The Experts below are selected from a list of 5907 Experts worldwide ranked by ideXlab platform
Athanasios V Vasilakos - One of the best experts on this subject based on the ideXlab platform.
-
time varying channel estimation for ofdm with extended Observation Window
International Workshop on VehiculAr Inter-NETworking, 2012Co-Authors: Dan Shan, Paul Richardson, Weidong Xiang, Athanasios V VasilakosAbstract:Orthogonal Frequency-Division Multiplexing (OFDM) systems operating in high mobility environments experience time-varying channel, which leads to serious inter-carrier-interferences. Basis Expansion Model (BEM) is a widely used technique to estimate the time-varying channel, but can only capture the time-varying characteristics within each single OFDM block one by one in traditional algorithms. A method to extend the Observation Window of BEM based algorithms to the range of multiple OFDM blocks is established and adapted to pilot-assisted OFDM systems, where pilot signals in multiple OFDM blocks are converted into time-domain and connected together. As a result, the time-varying channel can be estimated more accurately, with comparable complexity as those of traditional algorithms.
-
VANET@MOBICOM - Time-varying channel estimation for OFDM with extended Observation Window
Proceedings of the ninth ACM international workshop on Vehicular inter-networking systems and applications - VANET '12, 2012Co-Authors: Dan Shan, Paul Richardson, Weidong Xiang, Athanasios V VasilakosAbstract:Orthogonal Frequency-Division Multiplexing (OFDM) systems operating in high mobility environments experience time-varying channel, which leads to serious inter-carrier-interferences. Basis Expansion Model (BEM) is a widely used technique to estimate the time-varying channel, but can only capture the time-varying characteristics within each single OFDM block one by one in traditional algorithms. A method to extend the Observation Window of BEM based algorithms to the range of multiple OFDM blocks is established and adapted to pilot-assisted OFDM systems, where pilot signals in multiple OFDM blocks are converted into time-domain and connected together. As a result, the time-varying channel can be estimated more accurately, with comparable complexity as those of traditional algorithms.
Nur A. Touba - One of the best experts on this subject based on the ideXlab platform.
-
Improved Trace Buffer Observation via Selective Data Capture Using 2-D Compaction for Post-Silicon Debug
IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2013Co-Authors: Joon-sung Yang, Nur A. ToubaAbstract:This paper presents a novel technique for extending the capacity of trace buffers when capturing debug data during post-silicon debug. It exploits the fact that is it not necessary to capture error-free data in the trace buffer since that information can be obtained from simulation. A selective data capture method is proposed in this paper that only captures debug data during clock cycles in which errors are present. The proposed debug method requires only three debug sessions. The first session estimates a rough error rate, the second session identifies a set of suspect clock cycles where errors may be present, and the third session captures the suspect clock cycles in the trace buffer. The suspect clock cycles are determined through a 2-D compaction technique using multiple-input signature register signatures and cycling register signatures. Intersecting both signatures generates a small number of suspect clock cycles for which the trace buffer needs to capture. The effective Observation Window of the trace buffer can be expanded significantly, by up to orders of magnitude. Experimental results indicate very significant increases in the effective Observation Window for a trace buffer can be obtained.
-
VTS - Expanding Trace Buffer Observation Window for In-System Silicon Debug through Selective Capture
26th IEEE VLSI Test Symposium (vts 2008), 2008Co-Authors: Joon-sung Yang, Nur A. ToubaAbstract:Trace buffers are commonly used to capture data during in-system silicon debug. This paper exploits the fact that it is not necessary to capture error-free data in the trace buffer since that information is obtainable from simulation. The trace buffer need only capture data during clock cycles in which errors are present. A three pass methodology is proposed. During the first pass, the rough error rate is measured, in the second pass, a set of suspect clock cycles where errors may be present is determined, and then in the third pass, the trace buffer captures only during the suspect clock cycles. In this manner, the effective Observation Window of the trace buffer can be expanded significantly, by up to orders of magnitude. This greatly increases the effectiveness of a given size trace buffer and can rapidly speed up the debug process. The suspect clock cycles are determined through a two dimensional (2-D) compaction technique using a combination of multiple-input signature register (MISR) signatures and cycling register signatures. By intersecting the signatures, the proposed 2-D compaction technique generates a small set of remaining suspect clock cycles for which the trace buffer needs to capture data. Experimental results indicate very significant increases in the effective Observation Window for a trace buffer can be obtained.
-
expanding trace buffer Observation Window for in system silicon debug through selective capture
VLSI Test Symposium, 2008Co-Authors: Joon-sung Yang, Nur A. ToubaAbstract:Trace buffers are commonly used to capture data during in-system silicon debug. This paper exploits the fact that it is not necessary to capture error-free data in the trace buffer since that information is obtainable from simulation. The trace buffer need only capture data during clock cycles in which errors are present. A three pass methodology is proposed. During the first pass, the rough error rate is measured, in the second pass, a set of suspect clock cycles where errors may be present is determined, and then in the third pass, the trace buffer captures only during the suspect clock cycles. In this manner, the effective Observation Window of the trace buffer can be expanded significantly, by up to orders of magnitude. This greatly increases the effectiveness of a given size trace buffer and can rapidly speed up the debug process. The suspect clock cycles are determined through a two dimensional (2-D) compaction technique using a combination of multiple-input signature register (MISR) signatures and cycling register signatures. By intersecting the signatures, the proposed 2-D compaction technique generates a small set of remaining suspect clock cycles for which the trace buffer needs to capture data. Experimental results indicate very significant increases in the effective Observation Window for a trace buffer can be obtained.
Zhaohui Chen - One of the best experts on this subject based on the ideXlab platform.
-
estimating surface carbon fluxes based on a local ensemble transform kalman filter with a short assimilation Window and a long Observation Window an observing system simulation experiment test in geos chem 10 1
Geoscientific Model Development, 2019Co-Authors: Eugenia Kalnay, Ning Zeng, Ghassem R. Asrar, Zhaohui ChenAbstract:Abstract. We developed a carbon data assimilation system to estimate surface carbon fluxes using the local ensemble transform Kalman filter (LETKF) and atmospheric transport model GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological field based on the Goddard Earth Observing System model, version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. (2011, 2012), who estimated the surface carbon fluxes in an observing system simulation experiment (OSSE) as evolving parameters in the assimilation of the atmospheric CO2 , using a short assimilation Window of 6 h. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO2 transport. After introducing new techniques such as “variable localization”, and increased Observation weights near the surface, they obtained accurate surface carbon fluxes at grid-point resolution. We developed a new version of the local ensemble transform Kalman filter related to the “running-in-place” (RIP) method used to accelerate the spin-up of ensemble Kalman filter (EnKF) data assimilation (Kalnay and Yang, 2010; Wang et al., 2013; Yang et al., 2012). Like RIP, the new assimilation system uses the “no cost smoothing” algorithm for the LETKF (Kalnay et al., 2007b), which allows shifting the Kalman filter solution forward or backward within an assimilation Window at no cost. In the new scheme a long “Observation Window” (e.g., 7 d or longer) is used to create a LETKF ensemble at 7 d. Then, the RIP smoother is used to obtain an accurate final analysis at 1 d. This new approach has the advantage of being based on a short assimilation Window, which makes it more accurate, and of having been exposed to the future 7 d Observations, which improves the analysis and accelerates the spin-up. The assimilation and Observation Windows are then shifted forward by 1 d, and the process is repeated. This reduces significantly the analysis error, suggesting that the newly developed assimilation method can be used with other Earth system models, especially in order to make greater use of Observations in conjunction with models.
-
Estimating Surface Carbon Fluxes Based on a Local Ensemble Transform Kalman Filter with a Short Assimilation Window and a Long Observation Window
2017Co-Authors: Yun Liu, Eugenia Kalnay, Ning Zeng, Ghassem R. Asrar, Zhaohui Chen, Binghao JiaAbstract:Abstract. We developed a Carbon data assimilation system to estimate the surface carbon fluxes using the Local Ensemble Transform Kalman Filter and atmospheric transfer model of GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological fields based on the Goddard Earth Observing System Model, Version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. (2011, 2012), who estimated the surface carbon fluxes in an Observing System Simulation Experiment (OSSE) mode, as evolving parameters in the assimilation of the atmospheric CO 2 , using a short assimilation Window of 6 hours. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO 2 transport. After introducing new techniques such as “variable localization”, and increased Observation weights near the surface, they obtained accurate carbon fluxes at grid point resolution. We developed a new version of the LETKF related to the “Running-in-Place” (RIP) method used to accelerate the spin-up of EnKF data assimilation (Kalnay and Yang, 2010; Wang et al., 2013, Yang et al., 2014). Like RIP, the new assimilation system uses the “no-cost smoothing” algorithm for the LETKF (Kalnay et al., 2007b), which allows shifting at no cost the Kalman Filter solution forward or backward within an assimilation Window. In the new scheme a long “Observation Window” (e.g., 7-days or longer) is used to create an LETKF ensemble at 7-days. Then, the RIP smoother is used to obtain an accurate final analysis at 1-day. This analysis has the advantage of being based on a short assimilation Window, which makes it more accurate, and of having been exposed to the future 7-days Observations, which accelerates the spin up. The assimilation and Observation Windows are then shifted forward by one day, and the process is repeated. This reduces significantly the analysis error, suggesting that this method could be used in other data assimilation problems. The newly developed assimilation method can be used with other Earth system models, especially for greater use of Observations in conjunction with models.
-
Estimating Surface Carbon Fluxes Based on a Local Ensemble Transform Kalman Filter with a Short Assimilation Window and a Long Observation Window
2017Co-Authors: Yun Liu, Eugenia Kalnay, Ning Zeng, Ghassem R. Asrar, Zhaohui Chen, Binghao JiaAbstract:Abstract. We developed a Carbon data assimilation system to estimate the surface carbon fluxes using the Local Ensemble Transform Kalman Filter and atmospheric transfer model of GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological fields based on the Goddard Earth Observing System Model, Version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. [2011, 2012], who estimated the surface carbon fluxes in an Observing System Simulation Experiment (OSSE) mode, as evolving parameters in the assimilation of the atmospheric CO2, using a short assimilation Window of 6 hours. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO2 transport. After introducing new techniques such as variable localization , and increased Observation weights near the surface, they obtained accurate carbon fluxes at grid point resolution. We developed a new version of the LETKF related to the Running-in-Place (RIP) method used to accelerate the spin-up of EnKF data assimilation [Kalnay and Yang, 2010; Wang et al., 2013, Yang et al., 2014]. Like RIP, the new assimilation system uses the no-cost smoothing algorithm for the LETKF [Kalnay et al., 2007b], which allows shifting at no cost the Kalman Filter solution forward or backward within an assimilation Window. In the new scheme a long Observation Window (e.g., 7-days or longer) is used to create an LETKF ensemble at 7-days. Then, the RIP smoother is used to obtain an accurate final analysis at 1-day. This analysis has the advantage of being based on a short assimilation Window, which makes it more accurate, and of having been exposed to the future 7-days Observations, which accelerates the spin up. The assimilation and Observation Windows are then shifted forward by one day, and the process is repeated. This reduces significantly the analysis error, suggesting that this method could be used in other data assimilation problems.
Eugenia Kalnay - One of the best experts on this subject based on the ideXlab platform.
-
estimating surface carbon fluxes based on a local ensemble transform kalman filter with a short assimilation Window and a long Observation Window an observing system simulation experiment test in geos chem 10 1
Geoscientific Model Development, 2019Co-Authors: Eugenia Kalnay, Ning Zeng, Ghassem R. Asrar, Zhaohui ChenAbstract:Abstract. We developed a carbon data assimilation system to estimate surface carbon fluxes using the local ensemble transform Kalman filter (LETKF) and atmospheric transport model GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological field based on the Goddard Earth Observing System model, version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. (2011, 2012), who estimated the surface carbon fluxes in an observing system simulation experiment (OSSE) as evolving parameters in the assimilation of the atmospheric CO2 , using a short assimilation Window of 6 h. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO2 transport. After introducing new techniques such as “variable localization”, and increased Observation weights near the surface, they obtained accurate surface carbon fluxes at grid-point resolution. We developed a new version of the local ensemble transform Kalman filter related to the “running-in-place” (RIP) method used to accelerate the spin-up of ensemble Kalman filter (EnKF) data assimilation (Kalnay and Yang, 2010; Wang et al., 2013; Yang et al., 2012). Like RIP, the new assimilation system uses the “no cost smoothing” algorithm for the LETKF (Kalnay et al., 2007b), which allows shifting the Kalman filter solution forward or backward within an assimilation Window at no cost. In the new scheme a long “Observation Window” (e.g., 7 d or longer) is used to create a LETKF ensemble at 7 d. Then, the RIP smoother is used to obtain an accurate final analysis at 1 d. This new approach has the advantage of being based on a short assimilation Window, which makes it more accurate, and of having been exposed to the future 7 d Observations, which improves the analysis and accelerates the spin-up. The assimilation and Observation Windows are then shifted forward by 1 d, and the process is repeated. This reduces significantly the analysis error, suggesting that the newly developed assimilation method can be used with other Earth system models, especially in order to make greater use of Observations in conjunction with models.
-
Estimating Surface Carbon Fluxes Based on a Local Ensemble Transform Kalman Filter with a Short Assimilation Window and a Long Observation Window
2017Co-Authors: Yun Liu, Eugenia Kalnay, Ning Zeng, Ghassem R. Asrar, Zhaohui Chen, Binghao JiaAbstract:Abstract. We developed a Carbon data assimilation system to estimate the surface carbon fluxes using the Local Ensemble Transform Kalman Filter and atmospheric transfer model of GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological fields based on the Goddard Earth Observing System Model, Version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. (2011, 2012), who estimated the surface carbon fluxes in an Observing System Simulation Experiment (OSSE) mode, as evolving parameters in the assimilation of the atmospheric CO 2 , using a short assimilation Window of 6 hours. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO 2 transport. After introducing new techniques such as “variable localization”, and increased Observation weights near the surface, they obtained accurate carbon fluxes at grid point resolution. We developed a new version of the LETKF related to the “Running-in-Place” (RIP) method used to accelerate the spin-up of EnKF data assimilation (Kalnay and Yang, 2010; Wang et al., 2013, Yang et al., 2014). Like RIP, the new assimilation system uses the “no-cost smoothing” algorithm for the LETKF (Kalnay et al., 2007b), which allows shifting at no cost the Kalman Filter solution forward or backward within an assimilation Window. In the new scheme a long “Observation Window” (e.g., 7-days or longer) is used to create an LETKF ensemble at 7-days. Then, the RIP smoother is used to obtain an accurate final analysis at 1-day. This analysis has the advantage of being based on a short assimilation Window, which makes it more accurate, and of having been exposed to the future 7-days Observations, which accelerates the spin up. The assimilation and Observation Windows are then shifted forward by one day, and the process is repeated. This reduces significantly the analysis error, suggesting that this method could be used in other data assimilation problems. The newly developed assimilation method can be used with other Earth system models, especially for greater use of Observations in conjunction with models.
-
Estimating Surface Carbon Fluxes Based on a Local Ensemble Transform Kalman Filter with a Short Assimilation Window and a Long Observation Window
2017Co-Authors: Yun Liu, Eugenia Kalnay, Ning Zeng, Ghassem R. Asrar, Zhaohui Chen, Binghao JiaAbstract:Abstract. We developed a Carbon data assimilation system to estimate the surface carbon fluxes using the Local Ensemble Transform Kalman Filter and atmospheric transfer model of GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological fields based on the Goddard Earth Observing System Model, Version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. [2011, 2012], who estimated the surface carbon fluxes in an Observing System Simulation Experiment (OSSE) mode, as evolving parameters in the assimilation of the atmospheric CO2, using a short assimilation Window of 6 hours. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO2 transport. After introducing new techniques such as variable localization , and increased Observation weights near the surface, they obtained accurate carbon fluxes at grid point resolution. We developed a new version of the LETKF related to the Running-in-Place (RIP) method used to accelerate the spin-up of EnKF data assimilation [Kalnay and Yang, 2010; Wang et al., 2013, Yang et al., 2014]. Like RIP, the new assimilation system uses the no-cost smoothing algorithm for the LETKF [Kalnay et al., 2007b], which allows shifting at no cost the Kalman Filter solution forward or backward within an assimilation Window. In the new scheme a long Observation Window (e.g., 7-days or longer) is used to create an LETKF ensemble at 7-days. Then, the RIP smoother is used to obtain an accurate final analysis at 1-day. This analysis has the advantage of being based on a short assimilation Window, which makes it more accurate, and of having been exposed to the future 7-days Observations, which accelerates the spin up. The assimilation and Observation Windows are then shifted forward by one day, and the process is repeated. This reduces significantly the analysis error, suggesting that this method could be used in other data assimilation problems.
Dan Shan - One of the best experts on this subject based on the ideXlab platform.
-
time varying channel estimation for ofdm with extended Observation Window
International Workshop on VehiculAr Inter-NETworking, 2012Co-Authors: Dan Shan, Paul Richardson, Weidong Xiang, Athanasios V VasilakosAbstract:Orthogonal Frequency-Division Multiplexing (OFDM) systems operating in high mobility environments experience time-varying channel, which leads to serious inter-carrier-interferences. Basis Expansion Model (BEM) is a widely used technique to estimate the time-varying channel, but can only capture the time-varying characteristics within each single OFDM block one by one in traditional algorithms. A method to extend the Observation Window of BEM based algorithms to the range of multiple OFDM blocks is established and adapted to pilot-assisted OFDM systems, where pilot signals in multiple OFDM blocks are converted into time-domain and connected together. As a result, the time-varying channel can be estimated more accurately, with comparable complexity as those of traditional algorithms.
-
VANET@MOBICOM - Time-varying channel estimation for OFDM with extended Observation Window
Proceedings of the ninth ACM international workshop on Vehicular inter-networking systems and applications - VANET '12, 2012Co-Authors: Dan Shan, Paul Richardson, Weidong Xiang, Athanasios V VasilakosAbstract:Orthogonal Frequency-Division Multiplexing (OFDM) systems operating in high mobility environments experience time-varying channel, which leads to serious inter-carrier-interferences. Basis Expansion Model (BEM) is a widely used technique to estimate the time-varying channel, but can only capture the time-varying characteristics within each single OFDM block one by one in traditional algorithms. A method to extend the Observation Window of BEM based algorithms to the range of multiple OFDM blocks is established and adapted to pilot-assisted OFDM systems, where pilot signals in multiple OFDM blocks are converted into time-domain and connected together. As a result, the time-varying channel can be estimated more accurately, with comparable complexity as those of traditional algorithms.
