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This paper considers fusion of dimension-reduced estimates in a decentralized sensor network. The benefits of a decentralized sensor network include modularity, robustness and flexibility. Moreover, since preprocessed data is exchanged between the agents it allows for reduced communication. Nevertheless, in certain applications the communication load is required to be reduced even further. One way to decrease the communication load is to exchange dimension-reduced estimates instead of full estimates. Previous work on this topic assumes global availability of covariance matrices, an assumption which is not realistic in decentralized applications. Hence, in this paper we consider the problem of deriving dimension-reduced estimates using only local information. The proposed solution is based on an estimate of the information common to the network. This common information estimate is computed locally at each agent by fusion of all information that is either received or transmitted by that agent. It is shown how the common information estimate is utilized for fusion of dimension-reduced estimates using two well-known fusion methods: the Kalman fuser which is optimal under the assumption of uncorrelated estimates, and covariance intersection. One main theoretical result is that the common information estimate allows for a decorrelation procedure such that uncorrelated estimates can be maintained. This property is crucial to be able to use the Kalman fuser without double counting of information. A numerical comparison suggests that the performance degradation of using the common information estimate, compared to having local access to the actual covariance matrices computed by other agents, is relatively small., Funding: Industry Excellence Center LINK-SIC - Swedish Governmental Agency for Innovation Systems (VINNOVA); Saab AB

This paper considers fusion of dimension-reduced estimates in a decentralized sensor network. The benefits of a decentralized sensor network include modularity, robustness and flexibility. Moreover, since preprocessed data is exchanged between the agents it allows for reduced communication. Nevertheless, in certain applications the communication load is required to be reduced even further. One way to decrease the communication load is to exchange dimension-reduced estimates instead of full estimates. Previous work on this topic assumes global availability of covariance matrices, an assumption which is not realistic in decentralized applications. Hence, in this paper we consider the problem of deriving dimension-reduced estimates using only local information. The proposed solution is based on an estimate of the information common to the network. This common information estimate is computed locally at each agent by fusion of all information that is either received or transmitted by that agent. It is shown how the common information estimate is utilized for fusion of dimension-reduced estimates using two well-known fusion methods: the Kalman fuser which is optimal under the assumption of uncorrelated estimates, and covariance intersection. One main theoretical result is that the common information estimate allows for a decorrelation procedure such that uncorrelated estimates can be maintained. This property is crucial to be able to use the Kalman fuser without double counting of information. A numerical comparison suggests that the performance degradation of using the common information estimate, compared to having local access to the actual covariance matrices computed by other agents, is relatively small., Funding: Industry Excellence Center LINK-SIC - Swedish Governmental Agency for Innovation Systems (VINNOVA); Saab AB

This paper considers fusion of dimension-reduced estimates in a decentralized sensor network. The benefits of a decentralized sensor network include modularity, robustness and flexibility. Moreover, since preprocessed data is exchanged between the agents it allows for reduced communication. Nevertheless, in certain applications the communication load is required to be reduced even further. One way to decrease the communication load is to exchange dimension-reduced estimates instead of full estimates. Previous work on this topic assumes global availability of covariance matrices, an assumption which is not realistic in decentralized applications. Hence, in this paper we consider the problem of deriving dimension-reduced estimates using only local information. The proposed solution is based on an estimate of the information common to the network. This common information estimate is computed locally at each agent by fusion of all information that is either received or transmitted by that agent. It is shown how the common information estimate is utilized for fusion of dimension-reduced estimates using two well-known fusion methods: the Kalman fuser which is optimal under the assumption of uncorrelated estimates, and covariance intersection. One main theoretical result is that the common information estimate allows for a decorrelation procedure such that uncorrelated estimates can be maintained. This property is crucial to be able to use the Kalman fuser without double counting of information. A numerical comparison suggests that the performance degradation of using the common information estimate, compared to having local access to the actual covariance matrices computed by other agents, is relatively small., Funding: Industry Excellence Center LINK-SIC - Swedish Governmental Agency for Innovation Systems (VINNOVA); Saab AB

This paper considers fusion of dimension-reduced estimates in a decentralized sensor network. The benefits of a decentralized sensor network include modularity, robustness and flexibility. Moreover, since preprocessed data is exchanged between the agents it allows for reduced communication. Nevertheless, in certain applications the communication load is required to be reduced even further. One way to decrease the communication load is to exchange dimension-reduced estimates instead of full estimates. Previous work on this topic assumes global availability of covariance matrices, an assumption which is not realistic in decentralized applications. Hence, in this paper we consider the problem of deriving dimension-reduced estimates using only local information. The proposed solution is based on an estimate of the information common to the network. This common information estimate is computed locally at each agent by fusion of all information that is either received or transmitted by that agent. It is shown how the common information estimate is utilized for fusion of dimension-reduced estimates using two well-known fusion methods: the Kalman fuser which is optimal under the assumption of uncorrelated estimates, and covariance intersection. One main theoretical result is that the common information estimate allows for a decorrelation procedure such that uncorrelated estimates can be maintained. This property is crucial to be able to use the Kalman fuser without double counting of information. A numerical comparison suggests that the performance degradation of using the common information estimate, compared to having local access to the actual covariance matrices computed by other agents, is relatively small., Funding: Industry Excellence Center LINK-SIC - Swedish Governmental Agency for Innovation Systems (VINNOVA); Saab AB

This paper introduces realizations of a reconfigurable finite-impulse-response (FIR) filter for simultaneous equalization and lowpass filtering. The proposed structures employ properties of both a variable bandwidth (VBW) filter and a variable equalizer (VE) with an adjustable coefficient using the Farrow structure, therefore they consist of weighted combinations of fixed subfilters. Design procedures using minimax optimization technique are provided. The paper includes a design example and complexity comparisons between the proposed structures of the reconfigurable lowpass equalizer (RLPE) and a common approach of using a regular FIR equalization filter requiring online redesign.

Unmanned Aerial Vehicles (UAVs) communications attracted significant research interest in recent years as a promising tool for future wireless communication systems (beyond fifth-generation (B5G)/sixth-generation (6G)) due to their numerous capabilities and broad applicability. In this paper we consider a UAV-enabled wireless communication system consisting of two ground base stations. Owing to distance, direct communication is not feasible for the ground stations. For this reason a UAV is used to travel periodically between the ground stations in order to enable communication. This communication model is widely used in single UAV architectures enabling throughput maximization and increased network security (especially in military applications). Since the performance of this communication system depends on several factors, in this paper we examine the Age of Information (AoI) performance of the system under different parameters, such as the number of packets exchanged between the ground base stations, the distance between the ground stations, the frequency that each ground station receives data packets and the SNR level since transmissions take place via an error-prone channel. Furthermore, we derive an analytical expression for the AoI considering the aforementioned parameters, and finally we provide simulation results that illustrate the impact of the network operating parameters on the age performance, considering the trade-off between the amount of data transferred through the network and the freshness of data exchanged between the terrestrial base stations., Funding Agencies|Hellenic Foundation for Research and Innovation (HFRI) [1411]

The news articles we read online can reveal a lot about us. Privacy aware groups have therefore long pushed for the use of HTTPS (encrypted end-to-end communication). In this paper, we present the design and evaluation of a lightweight framework that can (1) successfully identify individual news articles even when the articles are delivered over encrypted connections, and (2) separate between articles associated with different news websites even when the websites are delivered over the same infrastructure. Our results demonstrate that naive use of HTTPS is not enough to prevent attackers monitoring a user's connections from identifying articles that the user reads on the most popular news website. We also provide insights into what makes some websites more/less resilient to our attack, and we use Twitter data to evaluate the effectiveness of an example attack that in addition incorporates the popularity of individual news articles. We are the first to demonstrate and evaluate the practical effectiveness of this type of attack when applied on modern news websites, and our multi-website-based evaluation provides valuable insights into how websites can best protect themselves against this type of attacks. These insights are important for websites that want to help protect the privacy of their users., This paper won the best paper award at the conference.Funding: Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

Underwater surveillance using passive sonar and track-before-detect technology requires accurate models of the tracked signal and the background noise. However, in an underwater environment, the signal channel is time-varying and prior knowledge about the spatial distribution of the background noise is unavailable. In this paper, an autoregressive model that captures a time-varying signal level caused by multi-path propagation is presented. In addition, a multi-source model is proposed to describe spatially distributed background noise. The models are used in a Bernoulli filter track-before-detect framework and evaluated using both simulated and sea trial data. The simulations demonstrate clear improvements in terms of target loss and improved ability to discern the target from the noisy background. An evaluation of the track-before-detect algorithm on the sea trial data indicates a performance gain when incorporating the proposed models in underwater surveillance and tracking problems.

The surge in data generated by IoT sensors has increased the need for scalable and efficient data analysis methods, particularly for robust algorithms like quantile regression, which can be tailored to meet a variety of situations, including nonlinear relationships, distributions with heavy tails, and outliers. This paper presents a sub-gradient-based algorithm for distributed quantile regression with non-convex, and non-smooth sparse penalties such as the Minimax Concave Penalty (MCP) and Smoothly Clipped Absolute Deviation (SCAD). These penalties selectively shrink non-active coefficients towards zero, addressing the limitations of traditional penalties like the l(1)-penalty in sparse models. Existing quantile regression algorithms with non-convex penalties are designed for centralized cases, whereas our proposed method can be applied to distributed quantile regression using non-convex penalties, thereby improving estimation accuracy. We provide a convergence proof for our proposed algorithm and demonstrate through numerical simulations that it outperforms state-of-the-art algorithms in sparse and moderately sparse scenarios., Funding Agencies|Research Council of Norway

This paper proposes the modeling and analysis of multi-energy systems as multilayer networks. The aim is to assess the interdependence between different energy infrastructures. Multilayer network modeling enhances the one-dimensional graph-based approach employed to study the vulnerability and the topological characteristics of power grids. The centrality indices defined for one-dimensional networks are extended to multilayer networks, highlighting the differences emerging when interdependence is considered. The proposed multilayer framework is demonstrated on the 24-bus IEEE power grid coupled with a gas network with 7 nodes.

The QSAT problem, which asks to evaluate a quantified Boolean formula (QBF), is of fundamental interest in approximation, counting, decision, and probabilistic complexity and is also considered the prototypical PSPACE-complete problem. As such, it has previously been studied under various structural restrictions (parameters), most notably parameterizations of the primal graph representation of instances. Indeed, it is known that QSAT remains PSPACE-complete even when restricted to instances with constant treewidth of the primal graph, but the problem admits a double-exponential fixed-parameter algorithm parameterized by the vertex cover number (primal graph). However, prior works have left a gap in our understanding of the complexity of QSAT when viewed from the perspective of other natural representations of instances, most notably via incidence graphs. In this paper, we develop structure-aware reductions which allow us to obtain essentially tight lower bounds for highly restricted instances of QSAT, including instances whose incidence graphs have bounded treedepth or feedback vertex number. We complement these lower bounds with novel algorithms for QSAT which establish a nearly-complete picture of the problems complexity under standard graph-theoretic parameterizations. We also show implications for other natural graph representations, and obtain novel upper as well as lower bounds for QSAT under more fine-grained parameterizations of the primal graph., Funding Agencies|Austrian Science Fund (FWF) [J4656, P32830, Y1329]; Society for Research Funding Lower Austria (GFF) [ExzF-0004]; Vienna Science and Technology Fund (WWTF) [ICT19-060, ICT19-065]; ELLIIT - Swedish government

Hegselmann-Krause (HK) models exhibit complex behaviors which are not easily tractable through mathematical analysis. In this paper, a characterization of the steady-state behaviors of homogeneous HK models and sensitivity to confidence thresholds is discussed by commenting on existing and new numerical results. The typical decreasing of number of clusters and convergence time by increasing the confidence thresholds are discussed and motivations for the behavior of some counterexamples are provided. A tighter upper bound for the dependence of the number of clusters with respect to the confidence thresholds is proposed. Differences and analogies between the opinions evolution for symmetric and asymmetric HK models are commented.

The use of wildcard certificates and multi-domain certificates can impact how sensitive a certificate is to attacks and how many (sub)domains and machines may be impacted if a private key is compromised. Unfortunately, there are no globally agreed-upon best practices for these certificate types and the recommendations have changed many times over the years. In this paper, we present a 10-year longitudinal analysis of the usage of wildcard certificates and multi-domain certificates on the internet. Our analysis captures and highlights substantial differences in the heterogenous wildcard and multi-domain certificate practices. The results also show that there are several ways that CAs and domain owners have chosen to improve their practices, with many appearing to reduce the number of domains (and subdomains) for which each certificate is responsible., Funding Agencies|Swedish Foundation for Strategic Research (SSF); Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

Predicting pedestrian behavior when interacting with vehicles is one of the most critical challenges in the field of automated driving. Pedestrian crossing behavior is influenced by various interaction factors, including time to arrival, pedestrian waiting time, the presence of zebra crossing, and the properties and personality traits of both pedestrians and drivers. However, these factors have not been fully explored for use in predicting interaction outcomes. In this paper, we use machine learning to predict pedestrian crossing behavior including pedestrian crossing decision, crossing initiation time (CIT), and crossing duration (CD) when interacting with vehicles at unsignalized crossings. Distributed simulator data are utilized for predicting and analyzing the interaction factors. Compared with the logistic regression baseline model, our proposed neural network model improves the prediction accuracy and F1 score by 4.46% and 3.23%, respectively. Our model also reduces the root mean squared error (RMSE) for CIT and CD by 21.56% and 30.14% compared with the linear regression model. Additionally, we have analyzed the importance of interaction factors, and present the results of models using fewer factors. This provides information for model selection in different scenarios with limited input features., Funding Agencies|European research project "SHAPE-IT - Supporting the Interaction of Humans and Automated Vehicles: Preparing for the Environment of Tomorrow"; European Union [860410]

Recent studies of selective auditory attention have demonstrated that neural responses recorded with electroencephalogram (EEG) can be decoded to classify the attended talker in everyday multitalker cocktail-party environments. This is generally referred to as the auditory attention decoding (AAD) and could lead to a breakthrough for the next-generation of hearing aids (HAs) to have the ability to be cognitively controlled. The aim of this paper is to investigate whether cepstral analysis can be used as a more robust mapping between speech and EEG. Our preliminary analysis revealed an average AAD accuracy of 96%. Moreover, we observed a significant increase in auditory attention classification accuracies with our approach over the use of traditional AAD methods (7% absolute increase). Overall, our exploratory study could open a new avenue for developing new AAD methods to further advance hearing technology. We recognize that additional research is needed to elucidate the full potential of cepstral analysis for AAD.

This paper considers a communication system where a source sends time-sensitive information to its destination. We assume that both arrival and service processes of the messages are memoryless and the source has a single server with no buffer. Besides, we consider that the service is interrupted by an independent random process, which we model using an On-Off process. For this setup, we study the age of information for two queueing disciplines: 1) non-preemptive, where the messages arriving while the server is occupied are discarded, and 2) preemptive, where the in-service messages are replaced with newly arriving messages in the Off states. For these disciplines, we derive closed-form expressions for the mean peak age and mean age., Funding Agencies|US NSF [CNS-1814477, CNS-2225511]; Swedish Research Council (VR); ELLIIT; European Union (ETHER) [101096526]; Zenith

Extending the wireless power transfer range in miniaturized remotely powered micro-devices is a big challenge due to the very small effective area and low gain of the mm-sized antenna utilized in the micro-devices, which limits the harvested RF energy. This paper presents a method for increasing the separation distance between an external energy source antenna as a transmitter (TX) and micro device antenna as a receiver (RX) beyond 10 cm by utilizing various TX antennas, including a conventional loop antenna, multiple patch antennas, and a rectangular cavity antenna, and a 2-turn double-sided square loop RX antenna, sized 1.2mm x 1.2mm on FR4 substrate, which can be mounted on top of a CMOS SoC. The performance of the wireless power transfer system is evaluated and compared in different scenarios. At the 434 MHz ISM band, the results indicate that the highest peak power transfer efficiency of -20 dB and the highest harvested DC voltage of 4 V through an 8-stage Dickson RF-DC converter are obtained inside the rectangular hollow cavity sized 49.6cm x 49.6cm x 30.4cm, as TX, with an input TX power of 20 dBm. Furthermore, the multiple patch antennas have a power transfer efficiency of -39 dB and a harvested DC voltage of 2.5 V at a distance of 10 cm with an input TX power of 37 dBm. The specific absorption rate of both cases stays below the limits established by IEEE., Funding Agencies|Swedish Foundation for Strategic Research (SSF) [RMX18-0066]

An autonomous underwater vehicle (AUV) is a crewless robotic vehicle that dives into the water and performs without human assistance. This paper focuses on developing trajectory tracking control for bio-mimetic AUV system under uncertain environments. Therefore, a relatively new control technique called time delay-based estimation control is proposed for trajectory tracking under multiple uncertainties. This algorithm estimates the total disturbance in the system using immediate past information of input and output of feedback state and control variables. The benefit of this scheme is that it avoids assumptions about a priori upper bound information of disturbance. Further, the control structure is simple and does not require any high-frequency switching or high gain to nullify the effects of disturbance. The theoretical analysis of the proposed scheme guarantees the uniformly ultimate bounded stability of the closed-loop system. The numerical analysis is also carried out to validate the control performance of the given algorithm for lemniscate reference path tracking.

Embeddings are powerful tools for transforming complex and unstructured data into numeric formats suitable for computational analysis tasks. In this paper, we extend our previous work on using multiple embeddings for text similarity calculations to the field of networks. The embedding ensemble approach improves network reconstruction performance compared to single-embedding strategies. Our visual analytics methodology is successful in handling both text and network data, which demonstrates its generalizability beyond its originally presented scope.

Stacking (or stacked generalization) is an ensemble learning method with one main distinctiveness from the rest: even though several base models are trained on the original data set, their predictions are further used as input data for one or more metamodels arranged in at least one extra layer. Composing a stack of models can produce high-performance outcomes, but it usually involves a trial-and-error process. Therefore, our previously developed visual analytics system, StackGenVis, was mainly designed to assist users in choosing a set of top-performing and diverse models by measuring their predictive performance. However, it only employs a single logistic regression metamodel. In this paper, we investigate the impact of alternative metamodels on the performance of stacking ensembles using a novel visualization tool, called MetaStackVis. Our interactive tool helps users to visually explore different singular and pairs of metamodels according to their predictive probabilities and multiple validation metrics, as well as their ability to predict specific prob- lematic data instances. MetaStackVis was evaluated with a usage scenario based on a medical data set and via expert interviews.

The knowledge of channel covariance matrices is of paramount importance to the estimation of instantaneous channels and the design of beamforming vectors in multi-antenna systems. In practice, an abrupt change in channel covariance matrices may occur due to the change in the environment and the user location. Although several works have proposed efficient algorithms to estimate the channel covariance matrices after any change occurs, how to detect such a change accurately and quickly is still an open problem in the literature. In this paper, we focus on channel covariance change detection between a multiantenna base station (BS) and a single-antenna user equipment (UE). To provide theoretical performance limit, we first propose a genie-aided change detector based on the log-likelihood ratio (LLR) test assuming the channel covariance matrix after change is known, and characterize the corresponding missed detection and false alarm probabilities. Then, this paper considers the practical case where the channel covariance matrix after change is unknown. The maximum likelihood (ML) estimation technique is used to predict the covariance matrix based on the received pilot signals over a certain number of coherence blocks, building upon which the LLR-based change detector is employed. Numerical results show that our proposed scheme can detect the change with low error probability even when the number of channel samples is small such that the estimation of the covariance matrix is not that accurate. This result verifies the possibility to detect the channel covariance change both accurately and quickly in practice.

Cyber-Physical Systems (CPS) is a key concept in Industry 4.0, acting as a backbone to develop smart processes, machines and products. Multi-Agent Systems (MAS) is a suitable paradigm for the realization of such industrial CPS systems, supporting the distribution of intelligence and decision-making capabilities among a network of autonomous and cooperative agents. Standardization is a key factor for the acceptance of industrial CPS and agent-based systems, assuming a critical role in establishing specifications for the hardware integration, which is an important requirement for industrial agents. This paper focuses on the recently established IEEE 2660.1-2020 standard that defines a recommended practice to solve the interface problem when applying industrial agents, namely integrating intelligent software agents with low-level automation devices in the CPS context. The paper illustrates the applicability of the standard in three different application scenarios related to power and energy systems, factory automation and building automation, and discusses future directions in terms of standardization in the field of industrial agents, that are required for its wider adoption in the realization of industrial CPS solutions.

Monitoring conditions in the periglacial areas of Antarctica helps geographers and geologists to understand physical processes associated with mesoscale land systems. Analyzing these unique temporal datasets poses a significant challenge for domain experts, due to the complex and often incomplete data, for which corresponding exploratory tools are not available. In this paper, we present a novel visual analysis tool for extraction and interactive exploration of temporal measurements captured at the polar station at the James Ross Island in Antarctica. The tool allows domain experts to quickly extract information about the snow level, originating from a series of photos acquired by trail cameras. Using linked views, the domain experts can interactively explore and combine this information with other spatial and non-spatial measures, such as temperature or wind speed, to reveal the interplay of periglacial and aeolian processes. An abstracted interactive map of the area indicates the position of measurement spots to facilitate navigation. The design of the tool was made in tight collaboration with geographers, which resulted in an early prototype, tested in the pilot study. The following version of the tool and its usability has been evaluated in the user study with five domain experts and their feedback was incorporated into the final version, presented in this paper. This version was again discussed with two experts in an informal interview. Within these evaluations, they confirmed the significant benefit of the tool for their research tasks.

This paper presents a framework that fully leverages the advantages of a deferred rendering approach for the interactive visualization of large-scale datasets. Geometry buffers (G-Buffers) are generated and stored in situ, and shading is performed post hoc in an interactive image-based rendering front end. This decoupled framework has two major advantages. First, the G-Buffers only need to be computed and stored once-which corresponds to the most expensive part of the rendering pipeline. Second, the stored G-Buffers can later be consumed in an image-based rendering front end that enables users to interactively adjust various visualization parameters-such as the applied color map or the strength of ambient occlusion-where suitable choices are often not known a priori. This paper demonstrates the use of Cinema Darkroom on several real-world datasets, highlighting CDs ability to effectively decouple the complexity and size of the dataset from its visualization., Funding Agencies|U.S. Department of Homeland SecurityUnited States Department of Homeland Security (DHS) [2017-ST-061-QA0001, 17STQAC00001-03-03]; National Science Foundation ProgramNational Science Foundation (NSF) [1350573]; German research foundation (DFG)German Research Foundation (DFG) [IRTG 2057]; Swedish Foundation for Strategic Research (SSF)Swedish Foundation for Strategic Research [BD15-0082]; SeRC (Swedish e-Science Research Center); ELLIIT environment for strategic research in Sweden

This Paper describes how, based on a symbiosis of American and European thinking, "conceptual" labs for engineering courses have been developed in a series of design-based research projects that started in 1995. Researchers working in engineering education have been encouraged to "look for opportunities to translate research questions, theories, methods, and findings... across national and institutional boundaries", and urged to "think globally about the development of engineering education as a research field". Nevertheless, engineering education has been criticized for being insufficiently global in its practices. The aim of this paper is to meet these challenges. At first the design of labs was inspired by "interactive engagement" curricula such as "RealTime Physics" and "Workshop Physics" developed in the US, after they had been adapted to the Swedish setting and traditions. Variation theory, pragmatic and (post)phenomenological theories of the philosophy of technology, and activity theory influenced later development. Labs for advanced mechanics, and for introductory and advanced courses in electric circuit theory, were later developed using similar ideas. The labs utilized probeware and real-time computer-based measurement technologies as a mediating technology, and tasks were designed according to variation theory. Students learning in several designs of these labs has been studied by recording students activities and interactions by video, and using concept inventories such as the Force and Motion Conceptual Evaluation (FMCE). Some designs resulted in high achievement (normalized gains of 50-60%) on the conceptual tests, well in line with the results from the US. Furthermore, in the labs that led to high achievement, the technology was used help students to focus on important relationships and concepts, i.e. the technology functioned as a "cognitive tool". However, the implementation of probeware technology could also result poor achievement. This is explain, Funding Agencies|Swedish Research CouncilSwedish Research CouncilEuropean Commission; Swedish National Agency

The classical SIR model is a fundamental building block in most epidemiological models. Despite its widespread use, its properties in filtering and estimation applications are much less well explored. Independently of how the basic SIR model is integrated into more complex models, the fundamental question is whether the states and parameters can be estimated from a fusion of available numeric measurements. The problem studied in this paper focuses on the parameter and state estimation of a stochastic SIR model from assumed direct measurements of the number of infected people in the population, and the generalisation to other measurements is left for future research. In terms of parameter estimation, two components are discussed separately. The first component is model parameter estimation assuming that the all states are measured directly. The second component is state estimation assuming known parameters. These two components are combined into an iterative state and parameter estimator. This iterative method is compared to a straightforward approach based on state augmentation of the unknown parameters. Feasibility of the problem is studied from an information-theoretic point of view using the Cramer Rao Lower Bound (CRLB). Using simulated data resembling the first wave of Covid-19 in Sweden, the iterative method outperforms the state augmentation approach., Scalable Kalman Filters funded by the Swedish Research Council

This paper presents a variant of the Traveling Salesman Problem (TSP) with nonholonomic constraints and dynamic obstacles, with optimal control applications in the mining industry. The problem is discretized and an approach for solving the discretized problem to optimality is proposed. The proposed approach solves the three subproblems (waypoint ordering, heading at each waypoint and motion planning between waypoints) simultaneously using two nested graph-search planners. The higher-level planner solves the waypoint ordering and heading subproblems while making calls to the lower-level planner that solves the motion planning subproblem using a lattice-based motion planner. For the higher-level motion planner A* search is used and two different heuristics, a minimal spanning tree heuristic and a nearest insertion heuristic, are proposed and optimality bounds are proven. The proposed planner is evaluated on numerical examples and compared to Dijkstras algorithm. Furthermore, the performance and observed suboptimality are investigated when the minimal spanning tree heuristic cost is inflated., Funding Agencies|Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

Motion planning and decision-making while considering uncertainty is critical for an autonomous vehicle to safely and efficiently drive on a highway. This paper presents a new combined two-step approach for this problem, where a partially observable Markov decision process (POMDP) is tightly coupled with a scenario model predictive control (SCMPC) step. To generate the scenarios in the SCMPC step, the solution to the POMDP is used together with a novel scenario-reduction procedure, which selects a small representative subset of all scenarios considered in the POMDP. The resulting planner is evaluated in a simulation study where the impact of the two-step approach and the scenario-reduction method is shown., Funding Agencies|Swedens Innovation Agency

The classical SIR model is a fundamental building block in most epidemiological models. Despite its widespread use, its properties in filtering and estimation applications are much less well explored. Independently of how the basic SIR model is integrated into more complex models, the fundamental question is whether the states and parameters can be estimated from a fusion of available numeric measurements. The problem studied in this paper focuses on the parameter and state estimation of a stochastic SIR model from assumed direct measurements of the number of infected people in the population, and the generalisation to other measurements is left for future research. In terms of parameter estimation, two components are discussed separately. The first component is model parameter estimation assuming that the all states are measured directly. The second component is state estimation assuming known parameters. These two components are combined into an iterative state and parameter estimator. This iterative method is compared to a straightforward approach based on state augmentation of the unknown parameters. Feasibility of the problem is studied from an information-theoretic point of view using the Cramer Rao Lower Bound (CRLB). Using simulated data resembling the first wave of Covid-19 in Sweden, the iterative method outperforms the state augmentation approach., Scalable Kalman Filters funded by the Swedish Research Council

Motion planning and decision-making while considering uncertainty is critical for an autonomous vehicle to safely and efficiently drive on a highway. This paper presents a new combined two-step approach for this problem, where a partially observable Markov decision process (POMDP) is tightly coupled with a scenario model predictive control (SCMPC) step. To generate the scenarios in the SCMPC step, the solution to the POMDP is used together with a novel scenario-reduction procedure, which selects a small representative subset of all scenarios considered in the POMDP. The resulting planner is evaluated in a simulation study where the impact of the two-step approach and the scenario-reduction method is shown., Funding Agencies|Swedens Innovation Agency

This paper presents a variant of the Traveling Salesman Problem (TSP) with nonholonomic constraints and dynamic obstacles, with optimal control applications in the mining industry. The problem is discretized and an approach for solving the discretized problem to optimality is proposed. The proposed approach solves the three subproblems (waypoint ordering, heading at each waypoint and motion planning between waypoints) simultaneously using two nested graph-search planners. The higher-level planner solves the waypoint ordering and heading subproblems while making calls to the lower-level planner that solves the motion planning subproblem using a lattice-based motion planner. For the higher-level motion planner A* search is used and two different heuristics, a minimal spanning tree heuristic and a nearest insertion heuristic, are proposed and optimality bounds are proven. The proposed planner is evaluated on numerical examples and compared to Dijkstras algorithm. Furthermore, the performance and observed suboptimality are investigated when the minimal spanning tree heuristic cost is inflated., Funding Agencies|Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

This paper proposes an alternating direction method of multiplier (ADMM) based algorithm for solving the sparse robust phase retrieval with non-convex and non-smooth sparse penalties, such as minimax concave penalty (MCP). The accuracy of the robust phase retrieval, which employs an l(1) based estimator to handle outliers, can be improved in a sparse situation by adding a non-convex and non-smooth penalty function, such as MCP, which can provide sparsity with a low bias effect. This problem can be effectively solved using a novel proximal ADMM algorithm, and under mild conditions, the algorithm is shown to converge to a stationary point. Several simulation results are presented to verify the accuracy and efficiency of the proposed approach compared to existing methods., Funding Agencies|Research Council of Norway

The classical SIR model is a fundamental building block in most epidemiological models. Despite its widespread use, its properties in filtering and estimation applications are much less well explored. Independently of how the basic SIR model is integrated into more complex models, the fundamental question is whether the states and parameters can be estimated from a fusion of available numeric measurements. The problem studied in this paper focuses on the parameter and state estimation of a stochastic SIR model from assumed direct measurements of the number of infected people in the population, and the generalisation to other measurements is left for future research. In terms of parameter estimation, two components are discussed separately. The first component is model parameter estimation assuming that the all states are measured directly. The second component is state estimation assuming known parameters. These two components are combined into an iterative state and parameter estimator. This iterative method is compared to a straightforward approach based on state augmentation of the unknown parameters. Feasibility of the problem is studied from an information-theoretic point of view using the Cramer Rao Lower Bound (CRLB). Using simulated data resembling the first wave of Covid-19 in Sweden, the iterative method outperforms the state augmentation approach., Scalable Kalman Filters funded by the Swedish Research Council

Backscatter communication (BSC) is a promising solution for Internet-of-Things (IoT) connections due to its lowcomplexity, low-cost, and energy-efficient solution for sensors. There are several network infrastructure setups that can be used for BSC with IoT nodes/passive devices. One of them is a bistatic setup where there is a need for high dynamic range and high-resolution analog-to-digital converters at the reader side. In this paper, we investigate a bistatic BSC setup with multiple antennas. We propose a novel algorithm to suppress direct link interference between the carrier emitter (CE) and the reader using beamforming into the nullspace of the CEreader direct link to decrease the dynamic range of the system and increase the detection performance of the backscatter device (BSD). Further, we derive a Neyman-Pearson (NP) test and an exact closed-form expression for its performance in the detection of the BSD. Finally, simulation results show that the dynamic range of the system is significantly decreased and the detection performance of the BSD is increased by the proposed algorithm compared to a system not using beamforming in the CE, which could then be used in a host of different practical fields such as agriculture, transportation, factories, hospitals, smart cities, and smart homes., Funding Agencies|European Union [101013425]; ELLIIT; KAW foundation

Software-Defined Networking (SDN) architecture was developed to address the shortcomings of traditional network architectures. It allows system administrators to easily manage and configure the network by separating and abstracting the control plane from the data plane. All the knowledge and intelligence of SDN is concentrated in a software entity called the SDN controller, making the network programmable. However, a large-scale SDN architecture, particularly in the IoT domain, requires the implementation of a physically distributed control mechanism. Such a mechanism, based on the East/West interface raises many challenges in terms of scalability, reliability, security, consistency, and traceability. The development of the Blockchain allows addressing some of these challenges. In this paper, we present a design using Blockchain technology to improve SDNs in terms of trackability and discuss the adaptations required for large-scale deployment. Experimental results clearly show that the use of a proof-of-authority consensus algorithm in combination with a Merkle tree approach reduces the impact in terms of latency as well as in terms of Gas consumption.

This paper presents a nerve stimulation system implementation that contains full-wave rectifier-based energy harvester switching at 13.56MHz to generate stimulation current for accelerating the regeneration time of recovering damaged nerve. Reconfigurable eight-bit driver cells provide the selective option of controlling the stimulation current from as low as 4 mu A up to 0.92mA. The design is implemented in a standard 180nm CMOS process with a core area of 0.22mm(2) excluding a 3.6nF on-chip integrated capacitor which occupies 0.34mm(2) of the chip area. The fabricated chip is measured and characterized with coupled AC input amplitude of 2.2V and a driver load of 1k Omega. Furthermore, the measurement results verify the rectified DC output of 2V which implies a conversion ratio of 0.91.

Asymptotic theory for the regularized system identification has received increasing interests in recent years. In this paper, for the finite impulse response (FIR) model and filtered white noise inputs, we show the convergence in distribution of the Steins unbiased risk estimator (SURE) based hyper-parameter estimator and find factors that influence its convergence properties. In particular, we consider the ridge regression case to obtain closed-form expressions of the limit of the regression matrix and the variance of the limiting distribution of the SURE based hyper-parameter estimator, and then demonstrate their relation numerically., Funding Agencies|Thousand Youth Talents Plan funded by the central government of China - NSFC [61773329]; Shenzhen Science and Technology Innovation Council [Ji-20170189, JCY20170411102101881]; Robotic Discipline Development Fund from Shenzhen Government [2016-1418]; CUHKSZ [PF. 01.000249, 2014.0003.23]; Swedish Research Council [2019-04956]; Vinnovas center LINKSIC

If a concatenated Friedkin-Johnsen model is used to describe the evolution of the opinions of stubborn agents in a sequence of discussion events, then the social power achieved by the agents at the end of the discussions depends from the stubbornness coefficients adopted by the agents through the sequence of events. In this paper we assume that the agents are free to choose their stubbornness profiles, and ask ourselves what strategy should an agent follow in order to maximize its social power. Formulating the problem as a strategic game, we show that choosing the highest possible values of stubbornness in the early discussions leads to the highest possible social power., Funding Agencies|Swedish Research Council [2020-03701]; China Scholarship Council CSC [202004910739]; Shanghai Municipal Science and Technology Major Project [2021SHZDZX0100]; National Natural Science Foundation of China [61733018]

This paper presents a variant of the Traveling Salesman Problem (TSP) with nonholonomic constraints and dynamic obstacles, with optimal control applications in the mining industry. The problem is discretized and an approach for solving the discretized problem to optimality is proposed. The proposed approach solves the three subproblems (waypoint ordering, heading at each waypoint and motion planning between waypoints) simultaneously using two nested graph-search planners. The higher-level planner solves the waypoint ordering and heading subproblems while making calls to the lower-level planner that solves the motion planning subproblem using a lattice-based motion planner. For the higher-level motion planner A* search is used and two different heuristics, a minimal spanning tree heuristic and a nearest insertion heuristic, are proposed and optimality bounds are proven. The proposed planner is evaluated on numerical examples and compared to Dijkstras algorithm. Furthermore, the performance and observed suboptimality are investigated when the minimal spanning tree heuristic cost is inflated., Funding Agencies|Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

Screen-printed RuO2 pH electrodes are suitable for pH determination not only in water samples but also in different food matrixes, e.g., beverages, juices and milk. Nevertheless, the application of the screen-printed RuO2 electrodes in milk is impossible without covering the electrodes with a protective Nafion membrane that prevents the contamination of the pH-sensitive area of the electrode with sample residues. However, not much attention has been paid to the cleaning procedure of Nafion-covered screen-printed RuO2 electrodes (RuO2-Nf). In this paper, we show that cleaning the electrodes by soaking them in the solution of 5% pepsin in 0.1 M HCl allows restoring the electrode to its initial state for measuring pH in food samples., Funding Agencies|European Commission [H2020-MSCA-ITN-2018-813680]; Tallinn University of Technology development program [2014-2020.4.01.16-0032]

In this paper, we consider privacy aspects of wireless federated learning (FL) with Over-the-Air (OtA) transmission of gradient updates from multiple users/agents to an edge server. OtA FL enables the users to transmit their updates simultaneously with linear processing techniques, which improves resource efficiency. However, this setting is vulnerable to privacy leakage since an adversary node can hear directly the uncoded message. Traditional perturbation-based methods provide privacy protection while sacrificing the training accuracy due to the reduced signal-to-noise ratio. In this work, we aim at minimizing privacy leakage to the adversary and the degradation of model accuracy at the edge server at the same time. More explicitly, spatially correlated perturbations are added to the gradient vectors at the users before transmission. Using the zero-sum property of the correlated perturbations, the side effect of the added perturbation on the aggregated gradients at the edge server can be minimized In the meanwhile, the added perturbation will not be canceled out at the adversary, which prevents privacy leakage. Theoretical analysis of the perturbation covariance matrix, differential privacy, and model convergence is provided, based on which an optimization problem is formulated to jointly design the covariance matrix and the power scaling factor to balance between privacy protection and convergence performance. Simulation results validate the correlated perturbation approach can provide strong defense ability while guaranteeing high learning accuracy., Funding Agencies|Security Link; ELLIIT; KAW foundation

Base stations in 5G and beyond use advanced antenna systems (AASs), where multiple passive antenna elements and radio units are integrated into a single box. A critical bottleneck of such a system is the digital fronthaul between the AAS and baseband unit (BBU), which has limited capacity. In this paper, we study an AAS used for precoded downlink transmission over a multi-user multiple-input multiple-output (MU-MIMO) channel. First, we present the baseline quantization-unaware precoding scheme created when a precoder is computed at the BBU and then quantized to be sent over the fronthaul. We propose a new precoding design that is aware of the fronthaul quantization. We formulate an optimization problem to minimize the mean squared error at the receiver side. We rewrite the problem to utilize mixed-integer programming to solve it. The numerical results manifest that our proposed precoding greatly outperforms quantization-unaware precoding in terms of sum rate., Funding Agencies|Knut and Alice Wallenberg Foundation

This paper proposes a dynamic range (DR) extension technique based on a two-step conversion for high-sensitivity multi-element pseudo-resistive (MEPR) transimpedance amplifiers (TIA). In optical biomedical sensors targeted for fluorescence measurement applications, the front-ends high sensitivity and wide DR are critical for accurately recording cellular variations. In this work, the most significant bits (MSB) are extracted by a 2-bit current digital-to-analog converter (DAC) then the least significant bits (LSB) are extracted by a 10-bit successive approximation register (SAR) analog-to-digital converter (ADC). The current DACs are composed of identical pseudo-resistor (PR) elements used in the TIA feedback to make the MSB extraction robust against the process and mismatch variations. This technique preserves the linearity of the conversion while using the current DACs to improve the DR. The proposed front-end implemented in 65 nm CMOS technology achieves a DR of 72.3 dB with current detection of 1 pA up to 4.13 nA at a sampling rate of 1 kS/s. The total front-end consumes a power of 45 mu w from a 2.5 V supply., Funding Agencies|Swedish Foundation for Strategic Research (SSF) [RMX18-0066]

Burst super-resolution has received increased attention in recent years due to its applications in mobile photography. By merging information from multiple shifted images of a scene, burst super-resolution aims to recover details which otherwise cannot be obtained using a simple input image. This paper reviews the NTIRE 2022 challenge on burst super-resolution. In the challenge, the participants were tasked with generating a clean RGB image with 4x higher resolution, given a RAW noisy burst as input. That is, the methods need to perform joint denoising, demosaicking, and super-resolution. The challenge consisted of 2 tracks. Track 1 employed synthetic data, where pixel-accurate high-resolution ground truths are available. Track 2 on the other hand used real-world bursts captured from a handheld camera, along with approximately aligned reference images captured using a DSLR. 14 teams participated in the final testing phase. The top performing methods establish a new state-of-the-art on the burst super-resolution task., Funding Agencies|Huawei; Reality Labs; Bending Spoons; MediaTek; OPPO; Oddity; Voyage81; ETH Zurich (Computer Vision Lab); University of Wurzburg (CAIDAS)

Previous works on cell-free massive MIMO mostly consider physical-layer and fronthaul transport aspects. How to deploy cell-free massive MIMO functionality in a practical wireless system is an open problem. This paper proposes a new cell-free architecture that can be implemented on top of a virtualized cloud radio access network (V-CRAN). We aim to minimize the end-to-end power consumption by jointly considering the radio, optical fronthaul, virtualized cloud processing resources, and spectral efficiency requirements of the user equipments. The considered optimization problem is cast in a mixed binary second-order cone programming form and, thus, the global optimum can be found using a branch-and-bound algorithm. The optimal power-efficient solution of our proposed cell-free system is compared with conventional small-cell implemented using V-CRAN, to determine the benefits of cell-free networking. The numerical results demonstrate that cell-free massive MIMO increases the maximum rate substantially, which can be provided with almost the same energy per bit. We show that it is more power-efficient to activate cell-free massive MIMO already at low spectral efficiencies (above 1 bit/s/Hz)., Funding Agencies|CELTIC -NEXT project AI4Green; Vinnova, Swedish Innovation Agency

Identifying sociolinguistic attributes of inter-community interactions is essential for understanding the polarization of social network communities. A wide range of computational text and network analysis methods may be applicable for this task, however, interpretation of the respective results and investigation of particularly interesting cases and subnetworks are difficult due to the scale and complexity of the data, e.g., for the Reddit platform. In this poster paper, we present an interactive visual analysis interface that facilitates network exploration and comparison at different topological and multivariate attribute scales. Users are able to investigate text- and network-based properties of social network community interactions, identify anomalies of conflict starters, or gain insight into multivariate anomalies behind groups of negative social media posts.

An Intelligent Reflecting Surface (IRS) is an emerging technology for improving the data rate over wireless channels by controlling the underlying channel. In this paper, we describe a novel solution for IRS configuration to maximize the data rate over wideband channels. The optimization is obtained by online training of a deep generative neural network. Inspired by related works in image processing, this network is randomly initialized and acts as a regularization term for the optimization process since the structure of the generator is sufficient to capture a great deal of IRS statistics prior to any learning. In contrast to recent deep learning techniques for IRS configuration, the proposed technique does not require an offline training stage and can adapt quickly to any environment. Compared to the previous state-of-the-art algorithm, the proposed method is significantly faster and obtains IRS configurations that achieve higher data transmission rates.

How to manage the interference introduced by the enormous wireless devices is a crucial issue to address in the prospective sixth-generation (6G) communications. The treating interference as noise (TIN) optimality conditions are commonly used for interference management and thus attract significant interest in existing wireless systems. Cell-free massive multiple-input multiple-output (CF mMIMO) is a promising technology in 6G that exhibits high system throughput and excellent interference management by exploiting a large number of access points (APs) to serve the users collaboratively. In this paper, we take the first step on studying TIN in CF mMIMO systems from a stochastic geometry perspective by investigating the probability that the TIN conditions hold with spatially distributed network nodes. We propose a novel analytical framework for TIN in a CF mMIMO system with both Binomial Point Process (BPP) and Poisson Point Process (PPP) approximations. We derive the probability that the TIN conditions hold in close form using the PPP approximation. Numerical results validate our derived expressions and illustrate the impact of various system parameters on the probability that the TIN conditions hold., Funding Agencies|Fundamental Research Funds for the Central Universities [2021YJS001]; National Key R&D Program of China [2020YFB1807201]; National Natural Science Foundation of China [61971027, 61961130391]; Beijing Natural Science Foundation [L202013]; Natural Science Foundation of Jiangsu Province [BK20212002]; Royal Society Newton Advanced Fellowship [NA191006]; Frontiers Science Center for Smart Highspeed Railway System; Center for Industrial Information Technology (CENIIT); Excellence Center at Linkoping-Lund in Information Technology (ELLIIT)

Wireless power transfer (WPT) is an alternative technology to conventional batteries for powering Internet of things (IoT) devices. WPT is especially beneficial in situations when battery replacement is infeasible or expensive. It can also reduce battery-related e-waste. In this paper, we analyze the limits of adopting WPT technology for remote powering of IoT devices. We assume that an IoT device periodically harvests energy from a base station (BS) and transmits a data packet related to the sensor measurement under shadow fading channel conditions. Our goal is to characterize the epsilon-coverage range, where epsilon is the probability of the coverage. Our analysis shows a tradeoff between the coverage range and the rate of sensor measurements, where the maximal epsilon-coverage range is achieved as the sensor measurement rate approaches zero. We demonstrate that the weighted sum of the sleep power consumption and the harvesting sensitivity power of an IoT device limits the maximal e-coverage range. Beyond that range, the IoT device cannot harvest enough energy to operate. The desired rate of the sensor measurements also significantly impacts the epsilon-coverage range. Our results suggest that for an IoT device designed using current technology, the maximal 0.95-coverage range is in the order of 120 m. When high measurement rates are required, the coverage range drops to 50-100 m. Compared to battery-powered IoT devices, WPT is well-suited for medium-range applications plus when battery replacement is costly.

In this paper, we examine the energy consumption of a user equipment (UE) when it transmits a finite-sized data packet. The receiving base station (BS) controls a reconfigurable intelligent surface (RIS) that can be utilized to improve the channel conditions, if additional pilot signals are transmitted to configure the RIS. We derive a formula for the energy consumption taking both the pilot and data transmission powers into account. By dividing the RIS into subarrays consisting of multiple RIS elements using the same reflection coefficient, the pilot overhead can be tuned to minimize the energy consumption while maintaining parts of the aperture gain. Our analytical results show that there exists an energy-minimizing subarray size. For small data blocks and when the channel conditions between the BS and UE are favorable compared to the path to the RIS, the energy consumption is minimized using large subarrays. When the channel conditions to the RIS are better and the data blocks are large, it is preferable to use fewer elements per subarray and potentially configure the elements individually., Funding Agencies|Swedish Foundation for Strategic Research [FFL18-0277]