Aug
31
Optimization of Frame Structure for WiMAX Multi-hop Networks
August 31, 2009 | Comments Off
p class=”abstract”div class=”Abstract”To enhance the system throughput and to extend coverage of IEEE 802.16 networks, relay stations can be implemented. If a user
station is attached to the base station (BS) through several relays stations (RS), a multi-hop communication occurs. To enable
multi-hop communication, the IEEE 802.16j standard proposes two approaches how RSs can be implemented into the network. The
first approach groups BSs and several RSs into a multi-frame with repetition of relay zones. In the second approach, the BS
schedules several relay zones in one frame. While the first approach causes long packet delays, the second approach has high
requirements on RS’s processing capabilities. This paper proposes an optimized frame structure that allows using second approach
whilst the requirements on RS’s processing time are still kept in reasonable range. The obtained simulation results indicate
that packet delays in downlink and uplink direction can be significantly reduced.
/div/pul
lispan class=”labelName”Content Type /spanspan class=”labelValue”Book Chapter/span/liliDOI 10.1007/978-3-642-03841-9_10/lilispan class=”labelName”Authors/spanul
liPavel Mach, Czech Technical University in Prague Faculty of Electrical Engineering Technicka 2 Prague 16627 Czech Republic/liliRobert Bestak, Czech Technical University in Prague Faculty of Electrical Engineering Technicka 2 Prague 16627 Czech Republic/li
/ul/li
/ulul class=”parents”
ul class=”details”
lispan class=”header labelName”Book Series /spanspan class=”labelValue”a href=”http://www.springerlink.com/content/121483/”IFIP Advances in Information and Communication Technology/a/span/lilispan class=”labelName”Online ISSN /spanspan class=”labelValue”1868-422X/span/lilispan class=”labelName”Print ISSN /spanspan class=”labelValue”1868-4238/span/li
/ulul class=”details”
lispan class=”header labelName”Book Series Volume /spanspan class=”labelValue”Volume 308/2009/span/li
/ulul class=”details”
lispan class=”header labelName”Book /spanspan class=”labelValue”a href=”http://www.springerlink.com/content/k6k312p65848/”Wireless and Mobile Networking/a/span/lilispan class=”labelName”DOI /spanspan class=”labelValue”10.1007/978-3-642-03841-9/span/lilispan class=”labelName”Print ISBN /spanspan class=”labelValue”978-3-642-03840-2/span/li
/ul
/ul
Aug
31
On the Impact of Node Placement and Profile Point Selection on Indoor Localization
August 31, 2009 | Comments Off
p class=”abstract”div class=”Abstract”We present an indoor localization technique based on RF profiling using the received signal strength (RSS) measurements from
a set of pre–selected reference points. We do not attach any interpretative significance to the measurements other than use
them to calculate their difference from the measurements of the reference points. We study the performance of our technique
in an environment with multiple adjacent rooms and find that it gives better results compared to the application of the k-Nearest
Neighbor algorithm that has been used in the literature for the same task. We also study the proposed scheme and two other
well–known localization schemes, with respect to the sensitivity of the localization on the number and layout of the reference
points, as well as on the number and layout of the deployed fixed points (pegs) from where the measurements are collected.
We find that one can achieve good localization performance with either fewer reference points or with fewer pegs as long as
their layout is chosen carefully.
/div/pul
lispan class=”labelName”Content Type /spanspan class=”labelValue”Book Chapter/span/liliDOI 10.1007/978-3-642-03841-9_20/lilispan class=”labelName”Authors/spanul
liIsraat Tanzeena Haque, University of Alberta Computing Science Dept. Edmonton AB T6G 2E8 Canada/liliIoanis Nikolaidis, University of Alberta Computing Science Dept. Edmonton AB T6G 2E8 Canada/liliPawel Gburzynski, University of Alberta Computing Science Dept. Edmonton AB T6G 2E8 Canada/li
/ul/li
/ulul class=”parents”
ul class=”details”
lispan class=”header labelName”Book Series /spanspan class=”labelValue”a href=”http://www.springerlink.com/content/121483/”IFIP Advances in Information and Communication Technology/a/span/lilispan class=”labelName”Online ISSN /spanspan class=”labelValue”1868-422X/span/lilispan class=”labelName”Print ISSN /spanspan class=”labelValue”1868-4238/span/li
/ulul class=”details”
lispan class=”header labelName”Book Series Volume /spanspan class=”labelValue”Volume 308/2009/span/li
/ulul class=”details”
lispan class=”header labelName”Book /spanspan class=”labelValue”a href=”http://www.springerlink.com/content/k6k312p65848/”Wireless and Mobile Networking/a/span/lilispan class=”labelName”DOI /spanspan class=”labelValue”10.1007/978-3-642-03841-9/span/lilispan class=”labelName”Print ISBN /spanspan class=”labelValue”978-3-642-03840-2/span/li
/ul
/ul
Aug
30
Web 2.0 als Innovationsplattform
August 30, 2009 | Comments Off
p class=”abstract”div class=”Abstract”Vor dem Hintergrund immer kürzerer Innovationszyklen hängt die Wettbewerbsfähigkeit in vielen Märkten entscheidend davon ab,
wie gut es einem Unternehmen gelingt, multimediale Web-2.0-Collaboration gezielt zur Optimierung ihres Innovationsmanagements
einzusetzen. Wie das Beispiel Cisco zeigt, kommt es hierbei zum einen darauf an, möglichst viele Innovationsideen zu akquirieren
– sowohl innerhalb des eigenen Unternehmens als auch von externer Seite. Zum anderen aber werden klar definierte Evaluierungsprozesse
für diesen Ideenpool benötigt: Aussichtsreiche Innovationsanstöße müssen auf effiziente Weise selektiert werden, um sie anschließend
so schnell wie möglich zu marktreifen Lösungen und Produkten mit tragfähigen Geschäftsmodellen weiterzuentwickeln. Für beide
Aufgabenfelder hält das Web 2.0 ein geeignetes Instrumentarium bereit.
/div/pul
lispan class=”labelName”Content Type /spanspan class=”labelValue”Book Chapter/span/liliDOI 10.1007/978-3-8349-8242-1_23/lilispan class=”labelName”Authors/spanul
liJan Roschek/li
/ul/li
/ulul class=”parents”
ul class=”details”
lispan class=”header labelName”Book /spanspan class=”labelValue”a href=”http://www.springerlink.com/content/u17xj7/”Kommunikation als Erfolgsfaktor im Innovationsmanagement/a/span/lilispan class=”labelName”DOI /spanspan class=”labelValue”10.1007/978-3-8349-8242-1/span/lilispan class=”labelName”Online ISBN /spanspan class=”labelValue”978-3-8349-8242-1/span/lilispan class=”labelName”Print ISBN /spanspan class=”labelValue”978-3-8349-1659-4/span/li
/ulul class=”details”
lispan class=”header labelName”Book Part /spanspan class=”labelValue”a href=”http://www.springerlink.com/content/v46g92w7w71q/”Teil 4/a/span/li
/ul
/ul
Aug
30
Kommunikation als konstitutives Element im Innovationsmanagement
August 30, 2009 | Comments Off
p class=”abstract”div class=”Abstract”Die Parado×ie ist unübersehbar: Während Theorie und Praxis offene Innovationsprozesse sowie eine vielschichtige Einbindung
interner und externer Stakeholder fordern, werden die zugrunde liegenden Verständnisse von Innovation und Kommunikation nur
selten hinterfragt. Dadurch mangelt es dem Paradigma der Open Innovation an einem Fundament und einer Begründung jenseits
der ökonomischen Zweckrationalität. Genau diese lässt sich aber nur schwer nachweisen: Kosten und Nutzen verschiedener Konzepte
des Innovationsmanagements können aufgrund der großen Zahl interdependenter Einflussfaktoren nur näherungsweise berechnet
werden.
/div/pul
lispan class=”labelName”Content Type /spanspan class=”labelValue”Book Chapter/span/liliDOI 10.1007/978-3-8349-8242-1_2/lilispan class=”labelName”Authors/spanul
liAnsgar Zerfaß/li
/ul/li
/ulul class=”parents”
ul class=”details”
lispan class=”header labelName”Book /spanspan class=”labelValue”a href=”http://www.springerlink.com/content/u17xj7/”Kommunikation als Erfolgsfaktor im Innovationsmanagement/a/span/lilispan class=”labelName”DOI /spanspan class=”labelValue”10.1007/978-3-8349-8242-1/span/lilispan class=”labelName”Online ISBN /spanspan class=”labelValue”978-3-8349-8242-1/span/lilispan class=”labelName”Print ISBN /spanspan class=”labelValue”978-3-8349-1659-4/span/li
/ulul class=”details”
lispan class=”header labelName”Book Part /spanspan class=”labelValue”a href=”http://www.springerlink.com/content/j6k65025275v/”Teil 1/a/span/li
/ul
/ul
Aug
30
Social Software für Open Innovation
August 30, 2009 | Comments Off
p class=”abstract”div class=”Abstract”Unternehmen, die ihre Innovationsprozesse öffnen wollen, müssen die Integration der drei zentralen Gruppen von Innovatoren
– Kerninnovatoren, periphere Innovato-ren und externe Innovatoren – realisieren. Hierfür eignen sich verschiedene Lösungen
aus dem Bereich der, den Ideen des Web 2.0 folgenden, Social Software. Eine Strukturierung der Lösungen ist anhand des Unterstützungsschwerpunkts
nach Inhalt, Kommunikation sowie Identität und Netzwerk möglich. Dabei hat jede Innovatorengruppe spezifische Bedürfnisse
in ihren innovativen Aktivitäten, die durch das Angebot der passenden Social Software unterstützt werden können.
/div/pul
lispan class=”labelName”Content Type /spanspan class=”labelValue”Book Chapter/span/liliDOI 10.1007/978-3-8349-8242-1_8/lilispan class=”labelName”Authors/spanul
liMichael Koch/liliAngelika C. Bullinger/liliKathrin M. Möslein/li
/ul/li
/ulul class=”parents”
ul class=”details”
lispan class=”header labelName”Book /spanspan class=”labelValue”a href=”http://www.springerlink.com/content/u17xj7/”Kommunikation als Erfolgsfaktor im Innovationsmanagement/a/span/lilispan class=”labelName”DOI /spanspan class=”labelValue”10.1007/978-3-8349-8242-1/span/lilispan class=”labelName”Online ISBN /spanspan class=”labelValue”978-3-8349-8242-1/span/lilispan class=”labelName”Print ISBN /spanspan class=”labelValue”978-3-8349-1659-4/span/li
/ulul class=”details”
lispan class=”header labelName”Book Part /spanspan class=”labelValue”a href=”http://www.springerlink.com/content/n0v86n15258t/”Teil 2/a/span/li
/ul
/ul
Aug
29
Development of Technology Training for Destination Marketing Organisations
August 29, 2009 | Comments Off
p class=”abstract”div class=”Abstract”Most DMOs are not familiar with new technologies, and they have not had opportunities to learn about and evaluate dynamically
changing technologies. Therefore, this study aims to (a) develop educational content in an effort to provide tourism professionals
with basic training in new technologies and (b) discover their opinions about technologies through training programs. As training
content, tourism-related technologies were separated into Web-based technologies and hardware-based. In addition to the development
of training content, this study examined how familiar tourism professionals in DMOs are with these technologies, the perceived
usefulness of such technologies, and the DMOs’ willingness to use them after a training program on new tourism technologies.
Findings suggest that most tourism professionals were not familiar with these technologies and that You Tube was ranked as
the most helpful in their destination marketing, followed by RSS feeds and Google Earth.
/div/pul
lispan class=”labelName”Content Type /spanspan class=”labelValue”Book Chapter/span/liliDOI 10.1007/978-3-211-93971-0_33/lilispan class=”labelName”Authors/spanul
liByeong Cheol Lee, University of Illinois at Urbana-Champaign Department of Recreation, Sport, and Tourism USA/liliBruce Wicks, University of Illinois at Urbana-Champaign Department of Recreation, Sport, and Tourism USA/liliWei-Jue Huang, Clemson University Department of Parks, Recreation, and Tourism Management USA/li
/ul/li
/ulul class=”parents”
ul class=”details”
lispan class=”header labelName”Book /spanspan class=”labelValue”a href=”http://www.springerlink.com/content/l8162r/”Information and Communication Technologies in Tourism 2009/a/span/lilispan class=”labelName”DOI /spanspan class=”labelValue”10.1007/978-3-211-93971-0/span/lilispan class=”labelName”Online ISBN /spanspan class=”labelValue”978-3-211-93971-0/span/lilispan class=”labelName”Print ISBN /spanspan class=”labelValue”978-3-211-93970-3/span/li
/ulul class=”details”
lispan class=”header labelName”Book Part /spanspan class=”labelValue”a href=”http://www.springerlink.com/content/utr584m483u7/”8/a/span/li
/ul
/ul
Aug
29
Capital City Tourism: Online Destination Image of Washington, DC
August 29, 2009 | Comments Off
p class=”abstract”div class=”Abstract”National capital cities symbolize the image and prestige of a nation. Different from general urban tourism, “capital city
tourism” is an emerging topic in tourism research. Being both a capital and a destination, capital cities possess a unique
image that is favourable in tourism promotion. This study explores the destination image of national capital cities through
qualitative content analysis of travel-related websites on Washington, DC. Six analytical themes are identified: History,
Wealth, Power, Diversity, Ideal, and Patriotism. Findings suggest that national capital cities incorporate political messages
in its destination image, promoting not only the city itself as a destination but also national pride and identity.
/div/pul
lispan class=”labelName”Content Type /spanspan class=”labelValue”Book Chapter/span/liliDOI 10.1007/978-3-211-93971-0_30/lilispan class=”labelName”Authors/spanul
liWei-Jue Huang, Clemson University Department of Parks, Recreation Tourism Management USA/liliByeong Cheol Lee, University of Illinois at Urbana-Champaign Department of Recreation, Sport Tourism USA/li
/ul/li
/ulul class=”parents”
ul class=”details”
lispan class=”header labelName”Book /spanspan class=”labelValue”a href=”http://www.springerlink.com/content/l8162r/”Information and Communication Technologies in Tourism 2009/a/span/lilispan class=”labelName”DOI /spanspan class=”labelValue”10.1007/978-3-211-93971-0/span/lilispan class=”labelName”Online ISBN /spanspan class=”labelValue”978-3-211-93971-0/span/lilispan class=”labelName”Print ISBN /spanspan class=”labelValue”978-3-211-93970-3/span/li
/ulul class=”details”
lispan class=”header labelName”Book Part /spanspan class=”labelValue”a href=”http://www.springerlink.com/content/r8×6rr0716×0/”7/a/span/li
/ul
/ul
Aug
27
Found in Translation
August 27, 2009 | Comments Off
p class=”abstract”div class=”Abstract”We present a complete working system that gathers multilingual news items from the Web, translates them into English, categorises
them by topic and geographic location and presents them to the final user in a uniform way. Currently, the system crawls 560
news outlets, in 22 different languages, from the 27 European Union countries. Data gathering is based on RSS crawlers, machine
translation on Moses and the text categorisation on SVMs. The system also presents on a European map statistical information
about the amount of attention devoted to the various topics in each of the 27 EU countries. The integration of Support Vector
Machines, Statistical Machine Translation, Web Technologies and Computer Graphics delivers a complete system where modern
Statistical Machine Learning is used at multiple levels and is a crucial enabling part of the resulting functionality.
/div/pul
lispan class=”labelName”Content Type /spanspan class=”labelValue”Book Chapter/span/liliDOI 10.1007/978-3-642-04174-7_55/lilispan class=”labelName”Authors/spanul
liMarco Turchi, Queen’s Building Department of Engineering Mathematics/liliIlias Flaounas, Merchant Venturers Building, Bristol University Department of Computer Science Bristol United Kingdom/liliOmar Ali, Queen’s Building Department of Engineering Mathematics/liliTijl De Bie, Queen’s Building Department of Engineering Mathematics/liliTristan Snowsill, Queen’s Building Department of Engineering Mathematics/liliNello Cristianini, Queen’s Building Department of Engineering Mathematics/li
/ul/li
/ulul class=”parents”
ul class=”details”
lispan class=”header labelName”Book Series /spanspan class=”labelValue”a href=”http://www.springerlink.com/content/105633/”Lecture Notes in Computer Science/a/span/lilispan class=”labelName”Online ISSN /spanspan class=”labelValue”1611-3349/span/lilispan class=”labelName”Print ISSN /spanspan class=”labelValue”0302-9743/span/li
/ulul class=”details”
lispan class=”header labelName”Book Series Volume /spanspan class=”labelValue”Volume 5782/2009/span/li
/ulul class=”details”
lispan class=”header labelName”Book /spanspan class=”labelValue”a href=”http://www.springerlink.com/content/p1q374g75650/”Machine Learning and Knowledge Discovery in Databases/a/span/lilispan class=”labelName”DOI /spanspan class=”labelValue”10.1007/978-3-642-04174-7/span/lilispan class=”labelName”Print ISBN /spanspan class=”labelValue”978-3-642-04173-0/span/li
/ul
/ul
Aug
27
Overview of Supervised Learning
August 27, 2009 | Comments Off
p class=”abstract”div class=”Abstract”The first three examples described in Chapter 1 have several components in common. For each there is a set of variables that
might be denoted as iinputs/i, which are measured or preset. These have some influence on one or more ioutputs/i. For each example the goal is to use the inputs to predict the values of the outputs. This exercise is called isupervised learning/i.
div class=”AbstractPara”
div class=”"We have used the more modern Language of machine learning. In the statistical literature the inputs are often called the ipredictors/i, a term we will use interchangeably with inputs, and more classically the iindependent variables/i. In the pattern recognition literature the term ifeatures/i is preferred, which we use as well. The outputs are called the iresponses/i, or classically the idependent variables/i.
/div
/div
/div/pul
lispan class=”labelName”Content Type /spanspan class=”labelValue”Book Chapter/span/liliDOI 10.1007/978-0-387-84858-7_2/li
/ulul class=”parents”
ul class=”details”
lispan class=”header labelName”Book Series /spanspan class=”labelValue”a href=”http://www.springerlink.com/content/x1376g/”Springer Series in Statistics/a/span/lilispan class=”labelName”Print ISSN /spanspan class=”labelValue”0172-7397/span/li
/ulul class=”details”
lispan class=”header labelName”Book /spanspan class=”labelValue”a href=”http://www.springerlink.com/content/m3322u/”The Elements of Statistical Learning/a/span/lilispan class=”labelName”DOI /spanspan class=”labelValue”10.1007/978-0-387-84858-7/span/lilispan class=”labelName”Online ISBN /spanspan class=”labelValue”978-0-387-84858-7/span/lilispan class=”labelName”Print ISBN /spanspan class=”labelValue”978-0-387-84857-0/span/li
/ul
/ul
Aug
27
Random Forests
August 27, 2009 | Comments Off
p class=”abstract”div class=”Abstract”Bagging or ibootstrap aggregation/i Section 8.7 is a technique for reducing the variance of an estimated prediction function. Bagging seems to work especially
well for high-variance, low-bias procedures, such as trees. For regression, we simply fit the same regression tree many times
to bootstrap-sampled versions of the training data, and average the result. For classification, a icommittee/i of trees each cast a vote for the predicted class.
div class=”AbstractPara”
div class=”"Boosting in Chapter 10 was initially proposed as a committee method as well, although unlike bagging, the committee of iweak learners/i evolves over time, and the members cast a weighted vote. Boosting appears to dominate bagging on most problems, and became
the preferred choice.
/div
/div
/div/pul
lispan class=”labelName”Content Type /spanspan class=”labelValue”Book Chapter/span/liliDOI 10.1007/978-0-387-84858-7_15/li
/ulul class=”parents”
ul class=”details”
lispan class=”header labelName”Book Series /spanspan class=”labelValue”a href=”http://www.springerlink.com/content/x1376g/”Springer Series in Statistics/a/span/lilispan class=”labelName”Print ISSN /spanspan class=”labelValue”0172-7397/span/li
/ulul class=”details”
lispan class=”header labelName”Book /spanspan class=”labelValue”a href=”http://www.springerlink.com/content/m3322u/”The Elements of Statistical Learning/a/span/lilispan class=”labelName”DOI /spanspan class=”labelValue”10.1007/978-0-387-84858-7/span/lilispan class=”labelName”Online ISBN /spanspan class=”labelValue”978-0-387-84858-7/span/lilispan class=”labelName”Print ISBN /spanspan class=”labelValue”978-0-387-84857-0/span/li
/ul
/ul