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X-WR-CALDESC:Events for TECoSA
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DTSTART:20240101T000000
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DTSTART;TZID=UTC:20250306T150000
DTEND;TZID=UTC:20250306T160000
DTSTAMP:20250319T094227Z
CREATED:20250227T073358Z
LAST-MODIFIED:20250319T094227Z
UID:7659-1741273200-1741276800@www.tecosa.center.kth.se
SUMMARY:TECoSA Seminar - COSMOS: A Platform for Wireless Innovation
DESCRIPTION:We aim to bring you a TECoSA Seminar on the first Thursday of each month during term-time.  All are welcome to attend and we look forward to some lively discussions. Members can accept the Outlook invitations\, non-members can email tecosa-admin@kth.se to register.\nOur February seminar is with Ivan Seskar\, Chief Technologist and Director/IT at WINLAB\, Rutgers University. The seminar will take place via Zoom\, through this link. \nThe presentation from the seminar can be found here. \n \nAbstract: For seven years\, the COSMOS testbed in New York City has been a leading platform for cutting-edge wireless research\, spanning physical\,network\, and application layers. This presentation details its architecture\, capabilities\, and experimental framework. Past \nexperiments\, illustrating the exploration of technologies from full-duplex and mmWave communications to open-source 5G system integration and O-RAN Alliance participation\, will be highlighted. COSMOS’s impact on smart city development\, particularly in urban connectivity and infrastructure management\, will also be showcased. The presentation concludes with a discussion of upcoming hardware upgrades focused on FR3 and intelligent spectrum coordination experimentation. \nBio: Ivan Seskar is the Chief Technologist and Director/IT at WINLAB\, Rutgers University responsible for experimental systems and prototyping projects. He is currently the program director for the COSMOS project responsible for the New York City NSF PAWR deployment. He has also been the co-PI and project manager for all three phases of the NSF-supported ORBIT mid-scale testbed project at WINLAB\, successfully leading technology development and operations since the testbed was released as a community resource in 2005 and for which the team received the 2008 NSF Alexander Schwarzkopf Prize for Technological Innovation.  Ivan is a co-chair of the IEEE Future Networks Testbed Working Group\, member of the IEEE Standardization Programs Development Board and the co-founder and CTO of Upside Wireless Inc.
URL:https://www.tecosa.center.kth.se/event/tecosa-seminar-cosmos-a-platform-for-wireless-innovation/
CATEGORIES:Seminar
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DTSTART;TZID=UTC:20250312T130000
DTEND;TZID=UTC:20250312T163000
DTSTAMP:20250211T125703Z
CREATED:20250209T140739Z
LAST-MODIFIED:20250211T125703Z
UID:7616-1741784400-1741797000@www.tecosa.center.kth.se
SUMMARY:PhD Defense: "Efficient Strategies for Safety Assurance of Automated Driving Systems"
DESCRIPTION:TECoSA industrial PhD student (with Zenseact) Magnus Gyllenhammar will defend his thesis at F3 (Flodis)\, Lindstedtsvägen 26 & 28 at Sing-Sing\, KTH Campus. Contact Martin Törngren (martint@kth.se) if you are interested in attending. \nAbstract: By relieving the human driver of the responsibility of safely operating the vehicle\, Automated Driving Systems (ADSs) (colloquially known as self-driving cars) can free up time and possibly also reduce the number of road accidents. Paradoxically\, even though safety is one of the main expectations of ADSs\, it is also one of the major challenges and arguably one of the key reasons why we have yet to see widespread market deployment of such systems. Contrary to previous generations of automotive systems\, common development and safety assurance practises no longer suffice to accommodate the increased system complexity and operational uncertainty inherent to an ADS. Indeed\, concrete models and means to show safety fulfilment before deployment remain elusive. For that purpose\, this thesis focuses on efficient strategies for safety assurance of ADSs and explores this from three angles. \nFirstly\, a comprehensive review of the state of the art has been conducted to identify and structure available methods for providing (predictive) evidence of the safety of the ADS\, and to identify gaps and directions where further research is needed. \nSecondly\, the task of ensuring completeness of both the Verification and Validation (V\&V) as well as the safety requirements of the ADS has been explored. The appropriate definition\, formalisation and management of an Operational Design Domain (ODD) provide a means to ensure alignment between specification\, testing and operations of the ADS — suggesting one way of closing the completeness gap for the V\&V. Furthermore\, to address the exhaustiveness of the safety requirements\, this thesis proposes the use of a Quantitative Risk Norm (QRN) to elicit quantitative vehicle-level requirements. A QRN facilitates this exhaustiveness by considering frequencies of loss events (e.g. accidents) rather than requiring an enumeration of all possible hazards pertaining to the ADS. \nThirdly\, this thesis extends the concept of Precautionary Safety (PcS) proposing a methodology for connecting the quantitative safety requirements of the QRN and the runtime decisions of the ADS. This is enabled by augmenting the ADS’s situation awareness (SAW) with an understanding of its own ability to avoid different loss events. Using this enhances SAW model and by subsequently accounting for the uncertainties of the loss event probabilities\, enables an assessment of the QRN even when there is limited data available. Consequently\, the proposed methodology can ensure that the ADS indeed only takes decisions that are known to fulfil the QRN. \nJointly\, the work presented in this thesis paves a way for how to bridge quantitative safety requirements and runtime decision-making of the ADS\, and a possible strategy for efficient safety assurance of ADSs is outlined — drawing upon the contributions of the appended papers. There are still several open questions to understand the implications of this approach but the work showcased herein provides a solid foundation for such future work. \n\nDetails of the panel are shown below: \nSupervisors: Professor Martin Törngren (KTH)\, Jonas Fredriksson (Chalmers University)\, Gabriel Rodrigues de Campos (Zenseact) \nOpponent: Professor Phil Koopman (Carnegie Mellon University)
URL:https://www.tecosa.center.kth.se/event/phd-defense-efficient-strategies-for-safety-assurance-of-automated-driving-systems/
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DTSTART;TZID=UTC:20250331T130000
DTEND;TZID=UTC:20250331T163000
DTSTAMP:20250321T080200Z
CREATED:20250227T063657Z
LAST-MODIFIED:20250321T080200Z
UID:7653-1743426000-1743438600@www.tecosa.center.kth.se
SUMMARY:PhD Defense: Optimal Control and Coordination of Autonomous Intelligent Systems by Edge Computing
DESCRIPTION:TECoSA PhD student Kaige Tan will defend his thesis at Gladan\, Brinellvägen 85 at the Dept. of Engineering design\, KTH Campus. Contact Lei Feng (lfeng@kth.se) if you are interested in attending. The zoom link for the event can be found here. \nAbstract: Autonomous Intelligent Systems (AIS) are transforming various sectors by integrating advanced control theories\, artificial intelligence\, and cyber-physical systems. However\, AIS control development faces significant challenges\, including ensuring real-time responsiveness\, designing adaptive controllers for dynamic environments\, and coordinating multi-agent systems under uncertainties. These issues are exacerbated in resource-constrained settings\, where balancing computational demands and real-time performance is critical. \nTo mitigate these challenges\, this thesis leverages edge computing to enhance system performance\, so that data-driven methods and optimal control technologies become feasible for complex AIS applications. Edge computing is a scheme that brings computation\, communication\, and storage resources closer to data sources\, to achieve low-latency processing\, real-time adaptability\, and scalable solutions for AIS applications. It provides two key benefits: (1) offloading computationally intensive tasks to nearby edge servers\, so as to ensure responsive and efficient operations despite constrained resources onboard\, and (2) facilitating decentralized coordination among multiple agents by exploiting the edge server as a trustworthy node\, so as to improve system scalability\, reliability\, and collaborative decision-making. \nBuilding on the advantages of the offloading and coordinatio The dissertation will be at March 31st\, 13:00. The venue is Gladan\, Brinellvägen 85 at the Dept. of Engineering design\, KTH Campus. \nThe title of my thesis is: “Optimal Control and Coordination of Autonomous Intelligent Systems by Edge Computing”\, and the abstract is: \nAutonomous Intelligent Systems (AIS) are transforming various sectors by integrating advanced control theories\, artificial intelligence\, and cyber-physical systems. However\, AIS control development faces significant challenges\, including ensuring real-time responsiveness\, designing adaptive controllers for dynamic environments\, and coordinating multi-agent systems under uncertainties. These issues are exacerbated in resource-constrained settings\, where balancing computational demands and real-time performance is critical. \nTo mitigate these challenges\, this thesis leverages edge computing to enhance system performance\, so that data-driven methods and optimal control technologies become feasible for complex AIS applications. Edge computing is a scheme that brings computation\, communication\, and storage resources closer to data sources\, to achieve low-latency processing\, real-time adaptability\, and scalable solutions for AIS applications. It provides two key benefits: (1) offloading computationally intensive tasks to nearby edge servers\, so as to ensure responsive and efficient operations despite constrained resources onboard\, and (2) facilitating decentralized coordination among multiple agents by exploiting the edge server as a trustworthy node\, so as to improve system scalability\, reliability\, and collaborative decision-making. \nBuilding on the advantages of the offloading and coordination capabilities inherent in edge computing\, this thesis investigates how these features can be harnessed to overcome the limitations of AIS in achieving optimal control and coordination. Primary contributions of this thesis include: (1) the development of state estimation and data-driven optimal control algorithms\, which enables more precise estimation and control in nonlinear\, time-variant systems; (2) the design of edge-based computational task offloading algorithms to achieve real-time adaptive control and learning by distributing computationally intensive tasks\, which effectively balances latency and resource constraints; and (3) the introduction of decentralized optimization frameworks for multi-agent systems\, which enhances scalability\, robustness\, and coordination under communication constraints by leveraging edge servers as trustworthy nodes for efficient collaboration and decision-making. All contributions have been validated through case studies in soft robotics and connected autonomous vehicles\, demonstrating their effectiveness and advancements over existing methods. \nIn summary\, this thesis advances AIS capabilities by addressing real-time computational challenges and enabling optimal\, data-driven control and decentralized coordination. The integration of edge computing improves the efficiency\, scalability\, and adaptability of AIS\, offering promising opportunities for applications in autonomous mobility and other dynamic domains. \nDetails of the panel are shown below: \nPrincipal supervisor: Assoc. prof. Lei Feng\nCo-supervisor: Assoc. Prof. Fredrik Asplund\nChair at the defense: Assoc. Prof. Andreas Cronhjort\nOpponent: Assoc. Prof. Quanyan Zhu \nMembers of the grading committee: \nProf. Nicolce Murgovski\nAssoc. Prof. Ci Liang\nAssoc. Prof. Mohammad Ashjaei \nSubstitute: Prof. Jonas Mårtensson
URL:https://www.tecosa.center.kth.se/event/phd-defense-optimal-control-and-coordination-of-autonomous-intelligent-systems-by-edge-computing/
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