Event processing in web of things

The incoming digital revolution has the potential to drastically improve our productivity, reduce operational costs and improve the quality of the products. However, the realization of these promises requires the convergence of technologies — from edge computing to cloud, artificial intelligence, and the Internet of Things — blurring the lines between the physical and digital worlds. Although these technologies evolved independently over time, they are increasingly becoming intertwined. Their convergence will create an unprecedented level of automation, achieved via massive machine-to-machine interactions whose cornerstone are event processing tasks. This thesis explores the intersection of these technologies by making an in-depth analysis of their role in the life-cycle of event processing tasks, including their creation, placement and execution. First, it surveys currently existing Web standards, Internet drafts, and design patterns that are used in the creation of cloud-based event processing. Then, it investigates the reasons for event processing to start shifting towards the edge, alongside with the standards that are necessary for a smooth transition to occur. Finally, this work proposes the use of deep reinforcement learning methods for the placement and distribution of event processing tasks at the edge. Obtained results show that the proposed neural-based event placement method is capable of obtaining (near) optimal solutions in several scenarios and provide hints about future research directions.A nova revolução digital promete melhorar drasticamente a nossa produtividade, reduzir os custos operacionais e melhorar a qualidade dos produtos. A concretizac¸ ˜ao dessas promessas requer a convergˆencia de tecnologias – desde edge computing à cloud, inteligência artificial e Internet das coisas (IoT) – atenuando a linha que separa o mundo físico do digital. Embora as quatro tecnologias mencionadas tenham evoluído de forma independente ao longo do tempo, atualmente elas estão cada vez mais interligadas. A convergência destas tecnologias irá criar um nível de automatização sem precedentes.The research published in this work was supported by the Portuguese Foundation for Science and Technology (FCT) through CEOT (Center for Electronic, Optoelectronic and Telecommunications) funding (UID/MULTI/00631/2020) and by FCT Ph.D grant to Andriy Mazayev (SFRH/BD/138836/2018)

A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future

A High Altitude Platform Station (HAPS) is a network node that operates in the stratosphere at an of altitude around 20 km and is instrumental for providing communication services. Precipitated by technological innovations in the areas of autonomous avionics, array antennas, solar panel efficiency levels, and battery energy densities, and fueled by flourishing industry ecosystems, the HAPS has emerged as an indispensable component of next-generations of wireless networks. In this article, we provide a vision and framework for the HAPS networks of the future supported by a comprehensive and state-of-the-art literature review. We highlight the unrealized potential of HAPS systems and elaborate on their unique ability to serve metropolitan areas. The latest advancements and promising technologies in the HAPS energy and payload systems are discussed. The integration of the emerging Reconfigurable Smart Surface (RSS) technology in the communications payload of HAPS systems for providing a cost-effective deployment is proposed. A detailed overview of the radio resource management in HAPS systems is presented along with synergistic physical layer techniques, including Faster-Than-Nyquist (FTN) signaling. Numerous aspects of handoff management in HAPS systems are described. The notable contributions of Artificial Intelligence (AI) in HAPS, including machine learning in the design, topology management, handoff, and resource allocation aspects are emphasized. The extensive overview of the literature we provide is crucial for substantiating our vision that depicts the expected deployment opportunities and challenges in the next 10 years (next-generation networks), as well as in the subsequent 10 years (next-next-generation networks).Comment: To appear in IEEE Communications Surveys & Tutorial

System level power integrity transient analysis using a physics-based approach

With decreasing supply voltage level and massive demanding current on system chipset, power integrity design becomes more and more critical for system stability. The ultimate goal of well-designed power delivery network (PDN) is to deliver desired voltage level from the source to destination, in other words, to minimize voltage noise delivered to digital devices. The thesis is composed of three parts. The first part focuses on-die level power models including simplified chip power model (CPM) for system level analysis and the worst scenario current profile. The second part of this work introduces the physics-based equivalent circuit model to simplify the passive PDN model to RLC circuit netlist, to be compatible with any spice simulators and tremendously boost simulation speed. Then a novel system/chip level end-to-end transient model is proposed, including the die model and passive PDN model discussed in previous two chapters as well as a SIMPLIS based small signal VRM model. In the last part of the thesis, how to model voltage regulator module (VRM) is explicitly discussed. Different linear approximated VRM modeling approaches have been compared with the SIMPLIS small signal VRM model in both frequency domain and time domain. The comparison provides PI engineers a guideline to choose specific VRM model under specific circumstances. Finally yet importantly, a PDN optimization example was given. Other than previous PDN optimization approaches, a novel hybrid target impedance concept was proposed in this thesis, in order to improve system level PDN optimization process --Abstract, page iv

From Capture to Display: A Survey on Volumetric Video

Volumetric video, which offers immersive viewing experiences, is gaining increasing prominence. With its six degrees of freedom, it provides viewers with greater immersion and interactivity compared to traditional videos. Despite their potential, volumetric video services poses significant challenges. This survey conducts a comprehensive review of the existing literature on volumetric video. We firstly provide a general framework of volumetric video services, followed by a discussion on prerequisites for volumetric video, encompassing representations, open datasets, and quality assessment metrics. Then we delve into the current methodologies for each stage of the volumetric video service pipeline, detailing capturing, compression, transmission, rendering, and display techniques. Lastly, we explore various applications enabled by this pioneering technology and we present an array of research challenges and opportunities in the domain of volumetric video services. This survey aspires to provide a holistic understanding of this burgeoning field and shed light on potential future research trajectories, aiming to bring the vision of volumetric video to fruition.Comment: Submitte

Rhythmic Action Synchronizes Memory Replay During Reinforcement Learning

Our cognitive abilities - learning from the past, sensing the current environment, planning into the future, executing an action, and infusing value into an experience - all rely on precisely timed and widespread electrical communications across neural networks. The brain’s hippocampal formation receives multimodal input, forges episodic associations, and predicts future state. Oscillating electrical bursts originating from the hippocampus, termed ‘sharp-wave ripples’ (SWR), often contain patterns of previously expressed neural spike sequences, and are necessary for certain forms of learning and memory. The discharge of SWR-replay resonates in remote parts of the brain and displays specific characteristics depending on a subject’s state of awareness and sensory context. In the sleep state, when motoric repertoire is limited, waves of breathing synchronize neural activity in several regions of the brain, including SWRs of the hippocampus. During active sensation of the awake state, cyclic licking dynamically entrains taste-reward networks in subcortical and cortical areas throughout learning. However, the neural correlates linking oromotor movements in the active learning state to the memory system of the hippocampal formation have not yet been established. Given the recurrence of SWR-replay during rhythmic ingestion of reinforcement learning and the hierarchical coupling of orofacial behaviors, we hypothesized that repeated licking could provide the oscillatory framework to synchronize memory reactivation during active learning. We approach this question with new technology development to track licking events at a reward port (P-event) during behavior on a spatial alternation task. Additionally, we developed a modular brain implant to simultaneously record from hippocampal area CA1 and medial entorhinal cortex (MEC) - interconnected brain regions that are crucial to episodic memory processing. Along with the co-modulation of individual neurons by licking and SWRs, we provide the first evidence that SWRs detected in dorsal CA1 synchronize with the phase of P-event cycle during learning. Furthermore, we confirmed that SWRs occurring during licking bouts contain neural reactivation of active navigation and trigger enhanced ripple-frequency power in downstream MEC. These results connect movement with memory and may assist in addressing abnormal ingestion behaviors that negatively affect mental or physical healt