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The optimal handover strategy for each UE is generated by using the directed graph based model Three methods for handover optimization are compared in in Subsection 3.1.2. this subsection. The first method assumes the UE can predict its RSRP and make handover decision based on the directed In the graph based model, the two parameters w 1 and w 2 form graph model. (...) In this subsection, the w 1 is fixed by the results of the directed graph model with w 2 = 1. In the and different w 2 are evaluated to show the change of handover third method, the UE is always served by the strongest beam. number and average RSRP strength.
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Score: 845193.6 - https://www.itu.int/en/publica.../files/basic-html/page187.html
Data Source: un
The various curves correspond to values of pe, the weight of the modeled distortion in case of an error. (...) Nevertheless, the curves corresponding to medium code-rates are concentrated at the maximum of the achievable PSNR values indicating that the choice of the code-rate is not that critical when combined with our error modeling approach. (...) Eb=N0 for the sequences Foreman (top) and Mother and Daughter (bottom) for the optimal code- rate and error model parameter pe without feedback. quences that correspond to noise-spectral-energies in the range Eb=N0 = f14; 18; 22; 26;30g.
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Score: 845044 - https://www.itu.int/wftp3/av-a...video-site/9908_Ber/q15h24.pdf
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[364]  Report on the meeting of Working Party 3M (Geneva, 30 May - 10 June 2022)   Français   Español     Home : ITU-R : WP3M : Contributions : 364 Recently posted  -  Search Meeting Documents     [364]  Report on the meeting of Working Party 3M (Geneva, 30 May - 10 June 2022) Format Size Posted English Zip (Document)   358477 bytes 2022-06-20 [364] Annex 21 - Part 1 Word   429902 bytes 2022-06-20 [364] Annex 21 - Report of the meetings of Working Group 3M-4 (Digital Products) Zip (Document)   68524 bytes 2022-06-20 [364] Annex 20 - Part 2 Zip (Document)   90523 bytes 2022-06-20 [364] Annex 20 - Part 1 Word   221902 bytes 2022-06-20 [364] Annex 20 - Report on new measurements, table-keepers and formatted tables for SG 3 experimental database (DBSG3) Zip (Document)   1249347 bytes 2022-06-20 [364] Annex 19 - Part 2 Zip (Document)   3846381 bytes 2022-06-20 [364] Annex 19 - Part 1 Word   5143204 bytes 2022-06-20 [364] Annex 19 - Digital products for SG 3 website inclusion - Update of MATLAB/Octave implementation of Recommendation ITU-R P.2001 Zip (Document)   239833 bytes 2022-06-20 [364] Annex 18 - Part 2 Zip (Document)   1480092 bytes 2022-06-20 [364] Annex 18 - Part 1 Word   1739696 bytes 2022-06-20 [364] Annex 18 - Digital products for SG 3 website inclusion - Update of MATLAB/Octave implementation of Recommendation ITU-R P.452 Word   33491 bytes 2022-06-17 [364] Annex 17 - Terms of Reference of Correspondence Group 3K-3M-12 - Prediction of clutter loss Word   34180 bytes 2022-06-20 [364] Annex 16 - Terms of Reference of Correspondence Group 3M-4 - Software products, digital maps, and reference numerical data products Word   33536 bytes 2022-06-17 [364] Annex 15 - Correspondence Group 3K-3M-9 to study propagation of radiowaves along aeronautical paths Word   36483 bytes 2022-06-20 [364] Annex 14 - Terms of Reference of Correspondence Group 3J-3K-3M-14 to study issues relating to HAPS propagation model (30 May - 10 June 2022) Word   34113 bytes 2022-06-17 [364] Annex 13 - Correspondence Group 3K-3M-18 to study issues relating to Recommendations ITU-R P.1812 and ITU-R P.452 Word   33773 bytes 2022-06-20 [364] Annex 12 - Terms of Reference of Correspondence Group 3M-15 - Improvement of rain and total attenuation models in Recommendation ITU-R P.618 Word   35624 bytes 2022-06-17 [364] Annex 11 - Working document towards a preliminary draft revision of Recommendation ITU-R P.2108-1 - Prediction of clutter loss Word   3812441 bytes 2022-06-17 [364] Annex 10 - Working document towards a preliminary draft revision of Report ITU-R P.2402-0 - A method to predict the statistics of clutter loss for Earth-space and aeronautical paths Word   533188 bytes 2022-06-17 [364] Annex 9 - Working document towards a draft revision of Recommendation ITU-R P.2108-1 - Prediction of clutter loss     Word   35295 bytes 2022-06-20 [364] Annex 8 - Preliminary draft new Question ITU-R XXX/3 - Machine learning and methods required for radiowave propagation studies Word   377225 bytes 2022-06-20 [364] Annex 7 - Working document toward a preliminary draft revision to Recommendation ITU-R P.619-5 - Propagation data required for the evaluation of interference between stations in space and those on the surface of the Earth Word   1628734 bytes 2022-06-20 [364] Annex 6 - Working document towards a future revision of Recommendation ITU-R P.1409 - Propagation data and prediction methods for systems using high altitude platform stations and other elevated stations in the stratosphere at frequencies greater than about 0.7 GHz Word   1474990 bytes 2022-06-20 [364] Annex 5 - Preliminary draft new Fascicle 3M/FAS/X - Fascicle on hydrometeor scatter interference Word   482468 bytes 2022-06-20 [364] Annex 4 - Preliminary draft revision of Recommendation ITU-R P.452-17 (Hydrometeor Scatter) Prediction procedure for the evaluation of interference between stations on the surface of the Earth at frequencies above about 0.1 GHz Word   229623 bytes 2022-06-20 [364] Annex 3 - Preliminary draft revision of the clutter loss prediction method in Recommendation ITU-R P.452-17 - Prediction procedure for the evaluation of interference between stations on the surface of the Earth at frequencies above about 0.1 GHz Word   2870581 bytes 2022-06-20 [364] Annex 2 - Proposed harmonization of the troposcatter transmission loss prediction model in ITU-R P-series Recommendations Word   930685 bytes 2022-06-20 [364] Annex 1 - Considerations regarding a working document towards a preliminary draft revision of Recommendation ITU-R P.618 - Preliminary draft revisions and future work Word   112575 bytes 2022-07-14 [364]  - Report on the meeting of Working Party 3M (30 May - 10 June 2022)   Document : ITU-R WP3M  Contribution  364 Title : Report on the meeting of Working Party 3M (Geneva, 30 May - 10 June 2022) Date : 2022-06-10 Source : Chairman, WP 3M Access : Restricted to TIES users  [ITU-R] Top  -  Feedback  -  Contact Us  -  Copyright © ITU  2008 All Rights Reserved Contact for this page :  ITU-R Web coordinator Updated : 2022-07-14  
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Score: 844875.6 - https://www.itu.int/md/R19-WP3M-C-0364/_page.print
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The simplified H currently represents a Line-of-Sight (LoS) channel. A narrowband channel model [16] is used, but wideband models can be readily incorporated in case their extra computational cost is not an issue. (...) MACHINE LEARNING MODEL `=1 The parameters in Eq. (3) are obtained as follows. (...) P tx [t] − 2P d [t] r e [t] = (5) These angles are used to compose the steering vectors a t and P b [t] , a r . where P tx [t], P d [t], and P b [t] correspond, respectively, to the total amount (summation for all users) of transmitted, dropped 3.1 Traffic model and buffered packets at time t.
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Score: 842817.7 - https://www.itu.int/en/publica.../files/basic-html/page206.html
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Score: 842591.2 - https://www.itu.int/ml/lists/lists
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TIMSER Display a time series of a selected model life table column corresponding to a input series of mortality levels ……… 65 S. (...) CORMOR calculates and displays corresponding probabilities of dying for selected age groups and for all nine model life tables. TIMSER calculates and displays a time series of a selected model life table column corresponding to an input series of mortality levels.
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Score: 841846.2 - https://www.un.org/en/developm...f/mortality/mortpak_manual.pdf
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Based on a contribution by the Finnish government, an econometrician expert is working on the model application. The model and first estimations of elasticities were reviewed during a working meeting in January 2002. (...) If the priority is given to meet the deadlines, various options could be considered for limiting the scope/quality of the baseline study (for instance using another simpler model, using the ETTS V forecasts and updating them in co-operation with the national correspondents, etc.). 35. (...) The study should identify the subjects for further specific scenario analyses and define the quantitative impact, which could be modelled in the framework of the forest products and the forest resource models.
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Score: 840615.6 - https://unece.org/fileadmin/DA...cs/stats-24/english/2002-4.doc
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Please activate JavaScript in your web browser Sympa Menu Home Search for List(s) Search form Index of Lists Browse lists by categories Support Documentation FAQ TIES Account List Subscription ITU-T Homepage Login Mailing Lists Service Index of lists Index of lists a b c d e f g h i j k l m n o p q r s t u v w x y z others All ITU-T Standardization Sector a [ Ad-hoc group on CNC definition coordination ] [ Ad-hoc on "Future ICT Evolution for emerging Web Era" (Web3-adhoc) ] [ AI for Health ] [ General announcements and discussion related to AI commons and data ] c [ Correspondence Group for AIoT Activities (CG-AIoT) ] [ Correspondence Group on Green and Low-carbon Deployment and Operation for Information and Communication Network ] [ SG3 Correspondence Group on OTTs (CG-OTTs) ] [ SG17 Correspondence group on security capability and architecture (CG-SECAPA) ] [ Correspondence Group on efficient meeting management ] [ Correspondence group on trusted exchange of network digital forensics ] [ Collaboration on Intelligent Transport System (CITS) ] [ Council Working Group on the Preparation for the World Conference on International Telecommunications (CWG-WCIT) ] Correspondence group on WATTC Resolutions, Recommendations, and Opinion d [ DCGI ] [ Dynamic Coalition on Internet and Climate Change ] [ Mailing list for Working Group: Consumer Experience & Protection ] [ Mailing list for Working Group: DFS Ecosystem ] [ Mailing list for Working Group: Interoperability ] [ Mailing list for Working Group: Technology, Innovation & Competition ] f [ Focus Group on Aviation Applications of Cloud Computing for Flight Data Monitoring ] [ Focus Group on Artificial Intelligence (AI) and Internet of Things (IoT) for Digital Agriculture ] [ ITU-T Focus Group on autonomous and assisted driving (FG-AI4AD) ] [ General announcements and discussions for the ITU-T Focus Group on Environmental Efficiency for Artificial Intelligence and other Emerging Technologies (FG-AI4EE) ] [ Working Group 1 – Requirements of AI and other Emerging Technologies to Ensure Environmental Efficiency ] [ Working Group 2 – Assessment and Measurement of the Environmental Efficiency of AI and Emerging Technologies ] [ Working Group 3 – Implementation Guidelines of AI and Emerging Technologies for Environmental Efficiency ] [ General announcements and discussions for the ITU-T Focus Group on AI for health ] [ FG-AI4H Ad-hoc Group on digital technologies for COVID health emergency (AHG-DT4HE) ] [ FG-AI4H Topic Group Cardio (TG-Cardio), specific discussions for sub-topic on cardiac image analyses ] [ FG-AI4H Topic Group Cardio (TG-Cardio), specific discussions for sub-topic on clinical predictions ] [ FG-AI4H Topic Group on dermatology (TG-Derma) specific discussions ] [ FG-AI4H Topic Group on primary and secondary diabetes prediction (TG-Diabetes) ] [ FG-AI4H Topic Group on falls among the elderly (TG-Falls) specific discussions ] [ FG-AI4H Topic Group on human reproduction and fertility (TG-Fertility) specific discussions ] [ FG-AI4H Topic Group on AI for Malaria Detection (TG-Malaria) ] [ FG-AI4H Topic Group on musculoskeletal medicine (TG-MSK) specific discussions ] [ FG-AI4H Topic Group on ophthalmology (TG-Ophthalmo) specific discussions ] [ FG-AI4H Topic Group on outbreak detection (TG-Outbreaks) specific discussions ] [ FG-AI4H Topic Group on psychiatry (TG-PSY) specific discussions ] [ FG-AI4H Topic Group Symptom (TG-Symptom) specific discussions ] [ ITU-T Focus Group on AI for Natural Disaster Management (FG-AI4NDM) ] [ FG-AI4NDM Topic Group on Earth Data Monitoring ] [ FG-AI4NDM Topic Group on Flood Data Monitoring ] [FG-AI4NDM Topic Group on AI for Hail and Windstorm Hazard Mapping] [FG-AI4NDM Topic Group on AI for Insect Plague Monitoring and Detection] [ FG-AI4NDM Topic Group on Tsunami Data Monitoring ] [ FG-AI4NDM Working Group on Effective Communications (WG-Communications), specific discussions ] [ FG-AI4NDM Working Group on Data (WG-Data), specific 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The Joint Rapporteur Group is one way to support SG2 and SG3 in fulfilling their responsibilities under Resolution 64 ] [ Mailing list for Joint Rapporteur Group on Cloud Computing Management ] m [ Mailing list under the Focus Group on Smart Sustainable Cities to develop the Technical Report on Assessment of Energy & GHG from ICT in Cities and to provide feedback to the L.methodology_ICT in cities under Q18/5 ] Misuse Reports [ Memorandum of understanding on E-business signed between IEC/ISO/ITU and UN/ECE - MGT ONLY ] [ Project Resilience MVP Architecture ] [ Project Resilience MVP Data ] p [ SG12 P.BBQCG – Project on Parametric bitstream-based Quality Assessment of Cloud Gaming Services ] [ Partnership Coordination Panel on Telecom for Disaster ] [ ITU/IEEE joint discussion group on power line (tele)communication (PLC/PLT) ] [ Mailing list for a joint project on P.NATS Phase 2 / AVHD between Q14/12 and VQEG ] q [ SG12 QSDG – Quality of Service Development Group ] s [ ITU T informal 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performance requirements and assessment methods for multimedia including IPTV ] [ Development of parametric models and tools for audiovisual and multimedia quality measurement purposes ] [ Objective assessment of speech and sound transmission performance quality in networks ] [ Framework for diagnostic functions and their interaction with external objective models predicting media quality ] [ Performance of packet-based networks and other networking technologies ] [ Conferencing and telemeeting assessment ] [ Multimedia performance considerations for IP gateways ] [ Speech transmission characteristics of speech terminals for fixed circuit-switched, mobile and packet-switched (IP) networks ] [ Hands-free communication in vehicles ] [ Telephonometric methodologies for handset and headset terminals ] [ Analysis methods using complex measurement signals including their application for speech enhancement techniques and hands-free telephony ] [ Methods, tools and test plans for the 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Score: 839959.6 - https://www.itu.int/ml/lists/lists/itu_t
Data Source: un
Bit-rate saving in % vs. number of motion models For the case of average PSNR vs bit-rate, we show two plots per sequence: · Comparison of RD-performance between TMN-10 (ANCHOR), affine motion compensation using 1, 2, 4, 8, 16, 32, 64, and 99 models being warped from the previous frame. · Comparison of RD-performance between TMN-10 (ANCHOR), LTMP with 10 frames, and affine motion compensation using 1, 2, 4, 8, 16, 32, 64, and 99 models being warped from 10 LTMP frames. (...) The blue curve corresponds to affine motion compensation using using 1, 2, 4, 8, 16, 32, 64, and 99 models being warped from the previous frame while the red curve corresponds to affine motion compensation using 1, 2, 4, 8, 16, 32, 64, and 99 models being warped from 10 LTMP frames. · The same as before but at 35 dB PSNR. 0124816326499 0 2 4 6 8 10 12 14 16 18 20 # MODELS Bit-Rate Saving [%] News, QCIF, SKIP=2, QP=4,5,7,10,15,25, @ 35 dB PSNR TMN-10 + Affine MC LTMP(10) + Affine MC 0 10 20 30 40 50 60 70 26 26.5 27 27.5 28 28.5 29 29.5 30 30.5 31 31.5 32 32.5 33 33.5 34 34.5 35 35.5 36 36.5 37 37.5 38 38.5 39 39.5 40 Bit-Rate [kbps] PSNR [dB] Container Ship, QCIF, SKIP=2, QP=4,5,7,10,15,25, 10LTMP frame(s) ANCHOR 10 frames 1 Model 2 Models 4 Models 8 Models 16 Models 32 Models 64 Models 99 Models 0 10 20 30 40 50 60 70 26 26.5 27 27.5 28 28.5 29 29.5 30 30.5 31 31.5 32 32.5 33 33.5 34 34.5 35 35.5 36 36.5 37 37.5 38 38.5 39 39.5 40 Bit-Rate [kbps] PSNR [dB] Container Ship, QCIF, SKIP=2, QP=4,5,7,10,15,25, 1LTMP frame(s) ANCHOR 1 Model 2 Models 4 Models 8 Models 16 Models 32 Models 64 Models 99 Models 0 1 2 4 8 16 32 64 99 0 5 10 15 20 25 30 35 40 # MODELS Bit-Rate Saving [%] Container Ship, QCIF, SKIP=2, QP=4,5,7,10,15,25, @ 35 dB PSNR TMN-10 + Affine MC LTMP(10) + Affine MC 0 20 40 60 80 100 120 140 160 180 26 26.5 27 27.5 28 28.5 29 29.5 30 30.5 31 31.5 32 32.5 33 33.5 34 34.5 35 35.5 36 36.5 37 37.5 38 38.5 39 39.5 40 Bit-Rate [kbps] PSNR [dB] Foreman, QCIF, SKIP=2, QP=4,5,7,10,15,25, 1LTMP frame(s) ANCHOR 1 Model 2 Models 4 Models 8 Models 16 Models 32 Models 64 Models 99 Models 0 1 2 4 8 16 32 64 99 0 5 10 15 20 25 30 # MODELS Bit-Rate Saving [%] Foreman, QCIF, SKIP=2, QP=4,5,7,10,15,25, @ 32 dB PSNR TMN-10 + Affine MC LTMP(10) + Affine MC 0124816326499 0 5 10 15 20 25 30 35 40 # MODELS Bit-Rate Saving [%] Container Ship, QCIF, SKIP=2, QP=4,5,7,10,15,25, @ 32 dB PSNR TMN-10 + Affine MC LTMP(10) + Affine MC 0 20 40 60 80 100 120 28 28.5 29 29.5 30 30.5 31 31.5 32 32.5 33 33.5 34 34.5 35 35.5 36 36.5 37 37.5 38 38.5 39 39.5 40 Bit-Rate [kbps] PSNR [dB] Silent Voice, QCIF, SKIP=1, QP=4,5,7,10,15,25, 1LTMP frame(s) ANCHOR 1 Model 2 Models 4 Models 8 Models 16 Models 32 Models 64 Models 99 Models 10 20 30 40 50 60 70 80 90 100 110 28 28.5 29 29.5 30 30.5 31 31.5 32 32.5 33 33.5 34 34.5 35 35.5 36 36.5 37 37.5 38 38.5 39 39.5 40 Bit-Rate [kbps] PSNR [dB] Silent Voice, QCIF, SKIP=1, QP=4,5,7,10,15,25, 10LTMP frame(s) ANCHOR 10 frames 1 Model 2 Models 4 Models 8 Models 16 Models 32 Models 64 Models 99 Models 0 1 2 4 8 16 32 64 99 0 2 4 6 8 10 12 14 16 # MODELS Bit-Rate Saving [%] Silent Voice, QCIF, SKIP=1, QP=4,5,7,10,15,25, @ 32 dB PSNR TMN-10 + Affine MC LTMP(10) + Affine MC 0 1 2 4 8 16 32 64 99 -2 0 2 4 6 8 10 12 14 16 # MODELS Bit-Rate Saving [%] Silent Voice, QCIF, SKIP=1, QP=4,5,7,10,15,25, @ 35 dB PSNR TMN-10 + Affine MC LTMP(10) + Affine MC Please note that we only have to warp one reference frame when a motion model has been computed several times. This happens quite often for large numbers of models and sequences with a small amount of motion or static background.
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Score: 839891.8 - https://www.itu.int/wftp3/av-a...video-site/9908_Ber/q15h21.doc
Data Source: un
A key gap identified early in the process is the lack of an independent platform to evaluate AI models. In 2020, the FG-AI4H launched the Open Code Initiative ( OCI ) that is evolving into a complete assessment platform run by ITU/WHO in the cloud where the entire data and model ecosystem is integrated. (...) This is the first not-for-profit interagency AI-model assessment platform prototype, being developed in collaboration with country-level regulatory agencies that have an interest in independent model assessments before granting approval. (...) FG-AI4H has initiated a system for auditing use-case specific AI-models via the OCI described above. These audits will serve as blueprints for AI model approval and showcase the importance of a multidisciplinary approach to assess AI models.
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Score: 839645.3 - https://www.itu.int/en/ITU-T/f...ocuments/all/FGAI4H-N-052.docx
Data Source: un