This thesis consists of two research projects on wireless communication systems. In the first project, we propose a fast inphase and quadrature (I/Q) imbalance compensation technique for the analog quadrature modulators in direct conversion transmitters. The method needs no training sequence, no extra background data gathering process and no prior perfect knowledge of the envelope detector characteristics. In contrast to previous approaches, it uses points from both the linear and predictable nonlinear regions of the envelope detector to hasten convergence. We provide a least mean square (LMS) version and demonstrate that the quadrature modulator compensator converges. In the second project, we propose a technique to deceive the automatic gain control (AGC) block in an eavesdropper's receiver to increase wireless physical layer data transmission secrecy. By sharing a key with the legitimate receiver and fluctuating the transmitted signal power level in the transmitter side, a positive average secrecy capacity can be achieved even when an eavesdropper has the same or even better additive white gaussian noise (AWGN) channel condition. Then, the possible options that an eavesdropper may choose to fight against our technique are discussed and analyzed, and approaches to eliminate these options are proposed. We demonstrate that a positive average secrecy capacity can still be achieved when an eavesdropper uses these options.

Payment Channel Networks (PCNs) provide a solution to the scalability problem in blockchain technology. They facilitate multiple-hop transactions via payment channels between peers, allowing for the execution of several transactions before updating each node’s balance on the blockchain. However, the performance of the network in effectively routing payments is affected by unbalanced channels. This imbalance reduces the network’s ability to route payments in both directions within a channel, resulting in decreased overall performance. Previous research has identified unidirectional payment flows in the network as the underlying cause of this issue. The payment flow is based on the demand for payments between peers and the paths these payments take. The aim of this study is to investigate the relationship between payment patterns and the average channel imbalance, where the payment pattern is a combination of demand patterns and what pathfinding algorithm is used. The study uses a model of PCNs that allows the relationship of the variables of interest to be measured, while other impacting variables are controlled. A deterministic value of the average channel imbalance for a PCN topology, demand pattern, and pathfinding strategy is achieved by computing the steady-state imbalance. The results show that the less homogeneous the demand pattern is, the more asymmetric the payment flow becomes, and the more imbalanced the network’s channels become. The results confirm what previous research mentions: unidirectional flow causes the payment channel to become imbalanced. The demand pattern that generates unidirectional flow imbalance significantly affects the entire network, regardless of the pathfinding strategy employed. Even with the possibility of a pathfinding strategy influencing payment flow to make it less unidirectional, the inherent unbalanced nature of the demand pattern remains a considerable challenge. The results also show that the pathfinding strategy has a less significant impact than the demand pattern on the balance of the network over time. / Payment Channel Networks (PCN) tillåter fler transaktioner utföras på kortare tid för en mindre kostnad, än att publicera varje enskild transaktion direkt på blockkedja och är en bra lösning på skalbarhetsroblem kopplat till blockedjor som baseras på proof-of-work. PCNs möjliggör för flerhoppstransaktioner via noder som kan vidarebefodra flera betalningar på varje enskild betalningskanal de håller öppna med andra noder. Inte förrän en kanal stängs publiceras den senaste uppdateringen av saldot för enskild betalningskanal på blockkedjan. Trots detta lider nätverkets prestanda av att kanaler blir obalanserade och ökar risken för att transaktioner misslyckas routas. Tidigare forskning nämner att orsaken till att kanaler blir obalanserade är enriktade betalningsflöden i nätverket, vilket beror av betalningsflödet som är en kombination av betalningsefterfråga (vem vill betala vem) mellan noderna och vilka vägar som dessa betalningar tar. Syftet med denna studie är att undersöka sambandet mellan betalmönster och genomsnittlig kanalobalans, där betalmönstret är en kombination av efterfrågansmönster och vägvalsstrategier. Studien använder en PCN-modell som möjliggör att undersöka sambandet mellan de variabler som är tänkt ska mätas, samtidigt som andra variabler kan kontrolleras. Genom att beräkna den stabila obalansen uppnås ett deterministiskt värde för genomsnittlig kanalobalans i varje nätverkstopologi. Resultaten visar att ju mindre homogent efterfrågemönstret är, desto mer asymmetriskt blir betalningsflödet och desto mer obalanserade blir nätverkets kanaler. Resultatet bekräftar det som tidigare forskning nämner: enriktade flöden orsakar obalans i betalningskanalen. Specifikt är det efterfrågansmönstern som skapar enriktade flöden som har de högsta stabila obalansen, oavsett vilken vägvalsstrategi som används. Resultaten visar också att vägvalsstrategin har mindre betydelse än efterfrågemönstret, åtminstone för vägvalsstrategier som leder till liknande valda vägar.

Deep Learning (DL) has been broadly applied to solve big data problems in biomedical fields, which is most successful in image processing. Recently, many DL methods have been applied to analyze genomic studies. However, genomic data usually has too small a sample size to fit a complex network. They do not have common structural patterns like images to utilize pre-trained networks or take advantage of convolution layers. The concern of overusing DL methods motivates us to evaluate DL methods' performance versus popular non-deep Machine Learning (ML) methods for analyzing genomic data with a wide range of sample sizes. In this paper, we conduct a benchmark study using the UK Biobank data and its many random subsets with different sample sizes. The original UK Biobank data has about 500k participants. Each patient has comprehensive patient characteristics, disease histories, and genomic information, i.e., the genotypes of millions of Single-Nucleotide Polymorphism (SNPs). We are interested in predicting the risk of three lung diseases: asthma, COPD, and lung cancer. There are 205,238 participants have recorded disease outcomes for these three diseases. Five prediction models are investigated in this benchmark study, including three non-deep machine learning methods (Elastic Net, XGBoost, and SVM) and two deep learning methods (DNN and LSTM). Besides the most popular performance metrics, such as the F1-score, we promote the hit curve, a visual tool to describe the performance of predicting rare events. We discovered that DL methods frequently fail to outperform non-deep ML in analyzing genomic data, even in large datasets with over 200k samples. The experiment results suggest not overusing DL methods in genomic studies, even with biobank-level sample sizes. The performance differences between DL and non-deep ML decrease as the sample size of data increases. This suggests when the sample size of data is significant, further increasing sample sizes leads to more performance gain in DL methods. Hence, DL methods could be better if we analyze genomic data bigger than this study. / Graduate

Public school closures and the provision of occupational therapy services during the COVID-19 pandemic revealed inequities and barriers that affect Latinx families’ and children’s access to resources. In Massachusetts, many students receiving occupational therapy services may have missed mandated and necessary occupational therapy services due to systemic barriers and the lack of skills to advocate for their needs. Occupational therapy practitioners (OTPs) may have lacked the self-efficacy and ability to facilitate the families’ advocacy. Culturally appropriate evidence-based interventions are needed to serve Latinx families and their children and prevent them from being underserved. A literature review identified that OTPs might not have the necessary skills to work effectively with culturally diverse groups. This skills gap reduces the OTPs’ ability to provide culturally appropriate interventions and holistic care to Latinx children and their families. A proposed solution to this problem is the 6-month, theory- and evidence-based I AM. Intercultural Advocacy and Mentoring Program: Increasing Occupational Therapy Practitioners’ Advocacy Skills in Collaboration With Latinx Families With Young Children for OTPs who work with Latinx families and children. The program aims to promote OTPs’ self-efficacy, cultural humility, and rapport- and trust-building skills. The I AM Program will ensure professionals working with Latinx families and children practice cultural humility, thus enhancing services for the client and building strong, trusting relationships and thriving communities.

So far, numerous studies have been devoted to investigate the relationship between happiness and income by asking the question whether economic growth has a positive impact on happiness. However, the reversed relationship from happiness to economic output has received much less attention in the literature. This paper attempts to investigate such relationship by using data from the European Value Survey that contains subjective reported well-being (happiness, or life satisfaction) values across 47 European countries from 1981 to 2009. Gender imbalance is used as an instrument for happiness in order to disentangle the causal effect of happiness on income. Based on a derived Solow model, where labor efficiency is assumed to be positively affected by worker happiness, regression analyses suggest that the sense of happiness does have a positive and highly significant impact on GDP per worker. Robustness tests further show that the result also holds for life satisfaction. According to the results, the author recommends governments to use well-being oriented index, along with GDP to measure the overall economy.

Introduction: Technology has enabled remote work that is not location bound. Remote work is often performed by knowledge workers who focus on problemsolving and distributing information. New ways of working create a need for new information about the relationship between work and occupational balance. Aim:The aim of this study was to examine remote work´s impact on knowledge worker´s occupational balance from the perspectives of work-life balance and work-life conflict. Method: Method used was scoping review. Data were gathered with the help of electronic data bases. Results: Nine research papers and two Bachelor´s theses were analyzed for the study. Through content analysis the following categories were formed: How gender differences impact occupational balance, Environment in relation to balance, Type of work in relation to occupational balance, How family affects occupational balance and Means to maintain occupational balance. Conclusions: The findings indicate that remote work influences knowledge worker´s occupational balance. Family, type of work, environment, and gender affect the way balance is perceived. Worker and employer collaboration is important in maintaining occupational balance. Significance: As occupational balance may affect work efficiency and wellbeing positively; more research on both remote work and knowledge work should be performed

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Resource allocation across regions in an economy has been analysed by many authors, both from the efficiency and equity viewpoints. In general, these aspects are assumed to be conflicting - the attainment of higher growth rates and income levels in accordance with efficient resource allocation normally discrimminates against the relatively less well off sections of society. The literature concentrates largely on the allocation of resources across sectors, irrespective of geographic considerations, so that the decision to invest in a region seldom incorporates the area's absorptive capacity. The regional imbalance that results is shown just as clearly by the lack of high technology industry in some areas as it is by the (potential) congestion and overcrowding that characterises other regions. This study focuses on the relationship between the regional allocation of income generating activities and the total income generated for the nation. The growth potential of any area is defined by the availability of all essential facilities - service and repair facilities, transport and energy supplies being only a part of the picture. Factor supplies and the supply of credit, alongwith the high degree of interaction between regions and sectors also complement the picture. The low income potential of the peripheral areas of any nation is the result of a lack of these ancilliary facilities. However, once these bottlenecks are removed, the outlying areas normally depict higher growth rates than the core regions. In this study, a linear programming model is developed 1 in king the commodity, factor and asset markets of a nation, both across sectors and across regions. Thus, the commodity market of any region is related to the commodity, asset and factor markets of all regions. National absorptive capacity is now defined in terms of the potentials of all areas of the nation. The application of this model to Canada results in a set of optimal regional patterns of economic activity. Growth in any area is now encouraged only if the regional economy is not operating close to some capacity limitation, and if a full complement of goods and services, factors and assets is available. The results obtained justify these expectations because the model depicts a pattern of resource allocation that stresses areas where all facilities for growth are present. Thus, further investment in the traditional center Quebec and Ontario - is restricted, some critical thresholds regarding absorptive capacity having been hit. However, the regions where a full complement of services and asset supplies is not available - the Atlantic provinces - are not the alternatives. The relative ordering favours the modern manufacturing and service sec tors in Alberta and British Columbia. The model does suggest the existence of a tradeoff between national income and regional balance, since the imposition of regional balance constraints reduces the value of national consumption. However, even when regional equity constraints are imposed, the model suggests that more national income can be generated through reallocation of economic activity than was generated by the historical pattern of allocation. / Thesis / Doctor of Philosophy (PhD)

Direct-Down Conversion (DDC) principle based transceiver architectures are of interest to meet the diverse needs of present and future wireless systems. DDC transceivers have a simple structure with fewer analog components and offer low-cost, flexible and multi-standard solutions. However, DDC transceivers have certain circuit impairments affecting their performance in wide-band, high data rate and multi-user systems. IQ imbalance is one of the problems of DDC transceivers that limits their image rejection capabilities. Compensation techniques for frequency independent IQI arising due to gain and phase mismatches of the mixers in the I/Q paths of the transceiver have been widely discussed in the literature. However for wideband multi-channel transceivers, it is becoming increasingly important to address frequency dependent IQI arising due to mismatches in the analog I/Q lowpass filters. A hardware-efficient and standard independent digital estimation and compensation technique for frequency dependent IQI is introduced which is also capable of tracking time-varying IQI changes. The technique is blind and adaptive in nature, based on the second order statistical properties of complex random signals such as properness/circularity. A detailed performance analysis of the introduced technique is executed through computer simulations for various real-time operating scenarios. A novel technique for finding the optimal number of taps required for the adaptive IQI compensation filter is proposed and the performance of this technique is validated. In addition, a metric for the measure of properness is developed and used for error power and step size analysis. / Master of Science / A wireless transceiver consists of two major building blocks namely the RF front-end and digital baseband. The front-end performs functions such as frequency conversion, filtering, and amplification. Impurities because of deep-submicron fabrication lead to non-idealities of the front-end components which limit their accuracy and affect the performance of the overall transceiver. Complex (I/Q) mixing of baseband signals is preferred over real mixing because of its inherent trait of bandwidth efficiency. The I/Q paths enabling this complex mixing in the front-end may not be exactly identical thereby disturbing the perfect orthogonality of inphase and quadrature components leading to IQ Imbalance. The resultant IQ imbalance leads to an image of the signal formed at its mirror frequencies. Imbalances arising from mixers lead to an image of constant strength whereas I/Q low-pass filter mismatches lead to an image of varying strength across the Nyquist range. In addition, temperature effects cause slow variation in IQ imbalance with time. In this thesis a hardware efficient and standard-independent technique is introduced to compensate for performance degrading IQ imbalance. The technique is blind and adaptive in nature and uses second order statistical signal properties like circularity or properness for IQ imbalance estimation. The contribution of this work, which gives a key insight into the optimal number of taps required for the adaptive compensation filter improves the state-of-the-art technique. The performance of the technique is evaluated under various scenarios of interest and a detailed analysis of the results is presented.