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Background: The socio-technical characteristic of software engineering is acknowledged by many, while the technical side still dominates research. As software engineering is a human-intensive activity, the cognitive side of software engineering needs more exploration when trying to improve its efficiency.Aim: The aim of this study is to increase the understanding of the impact of cognitive load in

Software systems are increasingly depending on data, particularly with the rising use of machine learning, and developers are looking for new sources of data. Open Data Ecosystems (ODE) is an emerging concept for data sharing under public licenses in software ecosystems, similar to Open Source Software (OSS). It has certain similarities to Open Government Data (OGD), where public agencies share da

The notion of Cloud RAN is taking a prominent role in narrative for the next generation wireless infrastructure. It is also seen as a mean to industrial communication systems. In order to provide reliable wireless connectivity for industrial deployments, by conventional means, the cloud infrastructure needs to be reliable and incur little latency, which however, is contradictory to the stochastic

DevOps represent the tight connection between development and operations. To address challenges that arise on the bor- derline between development and operations, we conducted a study in collaboration with a Swedish company responsible for ticket management and sales in public transportation. The aim of our study was to explore and describe the existing DevOps environment, as well as to identify h

Large intelligent surface (LIS) is a technology that extends massive MIMO by considering an even greater number of antennas distributed throughout vast areas. In order to be able to implement this technology, it is crucial to consider decentralized architectures so as to make the whole system scalable. We consider a LIS divided into several LIS panels of smaller size, which can be located far away

While Bayesian Optimization (BO) is a very popular method for optimizing expensive black-box functions, it fails to leverage the experience of domain experts. This causes BO to waste function evaluations on bad design choices (e.g., machine learning hyperparameters) that the expert already knows to work poorly. To address this issue, we introduce Bayesian Optimization with a Prior for the Optimum

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

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

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