iTeleScope: Intelligent Video Telemetry and Classification in Real-Time using Software Defined Networking

Video continues to dominate network traffic, yet operators today have poor visibility into the number, duration, and resolutions of the video streams traversing their domain. Current approaches are inaccurate, expensive, or unscalable, as they rely on statistical sampling, middle-box hardware, or packet inspection software. We present {\em iTelescope}, the first intelligent, inexpensive, and scalable SDN-based solution for identifying and classifying video flows in real-time. Our solution is novel in combining dynamic flow rules with telemetry and machine learning, and is built on commodity OpenFlow switches and open-source software. We develop a fully functional system, train it in the lab using multiple machine learning algorithms, and validate its performance to show over 95\% accuracy in identifying and classifying video streams from many providers including Youtube and Netflix. Lastly, we conduct tests to demonstrate its scalability to tens of thousands of concurrent streams, and deploy it live on a campus network serving several hundred real users. Our system gives unprecedented fine-grained real-time visibility of video streaming performance to operators of enterprise and carrier networks at very low cost.Comment: 12 pages, 16 figure

Deployment characterization of a floatable tidal energy converter on a tidal channel, Ria Formosa, Portugal

This paper presents the results of a pilot experiment with an existing tidal energy converter (TEC), Evopod 1 kW floatable prototype, in a real test case scenario (Faro Channel, Ria Formosa, Portugal). A baseline marine geophysical, hydrodynamic and ecological study based on the experience collected on the test site is presented. The collected data was used to validate a hydro-morphodynamic model, allowing the selection of the installation area based on both operational and environmental constraints. Operational results related to the description of power generation capacity, energy capture area and proportion of energy flux are presented and discussed, including the failures occurring during the experimental setup. The data is now available to the scientific community and to TEC industry developers, enhancing the operational knowledge of TEC technology concerning efficiency, environmental effects, and interactions (i.e. device/environment). The results can be used by developers on the licensing process, on overcoming the commercial deployment barriers, on offering extra assurance and confidence to investors, who traditionally have seen environmental concerns as a barrier, and on providing the foundations whereupon similar deployment areas can be considered around the world for marine tidal energy extraction.Acknowledgements The paper is a contribution to the SCORE project, funded by the Portuguese Foundation for Science and Technology (FCT e PTDC/ AAG-TEC/1710/2014). Andre Pacheco was supported by the Portu- guese Foundation for Science and Technology under the Portuguese Researchers' Programme 2014 entitled “Exploring new concepts for extracting energy from tides” (IF/00286/2014/CP1234). Eduardo GGorbena has received funding for the OpTiCA project from the ~ Marie Skłodowska-Curie Actions of the European Union's H2020- MSCA-IF-EF-RI-2016/under REA grant agreement n [748747]. The authors would like to thank to the Portuguese Maritime Authorities and Sofareia SA for their help on the deployment.info:eu-repo/semantics/publishedVersio

Systems And Methods For Detecting Call Provenance From Call Audio

Various embodiments of the invention are detection systems and methods for detecting call provenance based on call audio. An exemplary embodiment of the detection system can comprise a characterization unit, a labeling unit, and an identification unit. The characterization unit can extract various characteristics of networks through which a call traversed, based on call audio. The labeling unit can be trained on prior call data and can identify one or more codecs used to encode the call, based on the call audio. The identification unit can utilize the characteristics of traversed networks and the identified codecs, and based on this information, the identification unit can provide a provenance fingerprint for the call. Based on the call provenance fingerprint, the detection system can identify, verify, or provide forensic information about a call audio source.Georgia Tech Research Corporatio