edu/techreports/2017/17-01/17-01.pdf">.pdf</a> ]
<blockquote><font size="-1">
Sensor networks are spatially distributed autonomous sensors that monitor the physical world around them and often communicate those reading over a network to a server or servers. Sensor networks can benefit from the generally “unlimited resources” of the cloud, namely processing, storage, and network resources. This literature review surveys the major components of distributed data management, namely, cloud computing, distributed persistence models, and distributed analytics.
</font></blockquote>
<p>
</p>
<p><a name="csdl2-17-02"></a>
Sergey Negrashov.
Compression and compressed sensing in bandwidth constrained sensor
networks.
Technical Report CSDL-17-02, University of Hawaii, Honolulu, HI,
March 2017.
[ <a href="http://csdl.ics.hawaii.edu/techreports/2017/17-02/17-02.pdf">.pdf</a> ]
<blockquote><font size="-1">
Improvements in sensor and radio technologies allow for creation of cheap sensors interconnected via radio links and the Internet. These advancements opened the door for creation of large area autonomous monitoring networks referred to as sensor networks. Bandwidth requirements of a wireless sensor network has a direct effect on its performance. This paper describes three bandwidth reduction methods: lossless compression, lossy compression, and compressed sensing.
</font></blockquote>
<p>
</p>
<p><a name="csdl2-17-03"></a>
Anthony J. Christe, Sergey Negrashov, Philip M. Johnson, Dylan Nakahodo, David
Badke, and David Aghalarpour.
Opq version 2: An architecture for distributed, real-time, high
performance power data acquisition, analysis, and visualization.
In <em>Proceedings of the Seventh Annual IEEE International
Conference on CYBER Technology in Automation, Control, and Intelligent
Systems</em>, Honolulu, HI, USA, July 2017.
[ <a href="http://csdl.ics.hawaii.edu/techreports/2017/17-03/17-03.pdf">.pdf</a> ]
<blockquote><font size="-1">
OpenPowerQuality (OPQ) is a framework that supports end-to-end capture, analysis, and visualizations of distributed real-time power quality (PQ) data. Version 2 of OPQ builds on version 1 by providing higher sampling rates, optional battery backup, end-to-end security, GPS synchronization, pluggable analysis, and a real-time visualization framework. OPQ provides real-time distributed power measurements which allows users to see both local PQ events and grid-wide PQ events. The OPQ project has three principal components: back-end hardware for making power measurements, middleware for data acquisition and analysis, and a front-end providing visualizations. OPQBox2 is a hardware platform that takes PQ measurements, provides onboard analysis, and securely transfers data to our middleware. The OPQ middleware performs filtering on the OPQBox2 sensor data and performs high-level PQ analysis. The results of our PQ analysis and real-time state of the sensor network are displayed using OPQView. We’ve collected distributed PQ datafrom locations across Oahu, Hawaii and have demonstrated our ability to detect both local and grid-wide power quality events.
</font></blockquote>
<p>
</p>
<p><a name="csdl2-17-04"></a>
Pavel Senin, Jessica Lin, Xing Wang, Tim Oates, Sunil Gandhi, Arnold
Boedihardjo, Crystal Chen, and Susan Frankenstein.
Grammarviz 3.0: Interactive discovery of variable-length time series
patterns.
<em>ACM Transactions on Knowledge Discovery from Data</em>, March
2017.
[ <a href="http://csdl.ics.hawaii.edu/techreports/2017/17-04/17-04.pdf">.pdf</a> ]
<blockquote><font size="-1">
The problems of recurrent and anomalous pattern discovery in time series, e.g. motifs and discords, respectively, have received a lot of attention from researchers in the past decade. However, since the pattern search space is usually intractable, most existing detection algorithms require that the patterns have discriminative characteristics and have its length known in advance and provided as input, which is an unreasonable requirement for many real-world problems. In addition, patterns of similar structure, but of different lengths may co-exist in a time series.
Addressing these issues, we have developed algorithms for variable length time series pattern discovery that are based on symbolic discretization and grammar inference – two techniques whose combination enables the structured reduction of the search space and discovery of the candidate patterns in linear time.
In this work we present GrammarViz 3.0 – a software package that provides implementations of proposed algorithms and GUI for interactive variable-length time series pattern discovery. The current version of the software provides an alternative grammar inference algorithm that improves the time series motif discovery workflow, and introduces an experimental procedure for automated discretization parameter selection that builds upon the minimum cardinality maximum cover principle and aids the time series recurrent and anomalous pattern discovery.
</font></blockquote>
<p>
</p>
<p><a name="csdl2-17-05"></a>
Amy M. Takayesu.
RADGRAD: Using degree planning, social networking, and gamification
to improve academic, professional, and social engagement during the
undergraduate computer science degree experience.
Technical Report CSDL-17-05, University of Hawaii, Honolulu, HI,
July 2017.
[ <a href="http://csdl.ics.hawaii.edu/techreports/2017/17-05/17-05.pdf">.pdf</a> ]
<blockquote><font size="-1">
A casual analysis of the Hawaii technology community site, TechHui, suggests that over the past decade, recent alumni and current undergraduates of the Information and Computer Science (ICS) program at the University of Hawaii at Manoa (UHM) have experienced several problems with various academic, professional, and social aspects of their ICS experience. Existing degree planning systems such as STAR, Starfish by Hobsons, Blackboard Planner and Coursicle fail to provide the specific support that ICS students need to create a complete and comprehensive degree plan. Existing academic social networks such as LinkedIn, TechHui and Rate My Professors fail to connect students closely with professors and alumni. Current popular video games suggest that several gamification features could encourage ICS students to achieve higher goals. A new system called RadGrad combines degree planning, social networking, and gamification in a novel way that aims to give ICS undergraduates the support they need to succeed and redefines what it means to have a successful degree experience. The overall goal of this thesis is to justify the initial RadGrad system design and establish baseline values for future studies. A baseline student survey conducted in Spring 2017 reveals current and more detailed student perceptions on the academic, professional, and social aspects of the ICS degree experience prior to using RadGrad. These baseline results can be used in a future study to measure if RadGrad has had any effects on the students.
</font></blockquote>
<p>
</p>