Classification of Sonar Targets in Air: A Neural Network Approach - Kroh, Patrick; Simon, Ralph; Rupitsch, Stefan (Sensors, 2019)
Ultrasonic sonar sensors are commonly used for contactless distance measurements in application areas such as automotive and mobile robotics. They can also be exploited to identify and classify sound-reflecting objects (targets), which may then be used as landmarks for navigation. In the presented work, sonar targets of different geometric shapes and sizes are classified with custom-engineered features. Artificial neural networks (ANNs) with multiple hidden layers are applied as classifiers and different features are tested as well as compared. We concentrate on features that are related to target strength estimates derived from pulse-compressed echoes. In doing so, one is able to distinguish different target geometries with a high rate of success and to perform tests with ANNs regarding their capabilities for size discrimination of targets with the same geometric shape. A comparison of achievable classifier performance with wideband and narrowband chirp excitation signals was conducted as well. The research indicates that our engineered features and excitation signals are suitable for the target classification task.
CRIS-Team WoS Importer:2019-04-12

Der Plasma-Lautsprecher als Lehrversuch - Hubert, Florian; Dorsch, Philipp; Kroh, Patrick; Günther, Hans-Michael; Rupitsch, Stefan (PhyDid A, Physik und Didaktik in Schule und Hochschule [Elektronische Ressource], 2021)
In Lehrveranstaltungen lassen sich physikalische Effekte und deren zugrunde liegende Theorie anschaulich anhand von Experimenten verdeutlichen. Besonderes Interesse wird hierbei durch optische, akustische und olfaktorische Sinneswahrnehmungen und vor allem durch die Kombination dieser Reize geweckt. Plasma-Lautsprecher stellen vor diesem Hintergrund ein hervorragendes Anschauungsobjekt für die Physikdidaktik im Fachgebiet „Akustik“ mit hohem motivierendem Potenzial dar. Im Rahmen dieses Betrags wird ein einfaches, verständliches und kostengünstiges Schaltungskonzept für einen Plasma-Lautsprecher vorgestellt und der zugrundeliegende Schallentstehungsmechanismus anschaulich erläutert. Anhand der vorgestellten Experimente können die Lernenden die akustischen Eigenschaften des Lautsprechers qualitativ und quantitativ untersuchen. Abschließend wird ein didaktisches Konzept für den Versuch gegeben. Der didaktische Schwerpunkt des Versuchs kann nach Vorwissen der Lernenden an die Zielgruppe angepasst werden.
Keywords: Entwicklung von Lehrversuchen; Didaktische Aufbereitung; neue Experimentieranordnung

Evaluation of Neural Network Architectures for Classification of Sonar Echoes in Air - Kroh, Patrick; Mrochen, Jan; Rupitsch, Stefan (Proceedings of IEEE Sensors, 2020)
In this contribution, we will classify sonar targets in air. The gained semantic environment information may be beneficial for autonomous mobile systems, such as robots and transport systems, for navigation in unknown as well as challenging settings with little or ambiguous optical and electromagnetic features. Examples for aforementioned environments may include food processing plants as well as medical buildings, in which obstacles are often comprised of transparent plastic or glass and may also have large shiny/reflecting surfaces. Targets are classified into three generic categories (flat, convex, concave), based on echoes from three subsequent recording positions. Multiple artificial neural networks with different architectures are designed, trained and evaluated as classifiers. The networks show promising prediction accuracy and an embedded implementation appears to be feasible. Especially, combinations of capsule networks and long short-term memory networks appear to be promising candidates for high classification performance.
CRIS-Team Scopus Importer:2021-04-19
Keywords: feature extraction; neural networks; sonar detection; sonar measurements

Frequency Dependent Sound Radiation of Ultrasonic Wedge Transducers - Ploß, Peter; Rupitsch, Stefan; Kroh, Patrick; Lerch, Reinhard (Sensors and Measuring Systems 2014; 17. ITG/GMA Symposium, 2014)

Low-power optimized, widely customizable, time-controlled wireless sensor/actuator data transmission with commercial off-the-shelf standard components - Kroh, Patrick; Fink, Michael; Günther, Hans-Michael; Lerch, Reinhard (Proceedings - AMA Conferences 2017 with SENSOR and IRS², 2017)
For common sensor/actuator applications in which signal data is only forwarded, ALOHA-protocol-based radio transmissions as well as full-stack radio solutions do not appear to be the most energy-efficient solutions. Thus, an own low-power and widely customizable radio solution has been developed. It is based on standard general purpose radio components and achieves high energy efficiency by use of time-controlled (TDMA) low duty-cycle transmissions. The presented solution is especially well suited to the task of periodic transmissions of measurement values. For demonstration, a sensor node has been implemented and the radio soluti performance has been evaluated.
Keywords: Wireless; Sensor; Low-Power; TDMA; Standard Components

Neural Network-Based Detection of Ultrasonic Targets with Respect to Noise and Number of Sampling Positions - Kroh, Patrick; Simon, Ralph; Rupitsch, Stefan (2019 IEEE International Ultrasonics Symposium (IUS), 2019)
A neural network-based approach for detection of sonar targets in air is presented in this contribution. Our approach may facilitate autonomous mobile systems to reliably detect and classify objects in their surrounding by using sonar information. This task might be extremely important in changing as well as unorganized environments. We perform target identification with long short-term memory networks as classifiers. Such are capable of dealing with variable numbers of echoes from multiple positions per input sequence, which facilitates more flexible operation. The impact of the number of recording positions per sequence and of noise is investigated. Furthermore, we demonstrate the improvement in classification performance in comparison to previously obtained results from multilayer perceptron
Keywords: sonar measurements; sonar detection; neural networks; feature extraction