Papers

In the early stages of a pandemic, identification of a small number of infected individuals through large-scale screening is critical for pandemic control, yet remains challenging under limited reagents and testing capacity. Existing group testing methods suffer from either high computational complexity or low identification accuracy. Even worse, no available methods provide theoretically rigorous analysis for sparse identification with hard constraints caused by the sample usage constraint and the dilution effect existing ubiquitously in practical applications. In this article, we propose the Logic Screening method (LoSc), an ultra fast, accurate, and theoretically grounded framework for large-scale screening. LoSc introduces a novel decoding algorithm with a very simple selection strategy, achieving identification of all positives with only O(klogn) pooled tests. The decoding relies only on logical operations, enabling direct hardware implementation and yielding ultra fast computational implementation. Moreover, LoSc explicitly incorporates dilution and sample usage constraints into pooling designs, and establishes theoretical guarantees to guide optimal pooling configurations. Extensive simulations confirm the superior effectiveness, efficiency, and scalability. We believe LoSc offers a fast and reliable solution for automatic large-scale screening.

The cloud radio access network (C-RAN) mitigates inter-cell interference by jointly processing the observations of distributed remote units (RUs) at a centralized unit (CU), but limited fronthaul capacity forces each RU to compress its received signal. Under transform-compress-forward, an RU transforms its signal and quantizes the resulting coefficients, with bit allocation distributing a finite bit budget across them. Classical reverse waterfilling assumes Gaussian sources, yet practical finite-alphabet symbols carry mutual information that saturates at $\log_2 M$, leaving bit allocation for such inputs unresolved. We address this by formulating bit allocation as maximizing the finite-alphabet generalized mutual information (GMI) achieved after linear MMSE (LMMSE) detection at the CU. Via the I-MMSE relation, this yields a fixed-point update whose converged solution decomposes into a vessel height, a shared water level, and a finite-alphabet mercury level; we term it {reverse mercury/waterfilling} (RMWF). Numerical results show that RMWF sustains end-to-end rate under tight fronthaul budgets and remains robust under antenna scaling, which is increasingly consequential as antenna counts outpace fronthaul capacity in modern C-RAN.

This paper proposes a multimodal learning framework that integrates camera images, LiDAR point clouds, and radio-frequency (RF) pilots to realize multi-user multiple-input single-output (MU-MISO) beamforming optimization in mixed line-of-sight (LoS) and non-line-of-sight (NLoS) environments. In LoS-dominant environments, beamforming can be designed by utilizing sensing information related to user locations. However, LoS paths cannot be guaranteed to exist for all users in realistic dynamic environments due to blockages. To characterize NLoS propagation, detailed three-dimensional (3D) information of surrounding obstacles is required. This paper integrates camera images and LiDAR points with RF pilots to capture detailed 3D geometric, object-class, and material information of users and surrounding objects. The obtained multimodal information provides useful cues for both direct LoS paths and reflected NLoS propagation, and provides rich channel-related information for beamforming design. Specifically, a bounding-box detection neural network is first used to extract two-dimensional spatial, object-class, and material information from camera images. The image-derived information then guides the selection of LiDAR points to obtain 3D geometric information of users and surrounding obstacles, and assigns object-class and material labels to the selected points. The image-guided LiDAR features, together with RF pilots, are jointly processed by a designed neural architecture to optimize the beamforming vectors. Simulation results show that the proposed method achieves a higher minimum rate than benchmarks.

This paper proposes structured channel-parameter estimators for reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) systems employing uniform rectangular arrays under line-of-sight propagation. By exploiting the Kronecker and multilinear structures of the cascaded channel, we introduce the Successive Kronecker Factorization (SKF) alongside two Third-Order Tensor Parameter Estimation (TOPE) methods: TOPE-ALS and TOPE-HOSVD. These estimators recover spatial signatures from the pilot-filtered signal matrix, and then decouple the corresponding horizontal and vertical spatial frequencies. This structured formulation achieves benchmark estimation accuracy while reducing computational complexity, offering a efficient solution for RIS-aided systems.

Accurate channel modeling and estimation of active reconfigurable intelligent surface (RIS)-assisted links with densely integrated elements are essential to fully unleashing this technology's potential. This work adopts a physically consistent model incorporating mutual coupling (MC) effects, modeled via scattering parameters, in RIS-aided communication. We formulate the MC-aware channel estimation as a compressed sensing (CS) problem. The MC effect leads to an increase in the sensing matrix dimensions. This increased dimensionality substantially elevates the complexity of the formulated CS problem. To overcome this, we propose a low-complexity estimator that leverages the structure of the scattering matrix and MC mechanisms to obtain a reduced-size design sensing matrix. Numerical results demonstrate that our approach outperforms MC-unaware estimators by several dBs, achieving accuracy comparable to fully MC-aware solutions but with significantly lower complexity.

Federated learning (FL) enables collaborative model training without sharing raw data, but its performance degrades under non-IID data and stochastic client participation. Remedies built on classical Federated Averaging (FedAvg) typically presuppose that client participation probabilities are known to the server, which is rarely the case in deployed systems. We first discuss and then characterize the optimization problem that distributionally agnostic FedAvg actually solves when participation is entirely unknown, possibly highly skewed, and of variable size across rounds: uniform aggregation is shown to minimize a well-defined stochastic objective, weighted by the participation-induced marginal, at a standard $\mathcal{O}(1/\sqrt{T})$ rate for convex and possibly nonsmooth losses. Building on this characterization, we propose Federated Risk-Averse Averaging (FedeRage), a risk-averse extension of FedAvg that embeds the Conditional Value-at-Risk (CVaR) into the local objective within a natural distributionally robust optimization (DRO) framework. FedeRage implicitly upweights high-loss and infrequently participating clients while adding only a single scalar per-client, and admits an $\mathcal{O}(\kappa/\sqrt{T})$ rate in which the factor $\kappa$ is the upper bound on the “price" of risk aversion. In contrast with aggregation-alignment schemes based on optimal transport, which require the availability distribution as an input, FedeRage remains agnostic to it. Several experiments on three heterogeneous benchmarks indicate consistent improvements over state-of-the-art methods in accuracy, fairness, and convergence speed.

Localizing a radio-frequency (RF) transmitter from received signals often requires a model of the environment to predict how obstacles block and reflect the signal. In many robotic applications, however, only a partial map is available, particularly when a robot localizes the source while exploring with simultaneous localization and mapping (SLAM). We study single-snapshot transmitter localization on such partially explored maps and compare two approaches that output a posterior over transmitter locations. The first extends a digital-twin method, which ray-traces every candidate location, to partial maps by treating unexplored space as free and training on mixed map coverage. The second, MAPLE-RF, encodes estimated path angles of arrival and signal-to-noise ratios as grid channels aligned with map knownness, occupancy, and line-of-sight visibility, and a U-Net scores all candidate positions in one pass without simulating propagation at inference. Ray-tracing simulations of indoor environments indicate that training on mixed map coverage is essential for both approaches. The digital-twin approach is more accurate on most single-snapshot metrics, while MAPLE-RF comes close at a query cost that does not depend on the propagation model and is more than two orders of magnitude below a fresh full-grid query with general-purpose ray tracing. Both outperform Gaussian and Gaussian-mixture baselines, and on exploration routes guided by its own estimates, fused MAPLE-RF posteriors place more probability near the source than the compared methods. Code and data will be released.

Spinning frequency-modulated continuous-wave (FMCW) radars have been gaining popularity in autonomous vehicle perception on account of their robustness to adverse weather conditions and 360{\deg} field of view. Recently, scanning radars have also been shown capable of generating per-azimuth Doppler velocity. In this paper, we investigate whether these Doppler velocity measurements improve spinning radar vehicle detection and tracking performance. For detection, we estimate the ego motion and use it to undo the Doppler range distortion of the radar image before passing it to a network. For tracking, we propose a new way to estimate a per-vehicle velocity and use it as a prior for the tracker's motion model. Since Doppler-enabled spinning radar data is not available in any dataset with ground-truth dynamic object labels, our first contribution is an automatic labelling pipeline that uses an ensemble of fine-tuned off-the-shelf lidar detectors to label all 643 km of the Boreas Road Trip dataset. We then transfer detections to radar, and use over 250 km of vehicle-dense sequences as ground-truth training data. By training and evaluating two state-of-the-art detectors, we show that Doppler undistortion can improve detection accuracy by up to $2.37$ points on mean average precision. Furthermore, we show that the Doppler velocity prior can improve tracking accuracy by $13.68$ points on multi-object tracking accuracy (MOTA) versus the zero-velocity initialization baseline, while achieving $99.7\%$ of the MOTA obtained using ground-truth velocities as the prior.

Objective: The aim of this study was to enhance patient comfort and diagnostic accuracy in urodynamic studies (UDS) by developing a novel framework that uses a single vesical pressure (Pves) channel combined with a surface abdominal electromyography (AEMG) electrode array to estimate detrusor pressure (Pdet) without the need for an abdominal catheter. The proposed method was designed to operate in real-time, and to be compatible with current UDS clinical hardware. Methods: 40 UDS studies were performed with AEMG measured simultaneously with both Pves and Pabd. A predefined, provocative abdominal maneuver was used before each study to ensure accurate coupling between the AEMG signal and associated abdominal pressures seen in Pves. Criteria were set to determine if coupling was sufficient for Pdet estimation from Pves and AEMG. Post-hoc signal processing was used to further refine and evaluate estimated Pdet using automated, non-causal event detection. An event-based scoring metric was used to determine how accurately this approach estimated Pdet by calculating the percentage of signal contained in the appropriate channel for that event (Pdet vs abdominal pressure). Results: The results showed that 30 of the 40 cases indicated a "Go" status after the provocative maneuver, demonstrating feasibility of this method in a majority of UDS studies. The scoring metric, along with correlation analysis, confirmed accuracy of estimated Pdet with scores ranging from 85% to 95% with median at 93%. Significance: This innovative approach has the potential to revolutionize urodynamic studies by providing a more comfortable and accurate diagnostic tool for assessing bladder function. This is particularly beneficial for vulnerable populations, such as the elderly and children, reducing the need for multiple catheters and improving the overall patient experience during UDS.

The conventional distinction between forced and natural oscillations treats the oscillation source as external to a fixed power-system boundary. This paper develops a coupling-aware dynamical interpretation by explicitly accounting for the directionality of source-grid coupling. It examines when prescribed forcing and a one-way autonomous source are observationally equivalent, and when grid measurements can distinguish the presence of grid-to-source feedback. Matched interface signals yield exact equivalence under the same deterministic grid initial-value problem and measurement map. Bidirectional feedback enables source-grid eigenvalue interaction in the augmented autonomous model, while a reduced two-mode model shows that the interaction depends jointly on loop coupling and complex modal separation. The resulting framework establishes source-grid coupling directionality as a feedback-based dynamical boundary and clarifies when feedback can be inferred from grid-side measurements and why weak feedback may remain unresolved. Simulation studies on a two-area system demonstrate feedback detection within a calibrated source family, while source-model mismatch can be confounded with feedback. A WECC 243-bus system study demonstrates feasibility on a large system and shows that feedback detection need not require exact candidate selection.

Robots that localize a radio transmitter need more than a point estimate: in cluttered rooms, one measurement is often consistent with several transmitter locations and, because upper-mid-band antennas are directional, several headings. We present MAGNETAR, which infers a joint posterior over planar transmitter position and heading from a single asynchronous radio-frequency (RF) multipath snapshot, represented by angle-of-arrival and signal-to-noise-ratio estimates, given the room layout and receiver pose. Among our five neural scorers, MAGNETAR adopts a shared 2D U-Net conditioned on each candidate heading, jointly normalizing scores over a discretized position-heading grid. Training uses real-to-sim-calibrated 10 GHz simulations and a small measured subset. Grid-based joint posteriors outperform parametric ones on held-out simulations, the heading-conditioned scorer transfers best to robotic measurements, and fusing joint posteriors improves on fusing position-only marginals.

Computational models of auditory physiology commonly target specific responses or stages of the auditory pathway, limiting their ability to integrate findings across experimental paradigms and neural timescales. We present a foundation model of human auditory electrophysiology: a causal neural network trained to map binaural acoustic waveforms directly to high-sample-rate EEG. The model was trained on approximately 250 hours of EEG data from 92 subjects, with varied electrode montages and stimuli spanning tonebursts, speech, and music. We tested whether the model recovered effects of stimulus rate, frequency, and presentation method on auditory brainstem responses (ABRs); subcortical and cortical temporal response functions (TRFs) to continuous speech; and the click-evoked binaural interaction component (BIC). Predicted ABRs and TRFs reproduced established response morphology and stimulus-dependent effects, with model-grand-average correlations falling within the corresponding subject-level human distributions. The model-predicted BIC metrics closely resembled the values reported in the literature. These findings demonstrate that a single audio-to-EEG model can capture auditory physiology across paradigms and timescales, supporting future in silico experimentation and hearing technology applications.

The classical Whittle formula provides a closed-form expression for the asymptotic Fisher information matrix (FIM) rate of multidimensional, real-valued, zero-mean, purely nondeterministic stationary Gaussian processes (GPs), expressed in terms of their parameterized spectra within a maximum-likelihood framework. However, the Gaussian assumption underlying this result restricts its applicability to many real-world signals exhibiting heavy-tailed or non-Gaussian behavior. In this paper, we extend Whittle's result to multidimensional, real-valued stationary compound Gaussian processes (CGPs) with arbitrary mean, under a unified framework encompassing fully known, parameterized, and completely unknown density generators. Building upon the Slepian-Bangs formula for $n$ consecutive observations and extending it to the semiparametric setting, we leverage the asymptotic properties of block Toeplitz matrices to obtain a closed-form spectral-domain expression for the asymptotic FIM rate. The resulting expression, common to all density generator families, generalizes the standard zero-mean Gaussian formula by incorporating a distribution-dependent contribution associated with the nonzero mean, and an additional covariance term that is invariant to the choice of non-Gaussian distribution. This extension enables efficient and theoretically grounded performance analysis for heavy-tailed signal processing applications.

In the early stage of a pandemic, rapidly identifying a small number of infected individuals through large-scale screening is critical for pandemic control. Group testing has been widely used to improve testing efficiency and numerous studies have investigated the problem under noisy measurements, typically modeled as bit-flipping of test outcomes. However, these methods do not consider the constraints imposed by dilution, pool size, and the limit of detection (LOD), which can lead to false negatives when the viral load in a pool falls below the LOD. In addition, liquid dispensing errors, common in laboratory settings, affects diluted viral loads in a nonlinear manner. In this work, we introduce a novel measurement model that characterizes the process of sample pooling and dilution, incorporating LOD-induced binary quantization as well as liquid dispensing errors. For the case with known sparsity level, we propose a low-complexity decoding algorithm and provide theoretical guarantees for exact support recovery under both noiseless and noisy settings. For the case with unknown sparsity level, we develop a blind support recovery algorithm, along with a heuristic variant to enhance robustness, which can achieve exact support recovery with only O(klogn) measurements. Extensive simulations show that the proposed algorithms outperform existing combinatorial group testing algorithms, validating the effectiveness, efficiency and robustness in large-scale screening.

Group testing has been widely used to identify infected individuals with a limited number of tests, typically under the assumption of independent infections. Recent studies have exploited correlations among individuals, but often require additional information beyond the contact graph, such as community structures, interaction strengths, or detailed infection dynamics. Such information may be unavailable, incomplete or unreliable in practice. In this work, we assume that only the contact graph is known and develop a graph-aware group testing framework that exploits localized infection clustering in pooling design, fundamental limits of recovery, and decoding. Specifically, we propose an optimal transport-based pooling design that incorporates graph proximity and pooling constraints into a unified optimization framework. We prove that, under mild conditions, the proposed design eliminates uninfected individuals with higher probability than the Bernoulli pooling design, reducing the feasible search space for decoding. Then, we characterize the family of possible infected sets induced by localized infection clustering and derive necessary conditions on the number of tests required for exact recovery, revealing a lower testing requirement than that under the combinatorial prior. For decoding, we model the infection states of the population as a piecewise-constant graph signal and propose a graph total variation regularized decoder. We establish sufficient conditions for exact recovery under the Bernoulli pooling design in both noiseless and noisy settings, and show that O(Klog(n/K)) tests are sufficient under mild conditions in the noiseless case. Extensive simulations on synthetic and real-world networks demonstrate the effectiveness of the proposed framework and the benefit of exploiting graph-induced correlations in group testing.

Hollow-core fibres (HCFs) are a promising means of increasing the throughput of coherent transmission systems. In addition to their advantages in terms of low latency, nonlinearity and attenuation, HCFs can potentially improve the energy efficiency of coherent transmission systems by reducing the number of repeaters and enabling more efficient modulation formats than SMF links. However, the relationship between the link parameters (e.g. launch power, amplifier efficiency and transceiver noise) and the energy efficiency has not been explored. In this work, we investigate energy-efficient operating regimes in HCF transmission systems. We show that the optimum energy per bit in SMF systems is ultimately throughput-limited - maximising throughput will minimise energy per bit. In contrast, the transceiver-limited throughput of HCF leads to two separate launch power optima - minimum-energy-per-bit and maximum-throughput. We derive a closed-form equation for the minimum-energy-per-bit launch power for HCF links in terms of the link parameters, including the amplifier efficiency, transceiver power consumption and link gain. We use our model to explore the impact of span length and fibre attenuation in both operating regimes, showing how energy per bit considerations significantly impact the optimum span length. Optimising for energy efficiency can lead to 50% reduction in link energy per bit for only a 3% throughput penalty at 3000 km, whilst also reducing the required amplifier launch power from >33 dBm to <23 dBm. This work highlights the importance of including physical layer energy considerations in HCF link design.

Conventional bandwidth-limited ranging resolves delay on the scale of \(c_0/(2B)\), yet a large distributed aperture can support substantially finer localization through near-field carrier-phase diversity. This letter characterizes the coherent main-lobe width of the localization likelihood for pinching-antenna systems and shows that it is governed by the spread of the direction cosines subtended by the aperture at the target. The resulting expression recovers classical far-field angular and Fresnel-range scalings and reduces to a wavelength-scale cell when the aperture subtends a large solid angle. Guided by this characterization, a hierarchical maximum-likelihood search is developed, combining noncoherent coarse localization, coherent lobe selection, and local refinement. Numerical results under scattering demonstrate submillimeter localization above a threshold signal-to-noise ratio without localization failures in the tested ensemble.

Many signal processing tasks require intensive computations. Quantum computing promises to accelerate certain tasks, but algorithms must be designed around the limitations of quantum mechanics. This paper provides a quantum implementation of finite impulse response (FIR) filters, which are a widely used tool in classical signal processing. The filter can be parallelized and composed as part of larger quantum algorithms, with potential for speedups using quantum amplitude estimation and future fault-tolerant quantum hardware. To accomplish these properties, we introduce a state-space framework wherein sample-based signal processing is performed with unitary quantum circuits. We present the quantum delay gate as the quantum analog of the delay line in discrete-time signal processing. Unitary circuits intrinsically describe lossless systems, but we also implement lowpass and other bounded filters by emulating a projection operator. The FIR filter implementation is tested and demonstrated with a quantum circuit simulator.

Flow state, characterized by deep engagement and immersion during challenging activities, represents a valuable mental state with significant implications for learning, performance, and rehabilitation outcomes. While flow has been extensively studied behaviorally, objective neurophysiological detection methods suitable for real-world deployment remain limited. Electroencephalography (EEG) offers a promising avenue for flow detection due to its accessibility, portability, and superior temporal resolution; however, the utility of consumer-grade EEG devices for robust, and subject independent flow classification has been insufficiently explored. This study validates entropy-based biomarkers for flow state detection using two wearable EEG headsets, Muse-S and Emotiv Insight, across 45 participants performing adaptive Tetris gameplay. After denoising, we applied the Discrete Wavelet Transform (DWT) to decompose the signals into multiple frequency sub-bands. From each sub-band, entropy-based features, combining channel-wise measures (Slope Entropy, Distribution Entropy, Spectral Entropy) with cross-channel descriptors (Cross Distribution Entropy, Cross Spectral Entropy) were extracted. These features were then used as input to a Random Forest classifier (RF), evaluated with two validation schemes: Random Sampling (RS) and Leave-One-Subject-Out (LOSO). Under random sampling cross-validation, Random Forest classifiers achieved 97% mean accuracy; under the more rigorous leave-one-subject-out (LOSO) scheme, average accuracy reached 75%, demonstrating genuine cross-subject generalizability. Comprehensive multi-classifier validation (SVM-RBF, GentleBoost, k-NN, Fitted Discriminant, Naive Bayes) confirmed that entropy biomarkers are robust across diverse modeling frameworks.

This paper presents a scalable framework for high-precision near-field multipath channel parameter estimation in extremely large antenna array (ELAA) systems, enabling joint recovery of path number, path gains, angles, and ranges from a single noisy observation. The key idea is to interpret the near-field multipath channel as a superposition of spatial chirp components with spatially varying frequencies and exploit this structure through a partitioned ELAA architecture. Specifically, we establish a Chirp-coupled Subarray Far-field (CSF) model, where each near-field path is locally represented as a far-field sinusoid with a constant spatial frequency within each subarray, while these local spatial frequencies are coupled across subarrays through a linear relationship induced by the underlying spatial chirp, forming a path-specific chirp trajectory. Based on this model, we propose the CHirp-coupled Angular-Range estiMation (CHARM) algorithm, which performs gridless local frequency estimation followed by cross-subarray trajectory recovery. To mitigate the potential modeling mismatch of the CSF model, we further propose the enhanced CHARM (E-CHARM) algorithm, which refines the CHARM estimate under the near-field channel model through maximum likelihood. The computational complexity of the proposed algorithms scales linearly with the array size. Moreover, simulation results show that the proposed algorithms achieve reliable path-number detection, high-precision angle-range estimation, and accurate channel reconstruction.

In this work we present an experimental demonstration of one-dimensional Fourier-domain imaging using a fully-digital wirelessly coordinated coherent distributed antenna array (CDA) receiver. The nodes consist of two software-defined radios (SDRs) operating with independent system clocks performing wireless time, frequency, and phase coordination without a shared reference such as the global navigation satellite system (GNSS). Two spatially separated noise sources transmit independent noise waveforms with a bandwidth of 25 MHz and a carrier frequency of 915 MHz. Two sources were positioned at angles of $-6.28{\deg}$ and $12.95{\deg}$ off broadside and imaging was performed. The two sources are clearly resolved to within $3{\deg}$ of their expected locations in both the individual and combined source measurements and demonstrating the Fourier-domain image reconstruction principle using a fully-digitally coordinated distributed antenna array.

Accurate characterization of shallow cryosphere subsurface structures is critical for understanding snow and ice dynamics, evaluating environmental hazards, and informing climate-related decision making. Recent advances in unmanned aerial vehicles (UAVs) and compact radar instrumentation have enabled UAV-borne ground penetrating radar (GPR) as a non-contact alternative for subsurface sensing in these environments. By combining the safety-constrained mobility of unmanned aerial platforms with the penetration capability of radar, UAV-borne GPR facilitates rapid, wide-coverage and flexible surveys over snow and ice surfaces in cryosphere that are often inaccessible to traditional ground-based methods. This contribution reviews recent progress in UAV-borne GPR for cryosphere investigations, encompassing system architectures, operational strategies, and representative deployment practices. Furthermore, key data-processing methodologies are summarized for their roles in enhancing subsurface imaging and reconstruction fidelity. A case study from Mochou Lake in East Antarctica, illustrates these capabilities, yielding an ice-thickness estimation error of only 0.03 m when benchmarked against drilling measurements. Finally, the remaining challenges, particularly those associated with scanning coverage, real-time data processing, and detection accuracy, are discussed for future cryosphere applications.

We distinguish two regimes in task-oriented quantization with a known deterministic oracle action. For an unconstrained interior oracle and a smooth strongly concave utility, quantizing the oracle action by vector Lloyd-Max minimizes a mean-squared-error surrogate and achieves a $\beta/\alpha$ approximation to the optimal $K$-level task quantizer. The reduction is exact for isotropic quadratic loss, and the corresponding task rate–distortion function is bracketed by two ordinary rate–distortion functions. Budget-constrained quadratic scheduling is different: the oracle satisfies a variational inequality, so quantizing water-filled actions is not generally optimal. We derive the exact Lloyd-type conditions for this case. The optimal action for a quantizer cell is water-filling evaluated at the cell's conditional-mean load, and the optimal encoder partitions load space into affine power-diagram cells. Thus the correct prescription is to quantize the load and water-fill its centroid. The distinction is material whenever a cell crosses water-filling active-set boundaries.

Parkinson's disease (PD) often manifests through speech impairments, facilitating accessible, non-invasive, and cost-effective severity assessment for early diagnosis and progression tracking. Despite advances in speech foundation models (SFMs), their cross-lingual generalization for PD severity multi-class classification remains underexplored due to limited labeled data, a lack of explainable methods and variability across languages and datasets. In this work, we evaluate pre-trained embeddings from four state-of-the-art open-source SFMs across three datasets in zero-shot and k-shot cross-lingual settings for multi-class PD severity assessment. Our results show that pre-trained speech embeddings enable meaningful cross-lingual transfer, although performance is sensitive to dataset properties, preprocessing, and adaptation strategy. Misclassifications under these conditions related to inter-speaker variability and atypical speech patterns highlight the need for more robust feature extraction and modeling for PD severity assessment while emphasizing the importance of explainability for reliable clinical insights.

This paper presents the design, characterization, and large-signal measurement methodology of a high-order whip-mode flexural microresonator on a lithium tantalate-on-insulator (LTOI) unimorph platform. A tapered cantilever concentrates kinetic energy at the free tip through a structural velocity amplification effect, with a targeted whip mode at 9.175 MHz exhibiting a measured Q of 691 in air. The results indicate a substantially reduced susceptibility to viscous damping at high modal frequencies. In-air large-signal testing on a separate device confirms tip velocities up to 20 m/s before the reliable measurement range of the laser Doppler vibrometer (LDV) at the tapered tip is exceeded, while the device itself sustains drive levels up to 240 Vpp before failure. Transitioning to vacuum reveals photothermal-induced static bending of the LTOI cantilever under LDV laser illumination, an effect that prohibits direct velocity measurement for these resonators. Hence, it motivates an indirect extraction methodology to be implemented. In this work, a 3.85 times base-to-tip geometric amplification factor, independently calibrated at low drive, is applied to base velocity measurements to infer tip velocity under large-signal conditions. Using this approach with narrowband chirp excitation, a maximum extracted tip velocity of 58.9 m/s is obtained at 192 Vpp, with spectral analysis of the base velocity placing a conservative lower bound of 36.2 m/s on this estimate. Large-signal failure-mode analysis identifies Pt/Au electrode melting at 210 Vpp as the current velocity ceiling. These results suggest that geometric amplification in high-order flexural modes offers a viable pathway toward the high proof-mass velocities targeted for next-generation MEMS inertial sensors.

Biological tissues exhibit power-law frequency dependence of the attenuation coefficient. We derive closed-form weak-wave expressions for fundamental, second-, and third-harmonic pressure amplitudes of monochromatic acoustic plane waves in homogeneous media with arbitrary power-law attenuation to extend classical expressions for quadratic attenuation. For linear frequency dependence of attenuation, one-dimensional simulations using the k-Wave k-space pseudospectral solver yielded 35-42% (classical) and 0-2% (proposed) normalized root-mean-square errors for fundamental-band depletion. This extension supports improved fundamental-band B/A estimation for quantitative ultrasound tissue characterization. A proof-of-concept through-transmission application in phantoms with nearly linear attenuation yielded experimental B/A estimation errors of 23.7-52.7% (classical) and 4.0-5.9% (proposed).

Communication-driven multi-view sensing relies on routine communication transmissions for sensing acquisition, while the resulting sensing data at distributed devices must be uploaded to an edge server under limited communication resources. This creates a unique coupling between sensing acquisition and edge inference: the communication interval determines the source information, whereas the uplink condition determines how much information can be delivered to the server for sensing inference. To account for this coupling, we propose a source entropy-guided adaptive transmission framework. Specifically, we characterize the entropy of packet-triggered channel state information (CSI) as a function of the communication interval using a multi-output Gaussian process. The resulting analytical bound is compared with the available bit budget, determined by the transmission rate and latency requirement, to select between original-data and task-oriented transmission. For task-oriented transmission, we formulate the communication-constrained inference problem based on the information bottleneck and decompose it into adaptive distributed encoding and multi-view inference (ADE-MI), which avoids alternating optimization between the devices and the edge server. Experiments on the Widar3.0 multi-view CSI gesture recognition dataset show that the analytical bound closely follows the normalizing-flow numerical estimate, while ADE-MI outperforms task-oriented benchmarks under the same bit budget and the proposed framework further improves recognition accuracy under time-varying channels.

This paper studies phase-coherent Doppler-only sensing for bandwidth-limited sub-6 GHz D-MIMO ISAC. We consider a multistatic system with one transmitter and multiple synchronized single-antenna receivers, and develop noncoherent and coherent maximum-likelihood estimators together with their position error bounds. The analysis shows that noncoherent sensing relies only on Doppler and cannot localize static targets, whereas phase-coherent processing exploits carrier phase to provide additional position information. Simulations confirm the bounds and show that coherent processing significantly improves localization accuracy in the considered narrowband setting.

Running artificial intelligence (AI) models directly on edge devices such as smartphones, wearables, and drones offers low latency, pervasive scalability, and data privacy, but these devices rarely carry the computing capability that modern neural networks demand. Edge accelerators have been developed in response, yet each adds computing hardware to devices already constrained in size, weight, power, and cost (SWaP-C). An alternative lies in what these devices already carry: the frequency mixer in every wireless radio multiplies signals in time, natively performing convolution in the frequency domain. Here we introduce radio-frequency convolutional neural networks (RF-CNNs), which repurpose existing communication hardware for CNN inference. Multi-channel convolutions are mapped onto frequency tones for a passive mixer to execute in a single pass. We experimentally demonstrate that RF-CNN runs deep CNNs up to 26.4 million parameters and nine layers from classification of wireless signals and images to controllable image generation, close to full-precision performance. Because the weights arrive over the air and the analog hardware is shared with communication, the edge device spends energy only on data preparation and readout-down to 0.72 femtojoules per multiply-accumulate, two orders of magnitude less than it would cost on an added digital processor. These results suggest that deployed wireless infrastructure can bring efficient, state-of-the-art AI inference to the billions of devices it already connects.

Research on 4D millimeter-wave radar perception algorithms has flourished in recent years, extending from signal processing and object detection to semantic segmentation, motion estimation, occupancy prediction, and dynamic scene reconstruction. This review organizes the field according to the evolution of perception tasks and algorithms. It first introduces radar fundamentals, data representations, and quality-enhancement methods, and then reviews object-level perception, motion and localization, local and dense spatial perception, and dynamic scene understanding. Across these directions, we compare radar-only learning, multimodal fusion, and cross-modal supervision and knowledge distillation. Particular attention is paid to how elevation, Doppler measurements, and radar physical priors are exploited across tasks. We further summarize the task coverage, input data, annotations, and evaluation protocols of existing datasets, clarifying the empirical support for different research directions. Finally, we discuss the common challenges and future directions of 4D radar perception for autonomous driving. This review provides a task-oriented perspective on the transition from sparse object perception to dynamic spatial understanding.