The 10 most recently published documents
Interacting multiple model filters are most commonly used in the context of maneuvering targets, as they can represent the different dynamics of a real system by combining the estimates of multiple models. However, the interacting multiple model approach generally requires more computational effort than a single Kalman filter. In this work, down-sampling is used to reduce the computational effort. We propose an adaptive scheme to maintain the accuracy of the estimator to a defined level. To this end, the trace of the innovation covariance matrix is evaluated, and if it lies above a certain threshold, out-of-sequence measurements are iteratively used to improve the estimate until the uncertainty threshold is met. The approach is evaluated by Monte Carlo analysis. The results show that with this approach, the number of measurements to be processed, and thus the computational effort can be dynamically reduced, while the accuracy remains at a desired level.
In extended object tracking, basic parametric shapes such as ellipses and rectangles or non-parametric shape representations such as Fourier series or Gaussian processes can be utilized as shape priors. However, flexible non-parametric shape representations can be disproportionately detailed and computationally intensive for many applications. Therefore, we propose to adopt deformable superellipses for a low-dimensional and flexible representation of basic parametric shapes in this paper. We present a measurement model in 2D space that can cope with boundary and interior measurements simultaneously by recursively estimating an artificial noise variance for interior measurements. We investigate and compare the model in a simulated and real-world maritime scenario with the result that the combination of deformable superellipses and artificial measurement noise estimation performs better than state-of-the-art methods.
In extended object tracking, random matrices are commonly used to filter the mean and covariance matrix from measurement data. However, the relation from mean and covariance matrix to the extension parameters can become challenging when a lidar sensor is used. To address this, we propose virtual measurement models to estimate those parameters iteratively by adapting them, until the statistical moments of the measurements they would cause, match the random matrix result. While previous work has focused on 2D shapes, this paper extends the methodology to encompass 3D shapes such as cones, ellipsoids and rectangular cuboids. Additionally, we introduce a classification method based on Chamfer distances for identifying the best-fitting shape when the object’s shape is unknown. Our approach is evaluated through simulation studies and with real lidar data from maritime scenarios. The results indicate that a cone is the best representation for sailing boats, while ellipsoids are optimal for motorboats.
Agrivoltaics is an emerging technology and combines the agricultural and energy generation sectors by enabling dual land use. The use of photovoltaic modules on agricultural land, for example in overhead or interspace systems, which are the focus of this work, can create synergy effects from which both sectors can benefit.
The aim of this study is to analyse the potential of agrivoltaics in the Seychelles. The focus of the potential analysis is on an acceptance study in which the perception of 75 farmers towards agrivoltaics and their willingness to implement the new technology is analysed. The data collection was carried out with the help of personal surveys.
The results of the studies show that agrivoltaics have potential in the Seychelles. Potential was identified in the use of irrigation systems and the self-supply of electricity, among other things. The results of the study also show that there is a need for further research on agrivoltaics in the Seychelles, for example in the area of field studies/test set ups and financing concepts.
With the emergence of new sensor technologies, such as fiber optic sensors (FOSs), compared to traditional mechanical sensors, unobtrusive sleep monitoring has been a research focus for decades. This work aims to provide a guide to current bed-based sensor technologies with diverse applications in various settings. We conducted a retrospective literature review, summarizing the state-of-the-art research over the past decade on non-contact bed-based sensor technology in sleep monitoring. We developed a three-category terminology: unobtrusive sensor technology, application, and subject. A total of 263 unique articles were acquired from three databases and screened for relevance, resulting in 21 papers selected for in-depth analysis. The findings revealed eight types of sensors: six mechanical sensors (pressure, accelerometer, piezoelectric, load cell, electromechanical film (EMFI), and hydraulic) and two FOSs (fiber Bragg grating and microbend FOS) that are integrated with or positioned under the bed at three levels of unobtrusiveness. We identified 15 parameters, with heart rate (HR) (14) and respiratory rate (RR) (13) being the most frequently measured. These parameters are generally categorized into three applications: disease-related diagnosis (18), general sleep analysis (9), and general well-being (11). The results indicated that sleep apnea (5) and insomnia (2) were the most frequently detected sleep disorders. Additionally, 59.1% (13) of the systems were tested in a lab environment, with only one undergoing clinical trials. In summary, there is a clear lack of convincing proof of the systems’ effectiveness in continuous in-home sleep monitoring.
Advanced Classifiers and Feature Reduction for Accurate Insomnia Detection Using Multimodal Dataset
(2024)
Sleep deprivation is a significant contributor to various diseases, leading to poor cognitive function, decreased performance, and heart disorders. Insomnia, the most prevalent sleep disorder, requires more effective diagnosis and screening for proper treatment. Actigraphic data and its combination with physiological sensors like electroencephalogram (EEG), electrocardiogram (ECG), and body temperature have proven significant in predicting insomnia using machine learning methods. Studies focusing solely on actigraphic data achieved an accuracy of 84%, combining it with other wearable devices increased accuracy to 88%, and 2-channel EEG alone yielded an accuracy of 92%, but limits scalability and practicality in real-world settings. Here we show that using the hybrid approach of incorporating both recursive feature elimination (RFE) and principal component analysis (PCA) on sleep and heart data features yields outstanding results, with the multi-layer perception (MLP) achieving an accuracy of 95.83% and an F1 score of 0.93. The top-ranked features are predominantly sleep-related and time-domain RR interval. The dependent variables in our study have been extracted from the self-report Pittsburgh Sleep Quality Index questionnaire responses. Our findings emphasize the importance of tailoring feature sets and employing appropriate reduction techniques for optimal predictive modeling in sleep-related studies. Our results demonstrate that the ensemble classifiers generalize well on the dataset regardless of the feature count, while other algorithms are hindered by the curse of dimensionality.
The digital transformation urges organizations to become digital enterprises. Digital enterprises require the integration of business and IT to efficiently leverage digital technologies. However, there is a lack of a framework that guides organizations on what organizational capabilities are required to achieve business-IT integration. The goal of this paper is to identify these capabilities. From a single case study, we derived twelve organizational capabilities that a digital enterprise, driven by technology, should design in terms of its people, organizational structure, and tasks to establish business-IT integration. Thus, this paper provides guidance for organizations to approach business-IT integration as a foundation for their path into a digital enterprise.
In this study, we quantify and compare the energy saving potential of intelligent thermostats in a seminar room under five different scenarios using a combination of thermal simulations and measurements. Coupling the thermostats to occupancy and window contact sensors results to be the most effective installation to maximize energy savings under minimal loss of comfort by lower temperatures.
Im Sommersemester 2024 konnte ich mich während eines Freistellungssemesters ganz einem Forschungsthema widmen. Das Thema „Compressed Sensing“ stand im Mittelpunkt dieses Semesters. Dies ist eine moderne Methodik der Datenerfassung, die in mein Berufungsgebiet der „Sensorik und Messtechnik“ als Professor an der HTWG Konstanz fällt.
Zu Beginn dieses Semesters habe ich mich eingearbeitet in dieses Forschungs-Themengebiet. Dazu habe ich Fachbücher, wissenschaftliche Forschungsartikel und Review-Paper gesucht, teilweise angeschafft, und durchgearbeitet.
Nach dieser umfangreichen Literaturrecherche habe ich dann einige Software-Bibliotheken für diese Methodik evaluiert. Mehrere dieser Bibliotheken habe ich installiert und mit Anwendungsbeispielen evaluiert. Ein konkretes, industrielles Messverfahren für Lidar-Sensoren habe ich vertieft mit der Methode der „Finite Rate of Innovation“ untersucht und daraus ein Konzept für einen Forschungsantrag erarbeitet.
Zum Abschluss des Semesters habe ich mit mehreren Firmen der Region Kontakt aufgenommen und geklärt ob Interesse an diesem Forschungsthema besteht. Interessierte Firmen habe ich vor Ort besucht, mein Forschungsthema dort vorgestellt und die Möglichkeit von industriellen Anwendungsmöglichkeiten und Forschungs-Kooperationen diskutiert.