L'étude des caractéristiques faciales a suscité l'intérêt croissant de la communauté scientifique et des industriels. En effet, ces caractéristiques véhiculent des informations non verbales qui jouent un rôle clé dans la communication entre les hommes. De plus, elles sont très utiles pour permettre une interaction entre l'homme et la machine. De ce fait, l'étude automatique des caractéristiques faciales constitue une tâche primordiale pour diverses applications telles que les interfaces homme-machine, la science du comportement, la pratique clinique et la surveillance de l'état du conducteur. Dans cette thèse, nous nous intéressons à la surveillance de l'état du conducteur à travers l'analyse de ses caractéristiques faciales. Cette problématique sollicite un intérêt universel causé par le nombre croissant des accidents routiers, dont une grande partie est provoquée par une dégradation de la vigilance du conducteur, connue sous le nom de l'hypovigilance. En effet, nous pouvons distinguer trois états d'hypovigilance. Le premier, et le plus critique, est la somnolence qui se manifeste par une incapacité à se maintenir éveillé et se caractérise par les périodes de micro-sommeil correspondant à des endormissements de 2 à 6 secondes. Le second est la fatigue qui se définit par la difficulté croissante à maintenir une tâche à terme et se caractérise par une augmentation du nombre de bâillements. Le troisième est l'inattention qui se produit lorsque l'attention est détournée de l'activité de conduite et se caractérise par le maintien de la pose de la tête en une direction autre que frontale. L'objectif de cette thèse est de concevoir des approches permettant de détecter l'hypovigilance chez le conducteur en analysant ses caractéristiques faciales. En premier lieu, nous avons proposé une approche dédiée à la détection de la somnolence à partir de l'identification des périodes de micro-sommeil à travers l'analyse des yeux. En second lieu, nous avons introduit une approche permettant de relever la fatigue à partir de l'analyse de la bouche afin de détecter les bâillements. Du fait qu'il n'existe aucune base de données publique dédiée à la détection de l'hypovigilance, nous avons acquis et annoté notre propre base de données représentant différents sujets simulant des états d'hypovigilance sous des conditions d'éclairage réelles afin d'évaluer les performances de ces deux approches. En troisième lieu, nous avons développé deux nouveaux estimateurs de la pose de la tête pour permettre à la fois de détecter l'inattention du conducteur et de déterminer son état, même quand ses caractéristiques faciales (yeux et bouche) ne peuvent être analysées suite à des positions non-frontales de la tête. Nous avons évalué ces deux estimateurs sur la base de données publique Pointing'04. Ensuite, nous avons acquis et annoté une base de données représentant la variation de la pose de la tête du conducteur pour valider nos estimateurs sous un environnement de conduite. / Studying facial features has attracted increasing attention in both academic and industrial communities. Indeed, these features convey nonverbal information that plays a key role in humancommunication. Moreover, they are very useful to allow human-machine interactions. Therefore, the automatic study of facial features is an important task for various applications includingrobotics, human-machine interfaces, behavioral science, clinical practice and monitoring driver state. In this thesis, we focus our attention on monitoring driver state through its facial features analysis. This problematic solicits a universal interest caused by the increasing number of road accidents, principally induced by deterioration in the driver vigilance level, known as hypovigilance. Indeed, we can distinguish three hypovigilance states. The first and most critical one is drowsiness, which is manifested by an inability to keep awake and it is characterized by microsleep intervals of 2-6 seconds. The second one is fatigue, which is defined by the increasing difficulty of maintaining a task and it is characterized by an important number of yawns. The third and last one is the inattention that occurs when the attention is diverted from the driving activity and it is characterized by maintaining the head pose in a non-frontal direction.The aim of this thesis is to propose facial features based approaches allowing to identify driver hypovigilance. The first approach was proposed to detect drowsiness by identifying microsleepintervals through eye state analysis. The second one was developed to identify fatigue by detecting yawning through mouth analysis. Since no public hypovigilance database is available,we have acquired and annotated our own database representing different subjects simulating hypovigilance under real lighting conditions to evaluate the performance of these two approaches. Next, we have developed two driver head pose estimation approaches to detect its inattention and also to determine its vigilance level even if the facial features (eyes and mouth) cannot be analyzed because of non-frontal head positions. We evaluated these two estimators on the public database Pointing'04. Then, we have acquired and annotated a driver head pose database to evaluate our estimators in real driving conditions.

Melanoma is a heterogeneous neoplasm at the histomorphologic, immunophenotypic, and molecular levels. Melanoma with extreme histomorphologic heterogeneity can pose a diagnostic challenge in which the diagnosis may predominantly rely on its immunophenotypic profile. However, tumor survival and response to therapy are linked to tumor genetic heterogeneity rather than tumor morphology. Therefore, understating the molecular characteristics of such melanomas become indispensable. In this study, DNA was extracted from 11 morphologically distinct regions in eight formalin-fixed, paraffin-embedded melanomas. In each region, mutations in 50 cancer-related genes were tested using next-generation sequencing (NGS). A tumor was considered genetically heterogeneous if at least one non-overlapping mutation was identified either between the histologically distinct regions of the same tumor (intratumor heterogeneity) or among the histologically distinct regions of the paired primary and metastatic tumors within the same patient (intertumor heterogeneity). Our results revealed that genetic heterogeneity existed in all tumors as non-overlapping mutations were detected in every tested tumor (n = 5, 100%; intratumor: n = 2, 40%; intertumor: n = 3, 60%). Conversely, overlapping mutations were also detected in all the tested regions (n = 11, 100%). Melanomas exhibiting histomorphologic heterogeneity are often associated with genetic heterogeneity, which might contribute to tumor survival and poor response to therapy.

Each gene or protein has its own function which, when combined with others, allows the group to perform more complex behaviors, e.g. carry out a particular cellular task (functional module) or affect a particular disease phenotype (disease module). One of the major challenges in systems biology is to reveal the roles of genes or proteins in functional modules or disease modules. In the first part of the dissertation, I present a data-driven method, Correlation Set Analysis (CSA), for comprehensively detecting active regulators in disease populations by integrating co-expression analysis and specific types of literature-derived causal relationships. Instead of investigating the co-expression level between regulators and their targets, I focus on coherence of regulatees of a regulator, e.g. downstream targets of a transcription factor. Using simulated datasets I show that my method can reach high true positive rate and true negative rate (>80%) even the regulatory relationships is weak (only 20% of regulatees are co-expressed). Using three separate real biological datasets I was able to recover well-known and as- yet undescribed, active regulators for each disease population. In the second part of the dissertation, I develop and apply a new computational algorithm for detecting modules of functionally related genes that are likely to drive malignant transformation. The algorithm takes as input the identity and locations of a small number of known oncogenes (a seed set) on a human genome functional linkage network (FLN). It then searches for a boundary surrounding a gene set encompassing the seed, such that the magnitude of the difference in linkage weights between interior-interior gene pairs, and interior-exterior gene pairs is maximized. Starting with small seed sets for breast and ovarian cancer, I successfully identify known and novel drivers in both cancer types. In the third part of the dissertation, I propose a module based approach for expanding manually curated functional modules. I use the KEGG pathway database as an example and the results show that my approach can successfully suggest both validated pathway members (genes that are assigned to a particular pathway by other manually curated pathway databases) and novel candidate pathway genes.

Indiana University-Purdue University Indianapolis (IUPUI) / Self-driving is simply the capacity of a vehicle to drive itself without human intervention. To accomplish this, the vehicle utilizes mechanical and electronic parts, sensors, actuators and an AI computer. The on-board PC runs advanced programming, which permits the vehicle to see and comprehend its current circumstance dependent on sensor input, limit itself in that climate and plan the ideal course from point A to point B. Independent driving is not an easy task, and to create self-sufficient driving arrangements is an exceptionally significant ability in the present programming designing field. ROS is a robust and versatile communication middle ware (framework) tailored and widely used for robotics applications. This thesis work intends to show how ROS could be used to create independent driving programming by investigating self-governing driving issues, looking at existing arrangements and building up a model vehicle utilizing ROS. The main focus of this thesis is to develop and implement a one-tenth scale of an autonomous RACECAR equipped with Jetson Nano board as the on-board computer, PCA9685 as PWM driver, sensors, and a ROS based software architecture. Finally, by following the methods presented in this thesis, it is conceivable to build an autonomous RACECAR that runs on ROS. By following the means portrayed in this theory of work, it is conceivable to build up a self-governing vehicle.

The use of smartphones in cars is a common practice that can result in distracted drivers and accidents. Research has shown that using voice to interact with the devices is the least dangerous solution for users, but its implementation is limited and sub-optimal. Other techniques like proactivity have shown positive results but its presence in products is reduced. This study aims to define, through the synthesis and combination of previous research, a set of guidelines for the implementation of voice interfaces in smartphone apps that can safely offer relevant content to car drivers. Based on a review of the literature on testing for driving solutions, a series of online user evaluations were conducted across potential car drivers. The evaluations consisted of different behavioural scripts for the voice interface, which implemented diverse techniques to interact with drivers, and on the users’ thoughts and impressions. Analysis on the gathered data demonstrates that interacting with drivers through a voice interface and focusing on conciseness, politeness, proactiveness, offering relevant content and transparency of intent are fundamental to keep interactions engaging and relevant, as well as giving a sensation of assurance to the users. Further research is needed to validate the adequacy and safety of these guidelines in a real car environment. / Användning av smartphones i bilar är ett vanligt problem som kan resultera i distraherade förare och olyckor. Forskning har visat att användning av röst för att samverka med enheterna är den minst farliga lösningen för användare, men dess implementering är begränsad och suboptimal. Andra tekniker som proaktivitet har visat positiva resultat men dess närvaro i produkter minskar. Denna studie syftar till att definiera, genom syntes och kombination av tidigare forskning, en uppsättning riktlinjer för att implementera röstinterfacer i smartphone-appar som säkert kan erbjuda relevant innehåll till bilförare. Baserat på en genomgång av litteraturen om testning av körlösningar genomfördes en serie online-utvärderingar av potentiella förare. Utvärderingarna bestod av olika beteendeskript för röstinterfacen, som implementerade olika tekniker för att interagera med drivrutiner och om användarnas tankar och intryck. Analys av de insamlade uppgifterna visade att interaktion med förare genom en röstinterface och fokus på korthet, artighet, proaktivitet, erbjudande av relevant innehåll och avsiktsöppenhet var grundläggande för att hålla interaktioner relevanta och engagerande, samt att ge användarna en känsla av säkerhet. Ytterligare forskning behövs för att validera lämplighet och säkerhet för dessa riktlinjer i en verklig bilmiljö.

This dissertation presents two studies examining the interaction between workload history and driver mental workload. The first experiment focuses on testing for the presence of a hysteresis effect in the driving task. The second experiment examines the proposition that cueing impending periods of higher task demand can reduce the impact of any such potential hysteresis effects. Thirty-two licensed drivers served as participants and all served in both studies. Using the directions provided by a Heads-Up-Display navigation system, participants followed a pre-set route in the simulated environment. At specified points within the drive, the navigation system would purposefully fail which required drivers to relay a ten digit alphanumeric error code to a remote operator in order to reset the system. Results indicated that this increase in task demand from the navigation system's failure leads to a significant increase in perceived mental workload as compared to pre-failure periods. This increase in driver mental workload was not significantly reduced by the time the drive ended, indicating the presence of a hysteresis effect. In the second experiment, the navigation system provided a completely reliable visual warning before failure. Results indicate that cueing had neither an effect on perceived mental workload, nor any ameliorating effect on the hysteretic type effect seen in mental workload recovery. The conclusion of these findings being that the overall safety and efficiency of the surface transportation system would likely improve by designs which accommodate the periods immediately following a reduction in stress. Whether from leaving high demand areas such as work zones or in the period immediately after using a in-car information device such as a GPS or a cell phone, these post-high workload periods are associated with increased variability in driver inputs and levels of mental workload.

Drivers in simulators tend to drive faster than in a real car. The study aimed to examine if visual cues impact driver velocity in a simulator. This is important because of the tendency for users of to drive faster in simulators than in authentic driving situations. This is supposed to be caused by the lack of sufficient cues in the simulated environment to convey motion. The hypothesis advocates that the usage of visual cues would make simulated motion cues more realistic to assist the driver to make accurate judgements of their driving speed. Accurate judgements would in turn result in less speeding in the driver simulator. The experiment was conducted in a driver simulator in a collaboration with SAFE trafikskola. The experiment compared two conditions where visual cues were more and less present. The data was complimented with a survey to gather additional information. The result from the t-test showed a significant effect on the measured velocity, whereas the two-way ANOVA yielded no such impact. The repeated measures ANOVA contributed with significant results on the difference between the points of measure and gave no significant main effect between conditions. Together with the complimentary survey the conclusion was made that the usage of visual cues in a driver simulator can affect the velocity of the driver. The knowledge regarding visual cues in a simulated environments could be used to improve driver simulators. Future research has the possibility to investigate motion cues from other modalities than vision to increase realism in driver simulators.

Transcription factors (TFs) control gene expression by binding to highly specific DNA sequences in gene regulatory regions. This TF binding is central to control myriad biological processes. Indeed, transcriptional dysregulation has been associated with many diseases such as autoimmune diseases and cancer. In this thesis, I studied the transcriptional regulation of cytokines and gene transcriptional dysregulation in cancer. Cytokines are small proteins produced by immune cells that play a key role in the development of the immune system and response to pathogens and inflammation. I mined three decades of research and developed a user-friendly database, CytReg, containing 843 human and 647 mouse interactions between TFs and cytokines. I analyzed CytReg and integrated it with phenotypic and functional datasets to provide novel insights into the general principles that govern cytokine regulation. I also predicted novel cytokine promoter-TF interactions based on cytokine co-expression patterns and motif analysis, and studied the association of cytokine transcriptional dysregulation with disease. Transcriptional dysregulation can be caused by single nucleotide variants (SNVs) affecting TF binding sites (TFBS). Therefore, I created a database of altered TFBS (aTFBS-DB) by calculating the effect (gain/loss) of all possible SNVs across the human genome for 741 TFs. I showed how the probabilities to gain or disrupt TFBSs in regulatory regions differ between the major TF families, and that cis-eQTL SNVs are more likely to perturb TFBSs than common SNVs in the human population. To further study the effect of somatic SNVs in TFBS, I used the aTFBS-DB to develop TF-aware burden test (TFABT), a novel algorithm to predict cancer driver SNVs in gene promoters. I applied the TFABT to the Pan-Cancer Analysis of Whole Genomes (PCAWG) cohort and identified 2,555 candidate driver SNVs across 20 cancer types. Further, I characterized these cancer drivers using functional and biophysical assay data from three cancer cell lines, demonstrating that most SNVs alter transcriptional activity and differentially recruit cofactors. Taken together, these studies can be used as a blueprint to study transcriptional mechanisms in specific cellular processes (i.e. cytokine expression) and the effect of transcriptional dysregulation in disease (i.e. cancer).

The increasing market demand for wideband gap (WBG) power switches has led to heightened competition to increase converter power density, switching frequencies, and reduce form factor, among other factors. However, this technology has also brought about an increase in encounters with electromagnetic interference (EMI), posing significant challenges. Nevertheless, the maturation of power switches has been accompanied by an improvement in gate drive technology aimed at resolving EMI challenges, albeit at a higher component and cost expense. This thesis aims to design, analyze, and implement a recent innovative differential gate driver for a 1.2 kV SiC MOSFET full bridge module. The purpose of this design is to mitigate EMI, improve performance, and reduce the number of filtering elements that are typically required. The investigation into the impact of EMI on electrical systems involves exploring factors such as testing equipment, power supplies, and gate drive layout. Based on these considerations, system and sub-system level analyses are conducted to derive practical design recommendations for implementing the differential gate driver. Three gate drive PCBs are designed and evaluated through extensive double pulse tests (DPTs). Furthermore, continuous switching of the driver presents its own set of challenges that are not apparent during the DPTs, requiring further exploration of low-cost solutions. Finally, a comparison between custom and discrete module solutions employing 1.2 kV SiC MOSFETs is conducted, highlighting the advantages and disadvantages of each approach. The solutions proposed in this work are intended to be extended to other gate drive ICs, with the goal of providing valuable insights and guidelines for EMI suppression and gate driver performance enhancement. / Master of Science / The increasing demand for powerful and efficient electronic devices has led to competition to develop better converters with wideband gap (WBG) power switches. These switches can make electronics work faster and take up less space, but they can also cause electromagnetic interference (EMI) that can be problematic. Despite these challenges, advances in power switch technology have led to improvements in gate drive technology, which can help reduce EMI, albeit, sometimes, at a higher cost. This research aims to design and analyze an innovative differential gate driver for a 1.2 kV SiC MOSFET full bridge module that can help mitigate EMI, improve performance, and reduce the number of required filtering elements. A system-level analysis is conducted to identify critical noise paths and potential solutions in response to poor gate driver performance. Practical design recommendations are provided for implementing a differential gate driver, and three PCB designs are tested and evaluated to showcase the effectiveness of the proposed solutions. The work also includes a comparison between a custom module and discrete module solutions employing 1.2 kV SiC MOSFETs, highlighting the advantages and disadvantages of each approach. The findings are extended to other gate drivers that share similar performance specifications, demonstrating the potential and improvements that can be achieved with the suggested techniques. Overall, the study provides valuable insights and guidelines for EMI suppression and performance enhancement in power electronics systems utilizing differential gate drivers.

The job of a package delivery driver (PDD) is complex and demanding. These drivers must possess many skills in order to succeed in their work, including physical stamina, appropriate decision-making, positive customer interaction, and most importantly, operational safety. Companies must use significant resources, not only to provide insurance for existing drivers, but also to train new drivers to use their visual attention effectively while driving, and companies have a vested interest in ensuring that the most capable trainees are selected for jobs. Currently, subjective assessments of supervisors or managers are typically used to make these determinations. While these are valuable methods for assessing drivers, an objective measure of how well the driver is using his/her visual attention would both assist evaluators in making judgments, as well as make those judgments more accurate. The purpose of the study described herein was to 1) conduct a task analysis of the driving component of the PDD job responsibilities, and 2) create and test an objective measure that a package delivery company could use to evaluate the performance of its drivers. A detailed task analysis based on numerous observations of drivers in their normal work routines was conducted for this research in order to understand these complex tasks. A framework was created for understanding this system of tasks, which was then used to organize all tasks that drivers were observed to perform into more general, goal-oriented activities. Using this task analysis, incidents were identified that were observed while drivers were behind the wheel. This information demonstrated that breakdowns were occurring within the tasks drivers were performing and that improved methods of training and evaluations may be needed as a result. A construct of visual behavior called Head Down Time (HTD) was then created and tested. An individual HDT is defined as the sum of time of all eye gazes away from the primary display (i.e. windshield) between two distinct eye gazes at the primary display while the vehicle is in motion. HDT was evaluated for its ability to differentiate levels of experience between drivers, its relationship to types of route on which drivers delivered, and its relationship to the driving-related incidents that were observed. HDTs were shown to be differed significantly between drivers of low and high experience, with experienced drivers displaying shorter durations of HDT when compared to inexperienced drivers. HDTs also differed in duration when analyzed by the type of route upon which drivers operated. Commercial and urban routes, while not significantly different with respect to HDT, were shown to have increased HDT durations when compared to rural routes and, in turn, residential routes were found to have significantly longer HDTs than did rural routes and may have significantly shorter durations compared to commercial and urban. Finally, HDTs that were associated with observed driving incidents in terms of chronological proximity were shown to be of significantly longer duration than were HDTs that were not associated with incidents. All tests were conducted using appropriate statistical measures, including t-tests at a level of α = 0.05 for each dataset. Applications of this research include: 1) improvement of PDD training and evaluation methods through use of a detailed task analysis, 2) improvement in how package delivery companies define incidents and train PDD toward the prevention of incidents based on task analysis and observations as to incident frequency, and 3) the further development of HDT as a possible objective measure to supplement the training and evaluation of PDD. / Master of Science