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Objective. This study aimed to investigate the potential of contrastive learning to improve auditory attention decoding (AAD) using electroencephalography (EEG) data in challenging cocktail-party scenarios with competing speech and background noise. Approach. Three different models were implemented for comparison: a baseline linear model (LM), a non-LM without contrastive learning (NLM), and a
Objective: Enhancing the reliability of myoelectric controllers that decode motor intent is a pressing challenge in the field of bionic prosthetics. State-of-the-art research has mostly focused on Supervised Learning (SL) techniques to tackle this problem. However, obtaining high-quality labeled data that accurately represents muscle activity during daily usage remains difficult. We investigate th
In this paper we propose a technique to enhance the performance of a Proportional-Integral-Derivative (PID)-based control structure for Depth-of-Hypnosis control in total intravenous anesthesia when set-point changes are required during the maintenance phase. In particular, the PID controller, tuned for disturbance rejection, is integrated with a feedforward action based on Model Predictive Contro
Two-view estimation is a fundamental problem in 3D computer vision, and an important sub-task of multi-view estimation pipelines such as Structure-from-Motion (SfM) and Simultaneous Localization and Mapping (SLAM). In recent years, the main focus in the field has been on keypoint-based methods, where interest points are first detected and matched across the two images, followed by robust estimatio
Internet of Things (IoT) are one of the key enablers of personalized health. However, IoT devices often have stringent constraints in terms of resources, e.g., energy budget, and, therefore, limited possibilities to exploit the state-of-the-art Deep Neural Networks (DNNs). Energy-aware Neural Architecture Search (NAS) is proposed to tackle this challenge, by exploring lightweight DNN (DNN) archite
Being deeply embedded in the mythological framework of old-school Christian angelology, the theodicy presented in this article outlines a thoroughgoing and unexpectedly simple solution to the problem of evil. Unlike other Satan-oriented theodicies, whose central idea is that natural evil is reducible to moral evil by taking the malevolent actions of demons into consideration, it accounts for both Being deeply embedded in the mythological framework of old-school Christian angelology, the theodicy presented in this article outlines a thoroughgoing and unexpectedly simple solution to the problem of evil. Unlike other Satan-oriented theodicies, whose central idea is that natural evil is reducible to moral evil by taking the malevolent actions of demons into consideration, it accounts for both
Many problems in computer vision can be formulated as geometric estimation problems, i.e. given a collection of measurements (e.g. point correspondences) we wish to fit a model (e.g. an essential matrix) that agrees with our observations. This necessitates some measure of how much an observation 'agrees' with a given model. A natural choice is to consider the smallest perturbation that makes the o
JavaDL ArtifactJavaDL is a language and toolchain for analyzing Java at the source level. JavaDL is based on MetaDL, a variant of Datalog that adds syntactic pattern matching, here specialized to pattern matching on Java.
Dexterous in-hand manipulation offers significant potential to enhance robotic manipulator capabilities. This paper presents a sensori-motor architecture for in-hand slip-aware control, being embodied in a sensorized gripper. The gripper in our architecture features rapid closed-loop, low-level force control and is equipped with sensors capable of independently measuring contact forces and sliding
Normalizing flows (NFs) have been shown to be advantageous in modeling complex distributions and improving sampling efficiency for unbiased sampling. In this work, we propose a new class of continuous NFs, ascent continuous normalizing flows (ACNFs), that makes a base distribution converge faster to a target distribution. As solving such a flow is non-trivial and barely possible, we propose a prac
The understanding of small antennas has evolved tremendously since the initial investigations close to 80 years ago. In this presentation, we highlight some fundamental results on Q-factor, bandwidth, and gain.
Static program analysis plays a crucial role in ensuring the quality and security of software applications by detecting bugs and potential vulnerabilities in the code. Traditionally, these analyses are performed offline, either as part of the continuous integration/continuous deployment pipeline or overnight on the entire repository. However, this delayed feedback disrupts developer productivity,
Background: Testing is imperative to validate the functionalities and safety of autonomous driving systems. Simulated scenario-based testing is commonly adopted for autonomous driving systems, which aims to construct various driving scenarios and validate the autonomous driving systems in simulation. Nevertheless, identifying relevant test scenarios, especially critical ones that expose hazards or
Researchers have envisioned and pioneered data-driven programming assistance for developers based on their interaction with the tools via multiple sensors such as eye trackers, microphones, and AI. However, these new sensors gather sensitive data from programmers, to what extent users can accept them and in what form they may work well are largely unclear. Meanwhile, developer tools such as static
Static analysis tools play a crucial role in software development by detecting bugs and vulnerabilities. However, running these tools separately from the code editing process often causes developers to switch contexts, which can reduce productivity. Previous work has shown how Reference Attribute Grammars (RAGs) can be used for declarative implementation of competitive tooling for intraprocedural
Background and objective:This study aims to enhance the performance of a closed-loop anesthetic depth control system by fusing noise-corrupted clinical measurements with a non-perfect pharmacological model.Methods:We implement a Kalman filter to constitute a trade-off between model prediction and measurement signal dependence for depth of hypnosis (DoH) control using a previously evaluated PID con
In massive multiple-input multiple-output (MIMO) systems, the large size of channel state information (CSI) matrix significantly increases the computational complexity of uplink detection and size of required memory to store the channel data. To address these challenges, we propose to perform detection in the angular domain, where the channel information can be presented in a more condensed way. T
Multi-cluster federation is envisioned to be the next-generation cloud infrastructure, where it will play a vital part in the realization of concepts such as edge and fog computing. Orchestrating applications in federated environments poses new challenges to well-known research problems in various fields, such as load-balancing, auto-scaling, resource allocation and service migration. However, as
In recent years, because the cloud can provide huge advantages regarding storage and computing resources, industry has been motivated to move industrial control systems to the cloud. However, the cloud also introduces major security challenges, since moving control systems to the cloud can enable attackers to infiltrate the system and establish an attack that can lead to damages and disruptions wi
