Recent developments in Multiple-Input-Multiple-Output (MIMO) technology include packing a large number of antenna elements in a compact array to access the bandwidth benefits provided by higher mutual coupling (MC). The resulting super-wideband (SW) systems require a circuit-theoretic framework to handle the MC and channel models which span extremely large bands. Hence, in this paper, we make two key contributions. First, we develop a physically-consistent Rician channel model for use with SW systems. Secondly, we express the circuit-theoretic models in terms of a standard MIMO model, so that insights into the effects of antenna layouts, MC, and bandwidth can be made using standard communication theory. For example, we show the bandwidth widening resulting from the new channel model. In addition, we show that MC distorts line-of-sight paths which has beamforming implications. We also highlight the interaction between spatial correlation and MC and show that tight coupling reduces spatial correlations at low frequencies.

Earth observation has a crucial role in understanding and monitoring our planet's health and changes. Spaceborne Synthetic Aperture Radar (SAR) has become a valuable technology for Earth monitoring, leading to a massive expansion of satellite launches. However, within the limited radio frequency (RF) band, Radio Frequency Interference (RFI) poses a significant challenge for SAR technology. RFI can have a significant impact on the overall system performance and particularly on SAR image quality. To analyze and solve the interference problem, a simulator/emulator is required at the RF level to emulate and analyze the effects of different RFI sources on the final focused spaceborne SAR image. This paper presents an open-source RF-level SAR emulator for spaceborne applications called SEMUS. SEMUS is an integrated end-to-end framework for realistic spaceborne SAR scenarios that can generate raw RF data (Level-0) for an arbitrary scene and reconstruct the final SAR-focused image (Level-1). Moreover, the emulator is capable of injecting arbitrary RFI waveforms into the raw SAR data. The simulation results prove SEMUS's ability to generate high-quality Level-0 SAR data above Melbourne, Australia. Affirming its capability, SEMUS is able to reconstruct Level-1 free of RFI or contaminated with interference.

Ising machines offer a high-performance approach to solving combinatorial optimization problems by evolving toward the ground state of the system Hamiltonian. Among these, oscillator-based Ising machines have gained attention due to their ultra-low power consumption and efficiency in reaching optimal configurations. However, existing implementations are often constrained by specific graph topologies and problem constraints, limiting their scalability and versatility. In this work, we propose a mixed-signal oscillator-based Ising machine that utilizes digital coupling with programmable weights, enabling support for problems with arbitrary graph connections and weight values. By eliminating the need for direct analog coupling, our design significantly reduces hardware complexity while expanding the problem space to all-to-all connectivity. We present a 128-node digital-coupled ring oscillator Ising machine to solve weighted Max-Cut problems, demonstrating its optimality, efficiency, and computational robustness advantages. Comprehensive simulation studies address key challenges, including sampling frequency, resolution, coupling strength, and second harmonic injection schemes. Our results show that the proposed system achieves high success probabilities in finding near-optimal solutions efficiently, paving the way for scalable, reconfigurable Ising-based computing architectures.

In this paper, novel robust principal component analysis (RPCA) methods are proposed to exploit the local structure of datasets. The proposed methods are derived by minimizing the α -divergence between the sample distribution and the Gaussian density model. The α- divergence is used in different frameworks to represent variants of RPCA approaches including orthogonal, non-orthogonal, and sparse methods. We show that the classical PCA is a special case of our proposed methods where the α- divergence is reduced to the Kullback-Leibler (KL) divergence. It is shown in simulations that the proposed approaches recover the underlying principal components (PCs) by down-weighting the importance of structured and unstructured outliers. Furthermore, using simulated data, it is shown that the proposed methods can be applied to fMRI signal recovery and Foreground-Background (FB) separation in video analysis. Results on real world problems of FB separation as well as image reconstruction are also provided.

Antennas that can operate across multiple communication standards have remained a challenge. To address these limitations, we propose a Field-Programmable Radio Frequency Surface (FPRFS), which is based on manipulating current flow on its surface to achieve desirable RF characteristics. In this work, we demonstrate that substantial enhancements in radiation efficiency can be achieved while preserving the high reconfigurability of antenna structures implemented on the FPRFS. This is accomplished by utilizing an asymmetric excitation, directing the excitation to the low-loss contiguous surface, and dynamically manipulating the imaged return current on a segmented ground plane by switches. This important insight allows for adaptable antenna performance that weakly depends on the number of RF switches or their loss. We experimentally validate that FPRFS antennas can achieve efficiencies comparable to traditionally implemented antenna counterparts. This permits the FPRFS to be effectively utilized as a productive antenna and impedance-matching network with real-time reconfigurability. A field-programmable radio frequency surface (FPRFS) is proposed that can implement arbitrary antennas and impedance matching networks. An asymmetric excitation scheme is demonstrated to ensure radiation efficiency independent of the number of FPRFS switches.

Continuous nanohertz gravitational waves from individual supermassive black hole binaries may be detectable with pulsar timing arrays. A novel search strategy is developed, wherein intrinsic achromatic spin wandering is tracked simultaneously with the modulation induced by a single gravitational wave source in the pulse times of arrival. A two-step inference procedure is applied within a state-space framework, such that the modulation is tracked with a Kalman filter, which then provides a likelihood for nested sampling. The procedure estimates the static parameters in the problem, such as the sky position of the source, without fitting for ensemble-averaged statistics such as the power spectral density of the timing noise, and therefore complements traditional parameter estimation methods. It also returns the Bayes factor relating a model with a single gravitational wave source to one without, complementing traditional detection methods. It is shown via astrophysically representative software injections in Gaussian measurement noise that the procedure distinguishes a gravitational wave from pure noise down to a characteristic wave strain of h(0 )approximate to 2 x 10(-15). Full posterior distributions of model parameters are recovered and tested for accuracy. There is a bias of approximate to 0.3 rad in the marginalized one-dimensional posterior for the orbital inclination i, introduced by dropping the so-called pulsar terms. Smaller biases less than or similar to 10 are also observed in other static parameters.

Pulsar timing arrays (PTA) can detect continuous nanohertz gravitational waves (GW) emitted by individual supermassive black hole binaries. The data analysis procedure can be formulated within a time-domain, state-space framework, in which the radio timing observations are related to a temporal sequence of latent states, namely the intrinsic pulsar spin frequency. The achromatic wandering of the pulsar spin frequency is tracked using a Kalman filter concurrently with the pulse frequency modulation induced by a GW from a single source. The modulation is the sum of terms proportional to the GW strain at the Earth and at every pulsar in the array. Here, we generalize previous state-space formulations of the PTA problem to include the pulsar terms; that is, we copy the pulsar terms from traditional, non-state-space analyses over to the state-space framework. The performance of the generalized Kalman filter is tested using astrophysically representative software injections in Gaussian measurement noise. It is shown that including the pulsar terms corrects for previously identified biases in the parameter estimates (especially the sky position of the source) which also arise in traditional matched-filter analyses that exclude the pulsar terms. Additionally, including the pulsar terms decreases the minimum detectable strain by 14 percent. Overall, the study verifies that the pulsar terms do not raise any special extra impediments for the state-space framework, beyond those studied in traditional analyses. The inspiral-driven evolution of the wave frequency at the Earth and at the retarded time at every pulsar in the array is also investigated.

The role of fluids in earthquake rupture is key to understanding seismic hazards, particularly at subduction zones. The Shumagin Gap, Alaska, is notable due to a paucity of large earthquake nucleation and weak coupling between the overriding and subducting plates. Fluids have been hypothesized to explain these observations, but the source of the fluids remains unclear. Here we present an image of the subsurface electrical resistivity derived from marine magnetotelluric data collected in the Shumagin segment. The model reveals an approximately 50-km-wide conductive (that is, fluid-rich) zone near the plate interface with fluids sourced from the dehydration of slab mantle (15–25 km beneath the crust–mantle boundary). We find that the July 2020 megathrust earthquake—which nucleated near the Semidi segment and propagated westwards into the Shumagin segment—only ruptured the conductive portion of the plate interface. This suggests that slab mantle fluids can influence the seismogenic zone by, for example, creating patches that are prone to dynamic rupture. In contrast, updip of the slip patch is simultaneously resistive and weakly coupled, suggesting that fluids alone are not responsible for weak coupling and that plate roughness plays a role. More broadly, these results suggest that slab mantle fluids could be an underappreciated fluid source in the water budgets of forearc subduction zones.

This paper presents an innovative contact-less flexible microwave biosensor, capable of encircling laboratory con-tainers such as medical vials, thereby facilitating the characterization of their contents. The functionality of this sensor relies on the excitation of waveguide modes within a cylindrical coplanar waveguide (CCPW) that is filled with a water-based solution. Experimental measurements utilizing this device were conducted on aqueous solutions containing $\text{NaCl}$ , KCI, and ethanol. The obtained results provide evidence of the high sensitivity and reliability of the device, thereby substantiating its suitability for various characterization applications.

With the advent of large commercial constellations carrying synthetic aperture radars (SAR), the problem of radio frequency interference (RFI) between different SAR satellites will soon arise due to the shared use of spectrum. Such interference manifests as distortion and artificial artifacts which reduce the quality of SAR images. This paper proposes a novel approach to tackle the interference problem by exploiting the RFI signal as an additional source of illumination acting as a bistatic radar transmitter. As such, rather than treating the RFI as an adversarial signal that needs to be suppressed, we benefit from this signal to further improve the acquired image quality, where the primary image is generated based on the satellite’s own signal as a typical active monostatic source. In order to evaluate this approach, we first lay the geometry and signal model including the required sensor architecture that leverage the well-structured SAR waveform. Then, we present the required modifications to typical SAR signal processing workflow such that both monostatic and bistatic images are overlapped. Results based on realistic signal emulation are then shown for a typical interference scenario where the proposed hybrid approach demonstrates excellent potential to tackle SAR RFI problems.

Passive bistatic radar research is essential for accurate 3D target tracking, especially in the presence of missing or low-quality bearing information. Traditional extended Kalman filter (EKF) methods often introduce bias in such scenarios. To overcome this limitation, we propose employing the unscented Kalman filter (UKF) for handling the nonlinearities in 3D tracking, utilizing range and range-rate measurements. Additionally, we incorporate the probabilistic data association (PDA) algorithm with the UKF to handle cluttered environments. Through extensive simulations, we demonstrate a successful implementation of the UKF-PDA framework, showing that the proposed method effectively reduces bias and significantly advances tracking capabilities in passive bistatic radars.

Inference and hypothesis testing are typically constructed on the basis that a specific model holds for the data. To determine the veracity of conclusions drawn from such data analyses, one must be able to identify the presence of the assumed structure within the data. In this paper, a model verification test is developed for the presence of a random walk-like structure in the variations in the frequency of complex-valued sinusoidal signals measured in additive Gaussian noise. This test evaluates the joint inference of the random walk hypothesis tests found in economics literature that seek random walk behaviours in time series data, with an additional test to account for how the random walk behaves in frequency space.

Gravitational-wave observatories around the world are searching for continuous waves: persistent signals from sources, such as spinning neutron stars. These searches use sophisticated statistical techniques to look for weak signals in noisy data. In this paper, we demonstrate these techniques using a table-top model gravitational-wave detector: a Michelson interferometer where sound is used as an analog for gravitational waves. Using signal processing techniques from continuous-wave searches, we demonstrate the recovery of tones with constant and wandering frequencies. We also explore the use of the interferometer as a teaching tool for educators in physics and electrical engineering by using it as an "optical microphone" to capture music and speech. A range of filtering techniques used to recover signals from noisy data are detailed in the supplementary material of this article. Here, we present the highlights of our results using a combined notch plus Wiener filter and the statistical log minimum mean-square error (logMMSE) estimator. Using these techniques, we easily recover recordings of simple chords and drums, but complex music and speech are more challenging. This demonstration can be used by educators in undergraduate laboratories and can be adapted for communicating gravitational-wave and signal-processing topics to nonspecialist audiences.

Objective: In this paper, we aim to address the problem of subspace detection in the presence of locally-correlated complex Gaussian noise and interference. For applications like brain activity detection using functional magnetic resonance imaging (fMRI) data where the noise is possibly locally correlated, using the sample covariance estimator is not a suitable choice due to significant dependency of its accuracy on the number of observations. Methods: In this study, we take advantage of an assumed banded structure in the covariance matrix to model the local dependence in the noise and propose a new covariance estimation approach. In particular, we use the idea of factorizing the joint likelihood function into a few conditional likelihood terms and maximizing each term independently of the others. This process leads to an explicit estimator for banded covariance matrices which requires fewer observations to achieve the same accuracy as the sample covariance. This estimate is then fed into an adaptive matched filter, two-step Rao and two-step Wald tests for detection. Results: Simulation results reveal the superiority of the proposed methods over well known classical detectors. Finally, the proposed methods are applied to functional magnetic resonance imaging (fMRI) data to localize neural activities in the brain. Conclusion: The proposed method can offer better activation maps in terms of accuracy and spatial smoothness. Significance: The proposed methods can be seen as alternatives for standard detection approaches which are not perfectly aligned with the properties of fMRI data.

Continental rifting is a critical component of the plate tectonic paradigm, and occurs in more than one mode, phase, or stage. While rifting is typically facilitated by abundant magmatism, some rifting is not. We aim to develop a better understanding of the fundamental processes associated with magma-poor (dry) rifting. Here, we provide an overview of the NSF-funded Dry Rifting In the Albertine-Rhino graben (DRIAR) project, Uganda. The project goal is to apply geophysical, geological, geochemical, and geodynamic techniques to investigate the Northern Western Branch of the East African Rift System in Uganda. We test three hypotheses: (1) in magma-rich rifts, strain is accommodated through lithospheric weakening from melt, (2) in magma-poor rifts, melt is present below the surface and weakens the lithosphere such that strain is accommodated during upper crustal extension, and (3) in magma-poor rifts, there is no melt at depth and strain is accommodated along pre-existing structures such as inherited compositional, structural, and rheological lithospheric heterogeneities. Observational methods in this project include: passive seismic to constrain lithospheric structure and asthenospheric flow patterns; gravity to constrain variations in crustal and lithospheric thickness; magnetics to constrain the thermal structure of the upper crust; magnetotellurics to constrain lithospheric thickness and the presence of melt; GNSS to constrain surface motions, extension rates, and help characterize mantle flow; geologic mapping to document the geometry and kinematics of active faults; seismic reflection analyses of intra-rift faults to document temporal strain migration; geochemistry to identify and quantify mantle-derived fluids in hot springs and soil gases; and geodynamic modeling to develop new models of magma-poor rifting processes. Fieldwork will begin in January 2022 and the first DRIAR field school is planned for summer 2022. Geodynamic modeling work and morphometric analyses are already underway.

It is an open challenge to estimate systematically the physical parameters of neutron star interiors from pulsar timing data while separating spin wandering intrinsic to the pulsar (achromatic timing noise) from measurement noise and chromatic timing noise (due to propagation effects). In this paper, we formulate the classic two-component, crust-superfluid model of neutron star interiors as a noise-driven, linear dynamical system and use a state-space-based expectation–maximization method to estimate the system parameters using gravitational-wave and electromagnetic timing data. Monte Carlo simulations show that we can accurately estimate all six parameters of the two-component model provided that electromagnetic measurements of the crust angular velocity and gravitational-wave measurements of the core angular velocity are both available. When only electromagnetic data are available, we can recover the overall relaxation time-scale, the ensemble-averaged spin-down rate, and the strength of the white-noise torque on the crust. However, the estimates of the secular torques on the two components and white-noise torque on the superfluid are biased significantly.