Coming dissertations at Uppsala university
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Multi-Tag Backscatter Networks
Link: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-526292
There are billions of Internet of Things (IoT) devices distributed across the globe, and this growing number of interconnected IoT devices demand seamless networking and low-power communication. While many devices are powered with batteries, their limitations such as maintenance and environmental impact call for battery-free alternatives. Small battery-free devices are attractive for sensing as they can use backscatter communication and operate on harvested energy from their surroundings. This dissertation presents a collection of novel techniques for backscatter communication, a method that reduces energy consumption by several orders of magnitude compared to standard low-power radio communication. Backscatter communication provides a direction for implementing widespread networks of battery-free devices that can be used for ubiquitous sensing. However, real-world deployment of backscatter tags encounters challenges due to their constrained power budgets. Adding mechanisms for identification, scheduling, querying and relaying for backscatter should be done carefully offloading power consuming components and delegating tasks whenever possible to an external powerful device.
This dissertation advances the state of the art in two different kinds of backscatter networks: digital backscatter networks and analog backscatter networks. Like conventional RF devices, protocol-based digital backscatter tags encode and communicate binary data in packets, allowing these tags to interoperate with conventional IoT devices using protocols such as IEEE 802.15.4. Applications such as dense networks require tag-to-tag multi-hop communication which introduces challenges as the tags rely on an external signal. For digital backscatter, I present protocol-based multi-hop communication and develop a tool to test large tag-to-tag networks. By contrast, analog backscatter directly communicates the sensor readings by modulating the external signal. As the analog tags lack a packet structure and onboard computation, these tags require new ways to provide key network functionality. For analog backscatter I propose and implement novel techniques for identification, querying and reading high resolution sensor data without significantly increasing the limited power budget on the tag. The contributions outlined in this dissertation enable practical deployment of backscatter tags for sensing and communication applications.
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Cognition and the Machine : Exploring Human-AI Interaction via Predictive Processing
Link: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-526350
Artificial Intelligence is rapidly becoming ubiquitous in both professional settings and everyday life. The prospect is that modern machine-learning technology of Generative AI could help with decision-making, and thinking and reasoning alike. However, our understanding of human cognition, particularly thinking and reasoning, remains limited. This thesis proposes a conceptual framework for understanding the interaction between humans and AI, drawing on the cognitive theory of Predictive Processing. Predictive Processing posits that the brain constructs and employs models to predict and interpret the external world based on past experiences. It operates through a top-down simulation, where sensory feedback primarily highlights discrepancies between predictions and reality. This simulation extends to imagination, reasoning, and thinking, facilitated by offline mental simulations using domain-specific predictive models. Prediction models are structured hierarchically, with lower levels corresponding to concrete sensory input, while higher levels represent abstract, generalized relationships within specific domains. In human communication, abstract predictions play a crucial role, as individuals mentally simulate predictions and convey them through language to peers who then interpret them based on their own domain-models. Chatbots has the potential of being a similar kind of dialogue partner, evaluating your predictions of the world, helping you with interpretations or mutual examinations of problems. For human users it may seem as if it is a shared dialogue, but what AI based on current technology most of all lack, is the worldly connection and adaptive flexibility of the model it is based upon.
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Porous Materials and Their Cellulose-Based Composites : Synthesis, Nanoengineering, and Applications
Link: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-526314
Porous materials, such as porous carbons (PCs), metal-organic frameworks (MOFs), and covalent organic frameworks (COFs), show considerable potential across various fields because of their rich microporous and mesoporous structures and large surface areas, yet they grapple with challenges like environmentally unfriendly fabrication methods and poor processability. In this thesis, we investigated environmentally friendly fabrication methods for porous materials, nanoengineering techniques for processing these materials, and their potential applications.
Cladophora cellulose (CC), a naturally abundant biopolymer, was used to prepare PC via a one-step physical carbonization/activation method without using any corrosive activation agents. The obtained CC-derived PC (CPC) showed a high specific surface area (507.2 m2 g−1) and rich microporous structure. Additionally, we introduced a simple and environmentally friendly method for synthesizing imine-linked COFs at room temperature using water as the solvent. The method involves a key step in which aldehyde monomers are pre-activated by acetic acid, which promotes the aldehyde monomers to dissolve in water, enhancing their reactivity with amine monomers, and ensuring the formation of crystalline COFs. Consequently, we synthesized 16 distinct imine-linked COFs with high crystallinity and specific surface areas.
Furthermore, this thesis focusses on improving the poor processability of these materials caused by the infusible and insoluble nature of their powders. The poor processability of these porous materials makes them difficult to process into desired structures and shapes. Here, we introduce two nanoengineering methods: i) Interweaving porous materials with CC nanofibers (CNFs) to form CNF-porous material aqueous solutions; and ii) Interfacial synthesis of porous materials on the surface of carboxylated CNFs to form CNF@porous materials with nanofiber structures in aqueous solutions. The obtained composite suspensions can be fabricated into freestanding and flexible composite nanopapers via a vacuum filtration and drying process. In addition, they can be processed into freestanding aerogels through a freeze-drying process. Consequently, we have successfully prepared freestanding and flexible CC-CPC nanopapers and CC-CPC aerogels, c-CNT@COF/CNT/CNF nanopapers (c-CNT: carboxylated carbon nanotube), CNF@MOF nanopapers, and CNF@COF nanopapers and demonstrated their potential in various applications, from efficient CO2 capture and organic pollutant removal to advanced energy storage and solar vapor generation.
In summary, we used environmentally friendly methods to synthesize PC and imine-linked COFs, circumventing the need for corrosive chemical agents and toxic organic solvents, respectively. Furthermore, by combining CNFs with porous materials, we successfully created freestanding and flexible nanopapers and aerogels, thereby addressing the issue of poor processability associated with porous materials.