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The PID controller is the by far most frequently employed type of controller. As you read, billions of digitally implemented PID controllers are running, shaping the dynamic behavior of anything from the fan speed in your laptop to safety-critical components in nuclear power plants. Given the abundance of commissioned PID controllers, it is surprisingly hard to find a single source that provides a

Gaze-control and gaze-assistance in software development tools have so far been explored in the setting of code editing, but other developer activities like code review could also benefit from this kind of tool support.In this paper, we present GANDER, a platform for user studies on gaze-assisted code review. As a proof of concept, we extend the platform with an assistant that highlights name rela

It is common that PID control loops function satisfactorily most of the time, but that they have issues with violating input, state, or output constraints. While MPC solves this problem in principle, it is in practice not straightforward to replace a functioning PID controller with an MPC implementation. This is particularly true for loops that are critical to plant operation, where stops associat

This paper introduces an improved method for real-time brain computer interface control. We demonstrate how Bayesian optimization and feedback can be used to achieve faster statistical convergence by controlling the sequence of stimuli shown in a brain computer interface based on a visual oddball paradigm.

Decentralized Cloud Storage (DCS) is considered to be the future for sustainable data storage within Web 3.0, in which we will move from a single cloud service provider to creating an ecosystem where anybody could be a cloud storage provider. Currently, the cloud storage market is highly dominated by centralized players like Amazon S3, Google Cloud, Box, etc. Decentralized projects like Storj, Fil

With increased autonomy in marine vessels, autonomous surface vessels (ASVs) and conventionally manned vessels need to coexist at sea. Any relocation needs to include collision avoidance according to the traffic rules at sea, COLREGs. Here, a local collision-avoidance planner for an archipelago environment is presented. The optimization-based local planner presented considers a predefined nominal

Insects rely on path integration (vector-based navigation) and landmark guidance to perform sophisticated navigational feats, rivaling those seen in mammals. Bees in particular exhibit complex navigation behaviors including creating optimal routes and novel shortcuts between locations, an ability historically indicative of the presence of a cognitive map. A mammalian cognitive map has been widely

L-DOPA-induced dyskinesia (LID) is a debilitating complication of dopamine replacement therapy in Parkinson's disease and the most common hyperkinetic disorder of basal ganglia origin. Abnormal activity of striatal D1 and D2 spiny projection neurons (SPNs) is critical for LID, yet the link between SPN activity patterns and specific dyskinetic movements remains unknown. To explore this, we implemen

l-DOPA-induced dyskinesia (LID) is a prevalent complication of dopamine replacement therapy in Parkinson’s disease. In addition to its clinical importance, LID represents a paradigm to unravel how altered signaling downstream of dopamine receptors can disrupt mechanisms of synaptic plasticity and motor information processing in the cortico-basal ganglia network. This chapter focuses on experimenta

Marine megafauna species are affected by a wide range of anthropogenic threats. To evaluate the risk of such threats, species' vulnerability to each threat must first be determined. We build on the existing threats classification scheme and ranking system of the International Union for Conservation of Nature (IUCN) Red List of Threatened Species by assessing the vulnerability of 256 marine megafau

This paper addresses the path-tracking problem of time-optimal trajectories under model uncertainties, by proposing a real-time predictive scaling algorithm. The algorithm is formulated as a convex optimization problem, designed to balance the trade-off between improving feasibility and time optimality of a trajectory. The predicted trajectory is scaled based on the presence of path segments that

For patients with epilepsy, automatic epilepsy monitoring, i.e., the process of direct observation of the patient’s health status in real time, is crucial. Wearable systems provide the possibility of real-time epilepsy monitoring and alerting caregivers upon the occurrence of a seizure. In the context of the ICASSP 2023 Seizure Detection Challenge, we pro- pose a lightweight machine-learning frame

Eye tracking has been used as part of software engineering and computer science research for a long time, and during this time new techniques for machine learning (ML) have emerged. Some of those techniques are applicable to the analysis of eye-tracking data, and to some extent have been applied. However, there is no structured summary available on which ML techniques are used for analysis in diff

Epilepsy is one of the most common neurological disorders that is characterized by recurrent and unpredictable seizures. Wearable systems can be used to detect the onset of a seizure and notify family members and emergency units for rescue. The majority of state-of-the-art studies in the epilepsy domain currently explore modern machine learning techniques, e.g., deep neural networks, to accurately

Full-duplex (FD) communication improves spectral efficiency by allowing simultaneous transmission and reception on the same frequency band. This paper analyzes a two-tier network consisting of macrocells and picocells, where base stations (BSs) operate in FD mode while users operate in half-duplex (HD) mode. However, interference remains a major challenge in these systems, significantly impacting

Massive multiple input, multiple output (MIMO) systems are crucial for enhancing spectral efficiency and link reliability in next-generation wireless communications. However, high Peak-to-Average Power Ratio (PAPR) remains a significant challenge, leading to power inefficiencies and signal distortion. This paper presents a low complexity method for PAPR reduction using hard clipping tailored for f

Clustering multiple motions from observed point trajectories is a fundamental task in understanding dynamic scenes. Most motion models require multiple tracks to estimate their parameters, hence identifying clusters when multiple motions are observed is a very challenging task. This is even aggravated for high-dimensional motion models. The starting point of our work is that this high-dimensionali

The advances in highly automated and autonomous transportation systems over the last decade have generated great interest in topics in the safe navigation of land vehicles. With distributed control strategies employed in the majority of applications of autonomous vehicles, such as traffic and formation control, the much-required resilience takes the form of fault-tolerance with respect to informat

Robotic systems for manipulation tasks are increasingly expected to be easy to configure for new tasks. While in the past, robot programs were often written statically and tuned manually, the current, faster transition times call for robust, modular and interpretable solutions that also allow a robotic system to learn how to perform a task. We propose the method Behavior-based Bayesian Optimizatio