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Your search for "kognition" yielded 1816 hits

A Robust Direction of Arrival Estimation Method for Coherently Distributed Sources in an Impulsive Noise Environment

In this work, a computationally efficient evolutionary algorithm is proposed for estimating the direction of arrival (DOA) of coherently distributed (CD) sources corrupted by additive impulsive noise. The typical method, such as distributed signal parameter estimation (DSPE) method, requires a 2-D spectral peak search and cannot allow for coherent signals. The proposed method in this article uses

Designing FDA Radars Robust to Contaminated Shared Spectra

This paper considers the problem of jointly designing the transmit waveforms and weights for a frequency diverse array (FDA) in a spectrally congested environment in which unintentional spectral interferences exist. Exploiting the properties of the interference signal induced by the processing of the multi-channel mixing and low-pass filtering FDA receiver, the interference covariance matrix struc

A Novel MIMO-SAR Echo Separation Solution for Reducing the System Complexity : Spectrum Preprocessing and Segment Synthesis

The problem of echo separation using digital beamforming (DBF) on receive for multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) is of notable importance to allow for practical systems. Regrettably, current DBF-MIMO-SAR schemes, such as the short-term shift-orthogonal (STSO) scheme, are computationally cumbersome, increasing the required hardware complexity. To alleviate this pro

DDPC : Automated Data-Driven Power-Performance Controller Design on-the-fly for Latency-sensitive Web Services

Traditional power reduction techniques such as DVFS or RAPL are challenging to use with web services because they significantly affect the services' latency and throughput. Previous work suggested the use of controllers based on control theory or machine learning to reduce performance degradation under constrained power. However, generating these controllers is challenging as every web service app

Time series anomaly detection in helpline call trends for early detection of COVID-19 spread across Sweden, 2020

Timely detection and surveillance of disease community spread is a potent tool for implementing effective public health interventions. This study investigates the National Telehealth Service (1177 helpline) across 18 regions in Sweden in 2020 to identify early signals of community transmission of COVID-19 at the beginning of the pandemic. Focusing on calls related to key COVID-19 symptoms (cough,

Neurophysiological Treatment Effects of Mesdopetam, Pimavanserin and Amantadine in a Rodent Model of Levodopa-Induced Dyskinesia

Levodopa provides effective symptomatic treatment for Parkinson's disease. However, nonmotor symptoms are often insufficiently relieved, and its long-term use is complicated by motor fluctuations and dyskinesia. To clarify mechanisms of levodopa-induced dyskinesia and pharmacological interventions aimed at reducing dyskinetic symptoms, we have here characterized the neurophysiological activity pat

Impulsive-compulsive behaviours and striatal neuroactivity in mildly parkinsonian rats under D2/3 agonist and L-DOPA treatment

Dopamine replacement therapy for Parkinson's disease can induce impulsive-compulsive behaviours (ICBs). Here we compare the D2/3 agonist ropinirole and L-DOPA, given alone or combined, with regard to their potential to induce ICBs in rats sustaining bilateral striatal injections of 6-hydroxydopamine. Daily treatment with ropinirole (2.5 mg/kg), L-DOPA (24.0 mg/kg), or their combination was given f

Formal Local Implication Between Two Neural Networks

Given two neural network classifiers with the same in-put and output domains, our goal is to compare the two networksin relation to each other over an entire input region (e.g., within avicinity of an input sample). To this end, we establish the foundationof formal local implication between two networks, i.e., N2D=⇒ N1, in an entire input region D. That is, network N1 consistently makesa correct d

Fast Relative Pose Estimation using Relative Depth

In this paper, we revisit the problem of estimating the relative pose from a sparse set of point-correspondences. For each point-correspondence we also estimate the relative depth, i.e. the relative distance to the scene point in the two images. This yields an additional constraint, allowing us to use fewer matches in RANSAC to generate the pose candidates. In the paper we propose two novel minima

Revisiting the P3P Problem

One of the classical multi-view geometry problems is the so called P3P problem, where the absolute pose of a calibrated camera is determined from three 2D-to-3D correspondences. Since these solvers form a critical component of many vision systems (e.g. in localization and Structure-from-Motion), there have been significant effort in developing faster and more stable algorithms. While the current s

Validation Of An Artificial Intelligence System To Assess Postural Orientation During A Single-Leg Squat

Alteration in postural orientation (i.e., the ability to align body segments in relation to each other) can occur due to pain or injury and is suggested to be a risk factor for traumatic knee injuries and early onset and progression of knee osteoarthritis. However, clinical and biomechanical assessment of postural orientation is time-consuming, thus new and faster methods are needed in order to be

Binary Forward-Only Algorithms

Today, the overwhelming majority of Internet of Things (IoT) and mobile edge devices have extreme resource limitations, e.g., in terms of computing, memory, and energy. As a result, training Deep Neural Networks (DNNs) using the complex Backpropagation (BP) algorithm on such edge devices presents a major challenge. Forward-only algorithms have emerged as more computation- and memory-efficient alte

Membership Inference Attack in Random Forests

Machine Learning (ML) offers many opportunities, but its reliance on personal data raises privacy concerns. One such example is the Membership Inference Attack (MIA), which aims to determine whether a specific data point was part of a model’s training dataset. In this paper, we investigate this attack on Random Forests (RFs) and propose a method to quantify their vulnerability to MIA. We also demo

Federated Learning for Obstacle Detection to Assist the Visually-Impaired Using Augmented Reality

Vision impairment increases risks such as social isolation, mobility challenges, and falls. Wearable Augmented Reality (AR) devices with Artificial Intelligence (AI) can enhance sensory perception by enabling real-time recognition of obstacles, assisting visually impaired individuals during street navigation, aiming to reduce the risk of falls. In this paper, we propose a Federated Learning (FL) f

Karolina Löwgren

Hur gick dina tankar när du valde audionomprogrammet? – Jag hade tidigare läst lingvistik och fonetik. Jag började då att intressera mig för hörseln och audionomprogrammet kändes därför rätt för mig.Varför blev det Lunds universitet? – Jag bodde i Lund, men jag tyckte även att det lockande att Lunds universitet har ett magisterprogram i audiologi så att jag kunde läsa vidare efter kandidatprogramm

https://www.lu.se/lubas/i-uoh-lu-vgaud/karolina-lowgren - 2025-12-21

Will I stay or will I go? Eye morphology predicts individual migratory propensity in a partial migrant

Billions of animals undertake migratory journeys every year, with powerful consequences for ecosystem dynamics. Key behaviours that enable successful migration are often guided by the visual system. The amount and quality of information that animals can extract from visual scenes are directly related to structural eye size—larger eyes can house larger pupils, enhancing light-gathering capacity and

A machine learning model for prediction of 30-day primary graft failure after heart transplantation

Background: Primary graft failure (PGF) remains the most common cause of short-term mortality after heart transplantation. The main objective was to develop and validate a risk model for prediction of short-term mortality due to PGF after heart transplantation using the ISHLT Heart Transplant Registry. Methods: We developed a non-linear artificial neural networks (ANN) model to evaluate the associ

Multi-Target Sonar Localization Using an Optimal Mass Transport Framework

This article addresses the problem of multi-target localization from acoustic time delay measurements in a 3-D underwater environment, taking into account the depth-dependent sound speed of the underwater wave propagation. Specifically, we formulate the problem as an optimal mass transport (OMT) optimization over a grid of potential candidate positions. By exploiting an iterative refinement approa