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State-of-the-Art Sensors Technology in Germany 2022
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State-of-the-Art Sensors Technology in Germany 2022

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "State-of-the-Art Sensors Technologies".

Deadline for manuscript submissions: closed (10 November 2022) | Viewed by 7214

Special Issue Editor

Dr. Bakhtiar Feizizadeh

E-Mail Website
Guest Editor
Faculty of Mathematics and Natural Sciences, Department of Geography, Humboldt University of Berlin, Invalidenstrasse 42, 10115 Berlin, Germany
Interests: remote sensing; space science; image analysis; geoinformation techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to provide a comprehensive overview of state-of-the-art sensor technology in Germany. Research articles and reviews are sought that provide insight into any aspect of novel sensor development and application in Germany. Topics of interest include, but are not limited to, the following:

  • Remote Sensing;
  • Intelligent Sensors;
  • Optical Sensors;
  • Sensing and Imaging;
  • Environmental Sensing;
  • Sensors for Smart Agriculture;
  • Sensors and Robotics;
  • Radar Sensors;
  • Precision Agriculture;
  • climate Change Monitoring Technology;
  • Sensor Networks;
  • Electronic Sensors;
  • Materials and Technology;
  • Geospatial Methods;
  • Data Science;  
  • Internet of Things;
  • Biomedical Sensors;
  • Wearables;
  • Nanosensors;
  • Vehicular Sensing;
  • Navigation and Positioning;
  • Industrial Sensors;
  • Physical, Chemical and Biological Sensors.

Dr. Bakhtiar Feizizadeh
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

17 pages, 2584 KiB  
Article
Evaluation of the Influence of Machine Tools on the Accuracy of Indoor Positioning Systems
by Till Neuber, Anna-Maria Schmitt, Bastian Engelmann and Jan Schmitt
Sensors 2022, 22(24), 10015; https://doi.org/10.3390/s222410015 - 19 Dec 2022
Cited by 2 | Viewed by 1639
Abstract
In recent years, the use of indoor localization techniques has increased significantly in a large number of areas, including industry and healthcare, primarily for monitoring and tracking reasons. From the field of radio frequency technologies, an ultra-wideband (UWB) system offers comparatively high accuracy [...] Read more.
In recent years, the use of indoor localization techniques has increased significantly in a large number of areas, including industry and healthcare, primarily for monitoring and tracking reasons. From the field of radio frequency technologies, an ultra-wideband (UWB) system offers comparatively high accuracy and is therefore suitable for use cases with high precision requirements in position determination, for example for localizing an employee when interacting with a machine tool on the shopfloor. Indoor positioning systems with radio signals are influenced by environmental obstacles. Although the influence of building structures like walls and furniture was already analysed in the literature before, the influence of metal machine tools was not yet evaluated concerning the accuracy of the position determination. Accordingly, the research question for this article is defined: To what extent is the positioning accuracy of the UWB system influenced by a metal machine tool?The accuracy was measured in a test setup, which consists of a total of four scenarios in a production environment. For this purpose, the visual contact between the transmitter and the receiver modules, including the influence of further interfering factors of a commercially available indoor positioning system, was improved step by step from scenario 1 to 4. A laser tracker was used as the reference measuring device. The data was analysed based on the type A evaluation of standard uncertainty according to the guide to the expression of uncertainty in measurement (GUM). It was possible to show an improvement in standard deviation from 87.64cm±32.27cm to 6.07cm±2.24cm with confidence level 95% and thus provides conclusions about the setup of an indoor positioning system on the shopfloor. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Germany 2022)
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15 pages, 1492 KiB  
Article
xLength: Predicting Expected Ski Jump Length Shortly after Take-Off Using Deep Learning
by Johannes Link, Leo Schwinn, Falk Pulsmeyer, Thomas Kautz and Bjoern M. Eskofier
Sensors 2022, 22(21), 8474; https://doi.org/10.3390/s22218474 - 03 Nov 2022
Viewed by 2023
Abstract
With tracking systems becoming more widespread in sports research and regular training and competitions, more data are available for sports analytics and performance prediction. We analyzed 2523 ski jumps from 205 athletes on five venues. For every jump, the dataset includes the 3D [...] Read more.
With tracking systems becoming more widespread in sports research and regular training and competitions, more data are available for sports analytics and performance prediction. We analyzed 2523 ski jumps from 205 athletes on five venues. For every jump, the dataset includes the 3D trajectory, 3D velocity, skis’ orientation, and metadata such as wind, starting gate, and ski jumping hill data. Using this dataset, we aimed to predict the expected jump length (xLength) inspired by the expected goals metric in soccer (xG). We evaluate the performance of a fully connected neural network, a convolutional neural network (CNN), a long short-term memory (LSTM), and a ResNet architecture to estimate the xLength. For the prediction of the jump length one second after take-off, we achieve a mean absolute error (MAE) of 5.3 m for the generalization to new athletes and an MAE of 5.9 m for the generalization to new ski jumping hills using ResNet architectures. Additionally, we investigated the influence of the input time after the take-off on the predictions’ accuracy. As expected, the MAE becomes smaller with longer inputs. Due to the real-time transmission of the sensor’s data, xLength can be updated during the flight phase and used in live TV broadcasting. xLength could also be used as an analysis tool for experts to quantify the quality of the take-off and flight phases. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Germany 2022)
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21 pages, 5603 KiB  
Article
QADI as a New Method and Alternative to Kappa for Accuracy Assessment of Remote Sensing-Based Image Classification
by Bakhtiar Feizizadeh, Sadrolah Darabi, Thomas Blaschke and Tobia Lakes
Sensors 2022, 22(12), 4506; https://doi.org/10.3390/s22124506 - 14 Jun 2022
Cited by 14 | Viewed by 2983
Abstract
Classification is a very common image processing task. The accuracy of the classified map is typically assessed through a comparison with real-world situations or with available reference data to estimate the reliability of the classification results. Common accuracy assessment approaches are based on [...] Read more.
Classification is a very common image processing task. The accuracy of the classified map is typically assessed through a comparison with real-world situations or with available reference data to estimate the reliability of the classification results. Common accuracy assessment approaches are based on an error matrix and provide a measure for the overall accuracy. A frequently used index is the Kappa index. As the Kappa index has increasingly been criticized, various alternative measures have been investigated with minimal success in practice. In this article, we introduce a novel index that overcomes the limitations. Unlike Kappa, it is not sensitive to asymmetric distributions. The quantity and allocation disagreement index (QADI) index computes the degree of disagreement between the classification results and reference maps by counting wrongly labeled pixels as A and quantifying the difference in the pixel count for each class between the classified map and reference data as Q. These values are then used to determine a quantitative QADI index value, which indicates the value of disagreement and difference between a classification result and training data. It can also be used to generate a graph that indicates the degree to which each factor contributes to the disagreement. The efficiency of Kappa and QADI were compared in six use cases. The results indicate that the QADI index generates more reliable classification accuracy assessments than the traditional Kappa can do. We also developed a toolbox in a GIS software environment. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Germany 2022)
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