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  • 351.
    Zhou, Linghui
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Oechtering, Tobias J.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Skoglund, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Polar Codes for Biometric Identification and Authentication2021In: 2021 IEEE Information Theory Workshop, ITW 2021 - Proceedings, Institute of Electrical and Electronics Engineers (IEEE) , 2021Conference paper (Refereed)
    Abstract [en]

    In this work, we present a polar code design that offers a provably optimal solution for biometric identification systems allowing authentication under noisy enrollment with secrecy and privacy constraints. Binary symmetric memoryless source and channels are considered. It is shown that the proposed polar code design achieves the fundamental limits and satisfies more stringent secrecy constraints than previously in the literature. The proposed polar code design provides the first example of a code design that achieves the fundamental limits involving both identification and authentication.

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  • 352.
    Zucchet, Julien
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
    Non-Intrusive Load Monitoring to Assess Retrofitting Work2022Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Non-intrusive load monitoring (NILM) refers to a set of statistical methods for inferring information about a household from its electricity load curve, without adding any additional sensor. The aim of this master thesis is to adapt NILM techniques for the assessment of the efficiency of retrofitting work to provide a first version of a retrofitting assessment tool. Two models are developed: a model corresponding to a constrained optimization problem, and a hierarchical Bayesian mixture model. These models are tested on a set of houses that have electric heating (which are the main target of retrofitting work). These models offer a satisfactory accuracy retrofitting assessment for about half of the houses.

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  • 353.
    Ågren, Tove
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematics (Div.).
    Inverse Uncertainty Quantification for Sounding Rocket Dispersion2022Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Sounding rocket impact points are subject to dispersion due to uncertainties in simulation model parameters and perturbations of the rocket trajectory during flight. Estimating the area of dispersion assumes that associated model uncertainties and magnitude of perturbations have already been inferred. In this thesis, a method to inversely quantify uncertainty in rocket simulation models based on launch data is presented. We take on a probabilistic approach based on Bayesian hierarchical modeling, to address both epistemic and aleatory uncertainty while incorporating prior knowledge about the modeled system. Bayesian computational techniques, including Markov Chain Monte Carlo simulations and modular Bayesian analysis, are accounted for and employed in numerical case studies. Surrogate deep neural network models are shown to ease otherwise infeasible computational burden that posterior distribution exploration suffers from. Numerical experiments are carried out based on actual launch data from Esrange Space Center, serving as validation of the methodology and providing posterior distributions of the target dispersion parameters. The results imply almost certainly that the currently used dispersion parameters can be reduced, for all considered sources of uncertainty in the study. Updating said parameters accordingly yields a potential 20% decrease in theoretically estimated dispersion area, which is in good agreement with empirical observations.

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  • 354.
    Åkesson, Hugo
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematics (Div.).
    Structure of the space of extensions of barcodes2023Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Motivated by the recent development of noise systems, we try to describe, for fixed persistence modules \(X\) and \(Y\), the set of all persistence modules that are extensions of \(X\) by \(Y\), as well as their sizes. We restrict ourselves to tame persistence modules indexed by nonnegative numbers, and our notion of size is \((p,C)\)-norms, which is a generalization of \(p\)-norms. We prove that when \(X\) is a single bar, there is a monotone bijection between a set of antichains in the barcode of \(Y\) and the mentioned set of all extensions. A corollary is that the antichain consisting of maximal elements corresponds to the extension with maximal norm. Without this assumption on \(X\), we can reuse the previous result to construct a surjection from a set of tuples of antichains to the set of all extensions. We also conjecture that, with regards to this surjection, the tuple consisting of maximal antichains is mapped to the extension with maximal norm. We also provide some experimental justification for this conjecture.

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  • 355.
    Öijar Jansson, Agnes
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Probability, Mathematical Physics and Statistics.
    Estimating Real Estate Selling Prices using Multimodal Neural Networks2023Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This thesis examines whether housing price estimations can be improved by combining several modalities of data through the utilization of neural networks. The analysis is limited to apartments in the Stockholm municipality, and the applied modalities are residential attributes (tabular data) and photo montages (image data). The tabular data includes living area, number of rooms, age, latitude, longitude and ocean distance, while the image data contains montages of four images representing the kitchen, bathroom, living space and neighborhood through satellite imagery. Furthermore, the dataset comprises a total of 1154 apartments sold within a time frame of approximately six months, ending in June 2023.

    The analysis is conducted by designing three artificial neural networks and comparing their performances: a multilayer perceptron that predicts selling prices using tabular data, a convolutional neural network that predicts selling prices using image data, and a multimodal neural network that estimates sold prices taking both modalities as inputs. To facilitate the construction process, the multimodal neural network is designed by integrating the other models into its architecture. This is achieved through the concatenation of their outputs, which is then fed into a joint hidden layer.

    Before initiating the network development phase, the data is preprocessed appropriately, for example by excluding duplicates and dealing with missing values. In addition, images are categorized into room types via object detection, satellite images are collected, and photo montages are created. To obtain well-performing models, hyperparameter tuning is performed using methods such as grid search or random search. Moreover, the models are evaluated through three repetitions of 5-fold cross-validation with the mean absolute percentage error as performance metric.

    The analysis shows that the multimodal neural network exhibits a marginal but significant performance advantage compared to the multilayer perceptron, both in terms of cross-validation scores and test set outcomes. This result underscores the potential benefits of utilizing both image data and tabular data for predicting apartment selling prices through the application of neural networks. Furthermore, this work motivates a deeper investigation into these prediction methods using larger datasets for which the multimodal neural network may achieve even stronger predictive capacity

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  • 356.
    Öijar Jansson, Emma
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematics (Div.).
    Bayesian Estimation of Sea Clutter Parameters for Radar - A Stochastic Approach2023Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Radars operating at sea encounter a common phenomenon known as sea clutter, characterized by undesired reflections originating from the sea surface. This phenomenon can significantly impair the radar’s capacity to detect small, slow-moving targets. Therefore, it is crucial to gain a comprehensive understanding of the statistical attributes that describes the sea clutter. This comprehension is pivotal for the development of efficient signal processing strategies.

    The core of this work revolves around the imperative requirement for accurate statistical models to characterize sea clutter. Within this context, this work particularly explores the application of Field’s model. Field’s model describes the sea clutter process using three stochastic differential equations that form the dynamical process of the complex reflectivity of the sea surface. One equation describes the radar cross section, which is given by a Cox-Ingersoll-Ross process, parameterized by the parameters A and α. The other equations describe the speckle process, which is a complex Ornstein-Uhlenbeck process parameterized by B. The aim of this thesis is to explore the possibilities in estimating the parameters A, α and B in Field’s model through the application of Bayesian inference.

    To achieve this objective, Metropolis-Hastings and Sequential Monte Carlo methods are employed. The clutter data, represented by the complex reflectivity, is synthetically generated by using the Euler-Maruyma and Milstein schemes. Three algorithms are designed for estimating the sea clutter parameters. Two algorithms require 300 seconds of data and are based on the approach suggested by Clement Roussel in his PhD thesis [1]. Specifically, these algorithms employ the Metropolis-Hastings method for estimating A, α and B, respectively. As input data to the algorithms, estimators of the Cox-Ingersoll-Ross process and the real part of the Ornstein-Uhlenbeck process are utilized. In contrast, the last algorithm describes an approach that employs only 3 seconds of data. This algorithm is a Metropolis-Hastings method that incorporates a particle filter for approximation of likelihoods.

    For evaluation of the algorithms, two distinct sets of parameters are considered, leading to varying characteristics of the complex reflectivity. The two algorithms that require 300 seconds of data are ex- ecuted ten times for each parameter set. Evidently, the algorithm designed for estimating B generates values that closely aligns with the true values while the algorithm designed for estimating A and α does not yield as satisfactory results. Due to time constraints and the computational demands of the simulations, the last algorithm, requiring 3 seconds of data, is executed only twice for each parameter set. Remarkably, this algorithm generates estimates that agree with the true values, indicating strong performance. Nonetheless, additional simulations are required to conclusively confirm its robustness.

    To conclude, it is possible to estimate sea clutter parameters within Field’s model by using the applied methods of Bayesian inference. However, it is important to analyze the applicability of these methods for a large quantity of diverse clutter data. Moreover, their computational demands pose challenges in real-world applications. Future research should address the need for more computation- ally efficient methods to overcome this challenge.

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