The Microsoft Spectrum Observatory ThinkTank 2014 aims to create a forum where like-minded individuals come together to discuss and provide additional feedback so the platform can better serve the community\u2019s research needs even more than today. During this ThinkTank we will be presenting the vision of Microsoft Spectrum Observatory, demo of current version (which is being actively developed), describe its architecture and provide an opportunity for you to provide suggestions and feedback to make the Microsoft Spectrum Observatory a success. It is also an opportunity for the Spectrum Observatory development team to describe their future plans, look at research currently being done in this space, and how you can become part of this exciting project.<\/p>\n
What is Microsoft Spectrum Observatory?<\/strong><\/p>\n Website: http:\/\/observatory.microsoftspectrum.com\/<\/a><\/p>\n E-mail: mailto:spectrum_obs@microsoft.com<\/a><\/p>\n\n\n\n\n\n \n<\/p> Slides<\/span><\/a><\/p>\n <\/p><\/div>\n \n<\/p> Slides<\/span><\/a><\/p>\n <\/p><\/div>\n \n<\/p> Slides<\/span><\/a><\/p>\n Spectrum sensing has been an active research area for the past two decades. Nonetheless, current spectrum sensing systems provide only coarse occupancy data. They lack information about the detailed signal patterns in each band and can easily miss fleeting signals like radar.<\/p>\n We present SpecInsight, a system for acquiring a detailed view of 4 GHz of spectrum in real-time. SpecInsight\u2019s design addresses the intrinsic conflict between the need to quickly scan a wide spectrum and the desire for obtaining very detailed information about each band. Its key enabler is a learned database of signal patterns and a new scheduling algorithm that leverages these patterns to identify when to sample each band to maximize the probability of sensing active signals.<\/p>\n SpecInsight is implemented using off-the-shelf USRP radios with only tens of MHz of instant bandwidth, but is able to span 4 GHz of spectrum, and capture very low duty-cycle signals in the radar band. Using SpecInsight, we perform a large-scale study of the spectrum in 6 locations in the US that span major cities and suburban areas, and build a first-of-its-kind database of spectrum usage patterns.<\/p>\n <\/p><\/div>\n \n<\/p> Slides<\/span><\/a><\/p>\n The evolution of cognitive (secondary) networks to enable more efficient spectrum usage will rely on fast and accurate spectrum sensing\/mapping, supported by a suitable architecture for data integration and model building. In the first part of the talk, fundamental aspects of the wide-area RF mapping problem as a grand challenge will be highlighted; and some recent work at UW that underpin sub-system level trade-offs (between scan latency and channel status estimation accuracy) for channel sensing described. Next, the role of centralized databases in RF map creation for enabling primary-to-secondary and secondary-to-secondary coexistence is explored and a hybrid architecture proposed \u2013 that involves both distributed (crowd-sourced) local sensing as well as it\u2019s integration into databases. Finally, some ongoing work regarding a fundamental question: how much white space capacity is actually available \u2013 will be described.<\/p>\n <\/p><\/div>\n \n<\/p> Slides<\/span><\/a><\/p>\n How should regulators re-assign spectrum optimally? How do licensees identify spectrum usage in order to provision for future needs? How do Dynamic Spectrum Access devices determine on which frequency to operate? All these questions require knowledge about active transmitters, which is not straight-forward to obtain with currently-existing techniques. In this talk I am going to present TxMiner: a system that automatically identifies transmitters without prior knowledge of their characteristics. TxMiner makes use of machine learning methods in order to tease apart transmitters from raw spectrum measurements. I will start by outlining several key insights that enable TxMiner; I will then show results from transmitter identification using traces collected by Microsoft\u2019s Spectrum Observatory.<\/p>\n <\/p><\/div>\n \n<\/p> Slides<\/span><\/a><\/p>\n We are facing an increasing difficult challenge in spectrum management: how to perform real-time spectrum monitoring with strong coverage of deployed regions. Today\u2019s solutions use dedicated hardware that is bulky and expensive, making the monitoring task extremely difficult and cost prohibitive. We propose a practical alternative by leveraging the power of the masses, i.e. millions of wireless users, using low- cost, commoditized spectrum monitoring hardware. We envision an ecosystem where crowd sourced smartphone users perform automated and continuous spectrum measurements using their mobile devices, and report the results to a monitoring agency in real-time.<\/p>\n In this talk, we will introduce our initial feasibility study to verify the efficacy of the mobile monitoring platform compared to that of conventional monitoring devices. Our results indicate that commoditized real-time spectrum monitoring is indeed feasible in the near future. We conclude by discussing a set of open challenges and potential directions for follow-up research.<\/p>\n <\/p><\/div>\n \n<\/p> Slides<\/span><\/a><\/p>\n It has been three decades since the United States Federal Communications Commission (\u201cFCC\u201d) created the first unlicensed spectrum access. Although only available commercially in the last fifteen years, it is hard to imagine consumers doing without technologies now used in the unlicensed spectrum bands \u2013 Wi-Fi, Bluetooth, RFID. There are now many more wireless devices reliant on unlicensed access to spectrum than wireless devices reliant on licensed access to spectrum. Driven by growing demand that risks outstripping the current allocations of spectrum for wireless broadband applications, governments and regulators around the world are now looking at making more spectrum available on an exclusive-use licensed basis for 4G LTE, as well as make more spectrum available on an unlicensed (or licensed-exempt) basis for Wi-Fi and other technologies. Governments and regulators are likewise now looking at alternative forms of spectrum access which will not require incumbent licensees to be cleared and reallocated, such as spectrum sharing opportunities in the TV white spaces, 2.3 GHz, 3.5 GHz, and 5 GHz either on an unlicensed, licensed, or lightly-licensed basis. This presentation will provide an overview of efforts by governments and regulators around the world to address these issues \u2013 with a particular focus on alternative forms of spectrum access. This presentation will also discuss how spectrum observatories, standardization, technology trials, commercial pilots, partnerships, and industry and academic coalitions can and are being used to support these efforts.<\/p>\n <\/p><\/div>\n <\/p><\/div>\n\n\n\n\n\n Dr. Ranveer Chandra Microsoft<\/p>\n Dr. Victor Bahl Microsoft<\/p>\n Mr. Paul Garnett Microsoft Mr. Paul Mitchell Microsoft<\/p>\n Dr. Elizabeth Belding UCSB The Microsoft Spectrum Observatory ThinkTank 2014 aims to create a forum where like-minded individuals come together to discuss and provide additional feedback so the platform can better serve the community\u2019s research needs even more than today. During this ThinkTank we will be presenting the vision of Microsoft Spectrum Observatory, demo of current version (which is […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"msr_startdate":"2014-08-06","msr_enddate":"2014-08-06","msr_location":"Online","msr_expirationdate":"","msr_event_recording_link":"","msr_event_link":"","msr_event_link_redirect":false,"msr_event_time":"","msr_hide_region":false,"msr_private_event":true,"footnotes":""},"research-area":[13555],"msr-region":[256048],"msr-event-type":[197944],"msr-video-type":[],"msr-locale":[268875],"msr-program-audience":[],"msr-post-option":[],"msr-impact-theme":[],"class_list":["post-199987","msr-event","type-msr-event","status-publish","hentry","msr-research-area-search-information-retrieval","msr-region-global","msr-event-type-hosted-by-microsoft","msr-locale-en_us"],"msr_about":"\n\n\n\n\n The Microsoft Spectrum Observatory ThinkTank 2014 aims to create a forum where like-minded individuals come together to discuss and provide additional feedback so the platform can better serve the community\u2019s research needs even more than today. During this ThinkTank we will be presenting the vision of Microsoft Spectrum Observatory, demo of current version (which is being actively developed), describe its architecture and provide an opportunity for you to provide suggestions and feedback to make the Microsoft Spectrum Observatory a success. It is also an opportunity for the Spectrum Observatory development team to describe their future plans, look at research currently being done in this space, and how you can become part of this exciting project.<\/p>\n What is Microsoft Spectrum Observatory?<\/strong><\/p>\n Website: http:\/\/observatory.microsoftspectrum.com\/<\/a><\/p>\n E-mail: mailto:spectrum_obs@microsoft.com<\/a><\/p>\n\n\n\n\n\n \n<\/p> Slides<\/span><\/a><\/p>\n <\/p><\/div>\n \n<\/p> Slides<\/span><\/a><\/p>\n <\/p><\/div>\n \n<\/p> Slides<\/span><\/a><\/p>\n Spectrum sensing has been an active research area for the past two decades. Nonetheless, current spectrum sensing systems provide only coarse occupancy data. They lack information about the detailed signal patterns in each band and can easily miss fleeting signals like radar.<\/p>\n We present SpecInsight, a system for acquiring a detailed view of 4 GHz of spectrum in real-time. SpecInsight\u2019s design addresses the intrinsic conflict between the need to quickly scan a wide spectrum and the desire for obtaining very detailed information about each band. Its key enabler is a learned database of signal patterns and a new scheduling algorithm that leverages these patterns to identify when to sample each band to maximize the probability of sensing active signals.<\/p>\n SpecInsight is implemented using off-the-shelf USRP radios with only tens of MHz of instant bandwidth, but is able to span 4 GHz of spectrum, and capture very low duty-cycle signals in the radar band. Using SpecInsight, we perform a large-scale study of the spectrum in 6 locations in the US that span major cities and suburban areas, and build a first-of-its-kind database of spectrum usage patterns.<\/p>\n <\/p><\/div>\n \n<\/p> Slides<\/span><\/a><\/p>\n The evolution of cognitive (secondary) networks to enable more efficient spectrum usage will rely on fast and accurate spectrum sensing\/mapping, supported by a suitable architecture for data integration and model building. In the first part of the talk, fundamental aspects of the wide-area RF mapping problem as a grand challenge will be highlighted; and some recent work at UW that underpin sub-system level trade-offs (between scan latency and channel status estimation accuracy) for channel sensing described. Next, the role of centralized databases in RF map creation for enabling primary-to-secondary and secondary-to-secondary coexistence is explored and a hybrid architecture proposed \u2013 that involves both distributed (crowd-sourced) local sensing as well as it\u2019s integration into databases. Finally, some ongoing work regarding a fundamental question: how much white space capacity is actually available \u2013 will be described.<\/p>\n <\/p><\/div>\n \n<\/p> Slides<\/span><\/a><\/p>\n How should regulators re-assign spectrum optimally? How do licensees identify spectrum usage in order to provision for future needs? How do Dynamic Spectrum Access devices determine on which frequency to operate? All these questions require knowledge about active transmitters, which is not straight-forward to obtain with currently-existing techniques. In this talk I am going to present TxMiner: a system that automatically identifies transmitters without prior knowledge of their characteristics. TxMiner makes use of machine learning methods in order to tease apart transmitters from raw spectrum measurements. I will start by outlining several key insights that enable TxMiner; I will then show results from transmitter identification using traces collected by Microsoft\u2019s Spectrum Observatory.<\/p>\n <\/p><\/div>\n \n<\/p> Slides<\/span><\/a><\/p>\n We are facing an increasing difficult challenge in spectrum management: how to perform real-time spectrum monitoring with strong coverage of deployed regions. Today\u2019s solutions use dedicated hardware that is bulky and expensive, making the monitoring task extremely difficult and cost prohibitive. We propose a practical alternative by leveraging the power of the masses, i.e. millions of wireless users, using low- cost, commoditized spectrum monitoring hardware. We envision an ecosystem where crowd sourced smartphone users perform automated and continuous spectrum measurements using their mobile devices, and report the results to a monitoring agency in real-time.<\/p>\n In this talk, we will introduce our initial feasibility study to verify the efficacy of the mobile monitoring platform compared to that of conventional monitoring devices. Our results indicate that commoditized real-time spectrum monitoring is indeed feasible in the near future. We conclude by discussing a set of open challenges and potential directions for follow-up research.<\/p>\n <\/p><\/div>\n \n<\/p> Slides<\/span><\/a><\/p>\n It has been three decades since the United States Federal Communications Commission (\u201cFCC\u201d) created the first unlicensed spectrum access. Although only available commercially in the last fifteen years, it is hard to imagine consumers doing without technologies now used in the unlicensed spectrum bands \u2013 Wi-Fi, Bluetooth, RFID. There are now many more wireless devices reliant on unlicensed access to spectrum than wireless devices reliant on licensed access to spectrum. Driven by growing demand that risks outstripping the current allocations of spectrum for wireless broadband applications, governments and regulators around the world are now looking at making more spectrum available on an exclusive-use licensed basis for 4G LTE, as well as make more spectrum available on an unlicensed (or licensed-exempt) basis for Wi-Fi and other technologies. Governments and regulators are likewise now looking at alternative forms of spectrum access which will not require incumbent licensees to be cleared and reallocated, such as spectrum sharing opportunities in the TV white spaces, 2.3 GHz, 3.5 GHz, and 5 GHz either on an unlicensed, licensed, or lightly-licensed basis. This presentation will provide an overview of efforts by governments and regulators around the world to address these issues \u2013 with a particular focus on alternative forms of spectrum access. This presentation will also discuss how spectrum observatories, standardization, technology trials, commercial pilots, partnerships, and industry and academic coalitions can and are being used to support these efforts.<\/p>\n <\/p><\/div>\n <\/p><\/div>\n\n\n\n\n\n Dr. Ranveer Chandra Microsoft<\/p>\n Dr. Victor Bahl Microsoft<\/p>\n Mr. Paul Garnett Microsoft Mr. Paul Mitchell Microsoft<\/p>\n Dr. Elizabeth Belding UCSBMore information<\/h4>\n
\n\t\t\t\tUS Federal Government Spectrum Monitoring by Mr. Michael Cotton\t\t\t<\/h4>\n
\n\t\t\t\tTutorial of the Spectrum Observatory and a Walkthrough of the Technology by Mr. Anoop Gupta\t\t\t<\/h4>\n
\n\t\t\t\tBeyond Sensing: Multi-GHz Realtime Spectrum Analytics by Mr. Lixin Shi, MIT\t\t\t<\/h4>\n
\n\t\t\t\tIntegrated Sensing and Database Architecture for White Space Networking by Dr. Sumit Roy, University of Washington\t\t\t<\/h4>\n
\n\t\t\t\tTxMiner: Identifying Transmitters in Real-World Spectrum Measurements\t\t\t<\/h4>\n
\n\t\t\t\tTowards Commoditized Real-time Spectrum Monitoring\t\t\t<\/h4>\n
\n\t\t\t\tAn Overview of the Global Policy and Regulatory Landscape \u2013 Opportunities and Risks for Alternative Forms of Spectrum Access\t\t\t<\/h4>\n
\nMr. Anoop Gupta Microsoft<\/p>\n
\nDr. Joseph Camp SMU
\nDr. Minghua Chen CUHK
\nMr. Michael Cotton ITS
\nDr. Joseph Evans University of Kansas
\nDr. Kyle Jameison UCL
\nDr. Giovanni Pau UCLA
\nDr. Sumit Roy UW
\nMr. Lixin Shi MIT
\nDr. Michael Souryal NIST
\nDr. Kannan Srinivasan Ohio State
\nDr. Rouzbeh Yassini UNH
\nDr. Mariya Zheleva SUNY
\nDr. Heather Zheng UCSB<\/p>\n\n\n","protected":false},"excerpt":{"rendered":"More information<\/h4>\n
\n\t\t\t\tUS Federal Government Spectrum Monitoring by Mr. Michael Cotton\t\t\t<\/h4>\n
\n\t\t\t\tTutorial of the Spectrum Observatory and a Walkthrough of the Technology by Mr. Anoop Gupta\t\t\t<\/h4>\n
\n\t\t\t\tBeyond Sensing: Multi-GHz Realtime Spectrum Analytics by Mr. Lixin Shi, MIT\t\t\t<\/h4>\n
\n\t\t\t\tIntegrated Sensing and Database Architecture for White Space Networking by Dr. Sumit Roy, University of Washington\t\t\t<\/h4>\n
\n\t\t\t\tTxMiner: Identifying Transmitters in Real-World Spectrum Measurements\t\t\t<\/h4>\n
\n\t\t\t\tTowards Commoditized Real-time Spectrum Monitoring\t\t\t<\/h4>\n
\n\t\t\t\tAn Overview of the Global Policy and Regulatory Landscape \u2013 Opportunities and Risks for Alternative Forms of Spectrum Access\t\t\t<\/h4>\n
\nMr. Anoop Gupta Microsoft<\/p>\n
\nDr. Joseph Camp SMU
\nDr. Minghua Chen CUHK
\nMr. Michael Cotton ITS
\nDr. Joseph Evans University of Kansas
\nDr. Kyle Jameison UCL
\nDr. Giovanni Pau UCLA
\nDr. Sumit Roy UW
\nMr. Lixin Shi MIT
\nDr. Michael Souryal NIST
\nDr. Kannan Srinivasan Ohio State
\nDr. Rouzbeh Yassini UNH
\nDr. Mariya Zheleva SUNY
\nDr. Heather Zheng UCSB<\/p>\n\n\n","tab-content":[{"id":0,"name":"Summary","content":"The Microsoft Spectrum Observatory ThinkTank 2014 aims to create a forum where like-minded individuals come together to discuss and provide additional feedback so the platform can better serve the community\u2019s research needs even more than today. During this ThinkTank we will be presenting the vision of Microsoft Spectrum Observatory, demo of current version (which is being actively developed), describe its architecture and provide an opportunity for you to provide suggestions and feedback to make the Microsoft Spectrum Observatory a success. It is also an opportunity for the Spectrum Observatory development team to describe their future plans, look at research currently being done in this space, and how you can become part of this exciting project.\r\n\r\nWhat is Microsoft Spectrum Observatory?<\/strong>\r\nMore information<\/h4>\r\nWebsite: http:\/\/observatory.microsoftspectrum.com\/<\/a>\r\n\r\nE-mail: mailto:spectrum_obs@microsoft.com<\/a>"},{"id":1,"name":"Program","content":"[accordion]\r\n\r\n[panel header=\"US Federal Government Spectrum Monitoring by Mr. Michael Cotton\"]\r\n\r\nSlides<\/span><\/a>\r\n[\/panel]\r\n\r\n[panel header=\"Tutorial of the Spectrum Observatory and a Walkthrough of the Technology by Mr. Anoop Gupta\"]\r\n\r\nSlides<\/span><\/a>\r\n\r\n[\/panel]\r\n\r\n[panel header=\"Beyond Sensing: Multi-GHz Realtime Spectrum Analytics by Mr. Lixin Shi, MIT\"]\r\n\r\nSlides<\/span><\/a>\r\n\r\nSpectrum sensing has been an active research area for the past two decades. Nonetheless, current spectrum sensing systems provide only coarse occupancy data. They lack information about the detailed signal patterns in each band and can easily miss fleeting signals like radar.\r\n\r\nWe present SpecInsight, a system for acquiring a detailed view of 4 GHz of spectrum in real-time. SpecInsight\u2019s design addresses the intrinsic conflict between the need to quickly scan a wide spectrum and the desire for obtaining very detailed information about each band. Its key enabler is a learned database of signal patterns and a new scheduling algorithm that leverages these patterns to identify when to sample each band to maximize the probability of sensing active signals.\r\n\r\nSpecInsight is implemented using off-the-shelf USRP radios with only tens of MHz of instant bandwidth, but is able to span 4 GHz of spectrum, and capture very low duty-cycle signals in the radar band. Using SpecInsight, we perform a large-scale study of the spectrum in 6 locations in the US that span major cities and suburban areas, and build a first-of-its-kind database of spectrum usage patterns.\r\n[\/panel]\r\n[panel header=\"Integrated Sensing and Database Architecture for White Space Networking by Dr. Sumit Roy, University of Washington\"]\r\n\r\nSlides<\/span><\/a>\r\n\r\nThe evolution of cognitive (secondary) networks to enable more efficient spectrum usage will rely on fast and accurate spectrum sensing\/mapping, supported by a suitable architecture for data integration and model building. In the first part of the talk, fundamental aspects of the wide-area RF mapping problem as a grand challenge will be highlighted; and some recent work at UW that underpin sub-system level trade-offs (between scan latency and channel status estimation accuracy) for channel sensing described. Next, the role of centralized databases in RF map creation for enabling primary-to-secondary and secondary-to-secondary coexistence is explored and a hybrid architecture proposed \u2013 that involves both distributed (crowd-sourced) local sensing as well as it\u2019s integration into databases. Finally, some ongoing work regarding a fundamental question: how much white space capacity is actually available \u2013 will be described.\r\n\r\n[\/panel]\r\n[panel header=\"TxMiner: Identifying Transmitters in Real-World Spectrum Measurements\"]\r\n\r\nSlides<\/span><\/a>\r\n\r\nHow should regulators re-assign spectrum optimally? How do licensees identify spectrum usage in order to provision for future needs? How do Dynamic Spectrum Access devices determine on which frequency to operate? All these questions require knowledge about active transmitters, which is not straight-forward to obtain with currently-existing techniques. In this talk I am going to present TxMiner: a system that automatically identifies transmitters without prior knowledge of their characteristics. TxMiner makes use of machine learning methods in order to tease apart transmitters from raw spectrum measurements. I will start by outlining several key insights that enable TxMiner; I will then show results from transmitter identification using traces collected by Microsoft\u2019s Spectrum Observatory.\r\n[\/panel]\r\n[panel header=\"Towards Commoditized Real-time Spectrum Monitoring\"]\r\n\r\nSlides<\/span><\/a>\r\n\r\nWe are facing an increasing difficult challenge in spectrum management: how to perform real-time spectrum monitoring with strong coverage of deployed regions. Today\u2019s solutions use dedicated hardware that is bulky and expensive, making the monitoring task extremely difficult and cost prohibitive. We propose a practical alternative by leveraging the power of the masses, i.e. millions of wireless users, using low- cost, commoditized spectrum monitoring hardware. We envision an ecosystem where crowd sourced smartphone users perform automated and continuous spectrum measurements using their mobile devices, and report the results to a monitoring agency in real-time.\r\n\r\nIn this talk, we will introduce our initial feasibility study to verify the efficacy of the mobile monitoring platform compared to that of conventional monitoring devices. Our results indicate that commoditized real-time spectrum monitoring is indeed feasible in the near future. We conclude by discussing a set of open challenges and potential directions for follow-up research.\r\n\r\n[\/panel]\r\n[panel header=\"An Overview of the Global Policy and Regulatory Landscape \u2013 Opportunities and Risks for Alternative Forms of Spectrum Access\"]\r\n\r\nSlides<\/span><\/a>\r\n\r\nIt has been three decades since the United States Federal Communications Commission (\u201cFCC\u201d) created the first unlicensed spectrum access. Although only available commercially in the last fifteen years, it is hard to imagine consumers doing without technologies now used in the unlicensed spectrum bands \u2013 Wi-Fi, Bluetooth, RFID. There are now many more wireless devices reliant on unlicensed access to spectrum than wireless devices reliant on licensed access to spectrum. Driven by growing demand that risks outstripping the current allocations of spectrum for wireless broadband applications, governments and regulators around the world are now looking at making more spectrum available on an exclusive-use licensed basis for 4G LTE, as well as make more spectrum available on an unlicensed (or licensed-exempt) basis for Wi-Fi and other technologies. Governments and regulators are likewise now looking at alternative forms of spectrum access which will not require incumbent licensees to be cleared and reallocated, such as spectrum sharing opportunities in the TV white spaces, 2.3 GHz, 3.5 GHz, and 5 GHz either on an unlicensed, licensed, or lightly-licensed basis. This presentation will provide an overview of efforts by governments and regulators around the world to address these issues \u2013 with a particular focus on alternative forms of spectrum access. This presentation will also discuss how spectrum observatories, standardization, technology trials, commercial pilots, partnerships, and industry and academic coalitions can and are being used to support these efforts.\r\n\r\n[\/panel]\r\n[\/accordion]"},{"id":2,"name":"Attendees","content":"Dr. Ranveer Chandra\u00a0 Microsoft\r\n\r\nDr. Victor Bahl\u00a0\u00a0Microsoft\r\n\r\nMr. Paul Garnett\u00a0 Microsoft\r\nMr. Anoop Gupta\u00a0 Microsoft\r\n\r\nMr. Paul Mitchell\u00a0 Microsoft\r\n\r\nDr. Elizabeth Belding\u00a0 UCSB\r\nDr. Joseph Camp SMU\r\nDr. Minghua Chen\u00a0 CUHK\r\nMr. Michael Cotton\u00a0 ITS\r\nDr. Joseph Evans\u00a0 University of Kansas\r\nDr. Kyle Jameison\u00a0 UCL\r\nDr. Giovanni Pau\u00a0\u00a0 UCLA\r\nDr. Sumit Roy\u00a0 UW\r\nMr. Lixin Shi\u00a0 MIT\r\nDr. Michael Souryal\u00a0\u00a0 NIST\r\nDr. Kannan Srinivasan Ohio State\r\nDr. Rouzbeh Yassini UNH\r\nDr. Mariya Zheleva SUNY\r\nDr. Heather Zheng UCSB"}],"msr_startdate":"2014-08-06","msr_enddate":"2014-08-06","msr_event_time":"","msr_location":"Online","msr_event_link":"","msr_event_recording_link":"","msr_startdate_formatted":"August 6, 2014","msr_register_text":"Watch now","msr_cta_link":"","msr_cta_text":"","msr_cta_bi_name":"","featured_image_thumbnail":null,"event_excerpt":"The Microsoft Spectrum Observatory ThinkTank 2014 aims to create a forum where like-minded individuals come together to discuss and provide additional feedback so the platform can better serve the community\u2019s research needs even more than today. During this ThinkTank we will be presenting the vision of Microsoft Spectrum Observatory, demo of current version (which is being actively developed), describe its architecture and provide an opportunity for you to provide suggestions and feedback to make the…","msr_research_lab":[],"related-researchers":[{"type":"user_nicename","value":"bahl","display_name":"Victor Bahl","author_link":"Victor Bahl<\/a>","is_active":false,"user_id":31167,"last_first":"Bahl, Victor","people_section":0,"alias":"bahl"}],"msr_impact_theme":[],"related-academic-programs":[],"related-groups":[],"related-projects":[],"related-opportunities":[],"related-publications":[],"related-videos":[],"related-posts":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/199987"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-event"}],"version-history":[{"count":3,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/199987\/revisions"}],"predecessor-version":[{"id":874368,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/199987\/revisions\/874368"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=199987"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=199987"},{"taxonomy":"msr-region","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-region?post=199987"},{"taxonomy":"msr-event-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event-type?post=199987"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=199987"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=199987"},{"taxonomy":"msr-program-audience","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-program-audience?post=199987"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=199987"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=199987"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}