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Analysis of Control Systems Under Sensor Timing Misalignments

This paper presents an in-depth analysis of the stability and performance of control systems experiencing sensor timing misalignments, a common challenge in practical applications such as autonomous vehicles and aerospace systems. We model multichannel sensor delays as independent random variables, capturing the variability of real-world systems where different sensors exhibit distinct and non-con

A lung function threshold for survival? - FEV1Q and mortality in patients with COPD and chronic respiratory failure

INTRODUCTION: The FEV1 quotient (FEV1Q), calculated as the index between FEV1 and a theoretical lower survivable FEV1 threshold of 0.4L for females and 0.5L for males, has been investigated as a novel method of interpreting results from lung function testing. The applicability of the FEV1Q in populations with chronic respiratory failure has not been studied, and the continuous association between

ALogSCAN: A Self-Supervised Dual Network for Adaptive and Timely Log Anomaly Detection in Clouds

Logs are prevalent in modern cloud systems and serve as a valuable source of information for system maintenance. Over the years, many supervised, semi-supervised, and unsupervised log analysis methods have been proposed to detect system anomalies. In particular, semi-supervised methods have garnered increasing attention as they balance reduced labeled data requirements and optimal detection perfor

Privacy Preserving Localization via Coordinate Permutations

Recent methods on privacy-preserving image-based localization use a random line parameterization to protect the privacy of query images and database maps. The lifting of points to lines effectively drops one of the two geometric constraints traditionally used with point-to-point correspondences in structure-based localization. This leads to a significant loss of accuracy for the privacy-preserving

Force-based semantic representation and estimation of feature points for robotic cable manipulation with environmental contacts

This work demonstrates the utility of dual-arm robots with dual-wrist force-torque sensors in manipulating a Deformable Linear Object (DLO) within an unknown environment that imposes constraints on the DLO’s movement through contacts and fixtures. We propose a strategy to estimate the pose of unknown environmental contacts encountered during the manipulation of a DLO, classifying the induced const

Multipolar Opinion Evolution in Biased Networks

Motivated by empirical research on bias and opinion formation, we formulate a multidimensional nonlinear opinion-dynamical model where agents have individual biases, which are fixed, as well as opinions, which evolve. The dimensions represent competing options, of which each agent has a relative opinion, and are coupled through normalization of the opinion vector. This can capture, for example, an

FETCH : A Fast and Efficient Technique for Channel Selection in EEG Wearable Systems

The rapid development of wearable biomedical systems now enables real-time monitoring of electroencephalography (EEG) signals. Acquisition of these signals relies on electrodes. These systems must meet the design challenge of selecting an optimal set of electrodes that balances performance and usability constraints. The search for the optimal subset of electrodes from a larger set is a problem wit

Robust Incremental Structure-from-Motion with Hybrid Features

Structure-from-Motion (SfM) has become a ubiquitous tool for camera calibration and scene reconstruction with many downstream applications in computer vision and beyond. While the state-of-the-art SfM pipelines have reached a high level of maturity in well-textured and well-configured scenes over the last decades, they still fall short of robustly solving the SfM problem in challenging scenarios.

Oversampling-Based Control with Multi-Core and Edge Implementations

Digital control systems introduce unavoidable computational latencies. For some controllers this time delay inhibits practical use, even though they in theory could provide more efficient control. For example, solving an optimization problem each sampling period when using model predictive control. By sampling faster than the computation time and executing independent controllers on distributed ha

Jitter Propagation in Task Chains

Chains of tasks are ubiquitous and used in a broad spectrum of applications. In these chains, tasks execute according to their timing. Then, they communicate by writing to and reading from shared memory. The schedule of tasks and the read/write instants are naturally subject to uncertainties (variability in the execution time, interference due to shared resources of higher priority tasks, etc.). D

The impact of common and rare genetic variants on bradyarrhythmia development

To broaden our understanding of bradyarrhythmias and conduction disease, we performed common variant genome-wide association analyses in up to 1.3 million individuals and rare variant burden testing in 460,000 individuals for sinus node dysfunction (SND), distal conduction disease (DCD) and pacemaker (PM) implantation. We identified 13, 31 and 21 common variant loci for SND, DCD and PM, respective

Soft-Constrained Stochastic MPC of Markov Jump Linear Systems : Application to Real-Time Control With Deadline Overruns

Modern real-time control systems can sporadically exceed the computation deadlines, which may lead to a deterioration in performance or even instability if not actively accounted for. This letter proposes a stochastic model predictive control approach that incorporates deadline miss probabilities of subsequent control task executions in a scenario tree. To account for the effect of missed deadline

Stress Testing Control Loops in Cyber-Physical Systems—RCR Report

This is the Replicated Computational Results (RCR) Report for the article ‘Stress Testing Control Loops in Cyber-Physical Systems’. The article proposes a novel approach for testing Cyber-Physical Systems (CPS) based on the integration of the guarantees that can be provided with the control theoretical models into the software testing practices. This RCR report describes how to reproduce the empir

Towards a Framework for Dynamic Task Offloading in Real-Time Robotic Applications

Dynamic task offloading is essential for real-time robotic applications, enabling them to adapt to fluctuating computational demands and maintain efficiency under changing conditions. This paper introduces a dynamic task offloading framework that incorporates monitoring, decision making, offloading triggering, and performance monitoring to optimize resource usage by offloading real-time tasks to e

Projected climate change in Fennoscandia – and its relation to ensemble spread and global trends

The need for information about climate change is great. This information is usually based on climate model data, which often have systematic biases. Furthermore, climate information is based on ensembles of climate models, which raises the question about how such ensembles are affected by the choice of models and emission scenarios. Here, we aim to describe climate change in Sweden and neighbourin

Wall-to-Wall Mapping of Forest Canopy Height using ICESat-2 Data and Multi-source Remote Sensing Images in a Machine Learning Framework

Forest Canopy Height (FCH) is one of the key variables for understanding forest structure distribution and growth. Remotely sensed data such as the NASA Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) ATL08 provides accurate FCH measurements; however, its point-based nature limits spatial continuity. This study addresses the challenge by generating a continuous FCH map over the West Usambara

The role of manual gestures in second language comprehension : A simultaneous interpreting experiment

Manual gestures and speech form a single integrated system during nativelanguage comprehension. However, it remains unclear whether this hold forsecond language (L2) comprehension, more specifically for simultaneousinterpreting (SI), which involves comprehension in one language and simultaneousproduction in another. In a combined mismatch and priming paradigm,we presented Swedish speakers fluent i

Learning pharmacometric covariate model structures with symbolic regression networks

Efficiently finding covariate model structures that minimize the need for random effects to describe pharmacological data is challenging. The standard approach focuses on identification of relevant covariates, and present methodology lacks tools for automatic identification of covariate model structures. Although neural networks could potentially be used to approximate covariate-parameter relation

Scale fragilities in localized consensus dynamics

We consider distributed consensus in networks where the agents have integrator dynamics of order two or higher (n≥2). We assume all feedback to be localized in the sense that each agent has a bounded number of neighbors and consider a scaling of the network through the addition of agents in a modular manner, i.e., without re-tuning controller gains upon addition. We show that standard consensus al