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Accuracy of radar interferometry is often hindered by the atmospheric phase screen (APS). To address this limitation, the geostatistical approach known as Kriging has been employed to predict APS from sparse observations for compensation purposes. In this article, we propose an enhanced Kriging approach to achieve more accurate APS predictions in ground-based (GB) radar interferometry applications. Specifically, the Kriging system is augmented with a time-series measure through correlation analysis, effectively leveraging spatiotemporal information for APS prediction. The validity of the introduced Kriging method in the APS compensation framework was tested with Ku-band GB radar datasets collected over two different mountainous sites. A comparison of this method with simple Kriging reveals a noticeable improvement in APS prediction accuracy and temporal phase stability.

Synthetic aperture radar (SAR) coherence magnitude is an essential parameter in SAR interferometry. This is the reason why current interferometric wide area ground motion services require the estimation of the coherence magnitude as accurately and computationally effectively as possible. The objective of this article is to improve the accuracy of this coherence estimation compared to known estimators, especially when estimating low coherences and working with a small, i.e., N $< $ 30, but also large number of samples, i.e., hundred or more. Precisely, this article proposes the interferometric coherence magnitude estimation by composition of subsample estimates and machine learning (ML). The principle is to partition the given sample and to estimate coherences on these independent subsamples using different coherence magnitude estimators. It results in a nonparametric and automated statistical inference. It is shown that the composite ML estimator has a high estimation quality yet without prior information, provides a deterministic estimate and is numerically efficient, it is suitable for general interferometric synthetic aperture radar applications and operational systems. Adequate computational performance results from the fact that no iteration, numerical integration, bootstrapping, or bagging are part of the composite estimator.

This paper conducts a thorough examination of the 12th Video Browser Showdown (VBS) competition, a well-established international benchmarking campaign for interactive video search systems. The annual VBS competition has witnessed a steep rise in the popularity of multimodal embedding-based approaches in interactive video retrieval. Most of the thirteen systems participating in VBS 2023 utilized a CLIP-based cross-modal search model, allowing the specification of free-form text queries to search visual content. This shared emphasis on joint embedding models contributed to balanced performance across various teams. However, the distinguishing factors of the top-performing teams included the adept combination of multiple models and search modes, along with the capabilities of interactive interfaces to facilitate and refine the search process. Our work provides an overview of the state-of-the-art approaches employed by the participating systems and conducts a thorough analysis of their search logs, which record user interactions and results of their queries for each task. Our comprehensive examination of the VBS competition offers assessments of the effectiveness of the retrieval models, browsing efficiency, and user query patterns. Additionally, it provides valuable insights into the evolving landscape of interactive video retrieval and its future challenges.

This article presents a study of the relationship among decorrelation phase in synthetic aperture radar (SAR) interferogram, soil moisture, and water content in vegetation with the aim of mitigating the contribution of decorrelation phase in SAR interferometry estimates of terrain displacements. A methodology for the mitigation of the phase bias based on the temporal variation of the vegetation water content is presented. Decorrelation phases are computed using time series of Sentinel-1 images and compared with in situ measurements of soil moisture. It is shown that soil moisture can partially explain the observed values of decorrelation phases pointing out the role of vegetation water content. A new model is proposed to compute the contribution of vegetation to the decorrelation phase based on the normalized difference water index (NDWI) index. The methodology is applied to all short temporal baseline interferograms obtained from the time series of Sentinel-1 SAR images, using the NDWI maps generated from Sentinel-2 multispectral images. The cumulative displacement is computed by integrating the short temporal baseline interferograms, corrected for the land cover and soil moisture changes. It is shown that the proposed methodology can reduce the variance of estimated cumulative displacement in areas covered by vegetation.

Medium-voltage connected ultra-fast chargers are getting more popular for charging electric vehicles with large battery capacities. Here, the solution based on a modular multilevel converter is more promising, since the isolation stage can be realized as a single medium-frequency transformer interconnecting the modular multilevel converter to a single-phase ac/dc converter. A new operating scheme is proposed for this converter, enabling zero-voltage switching and nearly zero-current switching across the entire load range. In contrast to the conventional phase-shift control method, the proposed scheme effectively reduces the reactive power through the ac/dc converter, leading to decreased turn-off switching losses in the ac/dc converter and a lower RMS current stress in the power path. A control scheme, integrating the operating principle, is developed for the modular multilevel converter. The method is verified through simulation and measurements on a scaled-down prototype. The results validate the theoretical analysis and practical feasibility of the proposed operating principle and the developed control scheme.

Deep neural networks (DNNs) have proven their capabilities in the past years and play a significant role in environment perception for the challenging application of automated driving. They are employed for tasks such as detection, semantic segmentation, and sensor fusion. Despite tremendous research efforts, several issues still need to be addressed that limit the applicability of DNNs in automated driving. The bad generalization of DNNs to unseen domains is a major problem on the way to a safe, large-scale application, because manual annotation of new domains is costly, particularly for semantic segmentation. For this reason, methods are required to adapt DNNs to new domains without labeling effort. This task is termed unsupervised domain adaptation (UDA). While several different domain shifts challenge DNNs, the shift between synthetic and real data is of particular importance for automated driving, as it allows the use of simulation environments for DNN training. We present an overview of the current state of the art in this research field. We categorize and explain the different approaches for UDA. The number of considered publications is larger than any other survey on this topic. We also go far beyond the description of the UDA state-of-the-art, as we present a quantitative comparison of approaches and point out the latest trends in this field. We conduct a critical analysis of the state-of-the-art and highlight promising future research directions. With this survey, we aim to facilitate UDA research further and encourage scientists to exploit novel research directions.

Human-robot teaming receives an ever-increasing level of attention in research, development and industry. Novel approaches to task sharing in hybrid teams range from optimized schedules to intelligent cobot assistants with a high degree of autonomy. These approaches must prove their usefulness and benefits compared to manual work or full automation – particularly when it comes to assessing their potential for productive industrial use. This leverages demand for standardized, repeatable benchmarks to compare approaches and measure improvements in a reproducible way. Designing such benchmarks is challenging as numerous aspects, from safety considerations to human factors and team performance, must be considered. This survey seeks to contribute to the future development of benchmarks for the field of collaborative assembly, handling, and industrial cobot applications by giving a comprehensive overview of relevant metrics, evaluation strategies, and tasks for human-robot teams.

A novel synthesis method for holographic multi-feed antennas is presented to combine all sub-holograms into an angle-dependent shared holographic aperture. In order to find the global error minimum between the shared holographic aperture and all ideal sub-holograms, a non-pixel-based genetic optimization is used. For a more accurately implementation of the analytical impedance tensor an eigenvector approach taking all tensor components into account is introduced. The optimized holographic antenna has four feeds for an exemplary integration into a 2D-monopulse radar system and is realized on a fused silica wafer due to its lower loss at millimeter-wave frequencies compared to Teflon-based materials. The antenna prototype provides a measured gain of 23dBi, a polarization purity of $\mathrm {26 \ \text {dB}}$ and a side-lobe level of $\mathrm {20 \ \text {dB}}$ for feed 1, 2 and 4 is reached across 76GHz−81GHz. The S-parameters are measured from $\mathrm {60 \ \text {GHz}}$ to $\mathrm {90 \ \text {GHz}}$ , and a reflection coefficient of $-\,\,\mathrm {14\,\, \text {dB}}$ and an inter-port coupling $< -\,\,\mathrm {38\,\, \text {dB}}$ are achieved. The measured 2D-monopulse patterns provide a FOV of ang 10, the sum beam has a gain of 26dBi leading to an aperture efficiency of 45% and the differential beams show a null depth of $\mathrm {30 \ \text {dB}}$ .

Current interferometric wide area ground motion services require the estimation of the coherence magnitude as accurately and computationally effectively as possible. However, a precise and at the same time computationally efficient method is missing. Therefore, the objective of this article is to improve the empirical Bayesian coherence magnitude estimation in terms of accuracy and computational cost. Precisely, this article proposes the interferometric coherence magnitude estimation by machine learning (ML). It results in a nonparametric and automated statistical inference. However, applying ML in this estimation context is not straightforward. The number and the domain of possible input processes is infinite and it is not possible to train all possible input signals. It is shown that the expected channel amplitudes and the expected interferometric phase cause redundancies in the input signals allowing to solve this issue. Similar to the empirical Bayesian methods, a single parameter for the maximum underlaying coherence is used to model the prior. However, no prior or any shape of prior probability is easy to implement within the ML framework. The article reports on the bias, standard deviation and RMSE of the developed estimators. It was found that ML estimators improve the coherence estimation RMSE from small samples ($2 \leq N < 30$) and for small underlaying coherence compared to the conventional and empirical Bayes estimators. The developed ML coherence magnitude estimators are suitable and recommended for operational InSAR systems. For the estimation, the ML model is extremely fast evaluated because no iteration, numeric integration or bootstrapping is needed.

Usably secure ad-hoc device pairing fosters connectivity with hardware which is difficult to access (e.g., implanted) and grants convenience for ad-hoc short-term on-off pairing patterns (e.g. shared public domain). Examples are medical devices or fitness equipment. We present CardioID, an approach to extract features from heart rate variability for secure pairing keys that change with the randomness inherited in heart operation. Our processing chain is compatible with electrocardiogram (ECG, voltage), as well as ballistocardiogram (BCG, acceleration) type signals. Dissimilarities in locally generated sequences are accounted for using fuzzy cryptography exploiting Bose–Chaudhuri–Hocquenghem (BCH) codes. We propose a quantization to derive secure keys for cross BCG-ECG device pairing from heart-rate variability and analyze the performance in (inter- and intra-subject) BCG-to-ECG pairing. A secure communication protocol for Body Area Networks (BAN) is discussed. The attack surface of the protocol is analyzed, and we conduct a video-based attack study. In addition, two case studies with 5 (laboratory) and 20 (controlled in-field) subjects were conducted.

In common understanding, the fast switching speed of wide-bandgap devices leads to high overvoltage and oscillations, if no countermeasures are taken. Those countermeasures were introduced in the past, and include methods such as build-in gate resistors or low-inductive power modules. There is, however, a physical limit for the reduction of the parasitic inductance in the commutation cell. This is one of the reasons why the full potential of wide-bandgap devices cannot be entirely utilized. In contrast, the Zero Overvoltage Switching (ZOS) phenomenon can be used to theoretically unleash unlimited switching speed with no switching losses and voltage overshoots. This method triggers the inherent parasitic oscillating elements of a commutation cell to perform an ideal current commutation. This article investigates the physical effects and usage of this technology in real-world applications. The model of an ideal commutation cell is adapted to reality by introducing damping factors as well as the nonlinear behaviour of the parasitic capacitances. An extended ZOS area is presented, including the parasitic capacitances of two wide-bandgap devices. It allows today’s silicon carbide power modules to make use of the ZOS technology and perform at different power levels. Measurements with a commercially available power module were taken to verify the theoretical analysis and the derived equations.

SAR interferometry has developed rapidly in recent years and now allows measurements of subtle deformation of the Earth's surface with millimeter accuracy. All state-of-the-art processing methods require a precise coherence estimate. However, this estimate is a random variable and biased toward higher values. Up to now, little is published on the Bayesian estimation of the degree of coherence. The objective of the article is to develop empirical Bayesian estimators and to assess their characteristics by simulations. Bayesian estimation is understood as a regularization of the maximum likelihood estimation. The more information is used and the stricter the general prior, the more accurate the estimate will be. Three levels of prior information are developed: (1) an uninformative prior and an informative prior which can be implemented as (2a) less strict prior and (2b) strict prior. The informative priors are described by a single parameter only i.e., the maximum underlaying coherence. The article reports on the bias, the standard deviation and the root-mean-square error of the developed estimators. It was found that all empirical Bayes estimators improve the coherence estimation from small samples and for small underlaying coherences compared to the conventional sample estimator, e.g., a zero underlaying coherence is estimated by the expected a posteriori estimator without additional information with a 33.3% reduced bias using three samples only. Assuming the maximum underlaying coherence is 0.6, the bias is reduced by 51.3% for the strict prior and by 36.6% for the less strict prior. In addition, it was found that the methods work very well even for the extremely small sample size of only two values.

To successfully launch automated vehicles into the consumer market, there must be credible proof that the vehicles will operate safely. However, finding a method to validate the vehicles’ safe operation is a challenging problem. While scenario-based test approaches seem to be possible solutions, they require the execution of a large number of test cases. Several test benches, ranging from actual test vehicles to partly or fully simulated environments, are available to execute these test cases. Each test bench provides different elements, which in turn, have different parameters and parameter ranges. The composition of elements with their specific parameter values at a specific test bench that is used to execute a test case is referred to as a test bench configuration. However, selecting the most suitable test bench configuration is difficult. The selected test bench configuration determines whether the execution of a specific test case provides sufficiently valid test case results with respect to the intended purpose, for example, validating a vehicle’s safe operation. The effective and efficient execution of a large number of test cases requires a method for systematically assigning test cases to the most suitable test bench configuration. Based on a proposed method for classifying test bench configurations, we propose and illustrate a method for systematically assigning test cases to test bench configurations in a scenario-based test approach for automated vehicles. This assignment method allows for the effective and efficient execution of a large number of test cases while generating sufficiently valid test case results.

Real-time load information in public transport is of high importance for both passengers and service providers. Neural algorithms have shown a high performance on various object counting tasks and play a continually growing methodological role in developing automated passenger counting systems. However, the publication of public-space video footage is often contradicted by legal and ethical considerations to protect the passengers’ privacy. This work proposes an end-to-end Long Short-Term Memory network with a problem-adapted cost function that learned to count boarding and alighting passengers on a publicly available, comprehensive dataset of approx.13,000 manually annotated low-resolution 3D LiDAR video recordings (depth information only) from the doorways of a regional train. These depth recordings do not allow the identification of single individuals. For each door opening phase, the trained models predict the correct passenger count (ranging from 0 to 67) in approx.96% of boarding and alighting, respectively. Repeated training with different training and validation sets confirms the independence of this result from a specific test set.

Sleep monitoring using polysomnography (PSG) in hospitals can be considered expensive, so the preferable way is to use contactless and wearable sensors to monitor sleep daily by patients at home. In this study, the Internet-of-Things (IoT) platform was utilized for sleep monitoring with contactless or wearable sensors as an integrated system developed based on an event-driven and microservice architecture. Multiple services that respond to events are provided within the system. Electrocardiogram (ECG) data were used as the input in the sleep monitoring system. The combination of the weighted extreme learning machine (WELM) algorithm with particle swarm optimization (PSO) was used to process the ECG data, followed by fuzzy logic to measure sleep quality, then display the data on the dashboard. Based on the experimental results, the proposed architecture increased throughput by 34.76%, decreased response time by 55.85%, and reduced memory consumption by 37.26% per instance replication compared to the non-event-driven architecture. The accuracies of the sleep stage classification were 78.78% and 73.09% for the three and four classes, respectively, and the area under a receiver operating characteristic (ROC) curve (AUC) reached 0.89 for both the three and four class classifications.

Future driver assistance and autonomous driving systems require high-resolution 4D imaging radars that provide detailed and robust information about the vehicle's surroundings, even in poor weather or lighting conditions. In this work, a novel high-resolution radar system with 1728 virtual channels is presented, exceeding the state-of-the-art channel count for automotive radar sensors by a factor of 9. To realize the system, a new mixed feedthrough and distribution network topology is employed for the distribution of the ramp oscillator signal. A multilayer printed circuit board is designed and fabricated with all components assembled on the back side, while the radio frequency signal distribution is on a buried layer and only the antennas are on the front side. The array is optimized to enable both multipleinput multiple-output operation and transmit beamforming. A sparse array with both transmit and receive antennas close to the transceivers is realized to form a 2D array with a large unambiguous region of $130 \mathrm{^{\circ }}$ × $75 \mathrm{^{\circ }}$ with a maximal sidelobe level of $-15 \,\mathrm{{\rm dB}}$. The array features a $3 \,\mathrm{{\rm dB}}$ beamwidth of $0.78 \mathrm{^{\circ }}$ × $3.6 \mathrm{^{\circ }}$ in azimuth and elevation, respectively. Radar measurements in an anechoic chamber show that even the individual peaks of the absorber in the chamber can be detected and separated in the range-angle cut of the 4D radar image. The performance is validated by measurements of a parking lot, where cars, a pedestrian, a fence, and a street lamp can be detected, separated, and estimated correctly in size and position.

Skeletal muscles generate force, enabling movement through a series of fast electro-mechanical activations coordinated by the central nervous system. Understanding the underlying mechanism of such fast muscle dynamics is essential in neuromuscular diagnostics, rehabilitation medicine and sports biomechanics. The unique combination of electromyography (EMG) and ultrafast ultrasound imaging (UUI) provides valuable insights into both electrical and mechanical activity of muscle fibers simultaneously, the excitation-contraction (E-C) coupling. In this feasibility study we propose a novel non-invasive method to simultaneously track the propagation of both electrical and mechanical waves in muscles using high-density electromyography and ultrafast ultrasound imaging (5000 fps). Mechanical waves were extracted from the data through an axial tissue velocity estimator based on one-lag autocorrelation. The E-C coupling in electrically evoked twitch contractions of the Biceps Brachii in healthy participants could successfully be tracked. The excitation wave (i.e. action potential) had a velocity of 3.9±0.5ms−1 and the subsequent mechanical (i.e. contraction) wave had a velocity of 3.5±0.9ms−1. The experiment showed evidence that contracting sarcomeres that were already activated by the action potential (AP) pull on sarcomeres that were not yet reached by the AP, which was corroborated by simulated contractions of a newly developed multisegmental muscle fiber model, consisting of 500 sarcomeres in series. In conclusion, our method can track the electromechanical muscle dynamics with high spatio-temporal resolution. Ultimately, characterizing E-C coupling in patients with neuromuscular diseases (e.g. Duchenne or Becker muscular dystrophy) may assess contraction efficiency, monitor the progression of the disease, and determine the efficacy of new treatment options.

In this work, we present Point Transformer, a deep neural network that operates directly on unordered and unstructured point sets. We design Point Transformer to extract local and global features and relate both representations by introducing the local-global attention mechanism, which aims to capture spatial point relations and shape information. For that purpose, we propose SortNet, as part of the Point Transformer, which induces input permutation invariance by selecting points based on a learned score. The output of Point Transformer is a sorted and permutation invariant feature list that can directly be incorporated into common computer vision applications. We evaluate our approach on standard classification and part segmentation benchmarks to demonstrate competitive results compared to the prior work.

In times of climate change, growing world population, and the resulting scarcity of resources, efficient and economical usage of agricultural land is increasingly important and challenging at the same time. To avoid disadvantages of monocropping for soil and environment, it is advisable to practice intercropping of various plant species whenever possible. However, intercropping is challenging as it requires a balanced planting schedule due to individual cultivation time frames. Maintaining a continuous harvest throughout the season is important as it reduces logistical costs and related greenhouse gas emissions, and can also help to reduce food waste. Motivated by the prevention of food waste, this work proposes a flexible optimization method for a full harvest season of large crop ensembles that complies with given economical and environmental constraints. Our approach applies evolutionary algorithms and we further combine our evolution strategy with a sophisticated hierarchical loss function and adaptive mutation rate. We thus transfer the multi-objective into a pseudo-single-objective optimization problem, for which we obtain faster and better solutions than those of conventional approaches.

The parasitic capacitances of modern Si SJ MOSFETs are characterized by their non-linearity. At high voltages the total stored energy Eoss(VDC) in the output capacitance Coss(v) differs substantially from the energy in an equivalent linear capacitor Coss(tr) storing the same amount of charge. That difference requires the definition of an additional equivalent linear capacitor Coss(er) storing the same amount of energy at a specific voltage. However, the parasitic capacitances of current SiC and GaN devices have a more linear distribution of charge along the voltage. Moreover, the equivalent Coss(tr) and Coss(er) of SiC and GaN devices are smaller than the ones of a Si device with a similar Rds,on. In this work, the impact of the non-linear distribution of charge in the performance and the design of resonant ZVS converters is analyzed. A Si SJ device is compared to a SiC device of equivalent Coss(tr), and to a GaN device of equivalent Coss(er), in single device topologies and half-bridge based topologies, in full ZVS and in partial or full hard-switching. A prototype of 3300 W resonant LLC DCDC converter, with nominal 400 V input to 52 V output, was designed and built to demonstrate the validity of the analysis.