The Experts below are selected from a list of 291 Experts worldwide ranked by ideXlab platform
Kenneth K. Y. Wong - One of the best experts on this subject based on the ideXlab platform.
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CORRIGENDUM: Parametric spectro-temporal analyzer (PASTA) for real-time Optical Spectrum observation.
Scientific Reports, 2014Co-Authors: Chi Zhang, Jianbing Xu, P. C. Chui, Kenneth K. Y. WongAbstract:CORRIGENDUM: Parametric spectro-temporal analyzer (PASTA) for real-time Optical Spectrum observation
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parametric spectro temporal analyzer pasta for real time Optical Spectrum observation
Scientific Reports, 2013Co-Authors: Chi Zhang, Jianbing Xu, P. C. Chui, Kenneth K. Y. WongAbstract:Real-time Optical Spectrum analysis is an essential tool in observing ultrafast phenomena, such as the dynamic monitoring of Spectrum evolution. However, conventional method such as Optical Spectrum analyzers disperse the Spectrum in space and allocate it in time sequence by mechanical rotation of a grating, so are incapable of operating at high speed. A more recent method all-Optically stretches the Spectrum in time domain, but is limited by the allowable input condition. In view of these constraints, here we present a real-time Spectrum analyzer called parametric spectro-temporal analyzer (PASTA), which is based on the time-lens focusing mechanism. It achieves a frame rate as high as 100 MHz and accommodates various input conditions. As a proof of concept and also for the first time, we verify its applications in observing the dynamic Spectrum of a Fourier domain mode-locked laser, and the Spectrum evolution of a laser cavity during its stabilizing process.
Luis Velasco - One of the best experts on this subject based on the ideXlab platform.
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Smart Filterless Optical Networks Based on Optical Spectrum Analysis
2019 21st International Conference on Transparent Optical Networks (ICTON), 2019Co-Authors: Marc Ruiz, Andrea Sgambelluri, Filippo Cugini, Luis VelascoAbstract:Dynamic network operations can produce power fluctuations of the established connections in filterless Optical networks. In addition, the gridless nature of filterless networks make that some (un)intentional effects such as transponders laser drift might disrupt the proper operation of lightpaths. To overcome these issues, we present a monitoring system exploiting data analytics and cost-effective Optical Spectrum analyzers to achieve smart filterless network operation. Experimental measurements are used to validate the proposed data analytics-based approaches, as well as to find the optimal resolution to achieve maximum performance with minimum cost.
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Feature-Based Optical Spectrum Monitoring for Failure Detection and Identification
2019 21st International Conference on Transparent Optical Networks (ICTON), 2019Co-Authors: Behnam Shariati, Marc Ruiz, Jaume Comellas, Luis VelascoAbstract:In this paper, we explore the benefits of analysing the Optical Spectrum of lightpaths for soft-failure detection and identification in Spectrum Switched Optical Network. We present a framework exploiting machine learning (ML) based algorithms that uses descriptive models of the Optical Spectrum of a lightpath in different points along its route to detect whether the Optical signal experiences anomalies reflecting a failure in the intermediate nodes. Our proposal targets the two most common filter-related soft-failures; filter shift (FS) and filter tightening (FT), which noticeably deform the expected shape of the Optical Spectrum. In this regard, filter cascading is a key challenge as it affects the shape of the Optical Spectrum similar to FT. Our proposals avoid the misclassification of properly operating signals when normal filter cascading effects is present. Extensive numerical results are presented to compare the performance of the proposed approaches in terms of accuracy and robustness.
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Learning From the Optical Spectrum: Failure Detection and Identification
Journal of Lightwave Technology, 2019Co-Authors: Behnam Shariati, Marc Ruiz, Jaume Comellas, Luis VelascoAbstract:The availability of coarse-resolution cost-effective Optical Spectrum analyzers (OSAs) allows their widespread deployment in operators' networks. In this paper, we explore several machine learning approaches for soft-failure detection, identification, and localization that take advantage of OSAs. In particular, we present three different solutions for the two most common filter-related soft-failures: filter shift and tight filtering, which noticeably deform the expected shape of the Optical Spectrum. However, filter cascading is a key challenge as it affects the shape of the Optical Spectrum similarly to the tight filtering; the approaches are specifically designed to avoid the misclassification of properly operating signals when normal filter cascading effects are present. The proposed solutions are as follows: First, the multi-classifier approach, which uses features extracted directly from the Optical Spectrum; second, the single-classifier approach, which uses preprocessed features to compensate for filter cascading; and third, the residual-based approach, which uses a residual signal computed from subtracting the signal acquired by OSAs from an expected signal synthetically generated. Extensive numerical results are ultimately presented to compare the performance of the proposed approaches in terms of accuracy and robustness.
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Monitoring and data analytics: Analyzing the Optical Spectrum for soft-failure detection and identification
2018 International Conference on Optical Network Design and Modeling (ONDM), 2018Co-Authors: Behnam Shariati, Marc Ruiz, A. P. Vela, Luis VelascoAbstract:Failure detection is essential in Optical networks as a result of the huge amount of traffic that Optical connections support. Additionally, the cause of failure needs to be identified so failed resources can be excluded from the computation of restoration paths. In the case of soft-failures, their prompt detection, identification, and localization make that recovery can be triggered before excessive errors in Optical connections translate into errors on the supported services or even become disrupted. Therefore, Monitoring and Data Analytics (MDA) become of paramount importance in the case of soft-failures. In this paper, we review a MDA architecture that reduces remarkably detection and identification times, while facilitating failure localization. In addition, we rely on Optical Spectrum Analyzers (OSA) deployed in the Optical nodes as monitoring devices acquiring the Optical Spectrum of outgoing links. Analyzing the Optical Spectrum of Optical connections, specific soft-failures that affect the shape of the Spectrum can be detected. A workflow consisting of machine learning algorithms, designed to be integrated in the aforementioned MDA architecture, will be studied to analyze the Optical Spectrum of a given Optical connection acquired in a node and to determine whether a filter failure is affecting it, and in such case, what is the type of filter failure and its magnitude. Exhaustive results are presented allowing to evaluate the proposed method.
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ONDM - Monitoring and data analytics: Analyzing the Optical Spectrum for soft-failure detection and identification
2018 International Conference on Optical Network Design and Modeling (ONDM), 2018Co-Authors: Behnam Shariati, Marc Ruiz, Alba P. Vela, Luis VelascoAbstract:Failure detection is essential in Optical networks as a result of the huge amount of traffic that Optical connections support. Additionally, the cause of failure needs to be identified so failed resources can be excluded from the computation of restoration paths. In the case of soft-failures, their prompt detection, identification, and localization make that recovery can be triggered before excessive errors in Optical connections translate into errors on the supported services or even become disrupted. Therefore, Monitoring and Data Analytics (MDA) become of paramount importance in the case of soft-failures. In this paper, we review a MDA architecture that reduces remarkably detection and identification times, while facilitating failure localization. In addition, we rely on Optical Spectrum Analyzers (OSA) deployed in the Optical nodes as monitoring devices acquiring the Optical Spectrum of outgoing links. Analyzing the Optical Spectrum of Optical connections, specific soft-failures that affect the shape of the Spectrum can be detected. A workflow consisting of machine learning algorithms, designed to be integrated in the aforementioned MDA architecture, will be studied to analyze the Optical Spectrum of a given Optical connection acquired in a node and to determine whether a filter failure is affecting it, and in such case, what is the type of filter failure and its magnitude. Exhaustive results are presented allowing to evaluate the proposed method.
Chi Zhang - One of the best experts on this subject based on the ideXlab platform.
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CORRIGENDUM: Parametric spectro-temporal analyzer (PASTA) for real-time Optical Spectrum observation.
Scientific Reports, 2014Co-Authors: Chi Zhang, Jianbing Xu, P. C. Chui, Kenneth K. Y. WongAbstract:CORRIGENDUM: Parametric spectro-temporal analyzer (PASTA) for real-time Optical Spectrum observation
-
parametric spectro temporal analyzer pasta for real time Optical Spectrum observation
Scientific Reports, 2013Co-Authors: Chi Zhang, Jianbing Xu, P. C. Chui, Kenneth K. Y. WongAbstract:Real-time Optical Spectrum analysis is an essential tool in observing ultrafast phenomena, such as the dynamic monitoring of Spectrum evolution. However, conventional method such as Optical Spectrum analyzers disperse the Spectrum in space and allocate it in time sequence by mechanical rotation of a grating, so are incapable of operating at high speed. A more recent method all-Optically stretches the Spectrum in time domain, but is limited by the allowable input condition. In view of these constraints, here we present a real-time Spectrum analyzer called parametric spectro-temporal analyzer (PASTA), which is based on the time-lens focusing mechanism. It achieves a frame rate as high as 100 MHz and accommodates various input conditions. As a proof of concept and also for the first time, we verify its applications in observing the dynamic Spectrum of a Fourier domain mode-locked laser, and the Spectrum evolution of a laser cavity during its stabilizing process.
Marc Ruiz - One of the best experts on this subject based on the ideXlab platform.
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Smart Filterless Optical Networks Based on Optical Spectrum Analysis
2019 21st International Conference on Transparent Optical Networks (ICTON), 2019Co-Authors: Marc Ruiz, Andrea Sgambelluri, Filippo Cugini, Luis VelascoAbstract:Dynamic network operations can produce power fluctuations of the established connections in filterless Optical networks. In addition, the gridless nature of filterless networks make that some (un)intentional effects such as transponders laser drift might disrupt the proper operation of lightpaths. To overcome these issues, we present a monitoring system exploiting data analytics and cost-effective Optical Spectrum analyzers to achieve smart filterless network operation. Experimental measurements are used to validate the proposed data analytics-based approaches, as well as to find the optimal resolution to achieve maximum performance with minimum cost.
-
Feature-Based Optical Spectrum Monitoring for Failure Detection and Identification
2019 21st International Conference on Transparent Optical Networks (ICTON), 2019Co-Authors: Behnam Shariati, Marc Ruiz, Jaume Comellas, Luis VelascoAbstract:In this paper, we explore the benefits of analysing the Optical Spectrum of lightpaths for soft-failure detection and identification in Spectrum Switched Optical Network. We present a framework exploiting machine learning (ML) based algorithms that uses descriptive models of the Optical Spectrum of a lightpath in different points along its route to detect whether the Optical signal experiences anomalies reflecting a failure in the intermediate nodes. Our proposal targets the two most common filter-related soft-failures; filter shift (FS) and filter tightening (FT), which noticeably deform the expected shape of the Optical Spectrum. In this regard, filter cascading is a key challenge as it affects the shape of the Optical Spectrum similar to FT. Our proposals avoid the misclassification of properly operating signals when normal filter cascading effects is present. Extensive numerical results are presented to compare the performance of the proposed approaches in terms of accuracy and robustness.
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Learning From the Optical Spectrum: Failure Detection and Identification
Journal of Lightwave Technology, 2019Co-Authors: Behnam Shariati, Marc Ruiz, Jaume Comellas, Luis VelascoAbstract:The availability of coarse-resolution cost-effective Optical Spectrum analyzers (OSAs) allows their widespread deployment in operators' networks. In this paper, we explore several machine learning approaches for soft-failure detection, identification, and localization that take advantage of OSAs. In particular, we present three different solutions for the two most common filter-related soft-failures: filter shift and tight filtering, which noticeably deform the expected shape of the Optical Spectrum. However, filter cascading is a key challenge as it affects the shape of the Optical Spectrum similarly to the tight filtering; the approaches are specifically designed to avoid the misclassification of properly operating signals when normal filter cascading effects are present. The proposed solutions are as follows: First, the multi-classifier approach, which uses features extracted directly from the Optical Spectrum; second, the single-classifier approach, which uses preprocessed features to compensate for filter cascading; and third, the residual-based approach, which uses a residual signal computed from subtracting the signal acquired by OSAs from an expected signal synthetically generated. Extensive numerical results are ultimately presented to compare the performance of the proposed approaches in terms of accuracy and robustness.
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Monitoring and data analytics: Analyzing the Optical Spectrum for soft-failure detection and identification
2018 International Conference on Optical Network Design and Modeling (ONDM), 2018Co-Authors: Behnam Shariati, Marc Ruiz, A. P. Vela, Luis VelascoAbstract:Failure detection is essential in Optical networks as a result of the huge amount of traffic that Optical connections support. Additionally, the cause of failure needs to be identified so failed resources can be excluded from the computation of restoration paths. In the case of soft-failures, their prompt detection, identification, and localization make that recovery can be triggered before excessive errors in Optical connections translate into errors on the supported services or even become disrupted. Therefore, Monitoring and Data Analytics (MDA) become of paramount importance in the case of soft-failures. In this paper, we review a MDA architecture that reduces remarkably detection and identification times, while facilitating failure localization. In addition, we rely on Optical Spectrum Analyzers (OSA) deployed in the Optical nodes as monitoring devices acquiring the Optical Spectrum of outgoing links. Analyzing the Optical Spectrum of Optical connections, specific soft-failures that affect the shape of the Spectrum can be detected. A workflow consisting of machine learning algorithms, designed to be integrated in the aforementioned MDA architecture, will be studied to analyze the Optical Spectrum of a given Optical connection acquired in a node and to determine whether a filter failure is affecting it, and in such case, what is the type of filter failure and its magnitude. Exhaustive results are presented allowing to evaluate the proposed method.
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ONDM - Monitoring and data analytics: Analyzing the Optical Spectrum for soft-failure detection and identification
2018 International Conference on Optical Network Design and Modeling (ONDM), 2018Co-Authors: Behnam Shariati, Marc Ruiz, Alba P. Vela, Luis VelascoAbstract:Failure detection is essential in Optical networks as a result of the huge amount of traffic that Optical connections support. Additionally, the cause of failure needs to be identified so failed resources can be excluded from the computation of restoration paths. In the case of soft-failures, their prompt detection, identification, and localization make that recovery can be triggered before excessive errors in Optical connections translate into errors on the supported services or even become disrupted. Therefore, Monitoring and Data Analytics (MDA) become of paramount importance in the case of soft-failures. In this paper, we review a MDA architecture that reduces remarkably detection and identification times, while facilitating failure localization. In addition, we rely on Optical Spectrum Analyzers (OSA) deployed in the Optical nodes as monitoring devices acquiring the Optical Spectrum of outgoing links. Analyzing the Optical Spectrum of Optical connections, specific soft-failures that affect the shape of the Spectrum can be detected. A workflow consisting of machine learning algorithms, designed to be integrated in the aforementioned MDA architecture, will be studied to analyze the Optical Spectrum of a given Optical connection acquired in a node and to determine whether a filter failure is affecting it, and in such case, what is the type of filter failure and its magnitude. Exhaustive results are presented allowing to evaluate the proposed method.
Behnam Shariati - One of the best experts on this subject based on the ideXlab platform.
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Feature-Based Optical Spectrum Monitoring for Failure Detection and Identification
2019 21st International Conference on Transparent Optical Networks (ICTON), 2019Co-Authors: Behnam Shariati, Marc Ruiz, Jaume Comellas, Luis VelascoAbstract:In this paper, we explore the benefits of analysing the Optical Spectrum of lightpaths for soft-failure detection and identification in Spectrum Switched Optical Network. We present a framework exploiting machine learning (ML) based algorithms that uses descriptive models of the Optical Spectrum of a lightpath in different points along its route to detect whether the Optical signal experiences anomalies reflecting a failure in the intermediate nodes. Our proposal targets the two most common filter-related soft-failures; filter shift (FS) and filter tightening (FT), which noticeably deform the expected shape of the Optical Spectrum. In this regard, filter cascading is a key challenge as it affects the shape of the Optical Spectrum similar to FT. Our proposals avoid the misclassification of properly operating signals when normal filter cascading effects is present. Extensive numerical results are presented to compare the performance of the proposed approaches in terms of accuracy and robustness.
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Learning From the Optical Spectrum: Failure Detection and Identification
Journal of Lightwave Technology, 2019Co-Authors: Behnam Shariati, Marc Ruiz, Jaume Comellas, Luis VelascoAbstract:The availability of coarse-resolution cost-effective Optical Spectrum analyzers (OSAs) allows their widespread deployment in operators' networks. In this paper, we explore several machine learning approaches for soft-failure detection, identification, and localization that take advantage of OSAs. In particular, we present three different solutions for the two most common filter-related soft-failures: filter shift and tight filtering, which noticeably deform the expected shape of the Optical Spectrum. However, filter cascading is a key challenge as it affects the shape of the Optical Spectrum similarly to the tight filtering; the approaches are specifically designed to avoid the misclassification of properly operating signals when normal filter cascading effects are present. The proposed solutions are as follows: First, the multi-classifier approach, which uses features extracted directly from the Optical Spectrum; second, the single-classifier approach, which uses preprocessed features to compensate for filter cascading; and third, the residual-based approach, which uses a residual signal computed from subtracting the signal acquired by OSAs from an expected signal synthetically generated. Extensive numerical results are ultimately presented to compare the performance of the proposed approaches in terms of accuracy and robustness.
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Monitoring and data analytics: Analyzing the Optical Spectrum for soft-failure detection and identification
2018 International Conference on Optical Network Design and Modeling (ONDM), 2018Co-Authors: Behnam Shariati, Marc Ruiz, A. P. Vela, Luis VelascoAbstract:Failure detection is essential in Optical networks as a result of the huge amount of traffic that Optical connections support. Additionally, the cause of failure needs to be identified so failed resources can be excluded from the computation of restoration paths. In the case of soft-failures, their prompt detection, identification, and localization make that recovery can be triggered before excessive errors in Optical connections translate into errors on the supported services or even become disrupted. Therefore, Monitoring and Data Analytics (MDA) become of paramount importance in the case of soft-failures. In this paper, we review a MDA architecture that reduces remarkably detection and identification times, while facilitating failure localization. In addition, we rely on Optical Spectrum Analyzers (OSA) deployed in the Optical nodes as monitoring devices acquiring the Optical Spectrum of outgoing links. Analyzing the Optical Spectrum of Optical connections, specific soft-failures that affect the shape of the Spectrum can be detected. A workflow consisting of machine learning algorithms, designed to be integrated in the aforementioned MDA architecture, will be studied to analyze the Optical Spectrum of a given Optical connection acquired in a node and to determine whether a filter failure is affecting it, and in such case, what is the type of filter failure and its magnitude. Exhaustive results are presented allowing to evaluate the proposed method.
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ONDM - Monitoring and data analytics: Analyzing the Optical Spectrum for soft-failure detection and identification
2018 International Conference on Optical Network Design and Modeling (ONDM), 2018Co-Authors: Behnam Shariati, Marc Ruiz, Alba P. Vela, Luis VelascoAbstract:Failure detection is essential in Optical networks as a result of the huge amount of traffic that Optical connections support. Additionally, the cause of failure needs to be identified so failed resources can be excluded from the computation of restoration paths. In the case of soft-failures, their prompt detection, identification, and localization make that recovery can be triggered before excessive errors in Optical connections translate into errors on the supported services or even become disrupted. Therefore, Monitoring and Data Analytics (MDA) become of paramount importance in the case of soft-failures. In this paper, we review a MDA architecture that reduces remarkably detection and identification times, while facilitating failure localization. In addition, we rely on Optical Spectrum Analyzers (OSA) deployed in the Optical nodes as monitoring devices acquiring the Optical Spectrum of outgoing links. Analyzing the Optical Spectrum of Optical connections, specific soft-failures that affect the shape of the Spectrum can be detected. A workflow consisting of machine learning algorithms, designed to be integrated in the aforementioned MDA architecture, will be studied to analyze the Optical Spectrum of a given Optical connection acquired in a node and to determine whether a filter failure is affecting it, and in such case, what is the type of filter failure and its magnitude. Exhaustive results are presented allowing to evaluate the proposed method.
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Learning from the Optical Spectrum: Soft-Failure Identification and Localization [Invited]
2018 Optical Fiber Communications Conference and Exposition (OFC), 2018Co-Authors: Luis Velasco, Behnam Shariati, Jaume Comellas, Alba P. Vela, Marc RuizAbstract:The availability of coarse-resolution cost-effective Optical Spectrum Analyzers (OSA) allows its widespread deployment in operators' networks. In this paper, several machine learning approaches for failure identification and localization that take advantage of OSAs are presented.