The emergence of 6G and beyond networks is set to enable a range of novel services such as personalized highly immersive experiences, holographic teleportation, and human-like intelligent robotic applications. Such applications require a set of stringent sensing, communication, control, and intelligence requirements that mandate a leap in the design, analysis, and optimization of today's wireless networks. First, from a wireless communication standpoint, future 6G applications necessitate extreme requirements in terms of bidirectional data rates, near-zero latency, synchronization, and jitter. Concurrently, such services also need a sensing functionality to track, localize, and sense their environment. Owing to its abundant bandwidth, one may naturally resort to terahertz (THz) frequency bands (0.1 − 10 THz) so as to provide significant wireless capacity gains and enable high-resolution environment sensing. Nonetheless, operating a wireless system at the THz band is constrained by a very uncertain channel which brings forth novel challenges. In essence, these channel limitations lead to unreliable intermittent links ergo the short communication range and the high susceptibility to blockage and molecular absorption. Second, given that emerging wireless services are "intelligence-centric", today's communication links must be transformed from a mere bit-pipe into a brain-like reasoning system. Towards this end, one can exploit the concept of semantic communications, a revolutionary paradigm that promises to transform radio nodes into intelligent agents that can extract the underlying meaning (semantics) or significance in a data stream. However, to date, there has been a lack in holistic, fundamental, and scalable frameworks for building next-generation semantic communication networks based on rigorous and well-defined technical foundations. Henceforth, to panoramically develop the fully-fledged theoretical foundations of future 6G applications and guarantee affluent corresponding experiences, this dissertation thoroughly investigates two thrusts. The first thrust focuses on developing the analytical foundations of THz systems with a focus on network design, performance analysis, and system optimization. First, a novel and holistic vision that articulates the unique role of THz in 6G systems is proposed. This vision exposes the solutions and milestones necessary to unleash THz's true potential in next-generation wireless systems. Then, given that extended reality (XR) will be a staple application of 6G systems, a novel risk and tail-based performance analysis is proposed to evaluate the instantaneous performance of THz bands for specific ultimate virtual reality (VR) services. Here, the results showcase that abundant bandwidth and the molecular absorption effect have only a secondary effect on the reliability compared to the availability of line-of-sight. More importantly, the results highlight that average metrics overlook extreme events and tend to provide false positive performance guarantees. To address the identified challenges of THz systems, a risk-oriented learning-based design that exploits reconfigurable intelligent surfaces (RISs) is proposed so as to optimize the instantaneous reliability. Furthermore, the analytical results are extended to investigate the uplink freshness of augmented reality (AR) services. Here, a novel ruin-based performance is conducted that scrutinizes the peak age of information (PAoI) during extreme events. Next, a novel joint sensing, communication, and artificial intelligence (AI) framework is developed to turn every THz communication link failure into a sensing opportunity, with application to digital world experiences with XR. This framework enables the use of the same waveform, spectrum, and hardware for both sensing and communication functionalities. Furthermore, this sensing input is intelligently processed via a novel joint imputation and forecasting system that is designed via non-autoregressive and transformed-based generative AI tools. This joint system enables fine-graining the sensing input to smaller time slots, predicting missing values, and fore- casting sensing and environmental information about future XR user behavior. Then, a novel joint quality of personal experience (QoPE)-centric and sensing-driven optimization is formulated and solved via deep hysteretic multi-agent reinforcement learning tools. Essentially, this dissertation establishes a solid foundation for the future deployment of THz frequencies in next-generation wireless networks through the proposal of a comprehensive set of principles that draw on the theories of tail and risk, joint sensing and communication designs, and novel AI frameworks. By adopting a multi-faceted approach, this work contributes significantly to the understanding and practical implementation of THz technology, paving the way for its integration into a wide range of applications that demand high reliability, resilience, and an immersive user experience. In the second thrust of this dissertation, the very first theoretical foundations of semantic communication and AI-native wireless networks are developed. In particular, a rigorous and holistic vision of an end-to-end semantic communication network that is founded on novel concepts from AI, causal reasoning, transfer learning, and minimum description length theory is proposed. Within this framework, the dissertation demonstrates that moving from data-driven intelligence towards reasoning-driven intelligence requires identifying association (statistical) and causal logic. Additionally, to evaluate the performance of semantic communication networks, novel key performance indicators metrics that include new "reasoning capacity" measures that could go beyond Shannon's bound to capture the imminent convergence of computing and communication resources. Then, a novel contrastive learning framework is proposed so as to disentangle learnable and memoizable patterns in source data and make the data "semantic-ready". Through the development of a rigorous end-to-end semantic communication network founded on novel concepts from communication theory and AI, along with the proposal of novel performance metrics, this dissertation lays a solid foundation for the advancement of reasoning-driven intelligence in the field of wireless communication and paves the way for a wide range of future applications. Ultimately, the various analytical foundations presented in this dissertation will provide key guidelines that guarantee seamless experiences in future 6G applications, enable a successful deployment of THz wireless systems as a versatile band for integrated communication and sensing, and build future AI-native semantic communication networks. / Doctor of Philosophy / To date, the evolution of wireless networks has been driven by a chase for data rates, i.e., higher download or upload speeds. Nonetheless, future 6G applications (the generation succeeding today's fifth generation 5G), such as the metaverse, extended reality (encompassing augmented, mixed, and virtual reality), and fully autonomous robots and vehicles, necessitate a major leap in the design and functionality of a wireless network. Firstly, wireless networks must be able to perform functionalities that go beyond communications, encompassing control, sensing, and localization. Such functionalities enable a wide range of tasks such as remotely controlling a device, or tracking a mobile equipment with high precision. Secondly, wireless networks must be able to deliver experiences (e.g. provide the user a sense of immersion in a virtual world), in contrast to a mere service. To do so, extreme requirements in terms of data rate, latency, reliability, and sensing resolution must be met. Thirdly, intelligence must be native to wireless networks, which means that they must possess cognitive and reasoning abilities that enable them to think, act, and communicate like human beings. In this dissertation, the three aforementioned key enablers of future 6G experiences are examined. Essentially, one of the focuses of this dissertation is the design, analysis, and optimization of wireless networks operating at the so-called terahertz (THz) frequency band. The THz band is a quasi-optical (close to the visible light spectrum) frequency band that can enable wireless networks to potentially provide the extreme speeds needed (in terms of communications) and the high-resolution sensing. However, such frequency bands tend to be very susceptible to obstacles, humidity, and many other weather conditions. Therefore, this dissertation investigates the potential of such bands in meeting the demands of future 6G applications. Furthermore, novel solutions, enablers, and optimization frameworks are investigated to facilitate the successful deployment of this frequency band. To provide wireless networks with their reasoning ability, this dissertation comprehensively investigates the concept of semantic communications. In contrast to today's traditional communication frameworks that convert our data to binary bits (ones and zeros), semantic communication's goal is to enable networks to communicate meaning (semantics). To successfully engineer and deploy such networks, this dissertation proposes a novel suite of communication theoretic tools and key performance indicators. Subsequently, this dissertation proposes and analyzes a set of novel artificial intelligence (AI) tools that enable wireless networks to be equipped with the aforementioned cognitive and reasoning abilities. The outcomes of this dissertation have the potential to transform the way we interact with technology by catalyzing the deployment of holographic societies, revolutionizing the healthcare via remote augmented surgery, and facilitating the deployment of autonomous vehicles for a safer and more efficient transportation system. Additionally, the advancements in wireless networks and artificial intelligence proposed in this dissertation could also have a significant impact on various other industries, such as manufacturing, education, and defense, by enabling more efficient and intelligent systems. Ultimately, the societal impact of this research is far-reaching and could contribute to creating a more connected and advanced world.

Convergence of novel technologies with smart cities is evolving now. Specifically Artificial Intelligence (AI) by means of sensors and cameras is used to make sense of city data for multiple purposes. Recent COVID-19 pandemic has shown that cities worldwide try to use AI technology for assisting in decision making and see the impact of certain socio-economic measures of city authorities in connection to pandemic. This thesis is a Qualitative study of a Helsingborg city AI application project for temperature detection by means of thermal sensors located on Helsingborg, Sweden central station for anonymous measuring of people temperature. Specifically, this study aims to understand how aware are project team members of the ethical considerations in connection to AI application for health detection in smart city of Helsingborg, related decision making and how they perceive it.   Data was gathered by means of Qualitative interviews which were hold online. This study has shown that convergence of AI and a smart city raises important ethical questions and perceived by some respondents as possibility to change the attitude towards privacy if connected to crisis events such as pandemics. The case study shows that in the researched AI project project team members considered ethics in connection to AI in terms of technology, legal issues, open collaboration and open data sharing with citizens. At the same time this project is willing to challenge existing norms and drive forward the development of ethics in connection to AI usage in a smart city.

International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California / The science payload for NASA's Extreme Ultraviolet Explorer (EUVE) satellite is controlled from the EUVE Science Operations Center (ESOC) at the Center for EUV Astrophysics (CEA), University of California, Berkeley (UCB). The ESOC is in the process of a transition from a single staffed shift to an autonomous, zero-shift, "lights out" science payload operations scenario (a.k.a., 1:0). The purpose of the 1:0 transition is to automate all of the remaining routine, daily, controller telemetry monitoring and associated "shift" work. Building on the ESOC's recent success moving from three-shift to one-shift operations (completed in Feb 1995), the 1:0 transition will further reduce payload operations costs and will be a "proof of concept" for future missions; it is also in line with NASA's goals of "cheaper, faster, better" operations and with its desire to out-source missions like EUVE to academe and industry. This paper describes the 1:0 transition for the EUVE science payload: the purpose, goals, and benefits; the relevant science payload instrument health and safety considerations; the requirements for, and implementation of, the multi-phased approach; a cost/benefit analysis; and the various lessons learned along the way.

In the literature, various Mobile Ad hoc NETwork (MANET) routing protocols proposed. Each performs the best under specific context conditions, for example under high mobility or less volatile topologies. In existing MANET, the degradation in the routing protocol performance is always associated with changes in the network context. To date, no MANET routing protocol is able to produce optimal performance under all possible conditions. The core aim of this thesis is to solve the routing problem in mobile Ad hoc networks by introducing an optimum system that is in charge of the selection of the running routing protocol at all times, the system proposed in this thesis aims to address the degradation mentioned above. This optimisation system is a novel approach that can cope with the network performance’s degradation problem by switching to other routing protocol. The optimisation system proposed for MANET in this thesis adaptively selects the best routing protocol using an Artificial Intelligence mechanism according to the network context. In this thesis, MANET modelling helps in understanding the network performance through different contexts, as well as the models’ support to the optimisation system. Therefore, one of the main contributions of this thesis is the utilisation and comparison of various modelling techniques to create representative MANET performance models. Moreover, the proposed system uses an optimisation method to select the optimal communication routing protocol for the network context. Therefore, to build the proposed system, different optimisation techniques were utilised and compared to identify the best optimisation technique for the MANET intelligent system, which is also an important contribution of this thesis. The parameters selected to describe the network context were the network size and average mobility. The proposed system then functions by varying the routing mechanism with the time to keep the network performance at the best level. The selected protocol has been shown to produce a combination of: higher throughput, lower delay, fewer retransmission attempts, less data drop, and lower load, and was thus chosen on this basis. Validation test results indicate that the identified protocol can achieve both a better network performance quality than other routing protocols and a minimum cost function of 4.4%. The Ad hoc On Demand Distance Vector (AODV) protocol comes in second with a cost minimisation function of 27.5%, and the Optimised Link State Routing (OLSR) algorithm comes in third with a cost minimisation function of 29.8%. Finally, The Dynamic Source Routing (DSR) algorithm comes in last with a cost minimisation function of 38.3%.

Negation is a part of language that humans engage in pretty much from the onset of speech. Negation appears at first glance to be harder to grasp than object or action labels, yet this thesis explores how this family of ‘concepts’ could be acquired in a meaningful way by a humanoid robot based solely on the unconstrained dialogue with a human conversation partner. The earliest forms of negation appear to be linked to the affective or motivational state of the speaker. Therefore we developed a behavioural architecture which contains a motivational system. This motivational system feeds its state simultaneously to other subsystems for the purpose of symbol-grounding but also leads to the expression of the robot’s motivational state via a facial display of emotions and motivationally congruent body behaviours. In order to achieve the grounding of negative words we will examine two different mechanisms which provide an alternative to the established grounding via ostension with or without joint attention. Two large experiments were conducted to test these two mechanisms. One of these mechanisms is so called negative intent interpretation, the other one is a combination of physical and linguistic prohibition. Both mechanisms have been described in the literature on early child language development but have never been used in human-robot-interaction for the purpose of symbol grounding. As we will show, both mechanisms may operate simultaneously and we can exclude none of them as potential ontogenetic origin of negation.

The development of new technology plays a major role in today's society and several different industries. While some technologies have more or less an impact upon the whole working sector, one of the more recent and controversial technologies is Artificial Intelligence (AI). In recent years, this technology has evolved continuously and is spreading across several different industries. As it is clear that AI is reshaping the workplace, it is relevant to examine how and to what extent it is affecting the digital design profession. Purpose The purpose of this study is to gain insight into the current state of adoption of AI within digital design, including graphic design and web design. Furthermore, to explore the effects of AI on the nature of the profession, from the perspectives of professionals in the industry. While focusing on the creative process and the development of digital products, it investigates how the industry is experiencing the effects of AI in daily tasks and workflows. Furthermore, it examines if the implementation of AI has lead to the development of new work processes, or if traditional tasks remain but are carried out with AI tools as assistance. Method The research method is qualitative. Through literature reviews and by carrying out interviews with relevant designers currently working in the industry, the appropriate data is collected and analyzed. The interviews focus on understanding the participant’s perspective on the topic, their experiences of AI and what effect it has on their work. Through the interviews, the study identifies to what extent AI is used in creative processes, and sheds light on the general feelings towards AI, including expectations and concerns. Conclusions The findings show that the overall awareness surrounding AI is rather divided. AI is already implemented in various design processes and software, whether the designer is aware of it or not. It can thus be concluded that AI has affected the nature of the digital design profession. However, the effects vary depending on the specific role and the related tasks. Most are interested in learning more about it but natural skepticism and lack of knowledge about the technology remain an obstacle for implementing more AI in companies.

Problemformulering: På vilka sätt hanteras och påverkas en organisations intressenter av implementeringen av AI-system?  Syfte: Syftet med denna studie är att utifrån en organisations intressenters uppfattning kartlägga på vilka sätt intressenterna hanteras och påverkas av implementering av AI-system. Metod: Studien har utgångspunkt i kvalitativ forskningsstrategi med en deduktiv ansats. Den genomförda studien är en fallstudie, där Länsförsäkringar har studerats. Det empiriska materialet är insamlat genom tio semistrukturerade intervjuer.  Slutsats: Med vår studie kan vi se att implementeringen av Länsförsäkringars chatbot påverkar de anställda. Den potentiella utvecklingen av AI däremot tenderar att påverka flera intressentgrupper. Vidare kan vi se i studiens resultat svårigheter att identifiera organisationens intressenter samt svårigheter att prioritera och värdera intressenter, vilket överlag överensstämmer med den framtagna teorin gällande intressentmodellen. Vi kan därför dra slutsatsen att Länsförsäkringar bör identifiera intressenter och dess påverkan av utvecklingen av AI för veta hur intressenter ska hanteras. / Research question: In what ways is an organization's stakeholders handled and affected by the implementation of AI-systems? Purpose: The purpose of this study is to map, based on the perception of an organization's stakeholders, in what ways stakeholders are handled and affected by the implementation of AI-systems.  Method: The study is based on a qualitative research strategy with a deductive approach. The completed study is a case study, where Länsförsäkringar has been studied. The empirical material is collected through ten semi-structured interviews. Conclusion: With our study, can we see that the implementation of Länsförsäkringar's chatbot affects the employees. The potential development of AI, on the other hand, tends to affect several stakeholder groups. Furthermore, we can see in the results of the study difficulties in identifying the organization's stakeholders as well as difficulties in prioritizing and evaluating stakeholders, which is generally in line with the developed theory regarding the stakeholder model. We can therefore conclude that Länsförsäkringar should identify stakeholders and their impact on the development of AI in order to know how stakeholders should be handled.

Volvo Construction Equipment (CE) are facing the challenge of vibrations in their wheel loaders that generate disruptive noise and impact the driver's experience. These vibrations have been linked to the contact surface between the crown wheel and pinion gear in the vehicles drive-axles. In response, this thesis was created to develop an Artificial Intelligence (AI) system, which can identify outliers in a dataset containing images of the contact surfaces between the crown wheel and pinion gear. However, the dataset exhibits variations in image sharpness, exposure and centering of the crown wheel, which hinders its suitability for machine vision tasks. The varying quality of the images poses the challenge of accurately extracting relevant features required to analyze the images through machine learning algorithms. This research aims to address these challenges by investigating two research questions. (1) what method can be employed to extract the Region of Interest (ROI) in images of crown wheels? And (2) which method is suitable for detection of outliers within the ROI? To find answers to these questions, a literature study was conducted leading up to the implementation of two architectures: You Only Look Once (YOLO) v5 Oriented Bounding Boxes (OBB) and a Hybrid Autoencoder (BAE). Visual evaluation of the results showed promising outcomes particularly for the extraction of ROIs, where the relevant areas were accurately identified despite the large variations in image quality. The BAE successfully identified outliers that deviated from the majority, however, the results of the model were influenced by the differences in image quality, rather than the geometrical shape of the contact patterns. These findings suggest that using the same feature extraction method on a higher-quality dataset or employing a more robust segmentation method, could increase the likelihood of identifying the contact patterns responsible for the vibrations.

OpenAI recently introduced ChatGPT, a chatbot powered by the GPT-3 family of deep learninglanguage models (LLMs). With the aid of machine learning techniques, ChatGPT has been fine-tuned to improve its capacity to respond to a diverse range of queries, and it has been describedas one of the most advanced machine learning technologies currently available. While AI israpidly advancing and being integrated into society, the comprehension of people's attitudestowards these novel technologies is not progressing at the same rate. Prior research studies andliterature have highlighted the importance of assessing user sentiment towards newly launchedAI services. Evaluating the expressed attitudes towards the recently introduced ChatGPT canprovide valuable insights into the product's potential, as well as highlighting any challenges orproblems encountered by users. This paper presents a study that examines the attitudesexpressed on the social media platforms Twitter and Reddit. For data collection, this studyutilized social media data in the form of free text obtained through the APIs of Twitter andReddit. A qualitative analysis is carried out with the aid of a sentiment analysis tool to assesslanguage and categorize text data based on their expressed attitudes. This data is presented in aquantitative summary. The findings indicate a favorable disposition among users towardsChatGPT in general but that there are areas of concern where users have conveyed sentimentsof feeling intimidated or having a negative resonance with ChatGPT's capabilities andachievements. This study contributes to the existing understanding of user attitudes towardsChatGPT and highlights the necessity for further research to delve deeper into this area.

Background: To remain competitive in Industry 4.0, B2B suppliers must develop new and increasingly advanced digital services by incorporating AI. However, although being of interest to practitioners, academic research on successful AI implementation in B2B functional domains is lacking. Consequently, academics have stressed the importance of developing comprehensive frameworks within B2B marketing to accelerate the creation of strategic roadmaps for AI implementation. Purpose: The purpose of this study is to explore how AI can be utilized to enhance digital servitization, according to the perspectives of one supplier and several of its customers. The aim is to provide a framework that can assist practitioners in implementing value-adding AI services. Method: To fulfill the exploratory purpose of this study, a qualitative single-case research design was applied. The empirical data was collected through twelve in-depth semi-structured interviews.  Utilizing an inductive approach, the data has been analyzed and interpreted through a thematic analysis. Conclusion: Incorporating a complete S-D logic mindset by implementing the AI solution based on all five axioms was found to enhance digital servitization. A model displaying various servitization activities connected to these axioms arose, emphasizing their collective impact. Additionally, suppliers may enhance digital servitization through the implementation of AI by engaging in three transformational mechanisms, namely customization, automation, and agile co-development.