2,738 research outputs found
TechNews digests: Jan - Nov 2009
TechNews is a technology, news and analysis service aimed at anyone in the education sector keen to stay informed about technology developments, trends and issues. TechNews focuses on emerging technologies and other technology news. TechNews service : digests september 2004 till May 2010 Analysis pieces and News combined publish every 2 to 3 month
Distributed Online Machine Learning for Mobile Care Systems
Appendix D: Wavecomm Tech Docs removed for copyright reasonsTelecare and especially Mobile Care Systems are getting more and more
popular. They have two major benefits: first, they drastically improve
the living standards and even health outcomes for patients. In addition,
they allow significant cost savings for adult care by reducing the needs
for medical staff. A common drawback of current Mobile Care Systems
is that they are rather stationary in most cases and firmly installed in
patientsâ houses or flats, which makes them stay very near to or even in
their homes. There is also an upcoming second category of Mobile Care
Systems which are portable without restricting the moving space of the
patients, but with the major drawback that they have either very limited
computational abilities and only a rather low classification quality or,
which is most frequently, they only have a very short runtime on battery
and therefore indirectly restrict the freedom of moving of the patients
once again. These drawbacks are inherently caused by the restricted
computational resources and mainly the limitations of battery based power
supply of mobile computer systems.
This research investigates the application of novel Artificial Intelligence
(AI) and Machine Learning (ML) techniques to improve the operation of
2
Mobile Care Systems. As a result, based on the Evolving Connectionist
Systems (ECoS) paradigm, an innovative approach for a highly efficient
and self-optimising distributed online machine learning algorithm called
MECoS - Moving ECoS - is presented. It balances the conflicting needs
of providing a highly responsive complex and distributed online learning
classification algorithm by requiring only limited resources in the form of
computational power and energy. This approach overcomes the drawbacks
of current mobile systems and combines them with the advantages of
powerful stationary approaches. The research concludes that the practical
application of the presented MECoS algorithm offers substantial improvements
to the problems as highlighted within this thesis
Spatial Statistical Data Fusion on Java-enabled Machines in Ubiquitous Sensor Networks
Wireless Sensor Networks (WSN) consist of small, cheap devices that have a combination of sensing, computing and communication capabilities. They must be able to communicate and process data efficiently using minimum amount of energy and cover an area of interest with the minimum number of sensors. This thesis proposes the use of techniques that were designed for Geostatistics and applies them to WSN field. Kriging and Cokriging interpolation that can be considered as Information Fusion algorithms were tested to prove the feasibility of the methods to increase coverage. To reduce energy consumption, a compression method that models correlations based on variograms was developed. A second challenge is to establish the communication to the external networks and to react to unexpected events. A demonstrator that uses commercial Java-enabled devices was implemented. It is able to perform remote monitoring, send SMS alarms and deploy remote updates
Audiovisual preservation strategies, data models and value-chains
This is a report on preservation strategies, models and value-chains for digital file-based audiovisual content. The report includes: (a)current and emerging value-chains and business-models for audiovisual preservation;(b) a comparison of preservation strategies for audiovisual content including their strengths and weaknesses, and(c) a review of current preservation metadata models, and requirements for extension to support audiovisual files
Utilizing Object Compression for Better J2ME Remote Method Invocation in 2.5G Networks
This paper introduces two new Java 2 Platform Micro Edition (J2ME) Remote Method Invocation (RMI) packages. These packages make use of serialized object compression and encryption in order to respectively minimize the transmission time and to establish secure channels. The currently used J2ME RMI package does not provide either of these features. Our packages substantially outperform the existing Java package in the total time needed to compress, transmit, and decompress the object for General Packet Radio Service (GPRS) networks, often called 2.5G networks, even under adverse conditions. The results show that the extra time incurred to compress and decompress serialized objects is small compared to the time required to transmit the object without compression in GPRS networks. Existing RMI code for J2ME can be obliviously used with our new packages
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