Matrix pencil method for vital sign detection from signals acquired by microwave sensors. - In: Sensors, ISSN 1424-8220, Bd. 21 (2021), 17, 5735, insges. 24 S.
Microwave sensors have recently been introduced as high-temporal resolution sensors, which could be used in the contactless monitoring of artery pulsation and breathing. However, accurate and efficient signal processing methods are still required. In this paper, the matrix pencil method (MPM), as an efficient method with good frequency resolution, is applied to back-reflected microwave signals to extract vital signs. It is shown that decomposing of the signal to its damping exponentials fulfilled by MPM gives the opportunity to separate signals, e.g., breathing and heartbeat, with high precision. A publicly online dataset (GUARDIAN), obtained by a continuous wave microwave sensor, is applied to evaluate the performance of MPM. Two methods of bandpass filtering (BPF) and variational mode decomposition (VMD) are also implemented. In addition to the GUARDIAN dataset, these methods are also applied to signals acquired by an ultra-wideband (UWB) sensor. It is concluded that when the vital sign is sufficiently strong and pure, all methods, e.g., MPM, VMD, and BPF, are appropriate for vital sign monitoring. However, in noisy cases, MPM has better performance. Therefore, for non-contact microwave vital sign monitoring, which is usually subject to noisy situations, MPM is a powerful method.
https://doi.org/10.3390/s21175735
MC-COCO4V2P: multi-channel clustering-based congestion control for Vehicle-to-Pedestrian communication. - In: IEEE Transactions on Intelligent Vehicles, ISSN 2379-8858, Bd. 6 (2021), 3, S. 523-532
Vehicle-to-Pedestrian communication can extend crash prevention capabilities of the current driver assistance systems in vehicles. This requires vehicles and pedestrians to exchange safety messages with each other. However, as the number of pedestrians increases, the numerous safety messages transmitted by pedestrians can quickly congest the network. This can severely affect Vehicle-to-Pedestrian and Vehicle-to-Vehicle communication. Hence, a mechanism for the mitigation of network congestion caused by pedestrian safety messages is required. This article proposes a Multi-channel Clustering-based Congestion Control (MC-COCO4V2P) algorithm, a proactive and infrastructure-independent clustering-based approach to mitigate the network congestion caused by pedestrians. Our approach clusters pedestrians based on their location and direction and uses separate channels for exchanging cluster and safety messages, thereby reducing the control information overhead. It also employs a transmit power control mechanism to make the clustering mechanism energy efficient. Our results show that the clustering of pedestrians can significantly improve network performance and reduce the power consumption of pedestrians devices.
https://doi.org/10.1109/TIV.2020.3046694
Modeling and evaluation of the impact of motorcycles mobility on vehicular traffic. - In: Journal of Transportation Technologies, ISSN 2160-0481, Bd. 11 (2021), 3, S. 426-435
Traffic simulation can help to evaluate the impact of different mobility behaviors on the traffic flow from safety, efficiency, and environmental views. The objective of this paper is to extend the SUMO (Simulation of Urban Mobility) road traffic simulator to model and evaluate the impact of motorcycles mobility on vehicular traffic. First, we go through diverse mobility aspects and models for motorcycles in SUMO. Later, we opt for the most suitable mobility models of motorcycles. Finally, the impact of motorcycle mobility on different kinds of vehicles is investigated in terms of environment, fuel consumption, velocity and travel time. The result of modeling and evaluation shows that based on the mobility model of the motorcycle, vehicular traffic flow can be enhanced or deteriorated.
https://doi.org/10.4236/jtts.2021.113028
Using the multi-linear rank-(Lr, Lr, 1) decomposition for the detection of the 200 Hz band activity in somatosensory evoked magnetic fields and somatosensory evoked electrical potentials. - In: IEEE access, ISSN 2169-3536, Bd. 9 (2021), S. 106232-106244
Im Titel ist "r" tiefgestellt
https://doi.org/10.1109/ACCESS.2021.3100759
Vehicle classification in intelligent transport systems: an overview, methods and software perspective. - In: IEEE open journal of intelligent transportation systems, ISSN 2687-7813, Bd. 2 (2021), S. 173-194
https://doi.org/10.1109/OJITS.2021.3096756
Conditional and unconditional Cramér-Rao bounds for near-field localization in bistatic MIMO radar systems. - In: IEEE transactions on signal processing, ISSN 1941-0476, Bd. 69 (2021), S. 3220-3234
The location estimation problem has been attracting a lot of research interest in recent years due to its significance for different areas of signal processing. This paper deals with a bistatic MIMO radar system where the targets are located in the near-field region. In this work, we derive the Cramér-Rao bound (CRB) for bistatic MIMO radar systems using the exact spherical wavefront model to evaluate the performance of target parameter estimation algorithms. The conditional and unconditional CRBs are derived for a system with one and multiple targets. For the one target system, we provide an analytical inversion of the Fisher Information Matrix (FIM) and obtain closed-form analytical non-matrix expressions of the CRB corresponding to the Cartesian and spherical coordinates of the targets. We compare the derived conditional and unconditional CRB with the performance of state-of-the-art localization algorithms and analyse the dependence of the CRB on various system parameters.
https://doi.org/10.1109/TSP.2021.3082469
IEEE access special section editorial: millimeter-wave and terahertz propagation, channel modeling, and applications. - In: IEEE access, ISSN 2169-3536, Bd. 9 (2021), S. 67660-67666
The demand for ever-increasing wireless data transmission rates and throughput area-densities, especially with regard to microcellular networks, internet access, back-hauling, inter-device transmission, and sensing applications, has spurred the exploration of new spectra in the millimeter-wave (30-300 GHz) and terahertz bands (0.1-10 THz), and the study of techniques for multi-Gigabit transmission based on very high-gain antennas [items 1) and 2) in the Appendix].
https://doi.org/10.1109/ACCESS.2021.3076326
Locally optimal subsampling strategies for full matrix capture measurements in pipe inspection. - In: Applied Sciences, ISSN 2076-3417, Bd. 11 (2021), 9, 4291, S. 1-14
In ultrasonic non-destructive testing, array and matrix transducers are being employed for applications that require in-field steerability or which benefit from a higher number of insonification angles. Having many transmit channels, on the other hand, increases the measurement time and renders the use of array transducers unfeasible for many applications. In the literature, methods for reducing the number of required channels compared to the full matrix capture scheme have been proposed. Conventionally, these are based on choosing the aperture that is as wide as possible. In this publication, we investigate a scenario from the field of pipe inspection, where cracks have to be detected in specific areas near the weld. Consequently, the width of the aperture has to be chosen according to the region of interest at hand. On the basis of ray-tracing simulations which incorporate a model of the transducer directivity and beam spread at the interface, we derive application specific measures of the energy distribution over the array configuration for given regions of interest. These are used to determine feasible subsampling schemes. For the given scenario, the validity/quality of the derived subsampling schemes are compared on the basis of reconstructions using the conventional total focusing method as well as sparsity driven-reconstructions using the Fast Iterative Shrinkage-Thresholding Algorithm. The results can be used to effectively improve the measurement time for the given application without notable loss in defect detectability.
https://doi.org/10.3390/app11094291
Improving the performance of a radio-frequency localization system in adverse outdoor applications. - In: EURASIP journal on wireless communications and networking, ISSN 1687-1499, (2021), 123, S. 1-26
In outdoor RF localization systems, particularly where line of sight can not be guaranteed or where multipath effects are severe, information about the terrain may improve the position estimate's performance. Given the difficulties in obtaining real data, a ray-tracing fingerprint is a viable option. Nevertheless, although presenting good simulation results, the performance of systems trained with simulated features only suffer degradation when employed to process real-life data. This work intends to improve the localization accuracy when using ray-tracing fingerprints and a few field data obtained from an adverse environment where a large number of measurements is not an option. We employ a machine learning (ML) algorithm to explore the multipath information. We selected algorithms random forest and gradient boosting; both considered efficient tools in the literature. In a strict simulation scenario (simulated data for training, validating, and testing), we obtained the same good results found in the literature (error around 2 m). In a real-world system (simulated data for training, real data for validating and testing), both ML algorithms resulted in a mean positioning error around 100 ,m. We have also obtained experimental results for noisy (artificially added Gaussian noise) and mismatched (with a null subset of) features. From the simulations carried out in this work, our study revealed that enhancing the ML model with a few real-world data improves localization’s overall performance. From the machine ML algorithms employed herein, we also observed that, under noisy conditions, the random forest algorithm achieved a slightly better result than the gradient boosting algorithm. However, they achieved similar results in a mismatch experiment. This work’s practical implication is that multipath information, once rejected in old localization techniques, now represents a significant source of information whenever we have prior knowledge to train the ML algorithm.
https://doi.org/10.1186/s13638-021-02001-6
Estimation bounds of beat signal in the R-mode localization system. - In: IEEE access, ISSN 2169-3536, Bd. 9 (2021), S. 69278-69286
The R-Mode system is a terrestrial navigation system currently under development, which exploits existing means of medium frequency radio transmission. The positioning and timing performance depends on the estimation of the signals' phase offset, from which the ranging information is derived. For an analogous problem such as the single-tone phase estimation, the Cramér-Rao bound (CRB) describes the minimal achievable performance in the mean squared error sense. For R-Mode, the problem involves the estimation of the phase offset for a beat signal, which can be described as the difference of phase estimation for the two aiding carriers next to the signal. This estimates are not statistically independent for finite observation, as we show in this paper. The effect becomes stronger for short observation times, which are important for a near real time application. In this contribution, we are interested in phase offset estimation for the signal models relevant to R-Mode: a beat signal and a beat signal combined with an MSK signal. A closed-form lower CRB is proposed for the aforementioned signal models phase estimation, as well as a generalization of the bound for the phase-difference estimation. Based on this derivation, optimized bit sequences are shown to improve performance of the estimates. The validity of the proposal is verified based on a simulation setup. Measurements acquired during a measurement campaign serve to further justify the usefulness of the bound. Some possible applications of such a bound are R-Mode coverage prediction and the associated phase estimators' performance.
https://doi.org/10.1109/ACCESS.2021.3076845