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- Wilroth, Johanna, 1994-
(författare)
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Exploring Auditory Attention Using EEG
- 2024
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Listeners with normal-hearing often overlook their ability to comprehend speech in noisy environments effortlessly. Our brain’s adeptness at identifying and amplifying attended voices while suppressing unwanted background noise, known as the cocktail party problem, has been extensively researched for decades. Yet, many aspects of this complex puzzle remain unsolved and listeners with hearing-impairment still struggle to focus on a specific speaker in noisy environments. While recent intelligent hearing aids have improved noise suppression, the problem of deciding which speaker to enhance remains unsolved, leading to discomfort for many hearing aid users in noisy environments.In this thesis, we explore the complexities of the human brain in challenging auditory environments. Two datasets are investigated where participants were tasked to selectively attend to one of two competing voices, replicating a cocktail-party scenario. The auditory stimuli trigger neurons to generate electrical signals that propagate in all directions. When a substantial number of neurons fire simultaneously, their collective electrical signal becomes detectable by small electrodes placed on the head. This method of measuring brain activity, known as electroencephalography (EEG), holds potential to provide feedback to the hearing aids, enabling adjustments to enhance attended voice(s).EEG data is often noisy, incorporating neural responses with artifacts such as muscle movements, eye blinks and heartbeats. In the first contribution of this thesis, we focus on comparing different manual and automatic artifact-rejection techniques and assessing their impact on auditory attention decoding (AAD).While EEG measurements offer high temporal accuracy, spatial resolution is inferior compared to alternative tools like magnetoencephalography (MEG). This difference poses a considerable challenge for source localization with EEG data. In the second contribution of this thesis, we demonstrate anticipated activity in the auditory cortex using EEG data from a single listener, employing Neuro-Current Response Functions (NCRFs). This method, previously evaluated only with MEG data, holds significant promise in hearing aid development.EEG data may involve both linear and nonlinear components due to the propagation of the electrical signals through brain tissue, skull, and scalp with varying conductivities. In the third contribution, we aim to enhance source localization by introducing a binning-based nonlinear detection and compensation method. The results suggest that compensating for some nonlinear components produces more precise and synchronized source localization compared to original EEG data.In the fourth contribution, we present a novel domain adaptation framework that improves AAD performances for listeners with initially low classification accuracy. This framework focuses on classifying the direction (left or right) of attended speech and shows a significant accuracy improvement when transporting poor data from one listener to the domain of good data from different listeners.Taken together, the contributions of this thesis hold promise for improving the lives of hearing-impaired individuals by closing the loop between the brain and hearing aids.
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| 2. |
- Hellander, Anja, 1996-
(författare)
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On Optimal Integrated Task and Motion Planning with Applications to Tractor-Trailers
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- An important aspect in autonomous systems is the ability of a system to plan before acting. This includes both high-level task planning to determine what sequence of actions to take in order for the system to reach a goal state, as well as low-level motion planning to detail how to perform the actions required.While it is sometimes possible to plan hierarchically, i.e., to first compute a task plan and then compute motion plans for each action in the task plan, there are also many problem instances where this approach fails to find a feasible plan as not all task plans lead to motion-planning problems that have feasible solutions. For this reason, it is desirable to solve the two problems jointly rather than sequentially. Additionally, it is often desirable to find plans that optimize a performance measure, such as the energy used, the length of the path travelled by the system or the time required. This thesis focuses on the problem of finding joint task and motion plans that optimize a performance measure.The first contribution is a method for solving a joint task and motion planning problem, that can be formulated as a traveling salesman problem with dynamic obstacles and motion constraints, to resolution optimality. The proposed method uses a planner comprising two nested graph-search planners. Several different heuristics are considered and evaluated.The second contribution is a method for solving a joint task and motion planning problem, in the form of a rearrangement problem for a tractor-trailer system, to resolution optimality. The proposed method combines a task planner with motion planners, all based on heuristically guided graph search, and uses branch-and-bound techniques in order to improve the efficiency of the search algorithm.The final contribution is a method for improving task and motion plans for rearrangement problems using optimal control. The proposed method takes inspiration from finite-horizon optimal control and decomposes the optimization problem into several smaller optimization problems rather than solving one larger optimization problem. Compared to solving the original larger optimization problem, it is demonstrated that this can lead to reduced computation time without any significant decrease in solution quality.
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| 3. |
- Ljungberg, Fredrik, 1993-
(författare)
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Estimation of Nonlinear Greybox Models for Marine Applications
- 2020
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- As marine vessels are becoming increasingly autonomous, having accurate simulation models available is turning into an absolute necessity. This holds both for facilitation of development and for achieving satisfactory model-based control. When accurate ship models are sought, it is necessary to account for nonlinear hydrodynamic effects and to deal with environmental disturbances in a correct way. In this thesis, parameter estimators for nonlinear regression models where the regressors are second-order modulus functions are analyzed. This model class is referred to as second-order modulus models and is often used for greybox identification of marine vessels. The primary focus in the thesis is to find consistent estimators and for this an instrumental variable (IV) method is used.First, it is demonstrated that the accuracy of an IV estimator can be improved by conducting experiments where the input signal has a static offset of sufficient amplitude and the instruments are forced to have zero mean. This two-step procedure is shown to give consistent estimators for second-order modulus models in cases where an off-the-shelf applied IV method does not, in particular when measurement uncertainty is taken into account.Moreover, it is shown that the possibility of obtaining consistent parameter estimators for models of this type depends on how process disturbances enter the system and on the amount of prior knowledge about the disturbances’ probability distributions that is available. In cases where the first-order moments are known, the aforementioned approach gives consistent estimators even when disturbances enter the system before the nonlinearity. In order to obtain consistent estimators in cases where the first-order moments are unknown, a framework for estimating the first and second-order moments alongside the model parameters is suggested. The idea is to describe the environmental disturbances as stationary stochastic processes in an inertial frame and to utilize the fact that their effect on a vessel depends on the vessel’s attitude. It is consequently possible to infer information about the environmental disturbances by over time measuring the orientation of a vessel they are affecting. Furthermore, in cases where the process disturbances are of more general character it is shown that supplementary disturbance measurements can be used for achieving consistency.Different scenarios where consistency can be achieved for instrumental variable estimators of second-order modulus models are demonstrated, both in theory and by simulation examples. Finally, estimation results obtained using data from a full-scale marine vessel are presented.
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| 4. |
- Ljungberg, Fredrik, 1993-
(författare)
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Identification of Nonlinear Marine Systems
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- As marine vessels are becoming increasingly automated, having accurate simulation models available is turning into an absolute necessity. This holds both for the facilitation of development and for achieving satisfactory model-based control. Such models can be obtained through system identification, and in this thesis, particular emphasis is given to experiment design and parameter estimation, which constitute two central steps in the system identification process. The analysis is carried out for a special class of nonlinear regression models called second-order modulus models, which is a type of model that is often used for describing nonlinear hydrodynamic effects in greybox identification of ships.First, it is demonstrated that the accuracy of an instrumental variable (iv) estimator can be improved by conducting experiments where the input signal has a static offset of sufficient amplitude and the instruments are forced to have zero mean. This two-step procedure is shown to give consistent estimators for second-order modulus models in cases where an off-the-shelf applied iv method does not, in particular when measurement uncertainty is taken into account. Further, it is shown that the possibility of obtaining consistent parameter estimators for models of this type depends on how the process disturbances enter the system and on the amount of prior knowledge that is available about the disturbances’ probability distributions. In cases where the first-order moments are known, the aforementioned approach gives consistent estimators even when disturbances enter the system before the nonlinearity. To obtain consistent estimators in cases where the first-order moments are unknown, a framework for estimating auxiliary nuisance parameters that depend on the disturbances’ first and second-order moments is suggested. This can be done by describing the process disturbances as stationary stochastic processes in an inertial frame and utilizing the fact that their effect on a vessel depends on the vessel’s attitude.After this, the attention is more clearly focused on experiment design, and a systematic approach for choosing the most informative combination of independent sub-experiments out of a predefined set of candidates is proposed. Further, a technique to account for an upcoming subtraction of the instruments’ mean during the experiment design is suggested, and the consequences of various ways of having the mean subtracted are explored. Additionally, it is shown how the dictionary-based method for finding an excitation signal can be combined with a motion-planning framework to obtain a trajectory that is both informative and spatially feasible.The suggested methods are evaluated in experimental work and show promising results on both simulated and real data, the latter from a full-scale marine vessel as well as a small-scale model ship.
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| 5. |
- Shoja, Shamisa, 1991-
(författare)
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On Complexity Certification of Branch-and-Bound Methods for MILP and MIQP with Applications to Hybrid MPC
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In model predictive control (MPC), an optimization problem is solved at each time step, in which the system dynamics and constraints can directly be taken into account. The MPC concept can be further extended to the control of hybrid systems, where a part of the state and control variables has a discrete set of values. When applying MPC to linear hybrid systems with performance measures based on the 1-norm or the∞-norm, the resulting optimal control problem can be formulated as a mixed-integer linear program (MILP), while the optimal control problem with a quadratic performance measure can be cast as a mixed-integer quadratic program (MIQP). An efficient method to solve these non-convex MILP and MIQP problems is branch and bound (B&B) which relies on solving convex relaxations of the problem ordered in a binary search tree. For the safe and reliable real-time operation of hybrid MPC, it is desirable to have a priori guarantees on the worst-case complexity such that the computational requirements of the problem do not exceed the time and hardware capabilities.Motivated by this need, this thesis aims to certify the computational complexity of standard B&B methods for solving MILPs and MIQPs in terms of, e.g., the size of the search tree or the number of linear systems of equations (iterations) that are needed to be solved online to compute optimal solution. In particular, this knowledge enables us to compute relevant worst-case complexity bounds for the B&B-based MILP and MIQP solvers, which has significant importance in, e.g., real-time hybrid MPC where hard real-time requirements have to be fulfilled. The applicability of the proposed certification method is further extended to suboptimal B&B methods for solving MILPs, where the computational effort is reduced by relaxing the requirement to find a globally optimal solution to instead finding a suboptimal solution, considering three different suboptimal strategies. Finally, the proposed framework is extended to the cases where the performance of B&B is enhanced by considering three common start heuristic methods that can help to find good feasible solutions early in the B&B search process.
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