BEGIN:VCALENDAR
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PRODID:-//WASP - ECPv6.16.3//NONSGML v1.0//EN
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METHOD:PUBLISH
X-ORIGINAL-URL:https://wasp-sweden.org
X-WR-CALDESC:Events for WASP
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:+01:00
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BEGIN:VEVENT
DTSTART;TZID=+01:00:20230428T101500
DTEND;TZID=+01:00:20230428T235900
DTSTAMP:20260601T183551
CREATED:20230412T073742Z
LAST-MODIFIED:20260601T083836Z
UID:10000234-1682676900-1682726340@wasp-sweden.org
SUMMARY:PhD Defense: Learning to Analyze Visual Data Streams for Environment Perception
DESCRIPTION:Welcome to the Doctoral Defense of Emil Brissman \nDate and time: April 28th\, 10:15-13:15\n \nLocation: Ada Lovelace\, B Building\, Entrance 27\, 2nd floor\, Campus Valla\, Linköping University\, and online (see link below) \nDoctoral student: Emil Brissman\, Computer Vision Laboratory\, Department of of Electrical Engineering\, Linköping University\, and Saab Dynamics AB \nTitle: Learning to Analyze Visual Data Streams for Environment Perception \nSupervisor: Professor Michael Felsberg\, Computer Vision Laboratory\, Department of Electrical Engineering\, Linköping University \nOpponent: Professor Bastian Leibe\, RWTH Aachen University \nOriginal location: Ada Lovelace\, B Building\, Campus Valla\, Linköping University & online
URL:https://wasp-sweden.org/event/phd-defense-learning-to-analyze-visual-data-streams-for-environment-perception/
LOCATION:Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Linköping\, Sweden
CATEGORIES:Defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=+01:00:20230217T131500
DTEND;TZID=+01:00:20230217T235900
DTSTAMP:20260601T183551
CREATED:20230213T094856Z
LAST-MODIFIED:20260601T083838Z
UID:10000240-1676639700-1676678340@wasp-sweden.org
SUMMARY:PhD Defense: Bayesian Models for Spatiotemporal Data from Transportation Networks
DESCRIPTION:Welcome to the Doctoral Defense of Hector Rodriguez-Deniz \nDate and time: February 17\, 13:15\n \nLocation: Ada Lovelace\, Building B\, Entrance 27\, Campus Valla \nDoctoral student: Hector Rodriguez-Deniz\, Department of Computer and Information Science\, Linköping University \nTitle: Bayesian Models for Spatiotemperal Data from Transportation Networks \nSupervisor: Professor Mattias Villani\, Department of Computer and Information Science\, Linköping University \nOpponent: Professor Yusak Susilo\, University of Natural Resources and Life Sciences\, Australia \nOriginal location: Ada Lovelace\, B-House\, entrance 27\, Campus Valla\, Linköping
URL:https://wasp-sweden.org/event/phd-defense-bayesian-models-for-spatiotemporal-data-from-transportation-networks/
LOCATION:Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Linköping\, Sweden
CATEGORIES:Defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=+01:00:20221209T140000
DTEND;TZID=+01:00:20221209T235900
DTSTAMP:20260601T183551
CREATED:20221123T130133Z
LAST-MODIFIED:20260601T083842Z
UID:10000249-1670594400-1670630340@wasp-sweden.org
SUMMARY:PhD Defense: Safety-Aware Autonomous Systems: Preparing Robots for Life in the Real World
DESCRIPTION:Welcome to the Doctoral Defense of Mattias Tiger\n \nDate and time: December 9\, 14:00\n \nLocation: Ada Lovelace\, B-House\, entrance 27\, Campus Valla\, Linköping \nDoctoral student: Mattias Tiger\, Department of Computer and Information Science\, Linköping University \nTitle: Safety-Aware Autonomous Systems: Preparing Robots for Life in the Real World \nSupervisor: Professor Fredrik Heintz\, Department of Computer and Information Science\, Linköping University \nOpponent: Rachid Alami\, Dr.\, LAAS-CNRS\, France \nOriginal location: Ada Lovelace\, B-House\, entrance 27\, Campus Valla\, Linköping
URL:https://wasp-sweden.org/event/phd-defense-safety-aware-autonomous-systems-preparing-robots-for-life-in-the-real-world/
LOCATION:Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Linköping\, Sweden
CATEGORIES:Defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=+01:00:20221004T131500
DTEND;TZID=+01:00:20221004T235900
DTSTAMP:20260601T183551
CREATED:20220909T114024Z
LAST-MODIFIED:20260601T083846Z
UID:10000259-1664889300-1664927940@wasp-sweden.org
SUMMARY:PhD Defense: Prediction Methods for High Dimensional Data with Censored Covaria
DESCRIPTION:Welcome to the Doctoral Defense of Caroline Svahn\n \nDate and time: October 4\, 13:15\n \nLocation: Ada Lovelace\, B-House\, entrance 27\, Campus Valla and Zoom \nDoctoral student: Caroline Svahn\, Department of Computer and Information Science\, Linköping University\n \nTitle: Prediction Methods for High Dimensional Data with Censored Covariates \nSupervisor: Mattias Villani\, LiU \nOpponent: Professor Xavier de Luna\, Umeå University \nOriginal location: Ada Lovelace\, B-House\, entrance 27\, Campus Valla and Zoom
URL:https://wasp-sweden.org/event/phd-defense-prediction-methods-for-high-dimensional-data-with-censored-covaria/
LOCATION:Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Linköping\, Sweden
CATEGORIES:Defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=+01:00:20220603T101500
DTEND;TZID=+01:00:20220603T235900
DTSTAMP:20260601T183551
CREATED:20220506T083729Z
LAST-MODIFIED:20260601T083850Z
UID:10000271-1654251300-1654300740@wasp-sweden.org
SUMMARY:PhD Defense: Condition Monitoring in Mobile Mining Machinery
DESCRIPTION:Welcome to the Doctoral Defense of Erik Jakobsson\n \nDate and time: June 3\, 10:15\n \nLocation: Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Linköping \nDoctoral student: Erik Jakobsson\, Department of Electrical Engineering\, Vehicular Systems. Linköping University \nTitle: Condition Monitoring in Mobile Mining Machinery \nSupervisor: Professor Erik Frisk\, Department of Electrical Engineering\, Vehicular Systems. Linköping University\n \nOpponent: Professor Olga Fink\, Civil Engineering Institute\, EPFL\, Lausanne\, Switzerland
URL:https://wasp-sweden.org/event/phd-defense-condition-monitoring-in-mobile-mining-machinery/
LOCATION:Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Linköping\, Sweden
CATEGORIES:Defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=+01:00:20220311T100000
DTEND;TZID=+01:00:20220311T235900
DTSTAMP:20260601T183551
CREATED:20220214T065347Z
LAST-MODIFIED:20260601T083855Z
UID:10000284-1646992800-1647043140@wasp-sweden.org
SUMMARY:PhD Defense: Distributed Optimization for Control and Estimation
DESCRIPTION:Welcome to the Doctoral Defense of Shervin Parvini Ahmadi\nDate and time: March 11\, 10:00 – 13:15 \nLocation: Ada Lovelace\, B-house\, entrance 27\, Campus Valla\, Linköping University and online via Zoom \nDoctoral student: Shervin Parvini Ahmadi\, Department of Electrical Engineering\, Linköping University \nTitle: Distributed Optimization for Control and Estimation \nSupervisor: Professor Anders Hansson\, Linköping University \nOpponent: Tamas Keviczky\, TU Delft\, The Netherlands \nOriginal location: Ada Lovelace\, B-house\, entrance 27\, Campus Valla\, Linköping University and online via Zoom
URL:https://wasp-sweden.org/event/phd-defense-distributed-optimization-for-control-and-estimation/
LOCATION:Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Linköping\, Sweden
CATEGORIES:Defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=+01:00:20220119T090000
DTEND;TZID=+01:00:20220119T235900
DTSTAMP:20260601T183551
CREATED:20211222T121736Z
LAST-MODIFIED:20260601T083857Z
UID:10000288-1642582800-1642636740@wasp-sweden.org
SUMMARY:PhD Defense: Dynamic Visual Learning
DESCRIPTION:Welcome to Doctoral Defense of Joakim Johnander\n \nDate and time: January 19\, 09:00\n \nLocation: Ada Lovelace\, B-building\, Campus Valla \nDoctoral student: Joakim Johnander\, Computer Vision Laboratories\, ISY\, Linköping University \nTitel: Dynamic Visual Learning \nSupervisor: Michael Felsberg\, ISY\, Linköping University\n \nOpponent:  Professor Bernt Schiele\, Max-Planck-Institut\, Informatik
URL:https://wasp-sweden.org/event/phd-defense-dynamic-visual-learning/
LOCATION:Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Linköping\, Sweden
CATEGORIES:Defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=+01:00:20210506T151500
DTEND;TZID=+01:00:20210506T235900
DTSTAMP:20260601T183551
CREATED:20210408T150236Z
LAST-MODIFIED:20260601T083907Z
UID:10000317-1620314100-1620345540@wasp-sweden.org
SUMMARY:PhD Defense: Exploiting Direct Optimal Control for Motion Planning in Unstructured Environments
DESCRIPTION:Title: Exploiting Direct Optimal Control for Motion Planning in Unstructured Environments \nRespondent: Kristoffer Bergman \nOpponent: Prof. Emilio Frazzoli\, ETH Zürich \nSupervisor: Assoc. Prof. Daniel Axehill \nCo-supervisor: Prof. Torkel Glad \n\nDate: 2021-05-06 \nTime: 15:15 \nPlace: Zoom/Ada Lovelace https://liu-se.zoom.us/j/62231874057?pwd=L3orZW9XY1hNajF1by9MU2lvTkRWUT09 \nLanguage: English \nLink to thesis \n\nAbstract\nDuring the last decades\, motion planning for autonomous systems has become an important area of research. The high interest is not the least due to the development of systems such as self-driving cars\, unmanned aerial vehicles and robotic manipulators. The objective in optimal motion planning problems is to find feasible motion plans that also optimize a performance measure. From a control perspective\, the problem is an instance of an optimal control problem. This thesis addresses optimal motion planning problems for complex dynamical systems that operate in unstructured environments\, where no prior reference such as road-lane information is available. Some example scenarios are autonomous docking of vessels in harbors and autonomous parking of self-driving tractor-trailer vehicles at loading sites. The focus is to develop optimal motion planning algorithms that can reliably be applied to these types of problems. This is achieved by combining recent ideas from automatic control\, numerical optimization and robotics. \nThe first contribution is a systematic approach for computing local solutions to motion planning problems in challenging unstructured environments. The solutions are computed by combining homotopy methods and direct optimal control techniques. The general principle is to define a homotopy that transforms\, or preferably relaxes\, the original problem to an easily solved problem. The approach is demonstrated in motion planning problems in 2D and 3D environments\, where the presented method outperforms a state-of-the-art asymptotically optimal motion planner based on random sampling. \nThe second contribution is an optimization-based framework for automatic generation of motion primitives for lattice-based motion planners. Given a family of systems\, the user only needs to specify which principle types of motions that are relevant for the considered system family. Based on the selected principle motions and a selected system instance\, the framework computes a library of motion primitives by simultaneously optimizing the motions and the terminal states. \nThe final contribution of this thesis is a motion planning framework that combines the strengths of sampling-based planners with direct optimal control in a novel way. The sampling-based planner is applied to the problem in a first step using a discretized search space\, where the system dynamics and objective function are chosen to coincide with those used in a second step based on optimal control. This combination ensures that the sampling-based motion planner provides a feasible motion plan which is highly suitable as warm-start to the optimal control step. Furthermore\, the second step is modified such that it also can be applied in a receding-horizon fashion\, where the proposed combination of methods is used to provide theoretical guarantees in terms of recursive feasibility\, worst-case objective function value and convergence to the terminal state. The proposed motion planning framework is successfully applied to several problems in challenging unstructured environments for tractor-trailer vehicles. The framework is also applied and tailored for maritime navigation for vessels in archipelagos and harbors\, where it is able to compute energy-efficient trajectories which complies with the international regulations for preventing collisions at sea. \nOriginal location: Zoom/Ada Lovelace
URL:https://wasp-sweden.org/event/phd-defense-exploiting-direct-optimal-control-for-motion-planning-in-unstructured-environments/
LOCATION:Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Linköping\, Sweden
CATEGORIES:Defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=+01:00:20200207T131500
DTEND;TZID=+01:00:20200207T235900
DTSTAMP:20260601T183551
CREATED:20191220T095818Z
LAST-MODIFIED:20260601T083915Z
UID:10000336-1581081300-1581119940@wasp-sweden.org
SUMMARY:Timing-Based Localization using Multipath Information
DESCRIPTION:Degree and subject: Licentiate in Electrical Engineering with specialization in Automatic Control \nSpeaker: Andreas Bergström \nOpponent: Dr. Erik Leitinger\, TU Graz \nSupervisor: Prof. Fredrik Gustafsson \nCo-supervisors: Assoc. Prof. Gustaf Hendeby and Assoc. Prof. Fredrik Gunnarsson \nLanguage: English \nAbstract\nThe measurements of radio signals are commonly used for localization purposes where the goal is to determine the spatial position of one or multiple objects. In realistic scenarios\, any transmitted radio signal will be affected by the environment through reflections\, diffraction at edges and corners etc. This causes a phenomenon known as multipath propagation\, by which multiple instances of the transmitted signal having traversed different paths are heard by the receiver. These are known as Multi-Path Components (MPCs). The direct path (DP) between transmitter and receiver may also be occluded\, causing what is referred to as non-Line-of-Sight (non-LOS) conditions. As a consequence of these effects\, the estimated position of the object(s) may often be erroneous. \nThis thesis focuses on how to achieve better localization accuracy by accounting for the above-mentioned multipath propagation and non-LOS effects. It is proposed how to mitigate these in the context of positioning based on estimation of the DP between transmitter and receiver. It is also proposed how to constructively utilize the additional information about the environment which they implicitly provide. This is all done in a framework wherein a given signal model and a map of the surroundings are used to build a mathematical model of the radio environment\, from which the resulting MPCs are estimated. \nFirst\, methods to mitigate the adverse effects of multipath propagation and non-LOS conditions for positioning based on estimation of the DP between transmitter and receiver are presented. This is initially done by using robust statistical measurement error models based on aggregated error statistics\, where significant improvements are obtained without the need to provide detailed received signal information. The gains are seen to be even larger with up-to-date real-time information based on the estimated MPCs. \nSecond\, the association of the estimated MPCs with the signal paths predicted by the environmental model is addressed. This leads to a combinatorial problem which is approached with tools from multi-target tracking theory. A rich radio environment in terms of many MPCs gives better localization accuracy but causes the problem size to grow large — something which can be remedied by excluding less probable paths. Simulations indicate that in such environments\, the single best association hypothesis may be a reasonable approximation which avoids the calculation of a vast number of possible hypotheses. Accounting for erroneous measurements is crucial but may have drawbacks if no such are occurring. \nFinally\, theoretical localization performance bounds when utilizing all or a subset of the available MPCs are derived. A rich radio environment allows for good positioning accuracy using only a few transmitters/receivers\, assuming that these are used in the localization process. In contrast\, in a less rich environment where basically only the DP/LOS components are measurable\, more transmitters/receivers and/or the combination of downlink and uplink measurements are required to achieve the same accuracy. The receiver’s capability of distinguishing between multiple MPCs arriving approximately at the same time also affects the localization accuracy.
URL:https://wasp-sweden.org/event/timing-based-localization-using-multipath-information/
LOCATION:Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Linköping\, Sweden
CATEGORIES:Defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=+01:00:20191216T101500
DTEND;TZID=+01:00:20191216T235900
DTSTAMP:20260601T183551
CREATED:20191126T074826Z
LAST-MODIFIED:20260601T083916Z
UID:10000338-1576491300-1576540740@wasp-sweden.org
SUMMARY:Data-driven Condition Monitoring in Mining Vehicles
DESCRIPTION:Name: Erik Jakobsson\nDegree: Licentiate in Electrical Engineering\nTitle: Data-driven Condition Monitoring in Mining Vehicles\nLink to thesis: http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1371480&dswid=-8493 \nOriginal location: Ada Lovelace\, B-Huset\, Campus Valla\, Linköping University\, Linköping
URL:https://wasp-sweden.org/event/data-driven-condition-monitoring-in-mining-vehicles/
LOCATION:Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Linköping\, Sweden
CATEGORIES:Defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=+01:00:20191212T101500
DTEND;TZID=+01:00:20191212T235900
DTSTAMP:20260601T183551
CREATED:20191125T160748Z
LAST-MODIFIED:20260601T083916Z
UID:10000339-1576145700-1576195140@wasp-sweden.org
SUMMARY:Computation of Autonomous Safety Maneuvers Using Segmentation and Optimization
DESCRIPTION:Name: Pavel Anistratov \nDegree: Licentiate in Electrical Engineering with specialization in Vehicular Systems \nTitle: Computation of Autonomous Safety Maneuvers Using Segmentation and Optimization \nLink to thesis: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-162164 \nLink to calendar: https://old.liu.se/liu-nytt/kalendarium/?l=sv \nOriginal location: Ada Lovelace\, B-Huset\, Campus Valla\, Linköping University\, Linköping
URL:https://wasp-sweden.org/event/computation-of-autonomous-safety-maneuvers-using-segmentation-and-optimization/
LOCATION:Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Linköping\, Sweden
CATEGORIES:Defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=+01:00:20190614T000000
DTEND;TZID=+01:00:20190614T235959
DTSTAMP:20260601T183551
CREATED:20191112T103938Z
LAST-MODIFIED:20260601T083917Z
UID:10000340-1560470400-1560556799@wasp-sweden.org
SUMMARY:On Motion Planning Using Numerical Optimal Control
DESCRIPTION:Defense by Kristoffer Bergman\nFor: Licentiate in automatic control\nTitle: On Motion Planning Using Numerical Optimal Control\nDate and Time: 2019-06-14\nLocation: Ada Lovelace\, B-building\, Linköpings University\nOpponent: Assoc. Prof. Paolo Falcone\, Chalmers\nSupervisor: Assoc. Prof. Daniel Axehill\nCo-supervisors: Prof. Torkel Glad \nOriginal location: Ada Lovelace\, B-building\, Linköpings University
URL:https://wasp-sweden.org/event/on-motion-planning-using-numerical-optimal-control/
LOCATION:Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Ada Lovelace\, B-building\, Campus Valla\, Linköping University\, Linköping\, Sweden
CATEGORIES:Defense
END:VEVENT
END:VCALENDAR