Zeitschriftenaufsätze

Reconfigurable intelligent surfaces (RISs) are deemed as a potential technique for the future of the Internet of Things (IoT) due to their capability of smartly reconfiguring the wireless propagation environment using a large number of low-cost passive elements. To benefit from RIS technology, the problem of RIS-assisted channel state information (CSI) acquisition needs to be carefully considered. Existing channel estimation methods usually ignored the different channel characteristics of direct channel and reflected channels. In fact, the reflected channel can be smartly configured by adjusting the phase shifts of the RIS, which is different from the direct channel due to the different path loss exponents between the transmitter and receiver. Therefore, it is necessary to further develop a RIS-assisted channel estimation to determine the direct and reflected channels, respectively. In this paper, we study a RIS-assisted channel estimation that jointly exploits the properties of the direct and the reflected channel to provide more accurate CSI. The direct channel is estimated using weighted ℓ1 norm minimization, while the reflected channel is modeled based upon the robust ℓ1,τ norm minimization to sequentially estimate the channel parameters. Moreover, by combining the gradient descent and the alternating minimization method, a flexible and fast algorithm is developed to provide a feasible solution. Simulation results demonstrate that an RIS-aided MIMO system significantly reduces the active antennas/RF chains compared to other benchmark schemes.

This paper proposes novel linear precoding algorithms for Multiple-Input Multiple-Output Bit-Interleaved Coded Modulation (MIMO-BICM) systems that maximize the achievable rate subject to power constraints. To overcome the nonlinear and nonconvex nature of the optimization problem, we rewrite the achievable rate in terms of the log-likelihood ratio (LLR) and introduce manifold-based gradient ascent (MGA) precoding and low-complexity non-iterative algorithms. Simulation results show significant gains in achievable rate and block error rate compared to existing techniques. Additionally, we extend our investigation to linear precoding with the constraint that the precoding matrix is selected from the codebook type-I adopted in Fifth-Generation New Radio (5G NR) networks. We propose heuristic algorithms that exploit the Kronecker and Discrete Fourier Transform (DFT) structure of the codebook and consider the singular vector decomposition (SVD) precoder as the optimal reference precoder. The traditional exhaustive search methods require a high complexity, especially for large codebook sizes. However, our proposed algorithms apply a combination of direct estimation and a low-dimensional search for deriving the indices, resulting in a reduced number of codebook precoder candidates. Simulation results show that our proposed low-complexity algorithms perform comparably to exhaustive search baselines.

Bio-inspired algorithms and architectures are considered superior to classical architectures for certain applications. An important aspect with regard to the function of the human memory is the sorting according to important and unimportant experiences. Certain important experiences are stored significantly longer than less important ones. One criterion to make this distinction is the frequency of occurrence of a property. In this work an RSFQ circuit is presented, which performs this weighting in the learning process of a synapse. In a simulation study, the principle of the selective learning mechanism is shown to work and a variant of permanent memory is demonstrated.

The performance of superconducting electronic devices such as superconducting quantum bits (qubits) and superconducting quantum interference devices (SQUIDs) strongly relies on high-quality Josephson junctions (JJ) and their integration into surrounding circuit elements. Therefore, a corresponding fabrication technology should allow for the fabrication of all required elements including the JJs, inductances, capacitances and waveguides. For a long time, shadow evaporation technique was the state of the art for the implementation of sub-µm sized JJs based on aluminum for qubits of high coherence times. Although, the use of a single lithographic step represents a major advantage of this technique. However, shadowing effects limit sample size, device complexity, and thus scalability of the circuitry. To overcome these limitations and to meet the demands of next generation scalable quantum circuits, in this work we introduce our cross-type JJ aluminum technology, where JJs are defined by the overlap of two narrow perpendicular stripes. We discuss the technological challenges, with a focus on our newly developed dry etching process for patterning of the aluminum thin film. Compared to a lift-off based process, this advanced wafer-scale fabrication technology offers a high integration density and the required design flexibility. We will present first results on cross-type aluminum JJs.

Quantum sensing provides advanced technologies which significantly improve sensitivity and accuracy for sensing changes of motion, gravity, electric and magnetic field. Therein, quantum sensors for the detection of magnetic fields, so-called quantum magnetometers, are one of the most promising technological realizations. We firstly will provide a brief overview on methods in geophysical exploration benefitting from quantum magnetometers with resolution at the physical and technical limit. We will introduce recent developments on SQUID and OPM based sensors as specific implementations of a quantum magnetometer systems and application examples.

The fictitious surface charge method (FSCM) is used for the calculation of the induced electrical field in magnetic stimulation. The method was embedded and optimized in Python. It was designed to allow for the computation of large problems. An element-wise Jacobi method was combined with vectorized matrix operations to increase the parallelization capabilities and enable GPU computing. The induced fields are compared against an analytical solution for a homogeneous sphere and a FEM solution on a realistic head model. The results for both cases show that the normalized root mean square error of less than 0.5% can be achieved with the integral-free FSCM even on low-performance computer hardware.

This publication proposes a parametric data model and a gradient-based maximum likelihood estimator suitable for the description of delay-dispersive responses of multiple dynamic ultrawideband (UWB)-radar targets. The target responses are estimated jointly with the global target parameters range and velocity. The large relative bandwidth of UWB has consequences for model-based parameter estimation. On the one hand, the Doppler effect leads to a dispersive response in the Doppler spectrum and to a coupling of the target parameters that both need to be considered during modeling and estimation. On the other hand, the shape of an extended target results in a dispersive response in range, which can be resolved by the radar resolution. We consider this extended response as a parameter of interest, e.g., for the purpose of target recognition. Hence, we propose an efficient description and estimation of it by a finite impulse response (FIR) structure only imposing a restriction on the target’s dispersiveness in range. We evaluate the approach on simulations, compare it to state-of-the-art solutions, and provide a validation of the FIR model on measurements of a static scenario.

Currently, 5G low-earth orbit satellite communications offer enhanced wireless coverage beyond the reach of 5G terrestrial networks, with important implications, particularly for automated and connected vehicles. Such wireless automotive mass-market applications demand well-designed compact user equipment antenna terminals offering non-terrestrial jointly with terrestrial communications. The antenna should be low-profile, conformal, and meet specific parameter values for gain and operational frequency bandwidth, tailored to the intended applications, in line with the aesthetic design requirements of passenger cars. This work presents an original concept for a dual-band nested circularly polarized automotive user terminal that operates at the S-band frequencies around 3.5 GHz and Ka-band frequencies around 28 GHz, namely within the 5G new-radio bands n78 and n257, respectively. The proposed terminal is designed to be integrated into the plastic components of a passenger vehicle. The arrays consist of 2 × 2 aperture-coupled corner-truncated microstrip slot patch antenna elements for the n78 band and of 4 × 4 single-layer edge-truncated microstrip circular slot patch antenna elements for the n257 band. The embedded arrays offer, across the two bands, respectively, 9.9 and 13.7 dBi measured realized gain and 3-dB axial ratio bandwidths of 100 and 1500 MHz for the n78 and n257 bands along the broadside direction. Detailed link budget calculations anticipate uplink data rates of 21 and 6 Mbit/s, respectively, deeming it suitable for various automotive mobility and Internet-of-Things applications.

A framework is proposed for developing and evaluating algorithms for extracting multipath propagation components (MPCs) from measurements collected by channel sounders at millimeter-wave frequencies. Sounders equipped with an omni-directional transmitter and a receiver with a uniform planar array (UPA) are considered. An accurate mathematical model is developed for the spatial frequency response of the sounder that incorporates the non-ideal cross-polar beampatterns for the UPA elements. Due to the limited Field-of-View (FoV) of each element, the model is extended to accommodate multi-FoV measurements in distinct azimuth directions. A beamspace representation of the spatial frequency response is leveraged to develop three progressively complex algorithms aimed at solving the single-snapshot maximum likelihood estimation problem: greedy matching pursuit (CLEAN), space-alternative generalized expectation-maximization (SAGE), and RiMAX. The first two are based on purely specular MPCs whereas RiMAX also accommodates diffuse MPCs. Two approaches for performance evaluation are proposed, one with knowledge of ground truth parameters, and one based on reconstruction mean-squared error. The three algorithms are compared through a demanding channel model with hundreds of MPCs and through real measurements. The results demonstrate that CLEAN gives quite reasonable estimates which are improved by SAGE and RiMAX. Lessons learned and directions for future research are discussed.

Angiography is a very informative method for physicians such as cardiologists, neurologists and neuroscientists. The current modalities experience some shortages, e.g., ultrasound is very operator dependent. The computerized tomography (CT) and magnetic resonance (MR) angiography are very expensive and near infrared spectroscopy cannot capture the deep arteries. Microwave technology has the potential to address some of these issues while compromising between operator dependency, cost, speed, penetration depth and resolution. This paper studies the feasibility of microwave signals for monitoring of arteries. To this aim, a homogenous phantom mimicking body tissue is built. Four elastic tubes simulate arteries and a mechanical system creates pulsations in these arteries. A multiple input multiple output (MIMO) array of ultra-wideband (UWB) transmitters and receivers illuminates the phantom and captures the reflected signals over the desired observation time period. Since we are only interested in the imaging of dynamic parts, i.e., arteries, the static clutters can be suppressed easily by background subtraction method. To obtain a fast image of arteries, which are pulsating with the heartbeat rate, we calculate the Fourier transform of each channel of the MIMO system over the observation time and apply delay and sum (DAS) beamforming method on the heartbeat rate aligned spectral component. The results show that the lateral and longitudinal images and motion mode (M-mode) time series of different points of phantom have the potential to be used for diagnosis.

The block-term decompositions (BTD) represent tensors as a linear combination of low multilinear rank terms and can be explicitly related to the Canonical Polyadic decomposition (CPD). In this paper, we introduce the SECSI-BTD framework, which exploits the connection between two decompositions to estimate the block-terms of the rank-(Lr, Lr, 1) BTD. The proposed SECSI-BTD algorithm includes the initial calculation of the factor estimates using the SEmi-algebraic framework for approximate Canonical polyadic decompositions via SImultaneous Matrix Diagonalizations (SECSI), followed by clustering and refinement procedures that return the appropriate rank-(Lr, Lr, 1) BTD terms. Moreover, we introduce a new approach to estimate the multilinear rank structure of the tensor based on the HOSVD and $k$-means clustering. Since the proposed SECSI-BTD algorithm does not require a known rank structure but can still take advantage of the known ranks when available, it is more flexible than the existing techniques in the literature. Additionally, our algorithm does not require multiple initializations, and the simulation results show that it provides more accurate results and a better convergence behavior for an extensive range of SNRs.

In this article, a general introduction to the area of sensor array and multichannel signal processing is provided, including associated activities of the IEEE Signal Processing Society (SPS) Sensor Array and Multichannel (SAM) Technical Committee (TC). The main technological advances in five SAM subareas made in the past 25 years are then presented in detail, including beamforming, direction-of-arrival (DOA) estimation, sensor location optimization, target/source localization based on sensor arrays, and multiple-input multiple-output (MIMO) arrays. Six recent developments are also provided at the end to indicate possible promising directions for future SAM research, which are graph signal processing (GSP) for sensor networks; tensor-based array signal processing, quaternion-valued array signal processing, 1-bit and noncoherent sensor array signal processing, machine learning and artificial intelligence (AI) for sensor arrays; and array signal processing for next-generation communication systems.

Purpose The purpose of this paper is to present an alternative modeling approach in terms of the determination of a physically equivalent circuit model for one-dimensional (1D) planar metamaterials in the high-frequency regime, without a postprocessing optimization procedure. Thereby, an efficient implementation of physical relationships is aimed. Design/methodology/approach In this paper, a method based on quasi-stationary simulations and mathematical conversions to derive the values for a physically equivalent circuit model is proposed. Because the electromagnetic coupling mechanisms are investigated in detail, a simplification for the considered multiconductor transmission line structure is achieved. Findings The results show that the proposed modeling approach is an efficient and physically meaningful alternative to classical full-wave simulation techniques for the investigated inhomogeneous transmission line structure in both the time domain as well as in the frequency domain. In the course of this, the effort is reduced while a comparable accuracy is maintained, whereby specific coupling mechanisms are considered in circuit simulations. Originality/value The process to obtain information about physically interpretable lumped element values for a given structure or to determine a layout for known ones is simplified with the aid of the proposed approach. An advantageous adaption of the presented procedure to other areas of application is well conceivable.

We describe a routine to precisely localize cortical muscle representations within the primary motor cortex with transcranial magnetic stimulation (TMS) based on the functional relation between induced electric fields at the cortical level and peripheral muscle activation (motor-evoked potentials; MEPs). Besides providing insights into structure-function relationships, this routine lays the foundation for TMS dosing metrics based on subject-specific cortical electric field thresholds. MEPs for different coil positions and orientations are combined with electric field modeling, exploiting the causal nature of neuronal activation to pinpoint the cortical origin of the MEPs. This involves constructing an individual head model using magnetic resonance imaging, recording MEPs via electromyography during TMS and computing the induced electric fields with numerical modeling. The cortical muscle representations are determined by relating the TMS-induced electric fields to the MEP amplitudes. Subsequently, the coil position to optimally stimulate the origin of the identified cortical MEP can be determined by numerical modeling. The protocol requires 2 h of manual preparation, 10 h for the automated head model construction, one TMS session lasting 2 h, 12 h of computational postprocessing and an optional second TMS session lasting 30 min. A basic level of computer science expertise and standard TMS neuronavigation equipment suffices to perform the protocol.

The slice-wise multiplication of two tensors is required in a variety of tensor decompositions (including PARAFAC2 and PARATUCK2) and is encountered in many applications, including the analysis of multidimensional biomedical data (EEG, MEG, etc.) or multi-carrier multiple-input multiple-output (MIMO) systems. In this paper, we propose a new tensor representation that is not based on a slice-wise (matrix) description, but can be represented by a double contraction of two tensors. Such a double contraction of two tensors can be efficiently calculated via generalized unfoldings. It leads to new tensor models of the investigated system that do not depend on the chosen unfolding (in contrast to matrix models) and reveal the tensor structure of the data model, such that all possible unfoldings can be seen at the same time. As an example, we apply this new concept to the design of new receivers for multi-carrier MIMO systems in wireless communications. In particular, we consider MIMO-orthogonal frequency division multiplexing (OFDM) systems with and without Khatri-Rao coding. The proposed receivers exploit the channel correlation between adjacent subcarriers, require the same amount of training symbols as traditional OFDM techniques, but have an improved performance in terms of the symbol error rate. Furthermore, we show that the spectral efficiency of the Khatri-Rao-coded MIMO-OFDM can be increased by introducing cross-coding such that the “coding matrix” also contains useful information symbols. Considering this transmission technique, we derive a tensor model and two types of receivers for cross-coded MIMO-OFDM systems using the double contraction of two tensors.

Non-coherent transmission from multiple transmission-reception-points (TRPs), i.e., base stations, or base station panels to a user equipment (UE) is exploited in 5G New Radio (NR) to improve downlink reliability and cell-edge throughput. Ultra reliable low-latency communications (URLLC) and enhanced Mobile BroadBand (eMBB) are prominent target use-cases for multi-TRP or multi-panel transmissions. In Third-Generation Partnership Project (3GPP) Release 17 specifications, multi-TRP-based transmissions were specified for the physical downlink control channel (PDCCH) specifically to enhance its reliability and robustness. In this work, a comprehensive account of various multi-TRP reliability enhancement schemes applicable for the 5G NR PDCCH, including the ones supported by the 3GPP Release 17 specifications, is provided. The impact of the specifications for each scheme, UE and network complexity and their utility in various use-cases is studied. Their error performances are evaluated via link-level simulations using the evaluation criteria agreed in the 3GPP proceedings. The 3GPP-supported multi-TRP PDCCH repetition schemes, and the additionally proposed PDCCH repetition and diversity schemes are shown to be effective in improving 5G NR PDCCH reliability and combating link blockage in mmWave scenarios. The link-level simulations also provide insights for the implementation of the decoding schemes for the PDCCH enhancements under different channel conditions. Analysis of the performance, complexity and implementation constraints of the proposed PDCCH transmission schemes indicate their suitability to UEs with reduced-capability or stricter memory constraints and flexible network scheduling.

In this work, magnetoelectric MEMS sensors based on a TiN/AlN/Ni laminate are investigated for the first time in regards of the anisotropic elastic properties when using hard magnetic Nickel as magnetostrictive layer. The implications of crystalline, uniaxial and shape anisotropy are analysed arising from the anisotropic ΔE effect in differently oriented cantilevers with 25 µm length and 15˚ spacing. The ΔE effect is derived analytically to consider the angular dependency of the different anisotropies within the sensors. In the measured frequency spectra complex profiles are observable consisting of contributions from neighbouring structures which are connected by a common electrode. The crosstalk effect is strongly depending on the cantilever orientation and reflects the anisotropic mechanical properties of the material stack. The intensity of the crosstalk effect is increasing for shortened cantilevers and narrowing distance between structures. The ΔE effect is investigated based on cantilevers of different angular spacing and of a single cantilever that is rotated in the magnetic field. The derived peak sensitivities are reaching values of 1.15 and 1.31T-1. The angular dependency of the sensitivity is found to be approximately constant for differently oriented cantilevers. In contrast, for a singly rotated cantilever an angular dependency of the 4th order is observed.

The low-rank recovery is a powerful tool to restore images from incomplete and corrupted observations. Conventional low-rank recovery techniques employ the reweighted nuclear norm minimization, which requires performing the full singular value decomposition and thus is computationally expensive. Using the scheme of bilinear factorization, we propose the Reweighted Low-rank Matrix Factorization (RLMF) method for single channel image restoration. The RLMF method can not only inherit the computational efficiency of bilinear factorization, but also incorporate the empirical distribution of the singular values in natural images. Then, considering the correlation between image channels, we generalize the reweighted nuclear norm from matrices to tensors, and develop the Reweighted Low-rank Tensor Factorization (RLTF) method for multichannel image restoration. Moreover, we enhance the RLMF and RLTF methods by introducing the deep image prior information, which is capable of capturing the implicit image structure through the neural network architecture to improve restoration accuracy. Experimental results show the computational efficiency of the proposed low-rank factorization scheme, and the superior restoration accuracy of the proposed methods compared with the state-of-the-art methods.

In this paper, we introduce a Multilinear Generalized Singular Value Decomposition (ML-GSVD) for two or more matrices with one common dimension. The ML-GSVD extends the Generalized Singular Value decomposition (GSVD) of two matrices to higher orders. The proposed decomposition allows us to jointly factorize a set of matrices with one common dimension. In comparison with other approaches that extend the GSVD, the ML-GSVD preserves the essential properties of the original (matrix-based) GSVD, such as orthogonality of the second-mode factor matrices as well as the subspace structure of the third-mode factor matrices. We introduce an ALS-based algorithm to compute the ML-GSVD, which has been inspired by PARAFAC2 decomposition algorithms. In addition, we present an application of the ML-GSVD for transceiver optimization in multicast and unicast MIMO-OFDM systems. Our numerical results show that the proposed ML-GSVD multicast and unicast beamforming outperforms existing state-of-the-art schemes in terms of the sum rate.

Capacity, reliability, and latency are seen as key requirements of new emerging applications, namely vehicle-to-everything (V2X) and machine-type communication in future cellular networks. D2D communication is envisaged to be the enabler to accomplish the requirements for the applications as mentioned earlier. Due to the scarcity of radio resources, a hierarchical radio resource allocation, namely the sub-granting scheme, has been considered for the overlay D2D communication. In this paper, we investigate the assignment of underutilized radio resources from D2D communication to device-to-infrastructure communication, which are moving in a dynamic environment. The sub-granting assignment problem is cast as a maximization problem of the uplink cell throughput. Firstly, we evaluate the sub-granting signaling overhead due to mobility in a centralized sub-granting resource algorithm, dedicated sub-granting radio resource (DSGRR), and then a distributed heuristics algorithm, open sub-granting radio resource (OSGRR), is proposed and compared with the DSGRR algorithm and no sub-granting case. Simulation results show improved cell throughput for the OSGRR compared with other algorithms. Besides, it is observed that the overhead incurred by the OSGRR is less than the DSGRR while the achieved cell throughput is yet close to the maximum achievable uplink cell throughput.

A novel model for the covariance matrix of sampled observations by multiple-input-multiple-output (MIMO) sensing systems with parallel receiver channels will be presented. The model is of shifted Kronecker structure and accounts for two mutually independent noise processes: a coloured and a white one. The maximum-likelihood (ML) estimator is applied to identify this covariance model from observations. The ML estimator gives rise to a non-convex optimisation problem. Since no closed-form solution is available, an iterative, space-alternating Gauss-Newton algorithm is proposed to solve the optimisation problem. This approach repeatedly requires the evaluation of the ML cost function. Since the cost function composes of the inverse and determinant of the covariance matrix, its evaluation can be memory exhaustive, numerically unstable and computationally complex. A computational method is developed to overcome these issues, using the simultaneous matrix diagonalisation and exploiting the properties of the Kronecker product. Measurements by a MIMO radar are used to identify the covariance model and to demonstrate its benefits. The identified covariance model is used to whiten the measurements. The whitening reduces interfering, noise-like components, which enhances the signal-to-interference ratio and hence facilitates the target detection.

Background - When modeling transcranial electrical stimulation (TES) and transcranial magnetic stimulation (TMS) in the brain, the meninges - dura, arachnoid, and pia mater - are often neglected due to high computational costs. - Objective - We investigate the impact of the meningeal layers on the cortical electric field in TES and TMS while considering the headreco segmentation as the base model. - Method - We use T1/T2 MRI data from 16 subjects and apply the boundary element fast multipole method with adaptive mesh refinement, which enables us to accurately solve this problem and establish method convergence at reasonable computational cost. We compare electric fields in the presence and absence of various meninges for two brain areas (M1HAND and DLPFC) and for several distinct TES and TMS setups. - Results - Maximum electric fields in the cortex for focal TES consistently increase by approximately 30% on average when the meninges are present in the CSF volume. Their effect on the maximum field can be emulated by reducing the CSF conductivity from 1.65 S/m to approximately 0.85 S/m. In stark contrast to that, the TMS electric fields in the cortex are only weakly affected by the meningeal layers and slightly (∼6%) decrease on average when the meninges are included. - Conclusion - Our results quantify the influence of the meninges on the cortical TES and TMS electric fields. Both focal TES and TMS results are very consistent. The focal TES results are also in a good agreement with a prior relevant study. The solver and the mesh generator for the meningeal layers (compatible with SimNIBS) are available online.

Neuromorphic computer models are used to explain sensory perceptions. Auditory models generate cochleagrams, which resemble the spike distributions in the auditory nerve. Neuron ensembles along the auditory pathway transform sensory inputs step by step and at the end pitch is represented in auditory categorical spaces. In two previous articles in the series on periodicity pitch perception an extended auditory model had been successfully used for explaining periodicity pitch proved for various musical instrument generated tones and sung vowels. In this third part in the series the focus is on octopus cells as they are central sensitivity elements in auditory cognition processes. A powerful numerical model had been devised, in which auditory nerve fibers (ANFs) spike events are the inputs, triggering the impulse responses of the octopus cells. Efficient algorithms are developed and demonstrated to explain the behavior of octopus cells with a focus on a simple event-based hardware implementation of a layer of octopus neurons. The main finding is, that an octopus’ cell model in a local receptive field fine-tunes to a specific trajectory by a spike-timing-dependent plasticity (STDP) learning rule with synaptic pre-activation and the dendritic back-propagating signal as post condition. Successful learning explains away the teacher and there is thus no need for a temporally precise control of plasticity that distinguishes between learning and retrieval phases. Pitch learning is cascaded: At first octopus cells respond individually by self-adjustment to specific trajectories in their local receptive fields, then unions of octopus cells are collectively learned for pitch discrimination. Pitch estimation by inter-spike intervals is shown exemplary using two input scenarios: a simple sinus tone and a sung vowel. The model evaluation indicates an improvement in pitch estimation on a fixed time-scale.

Purpose - The purpose of this paper is to show the applicability of a discrete Hodge operator in the context of the De Rham cohomology to bicomplex-valued electromagnetic wave propagation problems. It was applied in the finite element method (FEM) to get a higher accuracy through conformal discretization. Therewith, merely the primal mesh is needed to discretize the full system of Maxwell equations. Design/methodology/approach - At the beginning, the theoretical background is presented. The bicomplex number system is used as a geometrical algebra to describe three-dimensional electromagnetic problems. Because we treat rotational field problems, Whitney edge elements are chosen in the FEM to realize a conformal discretization. Next, numerical simulations regarding practical wave propagation problems are performed and compared with the common FEM approach using the Helmholtz equation. Findings - Different field problems of three-dimensional electromagnetic wave propagation are treated to present the merits and shortcomings of the method, which calculates the electric and magnetic field at the same spatial location on a primal mesh. A significant improvement in accuracy is achieved, whereas fewer essential boundary conditions are necessary. Furthermore, no numerical dispersion is observed. Originality/value - A novel Hodge operator, which acts on bicomplex-valued cotangential spaces, is constructed and discretized as an edge-based finite element matrix. The interpretation of the proposed geometrical algebra in the language of the De Rham cohomology leads to a more comprehensive viewpoint than the classical treatment in FEM. The presented paper may motivate researchers to interpret the form of number system as a degree of freedom when modeling physical effects. Several relationships between physical quantities might be inherently implemented in such an algebra.

The employment of passive reflectors enables the millimeter-wave automotive radars to detect an approaching vehicle in non-line-of-sight conditions. In this paper, the installation of such reflectors above the sidewalk at an intersection is proposed and studied, avoiding pedestrians' blockage and road dust effect at ground level. Through the analysis of the backscattering power, it is shown that the suggested scheme may detect an approaching vehicle in the blind zone at distances of 30,łdots,50 m to the intersection point. Additionally, the analysis shows that efficient operation is highly dependent on the spatial orientation and size of the reflector. Even a few degrees rotation may change the detecting range by several meters. In turn, the larger area of the reflector may cover longer detecting distances, improving the radar scheme's overall performance. It is also shown that further performance enhancement can be achieved by employing a C-type radar, contributing an extra 5 dB to the backscattering power relative to an A-type radar. However, despite these improvements, the strongest scattering centre of the detectable vehicle is systematically identified to the bumper zone.

This letter presents a new approach to construct space-time block codes (STBCs) based on the Kronecker product. The proposed Kronecker-structured STBCs (K-STBCs) are specially designed for constant modulus constellations and include the classical linear dispersion (LD) codes as a special case. Its inherent Kronecker structure offers additional coding gains due to the mutual spreading of multiple space-time codewords. At the receiver, an efficient algorithm is derived by recasting the decoding of the K-STBCs as a rank-one tensor approximation problem. The proposed decoding algorithm provides a good bit error ratio (BER) performance especially, in the low signal-to-noise ratio (SNR) regime. In particular, our numerical results show that K-STBCs outperform the existing quasi-orthogonal STBCs due to the denoising gain offered by the proposed rank-one approximation based detector.

R-Mode is a terrestrial navigation system under development for the maritime domain that provides backup in case of a GNSS outage. This paper describes the first test results for real-time positioning on board a ship using medium frequency R-Mode signals. The estimation and positioning algorithms used are described in detail and it is shown how they are integrated into the R-Mode receiver developed by the German Aerospace Center. Moreover, during two daytime experiments with lower and higher dynamic movements of a ship in the Baltic Sea, we were able to achieve a 95% horizontal positioning accuracy of better then 12 m in the center of three R-Mode transmitters. This demonstrates the first time that the medium frequency R-Mode has provided positioning at sea.

Hochpräzise Wägesysteme nach dem Prinzip der elektromagnetischen Kraftkompensation (EMK), wie Massekomparatoren, finden trotz Neudefinition der Einheit Kilogramm weiterhin Anwendung bei der Realisierung sowie Weitergabe einer praktischen Masseskale, indem sie metrologische Massevergleiche anhand des elektrischen Stromes als Zwischengröße ermöglichen. Gleichzeitig haben Quanteninterferometer auf Basis einer supraleitenden Hochgeschwindigkeitselektronik das Potential, kleinste Änderungen des magnetischen Flusses im Femtotesla-Bereich aufzulösen und eröffnen somit einen alternativen Ansatz zum Erfassen kleinster Stromdifferenzen. Die Kombination dieser bislang größtenteils nur für Fundamentalexperimente in der elektrischen Metrologie ausgenutzten quantenelektrischen Effekte mit Systemen der Präzisionskraftmessung ist eine neuartige Ausgangsbasis zur Verbesserung der Messgenauigkeit dieser Referenzsysteme. Insbesondere bietet die Anwendung eines quantenbasierten Analog-zu-Digital-Wandlers ein deutliches Potential zum Erschließen bisher unerreichter Genauigkeiten. In diesem Beitrag werden die Ergebnisse erster experimenteller Arbeiten zum Nachweis des grundlegenden Funktionsprinzips präsentiert. Darüber hinaus erfolgt eine Abschätzung der Leistungsfähigkeit sowie des Entwicklungspotentials des vorgestellten quantenbasierten Stromsensors für hochpräzise EMK-Wägesysteme.

In this paper, the Tensor-Based Near-Field Localization (TeNFiLoc) algorithm is proposed for channel estimation and user localization on the uplink of a multi-carrier wireless communication system with a massive antenna array. OFDM is used as the modulation scheme and the user can be located in the near-field of the array. The exact spherical model of the impinging wavefronts allows TeNFiLoc to localize and identify the reflected paths and distinguish them from the Line-of-Sight (LoS) path. Some additional processing generally allows TeNFiLoc to localize user even in non-LoS scenarios, when the LoS path may be blocked or shadowed, provided that the number of reflected paths that reach the receiver is not less than three. It is also shown that perfect knowledge of the transmitted data is not necessarily required to perform channel estimation and user localization. Using a specific design of the transmitted signal, an additional low throughput, highly reliable communication link to the receiver can be established. Simulation results demonstrate the excellent localization accuracy of the TeNFiLoc algorithm and its applicability in practical scenarios.

In ultrasound nondestructive testing (NDT), a widespread approach is to take synthetic aperture measurements from the surface of a specimen to detect and locate defects within it. Based on these measurements, imaging is usually performed using the synthetic aperture focusing technique (SAFT). However, SAFT is suboptimal in terms of resolution and requires oversampling in the time domain to obtain a fine grid for the delay-and-sum (DAS). On the other hand, parametric reconstruction algorithms give better resolution, but their usage for imaging becomes computationally expensive due to the size of the parameter space and a large amount of measurement data in realistic 3-D scenarios when using oversampling. In the literature, the remedies to this are twofold. First, the amount of measurement data can be reduced using state-of-the-art sub-Nyquist sampling approaches to measure Fourier coefficients instead of time-domain samples. Second, parametric reconstruction algorithms mostly rely on matrix-vector operations that can be implemented efficiently by exploiting the underlying structure of the model. In this article, we propose and compare different strategies to choose the Fourier coefficients to be measured. Their asymptotic performance is compared by numerically evaluating the Cramér-Rao bound (CRB) for the localizability of the defect coordinates. These subsampling strategies are then combined with an l1-minimization scheme to compute 3-D reconstructions from the low-rate measurements. Compared to conventional DAS, this allows us to formulate a fully physically motivated forward model matrix. To enable this, the projection operations of the forward model matrix are implemented matrix-free by exploiting the underlying two-level Toeplitz structure. Finally, we show that high-resolution reconstructions from as low as a single Fourier coefficient per A-scan are possible based on simulated data and measurements from a steel specimen.

The 5th generation new radio (5G NR) standards create both enormous challenges and potential to address the spatio-spectral-temporal agility of wireless transmission. In the framework of a research unit at TU Ilmenau, various concepts were studied, including both approaches toward integrated circuits and distributed receiver front-ends (FEs). We report here on the latter approach, aiming at the proof-of-principle of the constituting FEs suitable for later modular extension. A millimeter-wave agile multi-beam FE with an integrated 4 by 1 antenna array for 5G wireless communications was designed, manufactured, and verified by measurements. The polarization is continuously electronically adjustable and the directions of signal reception are steerable by setting digital phase shifters. On purpose, these functions were realized by analog circuits, and digital signal processing was not applied. The agile polarization is created inside the analog, real-time capable FE in a novel manner and any external circuitry is omitted. The microstrip patch antenna array integrated into this module necessitated elaborate measurements within the scope of FE characterization, as the analog circuit and antenna form a single entity and cannot be assessed separately. Link measurements with broadband signals were successfully performed and analyzed in detail to determine the error vector magnitude contributions of the FE.

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.

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.

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].

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.

Phase-noise is a system impairment caused by the mismatch between the oscillators at the transmitter and the receiver. In OFDM systems, this induces inter-carrier-interference (ICI) by rotating the transmitted symbols. Thus it can cause severe system performance degradation. To reduce its effects, the phase-noise must be estimated or compensated. In this work, we propose a two-stage tensor-based receiver for a joint channel, phase-noise (PN), and data estimation in MIMO-OFDM systems. In the first stage, we show that the received signal at the pilot subcarriers can be modeled as a third-order PARAFAC tensor. Based on this model, we propose two algorithms for channel and phase-noise estimation at the pilot subcarriers. The first algorithm, based on the BALS (Bilinear Alternating Least Squares), is an iterative algorithm that estimates the channel gains and the phase-noise impairments. The second is a closed-form algorithm based on the LS-KRF (Least Squares - Khatri-Rao Factorization) that estimates the channel gains and the phase-noise terms through multiple rank-one factorizations. Both algorithms achieve similar performance, but in terms of computational complexity, we show that the LS-KRF becomes more attractive than the BALS as the number of receive antennas is increased. The second stage consists of data estimation, for which we propose a ZF (Zero-Forcing) receiver that capitalizes on the PARATuck tensor structure of the received signal at the data subcarriers using the Selective Kronecker Product (SKP) operator. Our numerical simulations show that the proposed receiver achieves an improved performance compared to the state-of-art receivers in terms of symbol error rate (SER) and normalized mean square error (NMSE) of the estimated channel and phase-noise matrices.

In this paper, we present a gridless channel estimation algorithm for a hybrid millimeter wave (mmWave) MIMO-OFDM system assuming a frequency-selective channel. The proposed algorithm is based on the Tensor-ESPRIT in DFT beamspace algorithm framework. First, we derive the R-dimensional (R-D) Standard/Unitary Tensor-ESPRIT in DFT beamspace framework and its analytic performance. We show that ESPRIT-type algorithms in a reduced-dimensional DFT beamspace can provide a significant performance gain over ESPRIT-type algorithms in full DFT beamspace and in element space under mild conditions. Afterwards, we develop a gridless channel estimation algorithm that is based on 3-D Tensor-ESPRIT in DFT beamspace algorithms. Numerical simulation results show that the proposed channel estimation algorithm can provide accurate channel estimates using only a few training resources.

The use of non-conventional materials is nowadays of much interest in scientific community. Magneto-rheological elastomers are hybrid materials, which in presence of magnetic fields state a change in their mechanical properties. They are composed by an elastomeric matrix with embedded magnetic particles. One of the most attractive features of these materials is that as soon as the magnetic field is removed from the material, the original mechanical properties are completely recovered, with negligible differences in comparison to the original state. This paper focuses on the study of magnetic characteristics of these smart materials, such as relative permeability and demagnetizing factors, for samples with different volume concentration of ferromagnetic particles.

Full Matrix Capture is a multi-channel data acquisition method which enables flexible, high resolution imaging using ultrasound arrays. However, the measurement time and data volume are increased considerably. Both of these costs can be circumvented via compressed sensing, which exploits prior knowledge of the underlying model and its sparsity to reduce the amount of data needed to produce a high resolution image. In order to design compression matrices that are physically realizable without sophisticated hardware constraints, structured subsampling patterns are designed and evaluated in this work. The design is based on the analysis of the Cramér–Rao Bound of a single scatterer in a homogeneous, isotropic medium. A numerical comparison of the point spread functions obtained with different compression matrices and the Fast Iterative Shrinkage/Thresholding Algorithm shows that the best performance is achieved when each transmit event can use a different subset of receiving elements and each receiving element uses a different section of the echo signal spectrum. Such a design has the advantage of outperforming other structured patterns to the extent that suboptimal selection matrices provide a good performance and can be efficiently computed with greedy approaches.

Leakage in frequency-modulated continuous-wave (FMCW) radar with a homodyne receiver induces strong signal components in the lower frequency parts of the radar observations. There, the dynamic range of the observations has been reduced, such that close and weak targets are hard to detect. In this letter, a signal processing method is proposed to mitigate the leakage. First, background subtraction is applied to cancel the leakage. As the cancellation is imperfect, a noisy signal portion remains: the leakage noise. A statistical model is developed to describe the leakage noise as a colored noise process. This model is parameterized from measurements and used to whiten the observations. As a result, the dynamic range is improved, and the close targets become better detectable.

Global Navigation Satellite Systems (GNSS) are crucial for applications that demand very accurate positioning. Tensor-based time-delay estimation methods, such as CPD-GEVD, DoA/KRF, and SECSI, combined with the GPS3 L1C signal, are capable of, significantly, mitigating the positioning degradation caused by multipath components. However, even though these schemes require an estimated model order, they assume that the number of multipath components is constant. In GNSS applications, the number of multipath components is time-varying in dynamic scenarios. Thus, in this paper, we propose a tensor-based framework with model order selection and high accuracy factor decomposition for time-delay estimation in dynamic multipath scenarios. Our proposed approach exploits the estimates of the model order for each slice by grouping the data tensor slices into sub-tensors to provide high accuracy factor decomposition. We further enhance the proposed approach by incorporating the tensor-based Multiple Denoising (MuDe).

To achieve a reliable communication with short data blocks, we propose a novel decoding strategy for Kronecker-structured constant modulus signals that provides low bit error ratios (BERs) especially in the low energy per bit to noise power spectral density ratio (E_b/N_o). The encoder exploits the fact that any M-PSK constellation can be factorized as Kronecker products of lower or equal order PSK constellation sets. A construction of two types of schemes is first derived. For such Kronecker-structured schemes, a conceptually simple decoding algorithm is proposed, referred to as Kronecker-RoD (rank-one detector). The decoder is based on a rank-one approximation of the "tensorized" received data block, has a built-in noise rejection capability and a smaller implementation complexity than state-of-the-art detectors. Compared with convolutional codes with hard and soft Viterbi decoding, Kronecker-RoD outperforms the latter in BER performance at same spectral efficiency.

In this paper a localization system of a passive 3-D coil is proposed and signal uncertainties due to the 3-D coil's arbitrary orientation are analyzed. The 3-D coil is excited by an alternating primary magnetic field. Geometrically distributed pick-up coils measure the 3-D coil's secondary field. By means of a simulated look-up table that assigns expected voltages from the pick-up coils to the positions of the 3-D coil, the position of the 3-D coil is deduced by a least-squares approach. A basic assumption is that the secondary field is invariant to the orientation of the 3-D coil. This allows a reduction of the computational effort for the look-up table generation and the table search during the localization phase since for each position the field distribution for only one orientation has to be calculated. However, the assumption of invariance to rotation is only valid for a dipole model. In this paper we investigate the localization error introduced by this assumption when using 3-D coils with a geometric extent in an inhomogeneous primary field. Optimized localization methods that decrease the statistical error are proposed. The theoretical results are verified with measurements conducted on a laboratory system.

Optical microdisk cavities with certain asymmetric shapes are known to possess unidirectional far-field emission properties. Here, we investigate arrays of these dielectric microresonators with respect to their emission properties resulting from the coherent behavior of the coupled constituents. This approach is inspired by electronic mesoscopic physics where the additional interference effects are known to enhance the properties of the individual system. As an example, we study the linear arrangement of nominally identical Lima¸con-shaped cavities and find mostly an increase of the portion of directional emitted light while its angular spread is largely diminished from 20 deg for the single cavity to about 3 deg for a linear array of 10 Lima¸con resonators, in fair agreement with a simple array model. Moreover, by varying the intercavity distance, we observe windows of reversion of the emission directionality and superdirectionality that can be interesting for applications like optical sensing or interconnects. We introduce a generalized array factor model that takes the coupling into account.

Magnetic sensors are widely used in automotive and industrial applications to measure linear or angular movements. Although the measurement of the magnetic field vector offers the opportunity to estimate all mechanical degrees of freedom, previous approaches could not be applied in these environments. This paper presents an improved method for sensing a magnetic source's position and orientation with six degrees of freedom. To solve the underlying inverse magnetostatic problem, an Unscented Kalman Filter in combination with an analytical model of the magnetic source's field is used. The performance of the method is demonstrated on the basis of a novel multiaxis input device. It comprises a cuboid magnet and a CMOS (Complementary metal-oxide-semiconductor) Hall-Sensor array with up to 36 elements. The localization algorithm and the measurement model are implemented in an efficient way to achieve real-time capability and sampling rates up to 80 Hz on embedded hardware. This outperforms known methods significantly and allows for a wide application of multi-degree of freedom sensors. Moreover, an absolute position and orientation accuracy of 71 [my]m and 1.4 are achieved. The work describes the basis for advanced input devices but can also be transferred to other kinds of magnetic localization problems.

Uncertainty surrounding ohmic tissue conductivity impedes accurate calculation of the electric fields generated by non-invasive brain stimulation. We present an efficient and generic technique for uncertainty and sensitivity analyses, which quantifies the reliability of field estimates and identifies the most influential parameters. For this purpose, we employ a non-intrusive generalized polynomial chaos expansion to compactly approximate the multidimensional dependency of the field on the conductivities. We demonstrate that the proposed pipeline yields detailed insight into the uncertainty of field estimates for transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS), identifies the most relevant tissue conductivities, and highlights characteristic differences between stimulation methods. Specifically, we test the influence of conductivity variations on (i) the magnitude of the electric field generated at each gray matter location, (ii) its normal component relative to the cortical sheet, (iii) its overall magnitude (indexed by the 98th percentile), and (iv) its overall spatial distribution. We show that TMS fields are generally less affected by conductivity variations than tDCS fields. For both TMS and tDCS, conductivity uncertainty causes much higher uncertainty in the magnitude as compared to the direction and overall spatial distribution of the electric field. Whereas the TMS fields were predominantly influenced by gray and white matter conductivity, the tDCS fields were additionally dependent on skull and scalp conductivities. Comprehensive uncertainty analyses of complex systems achieved by the proposed technique are not possible with classical methods, such as Monte Carlo sampling, without extreme computational effort. In addition, our method has the advantages of directly yielding interpretable and intuitive output metrics and of being easily adaptable to new problems.

Wireless Sensor Networks are popular solutions for various applications in order to reduce the efforts required by traditional cable-based systems. If the system in question is used to monitor and control physical processes, robust and reliable communication services have to be provided by the network. This is especially true for industrial or vehicular applications where the nodes are deployed in environments facing harsh propagation conditions. Usually, the effect of this on the system performance is studied on isolated layers of the OSI-reference model only. Therefore, we present results from a measurement campaign of vehicular environments that try to estimate the achievable robustness with respect to multiple OSI layers. Our results show that even if the link is rather poor, protocols on the upper layers are able to compensate this at the cost of additional delay.

This paper presents a scanning method for indoor mobile robot localization using the received signal strength indicator (RSSI) approach. The method eliminates the main drawback of the conventional fingerprint, whose database construction is time-consuming and which needs to be rebuilt every time a change in indoor environment occurs. It directly compares the column vectors of a kernel matrix and signal strength vector using the Euclidean distance as a metric. The highest resolution available in localization using a fingerprint is restricted by a resolution of a set of measurements performed prior to localization. In contrast, resolution using the scanning method can be easily changed using a denser grid of potential sources. Although slightly slower than the trilateration method, the scanning method outperforms it in terms of accuracy, and yields a reconstruction error of only 0. 08 m averaged over 1600 considered source points in a room with dimensions 9.7 m × 4.7 m × 3 m. Its localization time of 0. 39 s makes this method suitable for real-time localization and tracking.

Determination of the 3-D eddy current distribution inside a translatory moving conductor under a permanent magnet can accurately be done by using finite-element method (FEM). However, FEM calculations are very expensive, as they require discretization of the whole conductor volume. In this paper, we propose a new technique, to be called boundary element source method (BESM), where only boundary layers are discretized. The BESM is a modification of the hybrid boundary element method (HBEM). In the BESM, the concentrated point sources placed at the centers of boundary elements for the HBEM are replaced by distributed charge density over the area of the boundary element. This is especially useful in the regions, where neighboring boundary meshes significantly affect one another and when calculation point of eddy current is very close or belong to the surface of a boundary element. The method can handle arbitrary geometries of the specimen as well as the defect and arbitrary orientation of the magnetization vector. The accuracy of the proposed method is verified by comparing the results with the solutions obtained from a finite-element model. The proposed BESM approach is shown to be simple, robust, and computationally accurate.

We propose a procedure to determine optimal magnet systems in the framework of the nondestructive evaluation technique Lorentz force eddy-current testing (LET). The underlying optimization problem is clearly defined considering the problem specificity of nondestructive testing scenarios. The quantities involved are classified as design variables, and system and scaling parameters to provide a high level of generality. The objective function is defined as the absolute defect response signal (ADS) of the Lorentz force resulting from an inclusion inside the object under test. Associated constraints are defined according to the applied force sensor technology. A numerical procedure based on the finite-element method is proposed to evaluate the nonlinear objective and constraint functions, and the method of sequential quadratic programming is applied to determine unconstrained and constrained optimal magnet designs. Consequently, we propose a new magnet design based on the Halbach principle in combination with high saturation magnetization iron-cobalt alloys. The proposed magnet system outperforms currently available cylindrical magnets in terms of weight and performance. The corresponding defect response signal is increased up to 180% in the case of small defects located close to the surface of the specimen. The combination of active and passive magnetic materials provides an increase of the ADS by 15% compared with the magnet designs that are built solely from permanent magnet material. The proposed procedure provides a highly adaptive optimization strategy in the framework of LET and proposes new magnet systems with inherently improved characteristics.

The Josephson comparator is one of the building blocks of superconductor electronics. It is used as a decision element in all analog and digital circuits. For fast high-bandwidth analog-to-digital converters as well as for digital circuits with reduced switching energy its high sensitivity is most important. Thermal noise limits its decision uncertainty. In this work we analyze the influence of the bias current on the gray zone of a Josephson comparator. Circuit simulations are compared against experimental data. The experimental analysis shows a characteristic dependence of the gray zone on the bias current. We can identify a clear minimum value. This point is related to a certain clock frequency. At low frequencies the simulated relationship between the gray zone and the bias current is in very good agreement with experimental results. The comparison confirmed the circuit model and enables to adjust the design to certain applications to realize the best trade-off between clock frequency and grey zone. The article describes the experimental validation procedure and a derived normalized dependency in detail.

We evaluated the relationship between the gray zone width and the operating margin for comparators composed of quasi-one-junction superconducting quantum interference devices (QOSs) with shunt resistors, which are often used as high-speed readout circuits in multiple superconductor detector systems. The gray zone width is a good measure of current sensitivity of a single-bit comparator. We numerically analyzed the gray zone width of a QOS comparator and determined the circuit parameters. The gray zone width obtained from the experiments concurred with the results of the numerical analys is and was 2-3 [my]A at 4.2 K in a QOS comparator composed of three Nb/AlOx/Nb junctions with critical currents of less than 90 [my]A. The experimentally obtained operating margin for the bias current provided to the comparator was +-15% at the bias current of around 140 [my]A. These results show that QOS comparators are promising for readout circuits operating up to tens of GHz and imply that gray zone width is the thermal noise in the resistors at 4.2 K.

We propose an efficient multiplexing technique for superconducting nanowire single-photon detectors based on an orthogonal detector bias switching method enabling the extraction of the average count rate of a set of detectors by one readout line. We implemented a system prototype where the SNSPDs are connected to an integrated cryogenic readout and a pulse merger system based on rapid single flux quantum (RSFQ) electronics. We discuss the general scalability of this concept, analyze the environmental requirements which define the resolvability and the accuracy and demonstrate the feasibility of this approach with experimental results for a SNSPD array with four pixels.

The spectral matching of image luminance measuring devices (ILMD) is influenced by angular relationships at the object and the image side of the lens. To design the filters for spectral matching for ILMD, it is necessary to take this information into account. Here the influence of the angular relationship to be the spectral transmission of the filters has to be estimated. The impact of these relations to the spectral matching of ILMDs is explained.

The decision of a Josephson comparator is influenced by thermal noise which is limiting the practical sensitivity in many applications of superconductor electronics. We analyzed the Gaussian state distribution theoretically and identified the most effective way to improve the practical sensitivity of a Josephson comparator. We suggest a modified damping concept and demonstrate a gray zone width as low as 840 nA experimentally. This value is about ten times smaller than for conventional Josephson comparators and very close to the theoretical limit at liquid helium temperature.

Die Blendung ist ein wichtiges Gütemerkmal in der Außenbeleuchtung, die deshalb zu bewerten und zu minimieren ist. Zur Bewertung stehen verschiedene Kennzahlen zur Verfügung, die nur aufwändig mit konventioneller Messtechnik in der Praxis erfasst werden können. Mit bildauflösender Leuchtdichtemesstechnik kann hingegen dieser Messprozess vereinfacht werden. Der Artikel zeigt am Beispiel der Lichtimmission, wie dadurch beispielsweise das Blendmaß ks einfach und ausreichend genau bestimmt werden kann. Weitere Kennzahlen könnten bildaufgelöst gemessen werden, womit aber eine entsprechende Überarbeitung der Normen einhergehen müsste.

Microwave reflectometry might be a suitable tool for the thoracic surgeon to monitor edema formation of the lung during lung surgery. A new setup of microwave reflectometry for lung water measurements was developed and tested for clinical application. Three lung models were used for the microwave reflectometry tests: 1) the model of an ex vivo isolated perfused rat lung to investigate lung edema formation during ischemia-reperfusion (n=6), 2) the in situ lung of a human patient to demonstrate the feasibility of lung water monitoring during a surgical operation, 3) the model of an ex vivo isolated perfused human lung to investigate edema formation during postischemic reperfusion and to investigate the changes in water content in the region of a tumor. During human lung operation, significant changes in water content occurred in different lung areas. During isolated perfusion, a significant increase in lung water was measured in models 1) and 3) (P=0.03). Water content of tumor tissue was higher than in the surrounding healthy lung tissue. Microwave reflectometry offers a noninvasive approach to monitor lung edema formation in experimental models and during thoracic surgery.

During the application of simulation tools of computational electromagnetics it is sometimes difficult to decide whether the problems can be solved by the computation of electromagnetic fields or circuit simulation tools have to be applied additionally. The paper describes a typical situation in an electromagnetic CAD course, not only for beginners. The modelling and numerical simulation of a simple homopolar motor, similar to that what has been presented by Michael Faraday in 1821 firstly, was used as an example. To simulate the current flow in the permanent magnet the FEM software codes COMSOL Multiphysics including the integrated SPICE module and MAXWELL have been used. The correct simulation of the entire electric circuit as well as the precise modelling of the impressed current and of the electrode contacts turned out to be very important.

Measurements of extremely weak magnetic fields in unshielded environments require sensors providing a high dynamic range and slew rate while maintaining a high resolution. At present, analog Superconducting Quantum Interference Devices (SQUIDs) are the most sensitive sensors for magnetic flux. Due to their room-temperature feedback electronics necessary to maintain a stable point of operation, slew rates of those sensors are limited. The dynamic range is usually restricted by the resolution of classical analog to digital converters. The digital SQUID follows a different approach: a change of the magnetic field penetrating the pickup loop is translated by the sensor into a stream of voltage pulses. Each pulse represents the change of the magnetic flux inside the pickup loop by one flux quantum [Phi]0. By fast counting of these flux quanta, it is possible to outperform the analog SQUID in terms of dynamic range and maximum slew rate. We present our latest results with respect to the internal mechanisms of the digital SQUID and present a method to compensate the main source for nonlinear behavior of the sensor. The analyzed circuit was fabricated by FLUXONICS Foundry [1] in a 1 kA/cm2 niobium fabrication process.

We implemented small superconducting loops containing a frozen single flux quantum in single flux quantum (SFQ) electronics. Such an element acts as a pi-phaseshifter which is the missing complementary circuit element in SFQ-electronics and can significantly improve the circuit reliability and robustness. We designed a four bit ripple counter including toggle flip-fops (TFF) with these pi-phaseshifters. We analyzed the circuit stability by means of bit error rate (BER) measurements versus bias supply. The measured operation range of the counter in standard technique was + - 14% and could be improved to + - 24% for the new version utilizing pi-phaseshifters. These results are a clear proof for a more robust circuit realization achieved without any additional power consumption. Furthermore, it supports the reduction of present power consumption in circuits by using smaller switching energy as well as the development of more complex circuits because of a reduced sensitivity against technological parameter spread.

The rapid single-flux quantum electronics (RSFQ) is a superconducting, naturally digital circuit family which is currently close to being commercially applied. RSFQ is outstanding because of its very low switching energy resulting in very low power consumption. This advantage causes, however, a significant influence of thermal noise. For industrial applications, a certain noise immunity is required which is still a challenge, especially for circuits of higher complexity. Integrating phase-shifting elements is a new concept for further improvements concerning stability against the influence of thermal noise. We have already shown that the implementation of phase-shifting elements significantly reduces the influence of thermal noise on circuit behavior by experimentally analyzing the bit-error rate (Mielke et al 2009 IEEE Trans. Appl. Supercond. 19 621-5). Concepts which are easily implementable in standard niobium technology are especially promising. The π-phaseshifter consists of a superconducting loop which is able to store a single flux quantum. The loop current related to the stored flux creates a well-defined phase shift. To achieve the correct functionality of complex circuits it is essential to store exactly one flux quantum in each π-phaseshifter during the cooling down of the chip. Thus, for studying the feasibility of this new approach, the initialization reliability of the π-phaseshifter needs to be verified. We present an experimental investigation of this reliability to obtain a general assessment for the application of the π-phaseshifter in niobium technology. Furthermore, we compare the configuration shielded by a solid ground plane with a configuration with a ground-plane hole below the π-phaseshifter. Justified by the experimental results we suggest programmable RSFQ circuits based on π-phaseshifters. The characteristics of these devices can be influenced by a controlled initialization of the π-phaseshifter. The fabrication was performed by FLUXONICS Foundry.

State-of-the-art RSFQ circuits consist of Josephson junctions, inductances and bias current sources, where the inductances are practically used as functional elements. Even more, their value defines the functionality and therefore the conventional design process requires strict conditions for the wiring between Josephson junctions. The present design and optimization process does not take into account their parasitic capacitances to ground. This becomes an important issue for ultra fast applications above 100 GHz, but also for circuits dedicated to control quantum electronics. We report on a theoretical and experimental investigation of the influence of ground capacitances. The impedance matching between the connecting microstrip lines and the Josephson junction is a key issue for the correct operation of these circuits. We performed circuit simulations and collected experimental data of correct and incorrect operating circuits to derive design criteria. The critical reflection coefficient is found to be 0.35 and the particular design must use a smaller value for transfer and a larger value for storage of SFQ pulses, respectively.

The connection of RSFQ circuits with commercial room temperature electronics and amplifying SFQ pulses with high data rates require cryogenic interface amplifiers. Such amplifiers should provide extremely high bandwidth, low noise and low power consumption at the same time. Various hybrid amplifiers based on commercial p-HEMT transistors in an embedded microwave design were designed and characterized. Towards the p-HEMT transistor characterization at cryogenic temperature the biasing settings according to an optimum between voltage gain and low power consumption were determined. Thus a total power consumption of 2 mW and a voltage gain of 12 dB per single stage were achieved. For a preamplifier and an amplifier concept a multiple number of these stages were implemented in a microstrip and a coplanar transmission line design with a special matched interconnect taper towards the RSFQ components. Measurements of the amplifiers, the combination of an amplifier with a RSFQ circuit at 4.2 K showed their good performances without any disturbance of the RSFQ circuit. Due to the losses between the stages a total gain of 25 dB with a four stage amplifier was achieved; pulse rates went up to 3 Gb/s. The total power consumption was in the range of 8 mW. Further increase of data rates will be achieved by improving the matching between RSFQ output stage and amplifier.

In this paper, we investigate the application of the asynchronous logic approach for the realization of ultra high-speed digital electronics with high complexity. We evaluate the possible physical, technological, and schematical origins of restrictions limiting such an application, and propose solutions for their overcoming. Although our considerations are based on the rapid single flux quantum technique, the conclusions derived can be generalized about any type of digital information coding.

This paper investigates the influence of external field on the distribution of the critical current of Josephson junctions. The external field can cause trapped flux which may reduce the critical current. Experimental results show a formation of bunches in the distribution of the critical current when the external magnetic field rises a certain limit. From a theoretical point of view this formation can only be explained by attractive pinning points in the vicinity of the junction. The Josephson junctions were fabricated with the 1 kA/cm2 Nb/Al2O3/Nb trilayer process of FlUXONICS Foundry.

A cylindrical cell containing GaInSn alloy and aqueous solution of KOH, a highly simplified aluminium reduction cell, is constructed. A direct current is applied to the cylindrical cell to generate the magnetic field. In a range of amplitude and frequency of vertical vibration, a stable, non-axisymmetric wave pattern is produced, and the displacement of the oscillating interface is measured by a digital camcorder. The perturbation of the magnetic field caused by the non-axisymmetric interface is measured by fluxgate sensors and processed by fast Fourier transforms. The measurements are consistent with forward calculations, and have been exploited to reconstruct the deformed interface by solving an inverse problem so as to develop magnetic field tomography to reconstruct an unknown interface between two electrically conducting fluids.

Recently, we have proposed a novel technique for the design of Josephson transmission lines (JTLs). According to our theoretical estimations, this technique ist able to increase the propagation speed of the JTLs and to reduce drastically their dc bias consumption. Here, a sucessful experimental verification of these estimations is presented.