This project represents the search for an architecture within the physical, historical, and political situation that an existing city presents. Set within the physical bounds of Savannah, it builds upon an understanding of the city as a series of Utopian propositions existing subliminally and often incongruously. As such, the project concerns the making of public space--space to relieve the culturally disjointed condition of modern urban life by acting as a sort of stage for creative expression and collective improvisation. This also involves the making of characteristic places, which by energetically acknowledging, confronting, challenging, or amplifying the cityâ s conceptions of itself, have the potential to generate both physical and metaphysical transformations. Furthermore, in response to urban development paradigms that are either senselessly uncoordinated or mechanistically authoritarian, the project proposes an alternative: the structured interweaving of a â civic layerâ of these generative urban centers, each serving a different part of the city. The centers must function architecturally as the symbols and containers of civic life, providing space and programmatic flexibility to allow for open cultural engagement while aesthetically enlivening the urban fabric and serving collectively as an index to the city at large. / Master of Architecture

Relations among various nodes in the circuit, as captured by static and inductive invariants, have shown to have a positive impact on a wide range of EDA applications. Techniques such as boolean constraint propagation for static learning and assume-then-verify approach to reason about inductive invariants have been possible due to efficient SAT solvers. Although a significant amount of research effort has been dedicated to the development of effective invariant learning techniques over the years, the computation time for deriving powerful multi-node invariants is still a bottleneck for large circuits. Fast computation of static and inductive invariants is the primary focus of this thesis. We present a novel technique to reduce the cost of static learning by intelligently identifying redundant computations that may not yield new invariants, thereby achieving significant speedup. The process of inductive invariant reasoning relies on the assume-then-verify framework, which requires multiple iterations to complete, making it infeasible for cases with a large set of multi-node invariants. We present filtering techniques that can be applied to a diverse set of multi-node invariants to achieve a significant boost in performance of the invariant checker. Mining and reasoning about all possible potential multi-node invariants is simply infeasible. To alleviate this problem, strategies that narrow down the focus on specific types of powerful multi-node invariants are also presented. Experimental results reflect the promise of these techniques. As a measure of quality, the invariants are utilized for untestable fault identification and to constrain ATPG for path delay fault testing, with positive results. / Master of Science

Verification is an important step for Integrated Circuit (IC) design. In fact, literature has reported that up to 70% of the design effort is spent on checking if the design is functionally correct. One of the core verification tasks is Equivalence Checking (EC), which attempts to check if two structurally different designs are functionally equivalent for all reachable states. Powerful equivalence checking can also provide opportunities for more aggressive logic optimizations, meeting different goals such as smaller area, better performance, etc. The success of Combinational Equivalence Checking (CEC) has laid a foundation to industry-level combinational logic synthesis and optimization. However, Sequential Equivalence Checking (SEC) still faces much challenge, especially for those complex circuits that have different state encodings and few internal signal equivalences. In this thesis, we propose a novel simulation-based multi-node inductive invariant generation and pruning technique to check the equivalence of sequential circuits that have different state encodings and very few equivalent signals between them. By first grouping flip-flops into smaller subsets to make it scalable for large designs, we then propose a constrained logic synthesis technique to prune potential multi-node invariants without inadvertently losing important constraints. Our pruning technique guarantees the same conclusion for different instances (proving SEC or not) compared to previous approaches in which merging of such potential invariants might lose important relations if the merged relation does not turn out to be a true invariant. Experimental results show that the smaller invariant set can be very effective for sequential equivalence checking of such hard SEC instances. Our approach is up to 20x-- faster compared to previous mining-based methods for larger circuits. / Master of Science

The advent of spectrum sharing has increased the need for robust interference rejection methods. The Citizens Broadband Radio Service (CBRS) band is soon to be occupied by LTE waveforms and License Assisted Access (LAA) will have LTE signals coexisting with other signals in the 5 GHz band. In anticipation of this need, we present a method for interference rejection of cyclostationary signals, which can also help avoid interference through better detection of low power co-channel signals. The method proposed in this thesis consists of a frequency-shift (FRESH) filter which acts as a whitening filter, canceling the interference by exploiting its cyclostationarity. It learns the cyclostationary characteristics of the interferer blindly, through a property restoration algorithm which aims to drive the spectrum to white noise. The property restoration algorithm, inspired by the constant modulus algorithm (CMA), is applied to each frequency bin to determine the optimal coefficients for the proposed CMA FRESH whitening filter (CFW). The performance of the CFW in interference rejection is compared to a time-invariant version, and proposed use cases are analyzed. The use cases consist of the rejection of a high powered, wider bandwidth interferer which is masking the signal-of-interest (SOI). The interferer is rejected blindly, with no knowledge of its characteristics. We analyzed signal detection performance in the case that the SOI is another user with much lower power, for multiple types of SOIs ranging from BPSK to OFDM. We also deal with the case that the SOI is to be received and demodulated; we recover it and compare resulting bit error rates to state of the art FRESH filters. The results show significantly better signal detection and recovery. / Master of Science / Wireless communication is complicated by the fact that multiple radios may be attempting to transmit at the same frequency, time and location concurrently. This scenario may be a due to malicious intent by certain radios (jamming), or mere confusion due to a lack of knowledge that another radio is transmitting in the same channel. The latter scenario is more common due to congested wireless spectrum, as the number of devices increases exponentially. In either case, interference results. We present a novel interference rejection method in this work, one that is blind to the properties of the interferer and adapts to cancel it. It follows the philosophy of property restoration as extolled by the constant modulus algorithm (CMA) and is a frequency shift (FRESH) filter, hence the name. The process of restoring the wireless spectrum to white noise is what makes it a whitening filter, and is also how it adapts to cancel interference. Such a filter has myriad possible uses, and we examine the use case of rejecting interference to detect or recover the signal-of-interest (SOI) that we are attempting to receive. We present performance results in both cases and compare with conventional time-invariant filters and state of the art FRESH filters.

Rapidly-increasing medium-voltage power electronics applications in emerging industry systems, including electrical ships, more electric aircraft, and microgrids, have emphasized the critical need for highly energy-efficient, reliable, and fast switching devices. As a result, Wide-Bandgap (WBG) devices have gained considerable interest over conventional silicon-based switches in recent years. For example, emerging WBG devices have unlocked new dimensions for modern motor drive systems with increased efficiency, switching frequency, and superior power density. Commercially-developed WBG devices such as Silicon Carbide (SiC) and Gallium Nitride (GaN) offer promising opportunities to meet those pressing requirements. However, the fast switching operation of WBG devices may cause substantially increased EMI emissions in medium-voltage applications, which can decrease the overall system’s performance or merits of power converters. This will be particularly an issue in a system where electric ground is unavailable, such as an electric ship, as a large Electro-Magnetic Interference current will be circulating within the system. The EMI in the WBG switch module will be emitted up to 500 MHz. This is the near radio-frequency (RF) band whose impact had not been clearly understood or properly analyzed in the power electronics field until recently. With new and critical challenges in recent years, to reliably adopt WBG devices in emerging power systems, there has been significant effort to improve electromagnetic compatibility (EMC) with new EMI mitigation techniques that comply with existing standards, including International Special Committee on Radio Interference (CISPR), Federal Communications Commission (FCC), Department of Defense (DOD), International Electro-Technical Commission (IEC), etc. This research investigates the common-mode EMI in networked power electronics-enabled power systems. Common-mode EMI phase information is a vital degree of freedom in EMI study that has not been considered in the state of the art. The EMI phase information reduces EMI without implementing any active or passive filter circuit. An effective and less complex method is introduced to reduce EMI in power electronics network. The work includes developing hybrid filter with passive and virtual filter. Including virtual filter reduces the passive common mode choke weight and volume significantly. Finally, a simplified switching node capacitance characterization technique for packaged WBG SiC has been introduced.

Parallel architectures, including multi-core processors, many-core processors, and multi-node systems, have become commonplace, as it is no longer feasible to improve single-core performance through increasing its operating clock frequency. Furthermore, to keep up with the exponentially growing desire for more and more computational power, the number of cores/nodes in parallel architectures has continued to dramatically increase. On the other hand, many applications in well-established and emerging fields, such as bioinformatics, social network analysis, and graph processing, exhibit increasing irregularities in memory access, control flow, and communication patterns. While multiple techniques have been introduced into modern parallel architectures to tolerate these irregularities, many irregular applications still execute poorly on current parallel architectures, as their irregularities exceed the capabilities of these techniques. Therefore, it is critical to resolve irregularities in applications for parallel architectures. However, this is a very challenging task, as the irregularities are dynamic, and hence, unknown until runtime. To optimize irregular applications, many approaches have been proposed to improve data locality and reduce irregularities through computational and data transformations. However, there are two major drawbacks in these existing approaches that prevent them from achieving optimal performance. First, these approaches use local optimizations that exploit data locality and regularity locally within a loop or kernel. However, in many applications, there is hidden locality across loops or kernels. Second, these approaches use "one-size-fits-all'' methods that treat all irregular patterns equally and resolve them with a single method. However, many irregular applications have complex irregularities, which are mixtures of different types of irregularities and need differentiated optimizations. To overcome these two drawbacks, we propose a general methodology that includes a taxonomy of irregularities to help us analyze the irregular patterns in an application, and a set of adaptive transformations to reorder data and computation based on the characteristics of the application and architecture. By extending our adaptive data-reordering transformation on a single node, we propose a data-partitioning framework to resolve the load imbalance problem of irregular applications on multi-node systems. Unlike existing frameworks, which use "one-size-fits-all" methods to partition the input data by a single property, our framework provides a set of operations to transform the input data by multiple properties and generates the desired data-partitioning codes by composing these operations into a workflow. / Ph. D. / Irregular applications, which present unpredictable and irregular patterns of data accesses and computation, are increasingly important in well-established and emerging fields, such as biological data analysis, social network analysis, and machine learning, to deal with large datasets. On the other hand, current parallel processors, such as multi-core CPUs (central processing units), GPUs (graphics processing units), and computer clusters (i.e., groups of connected computers), are designed for regular applications and execute irregular applications poorly. Therefore, it is critical to optimize irregular applications for parallel processors. However, it is a very challenging task, as the irregular patterns are dynamic, and hence, unknown until application execution. To overcome this challenge, we propose a general methodology that includes a taxonomy of irregularities to help us analyze the irregular patterns in an application, and a set of adaptive transformations to reorder data and computation for exploring hidden regularities based on the characteristics of the application and processor. We apply our methodology on couples of important and complex irregular applications as case studies to demonstrate that it is effective and efficient.

2023 / In recent years, there has been a remarkable increase in the use of machine learning techniques for analyzing graphs and their associated applications such as node classification, link prediction, community detection, and generating new graph instances with desired characteristics. This motivates the desire to create innovative and effective algorithms, as well as explore the potential and constraints of modern deep learning techniques, which have garnered considerable attention. This dissertation makes contributions in both of these areas. First, we propose innovative and scalable methods that rely solely on local node information for both unsupervised and supervised graph learning tasks. Specifically, we emphasize the significance of local triangle counts in community detection and introduce a novel triangle-aware spectral sparsification algorithm that enhances the efficiency of this task. Secondly, we analyze a Twitter dataset and create a supervised learning framework that leverages the multiple layers of interaction among Twitter users, resulting in a more precise prediction of new links among them. The emergence of deep learning has sparked interest in the use of unsupervised node embeddings, which are low-dimensional vector representations of nodes, and have become the primary tool in many graph-based machine learning tasks. A fundamental question arises: Can real-world networks be accurately represented in a low-dimensional space? We contribute to the understanding of node embeddings in two significant ways. Firstly, we prove that any graph with bounded maximum degree can be embedded in low dimensions, and we offer an algorithm that accurately embeds real-world networks in a few dimensions, typically in the order of tenths. Secondly, we explore contemporary embedding techniques and find that their embeddings are not always precise, as different graphs can have similar low-dimensional representations. However, despite the lack of exactness, these methods successfully encode sufficient information for high performance on node classification tasks. Finally, we study graph generative models under a unique novel criterion: their ability to generate graphs that are simultaneously edge-diverse and rich in small-sized dense subgraphs. We show the limitations of edge independent graph generative models and develop a hierarchy of models that are progressively more powerful in terms of mimicking better real-world networks. We complement our analysis with simple baseline methods relying on dense subgraph detection that perform competitively against more complex methods.

Le noeud sinusal (NS) est le centre de l‟automatisme cardiaque. Grâce à son activité électrique spontanée, il dicte la fréquence cardiaque (FC) en réponse aux demandes physiologiques. A ce jour, le NS demeure un sujet de recherche important puisque les mécanismes moléculaires responsables de sa régulation sont encore méconnus. Par exemple, les processus menant à la bradycardie sinusale et à la maladie du sinus (MS) chez les personnes âgées sont mécompris et présentement l‟implantation d‟un stimulateur cardiaque demeure le seul traitement disponible. Ainsi, l‟objectif de cette thèse était de déterminer les changements moléculaires et cellulaires se produisant au niveau du NS en réponse à divers stimuli physiologiques et pathologiques afin d'établir leurs rôles potentiels dans la régulation de la FC et le développement de la MS. Dans les deux premiers chapitres, la grossesse est présentée comme modèle physiologique. En effet, la réponse adaptative aux demandes croissantes de la mère et du foetus engendre des changements physiologiques considérables au niveau du myocarde, dont une augmentation de la FC essentielle pour la perfusion adéquate des organes. Toutefois, cette augmentation peut aussi favoriser le développement d‟arythmies. Dans le troisième chapitre, l‟inflammation, un facteur présent lors du vieillissement et dans plusieurs pathologies où la MS se manifeste, a fait l‟objet d‟une étude dans le but de déterminer son rôle dans le développement de la MS. Les résultats obtenus dans cette thèse démontrent que la grossesse induit une hausse de la FC chez la souris gestante similaire à celle retrouvée chez la femme enceinte. Cette accélération était due à un remodelage électrique du NS. Plus spécifiquement, la fréquence des potentiels d‟action ainsi que la densité et l‟expression des courants pacemaker (If) et calcique de type L (ICaL) étaient augmentées. De plus, une accélération des transitoires calciques spontanés et de la vitesse de relâche calcique du réticulum sarcoplasmique a été observée. La régulation de l‟automaticité par un stimulus pathologique, l‟interleukine-1β, est abordée par la suite. L‟interleukine-1β, une cytokine ayant un rôle majeur comme médiateur inflammatoire, se retrouve en concentrations élevées dans plusieurs maladies associées avec la ii MS. Nos résultats démontrent que l‟interleukine-1β engendre une diminution de l‟automaticité associée à une réduction de If et ICaL dans les cardiomyocytes humains de type nodal dérivés de cellules souches induites pluripotentes (hiPSC-CM). En parallèle, le phénotype électrophysiologique et moléculaire des hiPSC-CM a été caractérisé démontrant leur homologie avec les cellules du NS humain adulte, les validant comme modèle in vitro de cellules nodales humaines. En conclusion, les études présentées dans cette thèse démontrent que le NS est plus qu‟un simple tissu régulé par l‟innervation autonome. En effet, son automaticité est dynamique et peut être influencée par des facteurs physiologiques ou pathologiques. Nos résultats contribuent ainsi à une meilleure compréhension des mécanismes sous-jacents à l‟automaticité. Ces avancées sont importantes non seulement pour la santé des femmes, mais aussi pour les individus souffrant de la MS. À terme, nous espérons que ces résultats contribueront au développement de stratégies thérapeutiques pour traiter des complications liées aux troubles d‟automaticité cardiaque. / The sinoatrial node (SAN) is the dominant cardiac pacemaker. With its spontaneous automaticity, it dictates rhythm and controls heart rate in response to varying physiological demands. Despite its modest size, the SAN is a very heterogeneous and complex structure that remains the topic of research efforts due, in part, to uncertainties in the mechanisms that regulate pacemaking in various conditions. For instance, the processes that lead to severe sinus bradycardia and SAN dysfunction (SND) in the elderly are unknown and to date, the implantation of electronic pacemaker remains the only SND treatment. Accordingly, the overall objective of this thesis was to explore and highlight the molecular and cellular changes that occur within the SAN in both physiological and pathological states, while determining how they contribute to regulation of heart rate and potentially SND. In the first two chapters, we present pregnancy as a physiological model considering it is a period during which substantial adaptive changes to the myocardium and increases in heart rate occur. Paradoxically, the rapid rate, which is essential for adequate organ perfusion of both mother and foetus, may also increase vulnerability to certain arrhythmias. In the third chapter, inflammation, a central process in pathology and common factor to several diseases and even ageing, was evaluated as potential underlying circumstance contributing to the development of sinus bradycardia and SND. Combinations of in vivo, ex vivo, biochemical, molecular and cellular approaches were used in order to generate an integrated understanding of the models we examined. Our data shows that in pregnant mice, an increase in heart rate similar to that of pregnant women occurs and was due to an electrical remodelling of the SAN. Specifically, an increase in action potential frequency of isolated individual SAN cells was observed. This was attributed to increased expression and density of pacemaker (If) and L-type Ca2+ currents (ICaL) along with a rapid spontaneous Ca2+ transient rate and faster intracellular sarcoplasmic reticulum Ca2+ release. We then demonstrate that the pro-inflammatory cytokine interleukin-1β which is a major inflammatory mediator that is upregulated in several diseases associated with SND, iv dramatically slows automaticity by reducing If and ICaL density in nodal-like cardiomyocytes derived from human-induced pluripotent stem cells (hiPSC-CM). Importantly, in that study, hiPSC-CMs were also physiologically and molecularly characterized revealing their high resemblance to adult human SAN and a potential use as a novel in vitro model to study pacemaking in humans. In conclusion, the results of this thesis demonstrate that the SAN is not a simple, neurally controlled tissue, but a rather dynamic pacemaker that undergoes extensive intrinsic remodelling during states of health and disease. The results contribute to understanding physiological mechanisms of pacemaking and how they are altered by disease and may be relevant for both women‟s health and the individuals affected by SND. Ultimately, we hope these findings will be helpful in the development of therapeutic strategies to treat pacemaking-related complications.

The emergency departments in Region Östergötland use pen and paper to a large extent when recording emergency care procedures and measurements. During treatment the patient should be the main focus. Because of this, recording of measurements done could be delayed or in worst case forgotten during stressful situations. The proposal of this project is the development of a prototype that tries to make the administrative work a passive procedure rather than an active one. The system developed uses a Raspberry Pi, along with Node-Red, which connects predefined patient data and medical records, with the clinical staff tending the patient. All these connections are initiated by mainly using RFID technology. The conclusion made with the developed system is that it should unload the staff with the recording of data and that it helps make a data logging a more passive work than today’s used methods. Along with a process that is easier to operate, the time spent on administrative work could be reduced with the proposed system.

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