Effect of contact pressure on porcine postmortem brain tissue impedance. - In: Proceedings of International Workshop on Impedance Spectroscopy, IWIS 2022, (2022), S. 36-40
In this experimental study we demonstrate the influence of contact pressure on porcine postmortem brain tissue impedance using a movable electrode array and a load cell. We show that the variation of the contact pressure between the tissue and the measurement probe leads to a coefficient of variation in the measured impedance of under 3%. Its influence can therefore be neglected in the investigated use case. Further, we fit the measured impedances to an equivalent circuit model and compare the resistance of grey and white matter brain tissue based on the model parameters.
https://doi.org/10.1109/IWIS57888.2022.9975122
Machine learning routing protocol in mobile IoT based on software-defined networking. - In: 2022 IEEE Conference on Network Function Virtualization and Software Defined Networks, (2022), S. 108-111
The Internet's pervasive influence in all aspects of life has caused the number of heterogeneous devices connected to this network to grow exponentially. As a result, recognizing these devices and their management has led to the emergence of a new paradigm called the “Internet of Things” (IoT). Sensor networks are the essential pillar of the Internet of Things. Due to their low cost and ease of deployment, they can be implemented in a structured or unstructured way in a dynamic physical environment to manage and monitor the dynamic conditions of the desired area in various applications. Nevertheless, what is noteworthy in this regard is the limited resources of sensor networks, which cannot meet the diverse needs of the Internet of Things, so appropriate solutions must be adopted to some challenges, such as scalability and routing in dynamic topologies. Against this challenge, the SDN paradigm has attracted massive attention because it is possible to add new capabilities to networks with limited resources to reduce the overhead caused by processing and computing in sensor nodes and delegate these energy-consuming tasks to the controller. On the other hand, machine learning techniques have also shown their ability to optimize routing and increase the quality of service, reliability, and security by using statistics and information obtained from these networks. However, less research has addressed sensor nodes' mobility and challenges in resource-constrained IoT networks.
https://doi.org/10.1109/NFV-SDN56302.2022.9974791
Versatile safe autonomous intersection management protocol for heterogeneous connected vehicles. - In: 2022 IEEE Vehicle Power and Propulsion Conference (VPPC), (2022), insges. 7 S.
In this paper, we employ a novel safe centralized intersection management (IM) scheme for heterogeneous classes of connected vehicles such as passenger vehicles (PVs), buses, vans, trucks, emergency vehicles (EVs), trams and also vulnerable road users (VRUs) including motorcycles, bicycles, and moped. A rule-based algorithm undertakes safe coordination using different criteria such as intersection traffic rules, vehicle types and road priorities. Besides, in the proposed system, dynamics and behavior of the road users, which might be totally distinct, are taken into account. These underlying characteristic behaviors comprise of reaction distance $(D_r)$, stopping distance $(D_s)$, braking distance $(D_b)$, braking lag distance $(D_bl)$ as well as deceleration (d) and velocity (v) of the vehicles in an urban environment. In addition, special lanes for cyclists and tram tracks are considered in the layout. Furthermore, in order to more properly image the realistic traffic situations, we have integrated the impact of various road conditions in our system in terms of dry, wet, snowy, and icy. Performance of the system is analyzed in several sparse and dense traffic situations with respect to different criteria such as speed and travel time. Additionally, efficiency is also compared to traditional intersection management systems like traffic lights (TLs).
https://doi.org/10.1109/VPPC55846.2022.10003461
Enhancement of vision-based 3D reconstruction systems using radar for smart farming. - In: 2022 IEEE International Workshop on Metrology for Agriculture and Forestry, (2022), S. 155-159
Digital field recordings are central to most precision agriculture systems since they can replicate the physical environment and thus monitor the state of an entire field or individual plants. Using different sensors, such as cameras and radar, data can be collected from various domains. Through the combination of radio wave propagation and visible light phenomena, it is possible to enhance, e.g., the optical condition of a fruit with internal parameters such as the water content. This paper proposes a method to correct sensor errors to perform data fusion. As an example, we observe a watermelon with camera and radar sensors and present a system architecture for the visualization of both sensors. For this purpose, we constructed a handheld platform on which both sensors are mounted. In our report, the radar is analyzed in terms of systematic and stochastic errors to formulate an angle-dependent mapping function for error correction. It is successfully shown that camera and radar data are correctly assigned with a watermelon used as a target object, demonstrated by a 3D reconstruction. The proposed system shows promising results for sensor overlay, but radar data remain challenging to interpret.
https://doi.org/10.1109/MetroAgriFor55389.2022.9964699
One-dimensional convolutional neural network for detection and mitigation of DDoS attacks in SDN. - In: Machine learning for networking, (2022), S. 11-28
In Software-Defined Networking (SDN), the controller plane is an essential component in managing network traffic because of its global knowledge of the network and its management applications. However, an attacker might attempt to direct malicious traffic towards the controller, paralyzing the entire network. In this work, a One-Dimensional Convolutional Neural Network (1D-CNN) is used to protect the controller evaluating entropy information. Therefore, the CICDDoS2019 dataset is used to investigate the proposed approach to train and evaluate the performance of the model and then examine the effectiveness of the proposal in the SDN environment. The experimental results manifest that the proposed approach achieves very high enhancements in terms of accuracy, precision, recall, F1 score, and Receiver Operating Characteristic (ROC) for the detection of Distributed Denial of Service (DDoS) attacks compared to one of the benchmarking state of the art approaches.
Comparison of angle-dependent scattering of convoluted and straight microwave absorbers. - In: AMTA 2022 proceedings, (2022), insges. 6 S.
The increasing complexity and sensitivity of wireless communication systems enforce the requirements for test environments such as anechoic chambers. The minimum achievable level of interference between desired signal and scattered copies is essentially determined by the reflectivity of the installed absorbers, emphasizing the importance of thoroughly characterizing the scattering behavior of absorbers. In this paper, the scattering off absorbers with different geometric shapes, namely convoluted, pyramidal, wedge, and flat, is investigated using a numerical unit-cell model. To verify the simulation model, the angle-dependent reflectivity of the convoluted absorbers was measured at different angles-of-incidence between 2GHz and 18GHz. The numerical results agree well with the measured reflectivity at representative angles-of-incidence, validating the numerical model and revealing the expected increase in reflectivity for increasing beam tilts. Further, it becomes apparent that the performance of all shapes decreases similarly at oblique incidence. These results contribute to build a comprehensive database on the angle- and frequency-dependent reflectivity of absorbers, in order to develop a consistent data body, e.g., for modelling anechoic environments.
https://doi.org/10.23919/AMTA55213.2022.9955000
Emulation of LTE link scenarios reproducibly derived from field-operational tests. - In: AMTA 2022 proceedings, (2022), insges. 6 S.
Virtual drive tests using the over-the-air/vehicle-in-the-loop method are becoming an essential part of testing vehicular radio systems. Different approaches ask which link scenarios and channel environments are relevant and should be tested. This paper deals with the systematic evaluation of field-operational tests and the implementation of virtual drive tests of LTE communication links focussing on the performance of the radio link close to the edges of the radio cells, which are identified as a relevant testing scenario. For this purpose, three test drives were performed on each of two test tracks. Close to cell edges the available data throughput is as much as a factor of 10 lower than the maximum available data throughput along the test track and reduced approximately by half compared to the average data throughput in the cell center. Instead of trying to recreate the entire test drive with high accuracy, this approach focusses on recreating the critical parts of a test drive in the laboratory, as these are the most likely to cause radio link failure in real operation. Therefore, the physical parameters in terms of serving signal strength and level of interfering signals were transferred to a wired virtual test, and the data rate was examined again. Despite some systematic differences between real drive test and virtual drive test, which could be clearly identified, it was possible to reproduce the behavior at the cell edge very precisely with deviations smaller than 5 %.
https://doi.org/10.23919/AMTA55213.2022.9954989
Analytical and experimental studies of ground reflections on bi-static radar signal propagation. - In: AMTA 2022 proceedings, (2022), insges. 6 S.
Progressing towards highly automated and connected vehicles, radar systems have evolved into reliable assistance systems for environmental perception, for a wide spectrum of traffic scenarios, and with them, accurate angle-dependent descriptions of reflectivities and scattering centers of traffic participants and road users. Depending on the electrical size of the radar object, the influence of possibly unwanted ground reflections can be significant in radar cross-section measurements. This paper presents an analytical model based on the transmitter, receiver, and single or multiple scattering center positions, that takes into account the geometric reflections at the ground floor and calculates the resulting interference. Considering also the bi-static crosstalk between the transmit and receive antennas, six different propagation paths are obtained, which differ in path delay and attenuation. Subsequent validation measurements in a semi-anechoic automotive antenna test facility confirm the analytical approach very well. Existing discrepancies between the single scattering center model and the measurements with a metal sphere could be corrected by a closer look at the position of the scattering center. Final measurements on realistic bicyclist dummies show that the model is also reliably applicable to extended radar targets.
https://doi.org/10.23919/AMTA55213.2022.9954954
Empirical estimation of ETSI ITS-G5 performance over an IPv6-based platform. - In: 2022 IEEE International Performance, Computing, and Communications Conference (IPCCC), (2022), S. 185-193
Intelligent transport systems (ITS) promise to leverage wireless communications among vehicles. Performance of wireless communication is of crucial importance and can severely impact safety, efficiency and infotainment of transport applications. In this paper, in order to promote the usage of IPv6 in vehicular networks, we deploy a wireless communication framework in a Linux environment based on the Internet protocol (IP). Further, we evaluate the behaviour of the ITS-G5 in various stationary and mobile scenarios to investigate the impact of environmental and radio factors on the performance of ITS-G5. Several measurements such as inter-reception time (IRT), packet loss, latency, end-to-end (E2E) latency, reception position error (RPE), and throughput are carried out to discover the limitations and strength of the technology in a real world test-bed.
https://doi.org/10.1109/IPCCC55026.2022.9894344
Deep learning aided interpolation of spatio-temporal nonstationary data. - In: 30th European Signal Processing Conference (EUSIPCO 2022), (2022), S. 2221-2225
Despite the growing interest in many fields, spatio-temporal (ST) interpolation remains challenging. Given ST nonstationary data distributed sparsely and irregularly over space, our objective is to obtain an equidistant representation of the region of interest (ROI). For this reason, an equidistant grid is defined within the ROI, where the available time series data are arranged, and the time series of the unobserved points are interpolated. Aiming to maintain the interpretability of the whole process while offering flexibility and fast execution, this work presents a ST interpolation frame-work which combines a statistical technique with deep learning. Our framework is generic and not confined to a specific application, which also provides the prediction confidence. To evaluate its validity, this framework is applied to ultrasound nondestructive testing (UT) data as an example. After the training with synthetic UT data sets, our framework is shown to yield accurate predictions when applied to measured UT data.
https://ieeexplore.ieee.org/document/9909600