Sensors are highly essential components in most modern control systems and are used in increasingly complex ways to improve system precision and reliability. Since they are generally susceptible to faults it is common to perform on-line fault diagnostics on sensor data to verify nominal behavior. This is especially important for safety critical systems where it can be imperative to identify, and react to, a fault before it increases in severity. An example of such a safety critical system is the propulsion control of a vehicle. In this thesis, three different model-based methods for Fault Detection and Isolation (FDI) are developed and tested with the aim of detecting and isolating sensor faults in the powertrain of an electric, center articulated, four-wheel-drive vehicle. First, kinematic models are derived that combine sensor data from all sensors related to propulsion. Second, the kinematic models are implemented in system observers to produce fault sensitive zero-mean residuals. Finally, fault isolation algorithms are derived, which detect and indicate different types of faults via evaluation of the observer residuals. The results show that all FDI methods can detect and isolate stochastic faults with high certainty, but that offset-type faults are hard to distinguish from modeling errors and are therefore easily attenuated by the system observers. Faults in accelerometer sensors need extra measures to be detectable, owing to the environment where the vehicle is typically operated. A nonlinear system model shows good conformity to the vehicle system, lending confidence to its further use as a driver for propulsion control.

Mathematical optimization is applicable to nearly every scientific discipline. This thesis specifically focuses on optimization applications to digital communications and signal processing. Within the digital communications framework, the channel encoder attempts to encode a message from a source (the sender) in such a way that the channel decoder can utilize the encoding to correct errors in the message caused by the transmission over the channel. Low-density parity-check (LDPC) codes are an especially popular code for this purpose. Following the channel encoder in the digital communications framework, the modulator converts the encoded message bits to a physical waveform, which is sent over the channel and converted back to bits at the demodulator. The modulator and demodulator present special challenges for what is known as the two-antenna problem. The main results of this work are two algorithms related to the development of optimization methods for LDPC codes and the two-antenna problem. Current methods for optimization of LDPC codes analyze the degree distribution pair asymptotically as block length approaches infinity. This effectively ignores the discrete nature of the space of valid degree distribution pairs for LDPC codes of finite block length. While large codes are likely to conform reasonably well to the infinite block length analysis, shorter codes have no such guarantee. Chapter 2 more thoroughly introduces LDPC codes, and Chapter 3 presents and analyzes an algorithm for completely enumerating the space of all valid degree distribution pairs for a given block length, code rate, maximum variable node degree, and maximum check node degree. This algorithm is then demonstrated on an example LDPC code of finite block length. Finally, we discuss how the result of this algorithm can be utilized by discrete optimization routines to form novel methods for the optimization of small block length LDPC codes. In order to solve the two-antenna problem, which is introduced in greater detail in Chapter 2, it is necessary to obtain reliable estimates of the timing offset and channel gains caused by the transmission of the signal through the channel. The timing offset estimator can be formulated as an optimization problem, and an optimization method used to solve it was previously developed. However, this optimization method does not utilize gradient information, and as a result is inefficient. Chapter 4 presents and analyzes an improved gradient-based optimization method that solves the two-antenna problem much more efficiently.

On sait depuis vingt ans maintenant qu'un ordinateur quantique permettrait de résoudre en temps polynomial plusieurs problèmes considérés comme difficiles dans le modèle classique de calcul, comme la factorisation ou le logarithme discret. Entre autres, un tel ordinateur mettrait à mal tous les systèmes de chiffrement à clé publique actuellement utilisés en pratique, mais sa réalisation se heurte, entre autres, aux phénomènes de décohérence qui viennent entacher l'état des qubits qui le constituent. Pour protéger ces qubits, on utilise des codes correcteurs quantiques, qui doivent non seulement être performants mais aussi munis d'un décodage très rapide, sous peine de voir s'accumuler les erreurs plus vite qu'on ne peut les corriger. Une solution très prometteuse est fournie par des équivalents quantiques des codes LDPC (Low Density Parity Check, à matrice de parité creuse). Ces codes classiques offrent beaucoup d'avantages : ils sont faciles à générer, rapides à décoder (grâce à un algorithme de décodage itératif) et performants. Mais leur version quantique se heurte (entre autres) à deux problèmes. On peut voir un code quantique comme une paire de codes classiques, dont les matrices de parité sont orthogonales entre elles. Le premier problème consiste alors à construire deux « bons » codes qui vérifient cette propriété. L'autre vient du décodage : chaque ligne de la matrice de parité d'un des codes fournit un mot de code de poids faible pour le second code. En réalité, dans un code quantique, les erreurs correspondantes sont bénignes et n'affectent pas le système, mais il est difficile d'en tenir compte avec l'algorithme de décodage itératif usuel. On étudie dans un premier temps une construction existante, basée sur un produit de deux codes classiques. Cette construction, qui possède de bonnes propriétés théoriques (dimension et distance minimale), s'est avérée décevante dans les performances pratiques, qui s'expliquent par la structure particulière du code produit. Nous proposons ensuite plusieurs variantes de cette construction, possédant potentiellement de bonnes propriétés de correction. Ensuite, on étudie des codes dits q-Aires~: ce type de construction, inspiré des codes classiques, consiste à agrandir un code LDPC existant en augmentant la taille de son alphabet. Cette construction, qui s'applique à n'importe quel code quantique 2-Régulier (c'est-À-Dire dont les matrices de parité possèdent exactement deux 1 par colonne), a donné de très bonnes performances dans le cas particulier du code torique. Ce code bien connu se décode usuellement très bien avec un algorithme spécifique, mais mal avec l'algorithme usuel de propagation de croyances. Enfin, un équivalent quantique des codes spatialement couplés est proposé. Cette idée vient également du monde classique, où elle améliore de façon spectaculaire les performances des codes LDPC : le décodage s'effectue en temps quasi-Linéaire et atteint, de manière prouvée, la capacité des canaux symétriques à entrées binaires. Si dans le cas quantique, la preuve éventuelle reste encore à faire, certaines constructions spatialement couplées ont abouti à d'excellentes performances, bien au-Delà de toutes les autres constructions de codes LDPC quantiques proposées jusqu'à présent. / Quantum information is a developping field of study with various applications (in cryptography, fast computing, ...). Its basic element, the qubit, is volatile : any measurement changes its value. This also applies to unvolontary measurements due to an imperfect insulation (as seen in any practical setting). Unless we can detect and correct these modifications, any quantum computation is bound to fail. These unwanted modifications remind us of errors that can happen in the transmission of a (classical) message. These errors can be accounted for with an error-Correcting code. For quantum errors, we need to set quantum error-Correcting codes. In order to prevent the clotting of errors that cannot be compensated, these quantum error-Correcting codes need to be both efficient and fast. Among classical error-Correcting codes, Low Density Parity Check (LDPC) codes provide many perks: They are easy to create, fast to decode (with an iterative decoding algorithme, known as belief propagation) and close to optimal. Their quantum equivalents should then be good candidates, even if they present two major drawbacks (among other less important ones). A quantum error correction code can be seen as a combination of two classical codes, with orthogonal parity-Check matrices. The first issue is the building of two efficient codes with this property. The other is in the decoding: each row of the parity-Check matrix from one code gives a low-Weight codeword of the other code. In fact, with quantum codes, corresponding errors do no affect the system, but are difficult to account for with the usual iterative decoding algorithm. In the first place, this thesis studies an existing construction, based on the product of two classical codes. This construction has good theoritical properties (dimension and minimal distance), but has shown disappointing practical results, which are explained by the resulting code's structure. Several variations, which could have good theoritical properties are also analyzed but produce no usable results at this time. We then move to the study of q-Ary codes. This construction, derived from classical codes, is the enlargement of an existing LDPC code through the augmentation of its alphabet. It applies to any 2-Regular quantum code (meaning with parity-Check matrices that have exactly two ones per column) and gives good performance with the well-Known toric code, which can be easily decoded with its own specific algorithm (but not that easily with the usual belief-Propagation algorithm). Finally this thesis explores a quantum equivalent of spatially coupled codes, an idea also derived from the classical field, where it greatly enhances the performance of LDPC codes. A result which has been proven. If, in its quantum form, a proof is still not derived, some spatially-Coupled constructions have lead to excellent performance, well beyond other recent constuctions.

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[pt] Modelos de previsão cambial são frequentemente preteridos em relação a passeios aleatórios, porém o trabalho mostra que em certos casos, principalmente à médio e longo prazo, modelos simples de previsão cambial podem ser melhores do que passeio aleatório em países emergentes com câmbio livre. O trabalho também mostra que não há uma reversão do câmbio real à sua média de longo prazo e que seu movimento após um choque é feito quase todo pelo câmbio nominal. / [en] Foward looking exchange models are frequently deprecated when comparing to a random walk. This work notes that under certain cenarios, mostly at medium and long run, simple models can be more accurated than random walk for emerging countries with free floating exchange rates, though. This work also notes that there is no real exchange rate s mean-reverting at long run and that most of it s path, after a shock, is done via nominal exchange rate.

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Optimal responsiveness to infant cues, an integral component of parental caregiving, is thought to be determined by multiple factors including maternal mood, previous experiences of caregiving, and infant characteristics. This thesis builds on previous work and details novel investigations of brain, affective, and attitudinal responses to infants/children. In the first two studies, we investigated the effects of maternal mood and parity (previous experiences of caregiving) on brain response to affectively positive infant pictures using functional magnetic resonance imaging (fMRI). We found that mothers with postpartum depression showed an elevated right amygdala response to infant pictures in comparison to mothers without PPD and depressed non- mothers. Further, functional connectivity between the bilateral amygdalae and the right insular cortex was negatively correlated with clinical symptoms (self-reported depression and anxiety). Therefore, as depression and anxiety symptoms increased amygdala to insular cortex connectivity decreases and vice versa. In the third study, we developed a novel data-driven infant cuteness (DDIcute) metric using the infant pictures presented in the first two studies. In line with ethological postulations on cuteness (infant characteristics), we found that infant pictures with a higher DDIcute score were associated with increased positive/approach-related affective responses and decreased negative/avoidant-related affective responses. In the fourth study, we investigated whether the sex differences in attitudes about children and childcare were moderated by pet ownership/care (previous experiences of caregiving). We found that pet ownership and care eliminated sex differences in attitudes about children and childcare. This effect was driven by males; compared to males with low levels of experience caring for pets, males with high levels of experience caring for pets had more positive attitudes about children and childcare. / Thesis / Doctor of Science (PhD) / The work presented in this thesis adds to literature on human caregiving. In different studies we investigated brain, emotional, and attitudinal responses to human babies and children in people with and without depression as well as in people with different levels of caregiving experience. In the first two studies, we measured brain response to smiling baby pictures in depressed and non-depressed mothers and non-mothers. The amygdala is a brain region that is involved in the neurobiology underlying both depression and maternal behaviour. We found that amygdala response was the highest among mothers with postpartum depression. We also found that the coupled activity of the left and right amygdalae and another brain region called the insula was altered in mothers with postpartum depression. In the third study, we measured facial features of the baby faces presented in the first two studies to develop a measure of cuteness. We also measured emotional responses to the baby faces. We found that cuter infants elicited more positive and less negative emotional responses. In the fourth study, we measured differences in attitudes about children and caregiving between men and women who did and did not own and care for pets—specifically dogs and/or cats. We found that compared to men who did not own and/or care for pets, men who had more experience caring for pets had more positive attitudes about children and childcare.

This thesis investigates the complex dynamics of foreign exchange (FX) risk affecting Swedish multinational corporations and their financial performance, with a focus on the impact of company size and periods of economic crisis. Amidst global economic interdependencies, these entities encounter substantial FX risks, primarily due to the volatility of the Swedish Krona (SEK) against major currencies. Utilizing a comprehensive dataset spanning from 2004 to 2023, this study employs an empirical approach grounded in the International Capital Asset Pricing Model (ICAPM) and Purchasing Power Parity (PPP) to analyze the correlation between currency fluctuations and stock valuations. The analysis reveals that both company size and economic crises significantly modulate the effects of FX risks, with larger companies often better positioned to manage these risks through sophisticated hedging strategies. Smaller firms, conversely, show greater sensitivity to economic disruptions, particularly during crises which heighten the volatility of FX impacts. The findings indicate that FX risks significantly influence the financial outcomes of these firms, with both direct impacts on stock returns and indirect effects through operational strategies. The thesis underscores the importance of robust risk management strategies and the potential for policy adjustments to mitigate adverse effects from currency volatility. The insights derived from this research aims to contribute to a deeper understanding of the financial economics of foreign exchange, providing implications for investors and multinational corporations operating in global markets.

Two dimensional magnetic recording (TDMR) achieves high areal densities by reducing the size of a bit comparable to the size of the magnetic grains resulting in two dimensional (2D) inter symbol interference (ISI) and very high media noise. Therefore, it is critical to handle the media noise along with the 2D ISI detection. In this paper, we tune the generalized belief propagation (GBP) algorithm to handle the media noise seen in TDMR. We also provide an intuition into the nature of hard decisions provided by the GBP algorithm. The performance of the GBP algorithm is evaluated over a Voronoi based TDMR channel model where the soft outputs from the GBP algorithm are used by a belief propagation (BP) algorithm to decode low-density parity check (LDPC) codes.

Window decoding schedules are very attractive for message passing decoding of spatially coupled LDPC codes. They take advantage of the inherent convolutional code structure and allow continuous transmission with low decoding latency and complexity. In this paper we show that the decoding complexity can be further reduced if suitable message passing schedules are applied within the decoding window. An improvement based schedule is presented that easily adapts to different ensemble structures, window sizes, and channel parameters. Its combination with a serial (on-demand) schedule is also considered. Results from a computer search based schedule are shown for comparison.