Current communication and network systems are not built for delay-sensitive applications. The most obvious fact is that the communication capacity is only achievable in theory with infinitely long codes, which means infinitely long delays. One remedy for this is to use shorter codes. Conceptually, there is a deeper reason for the difficulties in such solutions: in Shannon's original 1948 paper, he started out by stating that the "semantic aspects" of information is "irrelevant" to communications. Hence, in Shannon's communication system, as well as every network built after him, we put all information into a uniform bit-stream, regardless what meanings they carry, and we transmit these bits over the network as a single type of commodity. Consequently, the network system can only provide a uniform level of error protection and latency control to all these bits. We argue that such a single measure of latency, or Age of Information (AoI), is insufficient for military Internet of Things (IoT) applications that inherently connect the communication network with a cyber-physical system. For example, a self-driving military vehicle might send to the controller a front-view image. Clearly, not everything in the image is equally important for the purpose of steering the vehicle: an approaching vehicle is a much more urgent piece of information than a tree in the background. Similar examples can be seen for other military IoT devices, such as drones and sensors. In this work, we present a new approach that inherently extracts the most critical information in a Military Battlefield IoT scenario by using a metric - called H-Score. This ensures the neural network to only concentrate on the most important information and ignore all background information. We then carry out extensive evaluation of this a by testing it against various inputs, ranging from a vector of numbers to a 1000x1000 pixel image. Next, we introduce the concept of Manual Marginalization, which helps us to make independent decisions for each object in the image. We also develop a video game that captures the essence of a military battlefield scenario and test our developed algorithm here. Finally, we apply our approach on a simple Atari Space Invaders video game to shoot down enemies before they fire at us. / Master of Science / The IoT is transforming military and civilian environments into truly integrated cyberphysical systems (CPS), in which the dynamic physical world is tightly embedded with communication capabilities. This CPS nature of the military IoT will enable it to integrate a plethora of devices, ranging from small sensors to autonomous aerial, ground, and naval vehicles. This results in huge amount of information being transferred between the devices. However, not all the information is equally important. Broadly we can categorize information into two types: Critical and Non-Critical. For example in a military battlefield, the information about enemies is critical and information abouut the background trees is not so important. Therefore, it is essential to isolate the critical information from non-critical informaiton. This is the focus of our work. We use neural networks and some domain knowledge about the enemies to extract the critical information and use the extracted information to take control decisions. We then evalue the performance of this approach by testing it against various kinds of synthetic data sets. Finally we use an Atari Space Invaders video game to demonstrate how the extracted information can be used to make crucial decisions about enemies.

This thesis proposes a deep learning approach to predict stress at various stages of mechanical loading in 2-D representations of fiber-reinforced composites. More specifically, the full-field stress distribution at elastic and at an early stage of damage initiation is predicted based on the microstructural geometry. The required data set for the purposes of training and validation are generated via high-fidelity simulations of several randomly generated microstructural representations with complex geometries. Two deep learning approaches are employed and their performances are compared: fully convolutional generator and Pix2Pix translation. It is shown that both the utilized approaches can well predict the stress distributions at the designated loading stages with high accuracy. / M.S. / Fiber-reinforced composites are material types with excellent mechanical performance. They form the major material in the construction of space shuttles, aircraft, fancy cars, etc., the structures that are designed to be lightweight and at the same time extremely stiff and strong. Due to the broad application, especially in the sensitives industries, fiber-reinforced composites have always been a subject of meticulous research studies. The research studies to better understand the mechanical behavior of these composites has to be conducted on the micro-scale. Since the experimental studies on micro-scale are expensive and extremely limited, numerical simulations are normally adopted. Numerical simulations, however, are complex, time-consuming, and highly computationally expensive even when run on powerful supercomputers. Hence, this research aims to leverage artificial intelligence to reduce the complexity and computational cost associated with the existing high-fidelity simulation techniques. We propose a robust deep learning framework that can be used as a replacement for the conventional numerical simulations to predict important mechanical attributes of the fiber-reinforced composite materials on the micro-scale. The proposed framework is shown to have high accuracy in predicting complex phenomena including stress distributions at various stages of mechanical loading.

Historical photographs can generate significant cultural and economic value, but often their subjects go unidentified. However, if analyzed correctly, visual clues in these photographs can open up new directions in identifying unknown subjects. For example, many 19th century photographs contain painted backdrops that can be mapped to a specific photographer or location, but this research process is often manual, time-consuming, and unsuccessful. AI-based computer vision algorithms could be used to automatically identify painted backdrops or photographers or cluster photos with similar backdrops in order to aid researchers. However, it is unknown which computer vision algorithms are feasible for painted backdrop identification or which techniques work better than others. We present three studies evaluating four different types of image embeddings – Inception, CLIP, MAE, and pHash – across a variety of metrics and techniques. We find that a workflow using CLIP embeddings combined with a background classifier and simulated user feedback performs best. We also discuss implications for human-AI collaboration in visual analysis and new possibilities for digital humanities scholarship. / Master of Science / Historical photographs can generate significant cultural and economic value, but often their subjects go unidentified. However, if these photographs are analyzed correctly, clues in these photographs can open up new directions in identifying unknown subjects. For example, many 19th century photographs contain painted backdrops that can be mapped to a specific photographer or location, but this research process is often manual, time-consuming, and unsuccessful. Artificial Intelligence-based computer vision techniques could be used to automatically identify painted backdrops or photographers or group together photos with similar backdrops in order to aid researchers. However, it is unknown which computer vision techniques are feasible for painted backdrop identification or which techniques work better than others. We present three studies comparing four different types of computer vision techniques – Inception, CLIP, MAE, and pHash – across a variety of metrics. We find that a workflow that combines the CLIP computer vision technique, software that automatically classifies photo backgrounds, and simulated human feedback performs best. We also discuss implications for collaboration between humans and AI for analyzing images and new possibilities for academic research combining technology and history.

Lately, models utilizing channel impulse response (CIR) data for training deep neural networks used for positioning in radio networks have shown promise, particularly in simulated indoor environments. Research has extended to real outdoor setups as well. In this study, deep neural networks originally designed for image classification were employed to estimate positions using both real and simulated outdoor CIR data. A ray tracing simulator was utilized to generate a simulated dataset which corresponded to a real-world dataset. Models were trained and tested on both datasets. To facilitate training on simulated data and testing on real data, a generative adversarial network (GAN) was employed. The thesis concludes that deep neural networks can effectively be used in real outdoor scenarios, but dense data sampling is likely necessary to achieve satisfactory performance across an area. Additionally, it was found that the simulated data used in this study differed significantly from reality, and the employed GAN could not effectively bridge this gap. Consequently, models trained on simulated data performed poorly when tested on real data. However, it was found that deep neural networks significantly outperformed the baseline K-nearest neighbor algorithm when trained and tested on simulated data. However, this was the only case where such a significant advantage for the deep models was observed.

This thesis explores the use of machine learning (ML) and smartphone sensors to improve indoordistance estimation, a critical aspect of healthcare tests like the 6-minute walk test (6MWT). In order to make tests like the 6MWT more available, and lower the barrier for a patient toget tested, there are multiple problems which need to be solved: How can the distance data needed for these tests be collected reliably and remotely, and without having to rely on the patient reporting correct data; How can these tests be performed indoors, without relying on GPS or other GNSS, which are unreliable indoors. To tackle these challenges a convolutional neural network (CNN) trained on a dataset containing continuous ground truth was employed. An enhancement of an existing CNN model was done by collecting more training data, tuning hyper parameters, and testing it on a diverse dataset. The results of this thesis shows that when predicting distance walked on data from participants the CNN model has seen before, the precision meets clinical minimum for being able to show a change in the health condition. On real world data the performance suffers. Despite limitations due to the scope of data collection, the results still underscores the potential of ML for accurate and efficient indoor distance estimation and points to future research directions. / <p></p><p></p><p></p>

The increasing application of Neural Networks (NNs) in various fields has heightened the demand for specialized hardware to enhance performance and efficiency. Field-Programmable Gate Arrays (FPGAs) have emerged as a popular choice for implementing NN accelerators due to their flexibility, high performance, and ability to be customized for specific NN architectures. However, the trend of outsourcing Integrated Circuit (IC) design to third parties has introduced new security vulnerabilities, particularly in the form of Hardware Trojans (HTs). These malicious alterations can severely compromise the integrity and functionality of NN accelerators. Building upon this, this study investigates a novel type of HT that degrades the accuracy of Convolutional Neural Network (CNN) accelerators over time. Two variants of the attack are presented: Gradually Degrading Accuracy Trojan (GDAT) and Suddenly Degrading Accuracy Trojan (SDAT), implemented in various components of the CNN accelerator. The approach presented leverages a sensitivity analysis to identify the most impactful targets for the trojan and evaluates the attack’s effectiveness based on stealthiness, hardware overhead, and impact on accuracy.  The overhead of the attacks was found to be competitive when compared to other trojans, and has the potential to undermine trust and cause economic damages if deployed. Out of the components targeted, the memory component for the feature maps was identified as the most vulnerable to this attack, closely followed by the bias memory component. The feature map trojans resulted in a significant accuracy degradation of 78.16% with a 0.15% and 0.29% increase in Look-Up-Table (LUT) utilization for the SDAT and GDAT variants, respectively. In comparison, the bias trojans caused an accuracy degradation of 63.33% with a LUT utilization increase of 0.20% and 0.33% for the respective trojans. The power consumption overhead was consistent at 0.16% for both the attacks and trojan versions.

"Crowd counting" is a term used to describe the process of calculating the number of people in a given context; however, crowd counting has multiple challenges especially when images representing a given crowd span multiple cameras or images. In this thesis, we propose a crowd counting camera array and correction (CCCAC) method using a camera array of scaled, adjusted, geometrically corrected, combined, processed, and then corrected images to determine the number of people within the newly created combined crowd field. The purpose of CCCAC is to transform and combine valid regions from multiple images from different sources and order as a uniform proportioned set of images for a collage or discrete summation through a new precision counting architecture. Determining counts in this manner within normalized view (collage), results in superior counting accuracy than processing individual images and summing totals with prior models. Finally, the output from the counting model is adjusted with learned results over time to perfect the counting ability of the entire counting system itself. Results show that CCCAC crowd counting corrected and uncorrected methods perform superior to raw image processing methods.

The purpose of this paper is to study how the latest U.S government shutdown was framed and what kind of framing was used in two major news outlets, Fox News and CNN. The paper ask two questions first, how is the shutdown presented in CNN and Fox News coverage? And second, what examples of framing can be seen in the different company’s coverage? To find this out the paper uses a qualitative text analysis of several news articles from these outlets with framing theory as a theoretical background for the paper. The paper reaches the conclusion that for the most part in the more news focused articles both CNN and Fox News portray the shutdown as a conflict between different actors as well as a problem meanwhile the opinion pieces show similar framing but differ in that they often include a pro/anti Trump framing besides the conflict framing to the articles.

Cette œuvre représente une tentative cherchant à mettre en lumière les facteurs qui expliquent la visée des chaînes transnationales de l’information pour créer cet effet de neutralité dans la couverture éditoriale des conflits. Elle analyse également les conditions qui rendent possibles cette stratégie par les limites internes de la rédaction, ainsi que par le biais des attentes des publics. La position privilégiée de l'auteur, qui a depuis plus d'une décennie travaillé comme journaliste au sein de l'équipe éditoriale internationale d'Euronews, lui a permis de tester ses hypothèses et les constatations de l'intérieur de la chaine. Cette thèse examine les stratégies éditoriales des grands chaînes paneuropéens et mondiales d'information, en les plaçant dans la perspective de l'évolution historique de la diffusion transnationale et en analysant des évolutions actuelles dans le contexte de la mondialisation. En se concentrant en particulier sur la question de la neutralité déclarée habituellement dans les politiques éditoriales de ces chaînes, l'étude analyse les divergences dans l'interprétation de ce principe de base aussi bien par les équipes éditoriales comme par des groupes cibles des différents organismes de télédiffusion – un phénomène crucial qui créent des identités distinctes derrière la neutralité prétendue des flux d'information. Les méthodes employées dans la préparation de cette thèse intègrent l’observation participante à l’intérieur d’une chaine de nouvelles internationales de premier plan, une analyse de la perception du contenu des nouvelles par des téléspectateurs se classant parmi des camps politiques antagonistes, et une revue de la littérature et des publications périodiques russes et européennes. / This work represents an attempt to shed light onto the factors that explain the yearning of the transnational news channels to creating the impression of editorial neutrality in conflict coverage. It also demonstrates the conditionality of this guideline by the internal editorial limitations, as well as by the bias of the audience groups. The privileged position of the author, who has for more than a decade been working as a staff journalist within the international editorial team of Euronews, allowed him to test his hypotheses and findings from within. This dissertation examines the editorial strategies of the major paneuropean and global news channels, putting them in the perspective of historical evolution of transnational broadcasting and analysing the current developments in the context of the globalising world of today. Focusing in particular on the issue of commonly declared neutrality in the channels' editorial policies, the study analyses divergencies in interpreting this basic principle both by the editorial teams and by various audience groups of different broadcasters, creating distinct identities behind the supposedly neutral informational flows. The methods employed in preparing this dissertation include involved observational research of a leading international news channel, the analysis of the perception of news content by viewers ranking among confronting political camps, and reviews of topical Russian and European periodicals and literature.

Cette thèse étudie la représentation de l’Iran contemporain à travers le discours des chaînes d’information en continu arabes et occidentales. L’étude des chaînes d’information en continu est une excellente occasion d’analyser les différentes représentations de l’Autre en étudiant comment ces chaînes construisent différentes représentations des identités à travers des stéréotypes et un contraste idéologique réducteur entre «nous» et «eux». La problématique de cette recherche repose sur l'analyse comparative du discours des chaînes de télévisions transnationales (BBC, CNN et France 24, comme des chaînes occidentales et des chaînes Al-Jazeera et Al-Arabiya comme des chaînes arabes), et leurs façons de représenter, parmi les évènements du monde, l'Autre, en l'occurrence l'identité iranienne. Les médias transnationaux produisent et distribuent des nouvelles, des images et des contenus symboliques relatifs aux problèmes que les téléspectateurs auraient, principalement voire exclusivement appris auparavant (ou pas), à partir de leurs médias nationaux. L’étude de la représentation de l'Autre, est un modèle utile qui cherche à exposer d’une façon scientifique les routines du processus de représentation des médias et la dynamique sous-jacente du pouvoir des représentations télévisuelles de l'Autre. Ce qui précédait cette ère de la postmodernité était l’enfermement du regard médiatique dans les frontières des Nations ou bien des empires coloniaux. La globalisation a introduit l’Autre au cœur même du local. Les représentations stéréotypées et les images de l’Iran dans les journaux télévisés et les émissions des chaînes semblent rétablir les distances spatiales, politiques et socio-culturelles entre les pays et semblent reproduire la supériorité occidentale surtout pour les chaînes américaines. Les chaînes arabes sont axées sur une forte orientation religieuse, raciale et ethnique lors de leur couverture liée à l’Iran. L’information est influencée par le processus de cadrage. Le cadrage fait par des chaînes arabes et occidentales tend alors à refléter et à renforcer l'idéologie dominante du pays d’origine. Les résultats de l'étude soulignent que les nouvelles internationales peuvent être interprétées par une vue combinée, dans laquelle les influences de la propagande sur la couverture médiatique sont interconnectées avec le système des médias et des intérêts nationaux, et paradoxalement par l’ancrage dans le territoire local dépendant de l'idéologie dominante du pays. / This thesis examines the representation of contemporary Iran through the discourse of Arab and Western news channels. The study of news channels is an excellent opportunity to analyze the different representations of the Other by studying how these chains build different representations of identities through a reducing stereotypes and ideological contrast between "us" and "them ". The problem of this research is based on the comparative analysis of the discourse of transnational television channels (BBC, CNN, France 24, as Western channels and Al-Jazeera and Al-Arabiya as Arab channels), and ways of represent among the events of the world, the Other in this case the Iranian identity. Transnational media produce and distribute news, images and symbolic content related issues that viewers would primarily or exclusively learned before (or not) from their national media. The study of the representation of the Other, is a useful model that seeks to explain a scientific way routines process media representation and the underlying dynamics of the power of television representations of the Other. What preceded this era of postmodernity was enclosing the media look into the borders of Nations or colonial empires. Globalization has brought the Other at the heart of local. Stereotypical representations and images of Iran in the news and emissions chains seem restore spatial distances, political and socio-cultural relations between the countries and seem to reproduce Western superiority especially for U.S. channels. Arabic channels are based on a strong religious orientation, racial and ethnic in their coverage related to Iran. The information is influenced by the delineation process. Framing done by Arab and Western chains can be expected to reflect and reinforce the country of origin dominant ideology. The results of the study highlight that international news can be interpreted by a combined view, in which the influences of propaganda on media coverage are interconnected with the system of media and national interests, the territory under the dominant ideology of the country.