Electrical Engineering And Systems Science

A flexible and low-cost device for eddy current non-destructive testing made of off-the-shelf components is described. The proposed system is compact and easy to operate, and it consists of a dual H-bridge stepper motor driver, a coil winded in-house on an additively manufactured support, a tunnel magnetoresistance sensor, and a data generation/acquisition module. For the latter, two different commercial devices have been used, and both setups have been then tested on a benchmark sample to detect small artificial cracks. The system can flexibly generate the square pulse or square wave with tunable duration and frequency, as well as pseudo-noise binary waveforms that are here used in combination with pulse-compression to increase the inspection sensitivity with respect to standard pulsed eddy current testing. A benchmark sample was analysed, and all the defects were correctly located, demonstrating the good detection capability of the sensor. This was achieved by assembling a very low-cost handy device, which can be further improved in portability and performances with the use of different off-the-shelf components, and that can be easily integrated with single-board PC, paving the way for future developments in this field.

Real-time magnetic resonance imaging (RT-MRI) of human speech production is enabling significant advances in speech science, linguistics, bio-inspired speech technology development, and clinical applications. Easy access to RT-MRI is however limited, and comprehensive datasets with broad access are needed to catalyze research across numerous domains. The imaging of the rapidly moving articulators and dynamic airway shaping during speech demands high spatio-temporal resolution and robust reconstruction methods. Further, while reconstructed images have been published, to-date there is no open dataset providing raw multi-coil RT-MRI data from an optimized speech production experimental setup. Such datasets could enable new and improved methods for dynamic image reconstruction, artifact correction, feature extraction, and direct extraction of linguistically-relevant biomarkers. The present dataset offers a unique corpus of 2D sagittal-view RT-MRI videos along with synchronized audio for 75 subjects performing linguistically motivated speech tasks, alongside the corresponding first-ever public domain raw RT-MRI data. The dataset also includes 3D volumetric vocal tract MRI during sustained speech sounds and high-resolution static anatomical T2-weighted upper airway MRI for each subject.

Many multichannel systems use a linear filter to retrieve a signal of interest corrupted by noise whose statistics are partly unknown. The optimal filter in Gaussian noise requires knowledge of the noise covariance matrix Σ and in practice the latter is estimated from a set of training samples. An important issue concerns the characterization of the performance of such adaptive filters. This is generally achieved using as figure of merit the ratio of the signal to noise ratio (SNR) at the output of the adaptive filter to the SNR obtained with the clairvoyant -- known Σ -- filter. This problem has been studied extensively since the seventies and this document presents a concise overview of results published in the literature. We consider various cases about the training samples covariance matrix and we investigate fully adaptive, partially adaptive and regularized filters.

We present an automated methodology for using Automatic Vehicle Location measurements of public transportation vehicles to construct a probabilistic model. The model not only allows for accurate evaluation of service performance, but also makes it possible to study the effects of system modifications a priori. The methodology is almost entirely agnostic to otherwise important details of the service -- in particular its route and the location of stops. Instead, it infers this from the data using automated map generation techniques. The behaviour of vehicles in the model is analysed using computer simulation combined with statistical model checking. We present two case studies involving the Airlink service in Edinburgh and the Bellevue Express in Seattle. To demonstrate the usefulness of the approach, we analyse the impact of the scheduling strategies of bus holding and speed modification on the Airlink's performance. The data and code used to create the figures are publicly available online.

Complex ADMM-Net, a complex-valued neural network architecture inspired by the alternating direction method of multipliers (ADMM), is designed for interference removal in super-resolution stepped frequency radar angle-range-doppler imaging. Tailored to an uncooperative scenario wherein a MIMO radar shares spectrum with communications, the ADMM-Net recovers the radar image---which is assumed to be sparse---and simultaneously removes the communication interference, which is modeled as sparse in the frequency domain owing to spectrum underutilization. The scenario motivates an ℓ 1 -minimization problem whose ADMM iteration, in turn, undergirds the neural network design, yielding a set of generalized ADMM iterations that have learnable hyperparameters and operations. To train the network we use random data generated according to the radar and communication signal models. In numerical experiments ADMM-Net exhibits markedly lower error and computational cost than ADMM and CVX.

Millimeter-wave (mmWave) joint communication-radar (JCR) will enable high data rate communication and high-resolution radar sensing for applications such as autonomous driving. Prior JCR systems that are based on the mmWave communications hardware, however, suffer from a limited angular field-of-view and low estimation accuracy for radars due to the employed directional communication beam. In this paper, we propose an adaptive and fast combined waveform-beamforming design for the mmWave automotive JCR with a phased-array architecture that permits a trade-off between communication and radar performances. To rapidly estimate the mmWave automotive radar channel in the Doppler-angle domain with a wide field-of-view, our JCR design employs a few circulant shifts of the transmit beamformer and apply two-dimensional partial Fourier compressed sensing technique. We optimize these circulant shifts to achieve minimum coherence in compressed sensing. We evaluate the JCR performance trade-offs using a normalized mean square error (MSE) metric for radar estimation and a distortion MSE metric for data communication, which is analogous to the distortion metric in the rate distortion theory. Additionally, we develop a MSE-based weighted average optimization problem for the adaptive JCR combined waveform-beamforming design. Numerical results demonstrate that our proposed JCR design enables the estimation of short- and medium-range radar channels in the Doppler-angle domain with a low normalized MSE, at the expense of a small degradation in the communication distortion MSE.

A high-rate yet low-cost air-to-ground (A2G) communication backbone is conceived for integrating the space and terrestrial network by harnessing the opportunistic assistance of the passenger planes or high altitude platforms (HAPs) as mobile base stations (BSs) and millimetre wave communication. The airliners act as the network-provider for the terrestrial users while relying on satellite backhaul. A null-steered beamforming technique relying on a large-scale planar array is used for transmission by the airliner/HAP for achieving a high directional gain, hence minimizing the interference between the users. Furthermore, approximate spectral efficiency (SE) and area spectral efficiency (ASE) expressions are derived and quantified for diverse system parameters.

The use of low-resolution data converters in the radio-frequency (RF) chains of all-digital massive multiple-input multiple-output (MIMO) basestations promises significant reductions in power consumption, hardware costs, and interconnect bandwidth. We propose a quantization-aware data-detection algorithm which mitigates the performance loss of 1-bit quantized massive MIMO orthogonal frequency-division multiplexing (OFDM) systems. Since the system performance heavily depends on the quality of channel estimates, we also develop a nonlinear 1-bit channel estimation algorithm that builds upon the proposed data detection algorithm. We show that the proposed algorithms significantly outperform linear data detectors and channel estimators in terms of bit error rate. For the proposed nonlinear data detection algorithm, we develop a very large scale integration (VLSI) architecture and present implementation results on a Xilinx Virtex-7 field programmable gate array (FPGA). Our implementation results are, to the best of our knowledge, the first for 1-bit massive MU-MIMO-OFDM systems and demonstrate comparable hardware efficiency with respect to state-of-the-art linear data detectors designed for systems with high-resolution data converters, while achieving lower bit error rate.

Deployment of automated ground vehicles beyond the confines of sunny and dry climes will require sub-lane-level positioning techniques based on radio waves rather than near-visible-light radiation. Like human sight, lidar and cameras perform poorly in low-visibility conditions. This paper develops and demonstrates a novel technique for robust sub-50-cm-accurate urban ground vehicle positioning based on all-weather sensors. The technique incorporates a computationally-efficient globally-optimal radar scan batch registration algorithm into a larger estimation pipeline that fuses data from commercially-available low-cost automotive radars, low-cost inertial sensors, vehicle motion constraints, and, when available, precise GNSS measurements. Performance is evaluated on an extensive and realistic urban data set. Comparison against ground truth shows that during 60 minutes of GNSS-denied driving in the urban center of Austin, TX, the technique maintains 95th-percentile errors below 50 cm in horizontal position and 0.5 degrees in heading.

We present a gridless sparse iterative covariance-based estimation method based on alternating projections for direction-of-arrival (DOA) estimation. The gridless DOA estimation is formulated in the reconstruction of Toeplitz-structured low rank matrix, and is solved efficiently with alternating projections. The method improves resolution by achieving sparsity, deals with single-snapshot data and coherent arrivals, and, with co-prime arrays, estimates more DOAs than the number of sensors. We evaluate the proposed method using simulation results focusing on co-prime arrays.