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Security for outsourced control applications can be provided if the physical plant is enabled with a mechanism to verify the control signal received from the cloud. Recent developments in modern cryptography claim the applicability of verifiable computation techniques. Such techniques allow a client to check the correctness of a remote execution. This article delivers a proof of concept for applic
When it comes to innovation, economic growth, affluence and international attractiveness, there are currently few places in the world that can compare with the San Francisco Bay Area. However, new megatrends such as Sustainability, can challenge its attractiveness. Scholars talk about the “Nordic approach”.In the geographical area of Southern Scandinavia, ‘The Strait Area’, the focus is on Sustai
Bayesian Optimization has emerged as a crucial technique for optimizing costly, black-box functions where each evaluation comes at a high cost, such as in scientific experiments, and machine learning hyperparameter optimization. By combining probabilistic modeling with sequential decision-making, Bayesian Optimization achieves efficient exploration, guiding the search toward optimal parameters wit
To avoid over-treatment of prostate cancer patients following screening for elevated prostate-specific antigen (PSA) levels, keeping patients on active surveillance has been suggested as an alternative to radical treatment. This means reoccurring visits for patients with low-grade cancer to monitor progression. The Prostate Cancer Research International Active Surveillance (PRIAS) study was initia
This thesis explores the challenges and advancements in high-dimensional Bayesian optimization (HDBO), focusing on understanding, quantifying, and improving optimization techniques in high-dimensional spaces.Bayesian optimization (BO) is a powerful method for optimizing expensive black-box functions, but its effectiveness diminishes as the dimensionality of the search space increases due to the cu
Massive multiple-input multiple-out (MIMO) technology and mm wave are important technologies in 5G/6G to enhance the spectrum efficiency and system capacity. However, it has a lot of challenges in mm wave range, such as high hardware cost and power consumption in such systems. Hybrid beamforming is an interesting solution with less number of RF transvers than antennas. In this paper, we will prese
In this study, the authors develop knowledge and insights on how the perception of interestingness influences the structure and focus of conversations in entrepreneurial education (EE) research. In particular, the authors elaborate on what is perceived as interesting among different subgroups of EE researchers, and not least, how EE researchers can identify and engage in scholarly conversation wit
We demonstrate how to compute radome reference cases for benchmarkingradome codes. Radomes are electrically large structures, and to facilitate thecomputations a rotationally symmetric structure is assumed. We show howto implement this in the commercial software Comsol Multiphysics, and howto extract the relevant data for comparison. Two example geometries areanalyzed: a spherical shell radome, an
As 5G is entering maturity, the research interest has shifted towards 6G, and specially the new use cases that the future telecommunication infrastructure needs to support. These new use cases encompass much higher requirements, specifically: higher communication data-rates, larger number of users, higher accuracy in localization, possibility to wirelessly charge devices, among others.The radio ac
The Internet of Things (IoT) paradigm, has opened up the possibility of using the ubiquity of small devices to route information without the necessity of being connected to a Wide Area Network (WAN). Use cases of IoT devices sending updates that are routed and delivered by other IoT devices have been proposed in the literature. In this paper we focus on receivers only interested in the freshest up
Cell-Free networking is one of the prime candidatesfor 6G networks. Despite being capable of providing the 6Gneeds, practical limitations and considerations are often neglectedin current research. In this work, we introduce the conceptof federations to dynamically scale and select the best set ofresources, e.g., antennas, computing and data resources, to servea given application. Next to communica
Neural implicit representations have recently shown encouraging results in various domains, including promising progress in simultaneous localization and mapping (SLAM). Nevertheless, existing methods produce over- smoothed scene reconstructions and have difficulty scaling up to large scenes. These limitations are mainly due to their simple fully-connected network architecture that does not incorp
Low-dimensional parametric models are the de-facto standard in computer vision for intrinsic camera calibration. These models explicitly describe the mapping between incoming viewing rays and image pixels. In this paper, we explore an alternative approach which implicitly models the lens distortion. The main idea is to replace the parametric model with a regularization term that ensures the latent
Big Data analytics and Artificial Intelligence (AI) technologies have become the focus of recent research due to the large amount of data. Dimensionality reduction techniques are recognized as an important step in these analyses. The multidimensional nature of Quality of Experience (QoE) is based on a set of Influence Factors (IFs) whose dimensionality is preferable to be higher due to better QoE
Bayesian optimization (BO) has become an established framework and popular tool for hyperparameter optimization (HPO) of machine learning (ML) algorithms. While known for its sample-efficiency, vanilla BO can not utilize readily available prior beliefs the practitioner has on the potential location of the optimum. Thus, BO disregards a valuable source of information, reducing its appeal to ML prac
While Weighted Lasso sparse regression has appealing statistical guarantees that would entail a major real-world impact in finance, genomics, and brain imaging applications, it is typically scarcely adopted due to its complex high-dimensional space composed by thousands of hyperparameters. On the other hand, the latest progress with high-dimensional hyperparameter optimization (HD-HPO) methods for
