In this paper, we present a multipath-based simultaneous localization and mapping (SLAM) algorithm that continuously adapts mulitiple map feature (MF) models describing specularly reflected multipath components (MPCs) from flat surfaces and point-scattered MPCs, respectively. We develop a Bayesian model for sequential detection and estimation of interacting MF model parameters, MF states and mobil

Many concepts for future generations of wireless communication systems use coherent processing of signals from many distributed antennas. The aim is to improve communication reliability, capacity, and energy efficiency and provide possibilities for new applications through integrated communication and sensing. The large bandwidths available in the higher bands have inspired much work regarding sen

Understanding the evolution of multipath components (MPCs) in real radio channels is crucial to enhancing channel modeling and multipath-assisted positioning. This paper provides an experimental analysis of the behavior of MPCs originating from a standard building facade at millimeter wave (mmWave) frequencies. Utilizing a high-resolution channel parameter estimation method alongside a joint clust

In this work, we introduce a spatio-temporal kernel for Gaussian process (GP) regression-based sound field estimation. Notably, GPs have the attractive property that the sound field is a linear function of the measurements, allowing the field to be estimated efficiently from distributed microphone measurements. However, to ensure analytical tractability, most existing kernels for sound field estim

Interpretability has become a crucial component in the Machine Learning (ML) domain. This is particularly important in the context of medical and health applications, where the underlying reasons behind how an ML model makes a certain decision are as important as the decision itself for the experts. However, interpreting an ML model based on limited local data may potentially lead to inaccurate co

Intratelencephalic neurons are a crucial class of cortical principal neurons that heavily innervate the striatum and cortical areas bilaterally. Their extensive cortico-cortical and cortico-striatal connectivity enables sensorimotor integration within the telencephalon, but their role in motor control remains poorly understood. Here, we used a chemogenetic approach to explore the role of intratele

Anthropogenic activities threaten many wildlife populations by increasing mortality rates, making it crucial to identify the locations and causes of mortality to inform conservation actions. Technological advancements, such as GPS satellite tracking, enable precise recording of wildlife movements. High-resolution data from such devices can facilitate rapid carcass recovery and provide insights int

This work addresses the design of multi-agent coordination through high-order consensus protocols. While first-order consensus strategies are well-studied—with known robustness to uncertainties such as time delays, time-varying weights, and nonlinearities like saturations—the theoretical guarantees for high-order consensus are comparatively limited. We propose a compositional control framework tha

The human brain performs tasks with an outstanding energy efficiency, i.e., with approximately 20 Watts. The state-of-the-art Artificial/Deep Neural Networks (ANN/DNN), on the other hand, have recently been shown to consume massive amounts of energy. The training of these ANNs/DNNs is done almost exclusively based on the back-propagation algorithm, which is known to be biologically implausible. Th

Machine learning techniques often lack formal correctness guarantees, evidenced by the widespread adversarial examples that plague most deep-learning applications. This lack of formal guarantees resulted in several research efforts that aim at verifying Deep Neural Networks (DNNs), with a particular focus on safety-critical applications. However, formal verification techniques still face major sca

Context: Evidence-based software engineering (EBSE) aims to improve research utilization in practice. It relies on systematic methods to identify, appraise, and synthesize existing research findings to answer questions of interest for practice. However, the lack of practitioners’ involvement in these studies’ design, execution, and reporting indicates a lack of appreciation for the need for knowle

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

In this study, a combined workflow of computational methodologies is introduced to explore the transformative landscape of the ancient city of Savatra (Central Anatolia Region, Türkiye), which faces long-term risks stemming from natural and anthropogenic threats. Emphasis was placed on regional and local scale landscape analysis, employing aerial and ground-based remote-sensing techniques to unrav

We investigated whether sign-naïve learners can infer and learn the meaning of signs after minimal exposure to continuous, naturalistic input in the form of a weather forecast in Swedish Sign Language. Participants were L1-English adults. Two experimental groups watched the forecast once (N=40) or twice (N=42); a control group did not (N=42). Participants were then asked to assign meaning to 22 ta

Among the hundreds of known hot Jupiters (HJs), only five have been found to have companions on short-period orbits. Within this rare class of multiple planetary systems, the architecture of WASP-47 is unique, hosting an HJ (planet-b) with both an inner and an outer sub-Neptunian mass companion (-e and -d, respectively) as well as an additional non-transiting, long-period giant (-c). The small per

Using functional imaging and neural circuit reconstructions, a recent study reveals head direction neurons in the anterior hindbrain of zebrafish that resemble insect head-direction cells to a surprising degree.

Shape control strategies seek to bring deformable objects towards a desired target shape. However, conventional methods focus on reaching the target shape without considering the extent to which the object is deformed during the control process. Control actions may generate unnecessary deformations and thus, increase the possibility of object over-stressing and failure. In this letter, we tackle t

Control theory allows one to design controllers that are robust to external disturbances, model simplification, and modelling inaccuracy. Researchers have investigated whether the robustness carries on to the controller’s digital implementation, mostly looking at how the controller reacts to either communication or computational problems. Communication problems are typically modelled using random