This Master’s thesis focuses on the discursive power struggle between politicians and journalists on Finnish digital news articles where the politician’s tweets are embedded or quoted in using multimodal discourse analysis. Embedded and quoted tweets are one of the premier links between Twitter and digital news platforms but have for the most part been left out of the field of discourse analysis. This research will try to fill that gap, focusing on a time period of one month before the 2019 parliamentary election in Finland. The research material consists of 18 articles from two of the biggest digital news platforms in Finland, Iltalehti and Ilta-Sanomat. They are analyzed using Machin and Mayr’s seven-part scheme for critical discourse analysis, focusing on the embedded and quoted tweets in relation to the text’s discourse, and also the intertwined textual and the visual sides of the articles. The analysis shows that in most articles, the discourse portrayed in the tweets is not challenged by the journalist, meaning that the politicians most often come on top in the discursive power struggle. The analysis also displays that there are multiple ways of challenging the discourse, but they are seldom used in the power struggle. In conclusion, as the tweets’ discourses often go unchallenged, both the politicians and Twitter as a platform have arguably disproportionate power to influence both the discourse on digital news platforms, as well as the readers of those.

RISC-V is an open-source instruction set that has been gaining popularity in recent years, and, with support from large chip manufacturers like Intel and the benefits of its open-source nature, RISC-V devices are likely to continue gaining momentum. Many courses in a computer science program involve development on an embedded device. Usually, this device is of the ARM architecture, like a Raspberry Pi. With the increasing use of RISC-V, it may be beneficial to use a RISC-V embedded device in one of these classroom environments. This research intends to assist development on the SiFive HiFive1 RevB, which is a RISC-V embedded device. This device was chosen because of its ease of use, functionality-rich API, and affordability. In order to make developing with this board very approachable for a student, this research involved the development of a small suite of tools. These tools support common functionality like: building a source file into an executable ELF file, converting that ELF executable into an Intel HEX executable format that is required to run on the device, uploading the Intel HEX executable onto the device, and attaching a debug session to the program that is running on the device. With the help of this toolchain, developing on this RISC-V embedded device should be very approachable for most students.

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L’augmentation de la complexité des systèmes temps-réel répartis embarqués (TR2E) et leur implication dans de nombreux domaines de notre quotidien imposent de nouvelles mé thodes de réalisation. Dans les domaines dits critiques (transport, médecine...) ces systèmes doivent satisfaire des contraintes dures pour garantir leur bon fonctionnement et éviter toutes défaillances qui engendreraient des conséquences financières ou humaines dramatiques. L’Ingénierie Dirigée par les Modèles (IDM) introduit le “modèle” - i.e. une description abstraite du système - et un ensemble d’outils (édition, transformation...) permettant la simplification et l’automatisation des étapes de conception, de validation et de génération du système. Ainsi, différentes abstractions du système sont élaborées dans des formalismes spécifiques de manière à couvrir un aspect du système et à permettre la réutilisation des outils d’analyse et de génération existants. Cependant, ces multiples représentations évoluent à des niveaux d’abstractions différents et il n’est pas toujours évident de mettre en corrélation système analysé et système généré. Ce travail de thèse exploite les concepts et les mécanismes offerts par l’IDM pour améliorer la fiabilité du processus de réalisation des systèmes critiques basé sur les modèles. L’approche que nous avons définie repose sur la définition du langage de modélisation architecturale et comportementale AADL-HI Ravenscar - un sous-ensemble du langage AADL (Architecture Analysis & Design Language) et de son annexe comportementale - contraint pour permettre conjointement l’analyse et la génération de l’ensemble des composants de l’application y compris de son exécutif, avec une sémantique proche d’un langage de programmation impératif... / The increasing complexity of distributed realtime and embedded (DRE) systems and their implication in various domains imply new design and development methods. In safety- criticial domains such as space, aeronautical, transport or medicine, their failure could result in the failure of the mission, or in dramatic damages such as human losses. This particular class of systems comes with strong requirements to satisfy safety, reliability and security properties. The Model-driven Engineering (MDE) introduces the concept of «model» - an abstract description of the system and a set of tools (editor, transformation engine, code generator) to simplify and automatize the design, the validation and the implementation of the system. Thus, various abstractions are realized using different domain-specific modeling languages in order to assess one particular aspect of the system and to re-use model-based analysis tools and generative technologies. These various representations may share some commonalities but the consistency between them is hard to validate (for example : Is the analyzed system the same as the generated one ?).This PhD thesis leverages MDE concepts and mechanisms, to enhance the reliability of the model-based development process of DRE systems. Our approach is based on the definition of the architectural and behavioral modeling language AADLHI Ravenscar, a restriction of AADL (Architecture Analysis & Design Language) and its behavioral annex. This subset of AADL constructs, comes up with a semantic close to the one of an imperative programming language, to drive both the analysis and the code generation of the application components and its relying execution platform (middleware) components...

Extending research of framing anti-war protest is framed in the public sphere, this study examines theatre critics' reviews and viewers' responses to Tim Robbins' anti-war play Embedded. My research examines how two groups of publics interpreted Embedded: (1) professional theatre reviewers and (2) a sample of Utah State University students. It is important to note that the majority of the students who participated in this study are members of The Church of Jesus Christ of Latter-day Saints (Mormon), a consistently social and political conservative religious sect. Thus, how this specific group of viewers deciphered Embedded is of special interest. Critical analyses of both reviews and responses revealed the prominence of two seemingly irreconcilable partisan master frames in critics' and spectators' interpretations of the play's protest narratives. Although these frames seem to be incompatible, adherents to both "whining for peace" and "anti-war protest" consider protection of American democracy the primary goal. However, members of both groups define the role of anti-war protest in, and defense of, democracy differently. Examination of discourse suggests that marginalization of anti-war protest continues to be the privileged discourse. Overwhelming dismissal of Embedded's anti-war narratives by the majority of critics and Latter-day Saint (LDS) viewers indicates that dissent was framed according to cultural and societal values, which perpetuated conceptions of anti-war protest as deviant. Thus, in both public discussion and personal interpretation of Embedded's outward expression of protest, anti-war activism is perceived to be illegitimate when the United States is at war. Results suggest that most theatre critics and LDS viewers relied on values framing in their perception of the play, which negated complex and nuanced discussion regarding military action in Iraq. By broadening discussion of how anti-war dissent is framed by including theatre critics and individual viewers, this research provides insight into how dissenting action is perceived within a larger cultural context. As findings reveal, it is reasonable to conclude that marginalization of anti-war dissent is not limited to mass media. Rather, I argue that dismissal of protest may be perpetuated on a wider societal scale, a problematic trend, especially as protest is widely considered to be a valuable tenet of democratic practice.

Welded wire mesh has been used in the past as soil reinforcement in the construction of reinforced soil embankments. Involved in the design of these embankments is the external and internal stability. The internal stability has two failure mechanisms; tension failure and pullout failure of the welded wire mesh. This paper presents the results of laboratory tests on different sizes of welded wire mats embedded in different types of soils. These tests were performed on mats that are much larger than in previous tests. These tests measured the pullout resistance as a function of the number of embedded wires, the diameter of the wire, and the overburden pressure. This data is plotted to allow design of reinforced soil embankments in various types of soils and to compare the results with theoretical relationships.

Sensor networks including opportunistic networks of sensor-equipped smartphones as well as networks of embedded sensors can enable a wide range of applications including environmental monitoring, smart grids, intelligent transportation, and healthcare. In most real-world applications, to meet end-user requirements, the network operator needs to define and update the sensors' tasks dynamically, such as updating the parameters for sensor data collection or updating the sensors' code. Tasking sensor networks is necessary to reduce the effort in programming sensor networks. However, it is challenging due to dynamics and scale in terms of number of nodes, number of tasks, and sensing regions of the networks. In addition, tasking sensor networks must also be efficient in terms of bandwidth, latency, energy consumption, and memory usage. This dissertation identifies and addresses the problems of scalability and efficiency in tasking sensor networks. The first challenge in tasking sensor networks is to define a mechanism that represents multiple tasks and sensor groups efficiently taking into account the heterogeneity and mobility of sensors deployed over a large geographical region. Another challenge in tasking sensor networks in general, and embedded sensor networks in particular, is to design protocols that can not only efficiently disseminate tasks but also maintain a consistent view of the task to be performed among inherently unreliable and resource-limited sensors. We believe that a scalable and efficient tasking framework can greatly benefit the development and deployment of sensor network applications. Our thesis is that decoupling the task specification from task implementation using a spatial two-dimensional (2D) representation of a tasking region such as maps enables scalable, efficient, and resource-adaptive tasking over heterogeneous mobile sensor networks. In addition, reducing overhead in detecting inconsistencies across nodes enables scalable and efficient task dissemination and maintenance. We present the design, implementation, and evaluation of Zoom, a multiresolution tasking framework that efficiently encapsulates multiple tasks and sensor groups for sensor networks deployed in a large geographical region. The key ideas in Zoom are (i) decoupling task specification and task implementation to support heterogeneity, (ii) using maps for representing spatial sensor groups and tasks to scale with the number of sensor groups and sensing regions, and (iii) using image encoding techniques to reduce the map size and provide adaptation to sensor platforms with different resource capabilities. We present the design, implementation, and evaluation of our protocol, DHV, which efficiently disseminates task content and ensures that all nodes have up-to-date task content in sensor networks. It achieves this by minimizing both the redundant information in each message and the number of transmitted messages in the networks. DHV has been included in the official distribution of TinyOS, a popular operating system for embedded sensor networks. As sensor networks continue to develop, they will evolve from dedicated and single-purpose systems to open and multi-purpose large scale systems. Nodes in the network will be retasked frequently to support multiple applications and multiple users. We believe that this work is an important step in enabling seamless interaction between users and sensor networks and to make sensor networks more widely adopted.

Indiana University-Purdue University Indianapolis (IUPUI) / The nanotechnology materials have been used for high sensitivity sensing devices due to their ability to alter their properties in response to the environmental parameters such as temperature, pressure, gas, electromagnetic, and chemicals. The features of employing nanoparticles on top of graphene thin film have driven the hypothesis of achieving high sensing nanotechnology devices. This study demonstrates a novel approach for designing a low noise nanoparticle based gas sensing device with internet of things (IoT) capability. The system is capable of minimizing cross-talk between multiple channels of amplifiers arranged on one chip using guard rings. Graphene mono-layer is utilized as sensing material with the sensitivity catalyzed by addition of gold nano-particles on its surface. The signal from the sensing unit is received by an offset cancellation amplifying system using a system on chip (SoC) approach. IoT capability of the sensing device is developed using FRDM K64f micro-controller board which sends messages on IoT platform when a gas is sensed. The message is received by an application created and sent as an email or message to the user. This study details the mathematical models of the graphene based gas sensing devices, and the interface circuitry that drives the differential potentials, resulting from the sensing unit. The study presents the simulation and practical model of the device, detailing the design approach of the processing unit within the SoC system and wireless implementation of it. The sensing device was capable of sensing gas concentration from 5% to 100% using both the resistive and capacitive based models. The I-V characteristics of the FET sensing device was in agreeable with the other models. The SoC processing unit was designed using cadence tools, and simulation results showed very high CMRR that enable the amplifier to sense a very low signal received from the gas sensors. The cross talk noise was reduced by surrounding guard rings around the amplifier circuits. The layout was accomplished with 45nm technology and simulation showed an offset voltage of 17μV.

Indiana University-Purdue University Indianapolis (IUPUI) / An Autonomous vehicle depends on the combination of latest technology or the ADAS safety features such as Adaptive cruise control (ACC), Autonomous Emergency Braking (AEB), Automatic Parking, Blind Spot Monitor, Forward Collision Warning or Avoidance (FCW or FCA), Lane Departure Warning. The current trend follows incorporation of these technologies using the Artificial neural network or Deep neural network, as an imitation of the traditionally used algorithms. Recent research in the field of deep learning and development of competent processors for autonomous or self-driving car have shown amplitude of prospect, but there are many complexities for hardware deployment because of limited resources such as memory, computational power, and energy. Deployment of several mentioned ADAS safety feature using multiple sensors and individual processors, increases the integration complexity and also results in the distribution of the system, which is very pivotal for autonomous vehicles. This thesis attempts to tackle two important adas safety feature: Forward collision Warning, and Object Detection using the machine learning and Deep Neural Networks and there deployment in the autonomous embedded platform. 1. A machine learning based approach for the forward collision warning system in an autonomous vehicle. 2. 3-D object detection using Lidar and Camera which is primarily based on Lidar Point Clouds. The proposed forward collision warning model is based on the forward facing automotive radar providing the sensed input values such as acceleration, velocity and separation distance to a classifier algorithm which on the basis of supervised learning model, alerts the driver of possible collision. Decision Tress, Linear Regression, Support Vector Machine, Stochastic Gradient Descent, and a Fully Connected Neural Network is used for the prediction purpose. The second proposed methods uses object detection architecture, which combines the 2D object detectors and a contemporary 3D deep learning techniques. For this approach, the 2D object detectors is used first, which proposes a 2D bounding box on the images or video frames. Additionally a 3D object detection technique is used where the point clouds are instance segmented and based on raw point clouds density a 3D bounding box is predicted across the previously segmented objects.

Theme of the Master's thesis is issue of backup power units and its attributes. There is also complete proposal of UPS power unit which can be used to control and monitor backup batteries. Power supply is fully controlled via Ethernet interface using TCI/IP protocol. The other chapter describes complete development of TCI/IP GSM unit which can be used for communication interface between GSM and Ethernet protocol. UPS and GSM modules are developed in details. The Master.s thesis includes all needed information for HW unit development and also for controlling software as well. Universal solution of the modules allows number possibilities of further development.