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Humlum O.,University of Oslo | Humlum O.,University Center in Svalbard | Solheim J.-E.,University of Oslo | Stordahl K.,Telenor Norway
Global and Planetary Change | Year: 2011

Analytic climate models have provided the means to predict potential impacts on future climate by anthropogenic changes in atmospheric composition. However, future climate development will not only be influenced by anthropogenic changes, but also by natural variations. The knowledge on such natural variations and their detailed character, however, still remains incomplete. Here we present a new technique to identify the character of natural climate variations, and from this, to produce testable forecast of future climate. By means of Fourier and wavelet analyses climate series are decomposed into time-frequency space, to extract information on periodic signals embedded in the data series and their amplitude and variation over time. We chose to exemplify the potential of this technique by analysing two climate series, the Svalbard (78°N) surface air temperature series 1912-2010, and the last 4000. years of the reconstructed GISP2 surface temperature series from central Greenland. By this we are able to identify several cyclic climate variations which appear persistent on the time scales investigated. Finally, we demonstrate how such persistent natural variations can be used for hindcasting and forecasting climate. Our main focus is on identifying the character (timing, period, amplitude) of such recurrent natural climate variations, but we also comment on the likely physical explanations for some of the identified cyclic climate variations. The causes of millennial climate changes remain poorly understood, and this issue remains important for understanding causes for natural climate variability over decadal- and decennial time scales. We argue that Fourier and wavelet approaches like ours may contribute towards improved understanding of the role of such recurrent natural climate variations in the future climate development. © 2011 Elsevier B.V.

Humlum O.,University of Oslo | Humlum O.,University Center in Svalbard | Stordahl K.,Telenor Norway | Solheim J.-E.,University of Tromsø
Global and Planetary Change | Year: 2013

Using data series on atmospheric carbon dioxide and global temperatures we investigate the phase relation (leads/lags) between these for the period January 1980 to December 2011. Ice cores show atmospheric CO2 variations to lag behind atmospheric temperature changes on a century to millennium scale, but modern temperature is expected to lag changes in atmospheric CO2, as the atmospheric temperature increase since about 1975 generally is assumed to be caused by the modern increase in CO2. In our analysis we use eight well-known datasets: 1) globally averaged well-mixed marine boundary layer CO2 data, 2) HadCRUT3 surface air temperature data, 3) GISS surface air temperature data, 4) NCDC surface air temperature data, 5) HadSST2 sea surface data, 6) UAH lower troposphere temperature data series, 7) CDIAC data on release of anthropogene CO2, and 8) GWP data on volcanic eruptions. Annual cycles are present in all datasets except 7) and 8), and to remove the influence of these we analyze 12-month averaged data. We find a high degree of co-variation between all data series except 7) and 8), but with changes in CO2 always lagging changes in temperature. The maximum positive correlation between CO2 and temperature is found for CO2 lagging 11-12months in relation to global sea surface temperature, 9.5-10months to global surface air temperature, and about 9months to global lower troposphere temperature. The correlation between changes in ocean temperatures and atmospheric CO2 is high, but do not explain all observed changes. © 2012 Elsevier B.V.

Dublin, Feb. 27, 2017 (GLOBE NEWSWIRE) -- Research and Markets has announced the addition of SNS Research's new report "The VoLTE (Voice over LTE) Ecosystem: 2016 - 2030 - Opportunities, Challenges, Strategies & Forecasts" to their offering. The VoLTE (Voice over LTE) Ecosystem: 2016 - 2030 - Opportunities, Challenges, Strategies & Forecasts report presents an in-depth assessment of the VoLTE ecosystem including enabling technologies, key market drivers, challenges, collaborative initiatives, regulatory landscape, standardization, opportunities, operator case studies, future roadmap, value chain, ecosystem player profiles and strategies. The report also presents forecasts for VoLTE smartphone shipments, subscriptions, service revenue and infrastructure investments from 2016 till 2030. The forecasts cover 7 individual submarkets and 6 regions. VoLTE (Voice over LTE) technology allows a voice call to be placed over an LTE network, enabling mobile operators to reduce reliance on legacy circuit-switched networks. Powered by IMS (IP Multimedia Subsystem) architecture, VoLTE brings a host of benefits to operators ranging from the ability to refarm legacy 2G and 3G spectrum to offering their subscribers a differentiated service experience through capabilities such as HD voice and video telephony. First deployed by South Korean operators in 2012, VoLTE is beginning to gain momentum globally. As of Q4'2016, more than 80 mobile operators have commercially launched VoLTE services, and several roaming and interoperability agreements are already in place. Estimates suggest that VoLTE service revenue will grow at a CAGR of 34% between 2016 and 2020. By the end of 2020, VoLTE subscribers will account for more than $200 Billion in revenue. Although traditional voice services will constitute a major proportion of this figure, nearly 15% of the revenue will be driven by video calling and supplementary services. The report provides answers to the following key questions: - How big is the VoLTE opportunity? - What trends, challenges and barriers are influencing its growth? - How is the ecosystem evolving by segment and region? - What will the market size be in 2020 and at what rate will it grow? - Which regions and countries will see the highest percentage of growth? - How will VoLTE capable smartphone shipments grow over time? - Who are the key market players and what are their strategies? - How can VoLTE help operators in reducing the flow of voice subscribers to OTT application providers? - What are the prospects of Wi-Fi calling, RCS and WebRTC? - What much will operators invest in VoLTE service assurance solutions? - How can mobile operators and MVNOs capitalize on VoLTE to drive revenue growth? - How can VoLTE help operators in refarming their 2G and 3G spectrum assets? - What is the status of international roaming and VoLTE-to-VoLTE interconnection agreements? - What strategies should VoLTE solution providers and mobile operators adopt to remain competitive? Key Findings - Estimates suggest that by 2020 VoLTE services will account for over $200 Billion in annual service revenue, as mobile operators remain committed to VoLTE as the long term solution to secure a fully native IP-based telephony experience. - As the transition to VoLTE accelerates, mobile operators have already begun shutting down their legacy networks in a bid to reallocate additional spectrum to their LTE networks. - Japan and South Korea have already shut down their 2G networks, and multiple operators in other parts of the world, including the United States, are in the processing of switching off 2G services. Some operators, such as Telenor Norway, are seeking the closure of their 3G networks as early as 2020. - Nearly all VoLTE operators are integrating their VoLTE services with Wi-Fi calling in a bid to offer voice services in areas where their licensed spectrum coverage is limited. - The vendor ecosystem is continuing to consolidate with several acquisitions such as Sonus Networks' recent takeover of IP communications specialist Taqua. Key Topics Covered: 1: Introduction 1.1 Executive Summary 1.2 Topics Covered 1.3 Forecast Segmentation 1.4 Key Questions Answered 1.5 Key Findings 1.6 Methodology 1.7 Target Audience 1.8 Companies & Organizations Mentioned 2: An Overview of VoLTE 2.1 What is VoLTE? 2.2 Architectural Evolution of VoLTE 2.2.1 CSFB (Circuit-Switched Fallback): The First Step Towards VoLTE 2.2.2 The Push From CDMA Operators 2.2.3 Towards an IMS Based VoLTE Solution 2.2.4 SRVCC (Single Radio Voice Call Continuity) 2.2.5 Integrating Video Telephony 2.3 Key Enabling Technologies 2.3.1 VoLTE Infrastructure IMS Core: CSCF, HSS, BGCF & MGCF VoLTE Application Servers SBC (Session Border Controller) MRF (Media Resource Function) PCRF (Policy and Charging Rules Function) 2.3.2 VoLTE Devices 2.3.3 Roaming & Interconnection Technology LBO (Local Breakout) S8HR (S8 Home Routing) 2.4 Market Growth Drivers 2.4.1 Spectral Efficiency & Cost Reduction 2.4.2 Enabling HD Voice, Video Calling & Rich IP Communications 2.4.3 Improved Battery Life 2.4.4 Integration with Wi-Fi: Enhanced Indoor Voice Coverage 2.4.5 Bundling Voice with Other Services 2.4.6 Fighting the OTT Threat 2.5 Market Barriers 2.5.1 Initial Lack of Compatible Devices 2.5.2 Roaming & Interconnect Issues 2.5.3 Limited Revenue Potential 2.5.4 Service Assurance Challenges 3: Collaboration, Standardization & Regulatory Landscape 3.1 3GPP (3rd Generation Partnership Project) 3.1.1 Release 8 3.1.2 Release 9 3.1.3 Release 10 3.1.4 Release 11 3.1.5 Release 12, 13 & Beyond 3.2 GSMA 3.2.1 Feature Requirements IR.92: IMS Profile for Voice and SMS IR.94: IMS Profile for Conversational Video Service 3.2.2 Roaming, Interworking & Other Guidelines IR.64: IMS Service Centralization & Continuity Guidelines IR.65: IMS Roaming & Interworking Guidelines IR.88: LTE Roaming Guidelines 3.3 VoLTE Interworking Technology Consultation Group, Korea 4: VoLTE Deployment Case Studies 4.1 AT&T 4.2 China Mobile 4.3 DT (Deutsche Telekom) 4.4 Du (Emirates Integrated Telecommunications Company) 4.5 EE 4.6 KDDI Corporation 4.7 KT Corporation 4.8 LG Uplus 4.9 NTT DoCoMo 4.10 Orange 4.11 Reliance Jio Infocomm 4.12 Rogers Communications 4.13 Singtel Group 4.14 SK Telecom 4.15 SoftBank Group 4.16 Swisscom 4.17 Telefónica Group 4.18 Telstra 4.19 Verizon Communications 4.20 Vodafone Group 5: VoLTE Industry Roadmap & Value Chain 5.1 Industry Roadmap 5.1.1 2016 - 2020: Large Scale VoLTE Rollouts 5.1.2 2020 - 2025: Building New Services on VoLTE Architecture 5.1.3 2025 - 2030: Continued Investments with 5G Rollouts 5.2 Value Chain 5.2.1 Enabling Technology Providers 5.2.2 VoLTE & IMS Infrastructure Suppliers 5.2.3 VoLTE Device OEMs 5.2.4 Roaming, Billing & Supplementary Service Providers 5.2.5 Mobile Operators 5.2.6 Test, Measurement & Performance Specialists 6: Key Market Players 6.1 Accedian Networks 6.2 Affirmed Networks 6.3 ALEPO 6.4 Altair Semiconductor 6.5 Amdocs 6.6 Anritsu Corporation 6.7 Anritsu Corporation 6.8 Apple 6.9 Aptilo Networks 6.10 Aricent 6.11 Astellia 6.12 Asus (ASUSTeK Computer) 6.13 BICS 6.14 Broadcom 6.15 BroadSoft 6.16 BT Group 6.17 CCN (Cirrus Core Networks) 6.18 CellMining 6.19 Cellwize 6.20 CENX 6.21 CEVA 6.22 Cirpack 6.23 Cisco Systems 6.24 D2 Technologies 6.25 Dialogic Corporation 6.26 DigitalRoute 6.27 Ecrio 6.28 Empirix 6.29 Ericsson 6.30 EXFO 6.31 F5 Networks 6.32 Fujitsu 6.33 GCT Semiconductor 6.34 GENBAND 6.35 Gigamon 6.36 GL Communications 6.37 Hitachi 6.38 HPE (Hewlett Packard Enterprise) 6.39 HTC Corporation 6.40 Huawei 6.41 iBasis 6.42 IBM 6.43 Imagination Technologies 6.44 IMSWorkX 6.45 InfoVista 6.46 Intel Corporation 6.47 InterDigital 6.48 Interop Technologies 6.49 Iskratel 6.50 Italtel 6.51 Ixia 6.52 Keysight Technologies 6.53 Lenovo 6.54 LG Electronics 6.55 Metaswitch Networks 6.56 Mitel Networks Corporation 6.57 Mobileum 6.58 Monolith Software 6.59 Mushroom Networks 6.60 MYCOM OSI 6.61 Napatech 6.62 NEC Corporation 6.63 NetScout Systems 6.64 NewNet Communication Technologies 6.65 Nexus Telecom 6.66 Nokia Networks 6.67 NXP Semiconductors 6.68 OpenCloud 6.69 Openet 6.70 Optulink 6.71 Oracle Communications 6.72 Pantech 6.73 Polystar 6.74 Qualcomm 6.75 Quortus 6.76 RADCOM 6.77 Radisys Corporation 6.78 Redknee Solutions 6.79 Rohde & Schwarz 6.80 Samsung Electronics 6.81 Sandvine 6.82 Sansay 6.83 Sequans Communications 6.84 Sharp Corporation 6.85 SIGOS 6.86 Sonus Networks 6.87 Sony Mobile Communications 6.88 Spirent Communications 6.89 SPIRIT DSP 6.90 Spreadtrum Communications 6.91 Summit Tech 6.92 Syniverse 6.93 SysMech 6.94 TNS (Transaction Network Services) 6.95 Viavi Solutions 6.96 VMware 6.97 VoiceAge Corporation 6.98 Voipfuture 6.99 WIT Software 6.100 ZTE 7: Market Analysis & Forecasts 7.1 Global Outlook of VoLTE 7.2 VoLTE Subscriptions & Service Revenue 7.2.1 VoLTE Subscriptions 7.2.2 VoLTE Service Revenue 7.2.3 Segmentation by Application 7.2.4 Voice Telephony 7.2.5 Video & Supplementary Services 7.3 VoLTE Infrastructure 7.3.1 Segmentation by Submarket 7.3.2 CSCF Servers 7.3.3 SBCs 7.3.4 VoLTE Application Servers 7.3.5 Other IMS Elements (HSS, BGCF, MGCF & MRF) 7.3.6 VoLTE Capable PCRF Solutions 7.4 Segmentation by Region 7.4.1 Asia Pacific 7.4.2 Eastern Europe 7.4.3 Latin & Central America 7.4.4 Middle East & Africa 7.4.5 North America 7.4.6 Western Europe 8: Conclusion, Key Trends & Strategic Recommendations 8.1 Why is the Market Poised to Grow? 8.2 Competitive Industry Landscape: Acquisitions, Alliances & Consolidation 8.3 Geographic Outlook: Which Countries Offer the Highest Growth Potential? 8.4 Monetization: Can VoLTE Drive Revenue Growth? 8.5 Operator Branded OTT Services: Implications for VoLTE 8.6 Virtualization: Moving VoLTE to the Cloud 8.7 Growing Investments in VoLTE Service Assurance 8.8 Prospects of the EVS (Enhanced Voice Services) Codec 8.9 Convergence with Wi-Fi Calling 8.9.1 Moving Towards IMS-Based Wi-Fi Calling Services 8.9.2 Future Prospects 8.10 Opportunities for MVNOs 8.10.1 Enabling Service Differentiation 8.10.2 Growing MVNE (Mobile Virtual Network Enabler) Investments in VoLTE Infrastructure 8.10.3 How Big is the VoLTE Service Revenue Opportunity for MVNOS? 8.11 WebRTC: Friend or Foe? 8.12 Status of RCS Adoption 8.13 Prospects of Roaming and Interconnected VoLTE Services 8.14 MCPTT over VoLTE: Enabling Critical Communications 8.15 Strategic Recommendations 8.15.1 VoLTE Solution Providers 8.15.2 Mobile Operators & MVNOs For more information about this report visit

Undheim A.,Telenor Norway | Chilwan A.,Norwegian University of Science and Technology | Heegaard P.,Norwegian University of Science and Technology
Proceedings - 2011 12th IEEE/ACM International Conference on Grid Computing, Grid 2011 | Year: 2011

Cloud computing is the new trend in service delivery, and promises large cost savings and agility for the customers. However, some challenges still remain to be solved before widespread use can be seen. This is especially relevant for enterprises, which currently lack the necessary assurance for moving their critical data and applications to the cloud. The cloud SLAs are simply not good enough. This paper focuses on the availability attribute of a cloud SLA, and develops a complete model for cloud data centers, including the network. Different techniques for increasing the availability in a virtualized system are investigated, quantifying the resulting availability. The results show that depending on the failure rates, different deployment scenarios and fault-tolerance techniques can be used for achieving availability differentiation. However, large differences can be seen from using different priority levels for restarting of virtual machines. © 2011 IEEE.

Svanaes D.,Norwegian University of Science and Technology | Alsos O.A.,Norwegian University of Science and Technology | Dahl Y.,Norwegian University of Science and Technology | Dahl Y.,Telenor Norway
International Journal of Medical Informatics | Year: 2010

Background: While much is known about how to do usability testing of stationary Electronic Patient Record (EPR) systems, less is known about how to do usability testing of mobile ICT systems intended for use in clinical settings. Aim: Our aim is to provide a set of empirically based recommendations for usability testing of mobile ICT for clinical work. Method: We have conducted usability tests of two mobile EPR systems. Both tests have been done in full-scale models of hospital settings, and with multiple users simultaneously. We report here on the methodological aspects of these tests. Results: We found that the usability of the mobile EPR systems to a large extent were determined by factors that went beyond that of the graphical user interface. These factors include ergonomic aspects such as the ability to have both hands free, and social aspects such as to what extent the systems disturbs the face-to-face interaction between the health worker and the patient. Conclusions: To be able to measure usability issues that go beyond what can be found by a traditional stationary user interface evaluation, it is necessary to conduct usability tests of mobile EPR systems in physical environments that simulate the conditions of the work situation at a high level of realism. It is further in most cases necessary to test with a number of test subjects simultaneously. © 2008 Elsevier Ireland Ltd. All rights reserved.

Islam S.,TU Munich | Houmb S.H.,Telenor Norway
2010 4th International Conference on Research Challenges in Information Science - Proceedings, RCIS 2010 | Year: 2010

Software projects are often faced with unanticipated problems caused by e.g. changes in the development environment resulting in delays or threatening the ability of the project to succeed. Managing these uncertainties is a challenging task at all phases of the development, but nevertheless crucial in controlling schedule and costs. Therefore software development risks need to be controlled as early as possible. As software development risks are not merely of technical nature it is equally important to tackle non-technical risks. The paper presents a goal-driven software development risk management model (GSRM) that takes a holistic view on development, taking both technical and non-technical development components into consideration. The focus of the paper is on how to integrate GSRM and particularly the holistic risk perspective into requirements engineering. GSRM effectively identifies and makes explicit the critical project goals (for arriving at a successful project) and the risk factors that may obstruct these goals. GSRM also helps in planning how to employ control actions for mitigating risks and by that increase the ability to meet project goals. The integrated requirements engineering risk management model has been applied to an on-going development project in a low-cost development environment (Bangladesh). The result showed it to be relatively trivial to integrate the model into requirements engineering activities and that the model did indeed contribute to the overall project success. © 2010 IEEE.

Osterbo O.,Telenor Norway
Proceedings of the 2011 23rd International Teletraffic Congress, ITC 2011 | Year: 2011

Due to the variation of radio condition in LTE the obtainable bitrate for active users will vary. The two most important factors for the radio conditions are fading and pathloss. By including both fast fading and shadowing and attenuation due to distance we have developed analytical models to investigate obtainable bitrates for the basic resource unit in LTE. In addition we estimate the total cell throughput/capacity by taking scheduling into account. Particularly, the cell throughput is investigated for three types of scheduling algorithms; Round Robin, Proportional Fair and Max SINR (signal-to-interference- plus-noise ratio) were also fairness among users is part of the analysis. In addition models for cell throughput/capacity for a mixture of Guaranteed Bit Rate (GBR) and Non-GBR greedy users are derived. Numerical examples show that multi-user gain is large for the Max-SINR algorithm, but also the Proportional Fair algorithm gives relative large gain relative to plain Round Robin. The Max-SINR has the weakness that it is highly unfair when it comes to capacity distribution among users. Further, the model visualize that use of GBR with high rates may cause problems in LTE due to the high demand for radio resources for users with low SINR, i.e. at cell edge. Hence, for GBR sources the allowed guaranteed rate should be limited. © 2011 ITC.

This article develops the concept of quality of user experience by focusing on requirements for its measurement and communication for use in industry. Whereas more typical work involves either modeling media quality based on technical measures or user subjective assessment of media quality, it is argued that QoE measurement should include objective measures of the process and outcomes of usage. Also, subjective measures should be collected as quantitative data rather than qualitative data as is currently common, because quantitative data enables combined analyses of objective and subjective measures for deriving global QoE ratings. Global ratings are important for optimizing the communicability of results to target audiences. Examples of measures and scales are provided. A structured approach to defining and measuring QoE in relation to QoS is explained and illustrated. © 2010 IEEE.

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