Papers

Acquiring dense channel information over many locations and beams incurs considerable pilot and processing overhead. Radio maps and channel knowledge maps (CKMs) reduce this overhead by reusing site-specific channel information, but their contents must be refreshed when new measurements or environmental observations become available. This paper introduces an electromagnetic twin as an updatable digital representation that uses sparse channel evidence to reconstruct the wireless state requested by communication queries. Rather than replacing radio maps or CKMs, the twin uses a CKM as channel memory, combines it with registered scene information, and regenerates its outputs after each evidence update. We instantiate this idea by completing a two-dimensional channel-gain field from sparse samples and an incomplete floor plan. A learned RF completion backbone recovers the main propagation structure, and a lightweight residual adapter tests whether frozen CLIP features provide useful side information. With $4\%$ measured locations and $55\%$ missing semantic objects, the RF backbone attains $4.44$ dB RMSE, compared with $8.29$ dB for CKM interpolation and $8.38$ dB for an incomplete physics prior. Residual adaptation reduces RMSE by a paired mean of $0.135$ dB (95% confidence interval: $0.100$–$0.169$ dB), but a same-capacity random-feature control is statistically indistinguishable from the CLIP-conditioned adapter. The results therefore support the measurement–update–query loop and lightweight residual correction, while avoiding an unsupported attribution of the correction to visual semantics.

This work addresses the limitations of CMOS at terahertz (THz) frequencies, where charge transit time and parasitic capacitances restrict the maximum operating frequency, fmax. As transistor dimensions shrink, reduced current handling capabilities further challenge CMOS, necessitating novel circuit design approaches for the THz domain. By leveraging the plasma characteristics of electron channels in CMOS transistors, this study explores a potential solution for THz signal amplification. Key mechanisms in plasma wave amplification within a continuum transistor array (CTA) formed by 28 nm fully depleted silicon-on-insulator (FD-SOI) CMOS transistors are investigated. A hydrodynamic transport model combined with Pierce's theory is presented to describe plasma wave propagation along the CTA. Simulations of gated amplifiers demonstrate the potential for THz signal amplification in advanced fabrication nodes. Finally, a proof-of-concept plasma wave amplifier operating at 700 GHz has been designed and fabricated, exhibiting amplification along the plasma wave propagation path.

Pinching-antenna systems (PASS) provide a large effective aperture and substantial path-loss reduction at low hardware cost, making them attractive for integrated sensing and communication (ISAC). Under wideband OFDM operation, however, the antennas on each waveguide impose nonlinear, position-dependent group delays on the same baseband signal, giving rise to waveguide dispersion that severely degrades range estimation. To this end, this paper proposes a two-stage ISAC framework comprising communication-aware beamforming and antenna placement followed by a dispersion-aware target localization stage. A fractional-programming beamformer and an element-wise coordinate-descent placement jointly maximize the downlink sum rate under a sensing beampattern-gain constraint, and the resulting optimized beamformer and placement determine the effective sensing channel used by the localization stage. The proposed DisPersion-aware Localization Network (DiPL-Net) detects targets from the received signal via a dispersion-aware score map built on a physics-derived range dictionary. Its convolutional backbone employs dual-kernel residual blocks, each pairing a short kernel matched to the OFDM main lobe with a long kernel matched to the dispersion tail, so as to deconvolve the dispersion and restore a sharp target peak at each true target. Simulation results show substantial localization gains over various sensing baselines while preserving the achievable communication rate.

Upgrading Single-Wire Earth Return (SWER) networks for smart grid capabilities requires a reliable communications technology. Narrowband Power Line Communication (NB-PLC) is a potential low cost solution. Real-world deployment is challenging due to the severe, frequency-dependent attenuation caused by complex earth-return paths and heterogeneous network infrastructure. To accurately characterize the communication channel, this paper develops a high-frequency (up to 300 kHz) digital twin of an operational SWER network. The digital twin integrates a segment-by-segment transmission line model with Vector Network Analyzer (VNA) measurements of physical grid hardware, replacing standard uniform assumptions with empirical component responses. Parametric sensitivity analysis demonstrates that distributed environmental factors, such as soil moisture and line sag, act as uniform magnitude offsets. Conversely, the conductor's magnetic permeability and local injection-transformer impedances dictate the channel's resonant spectral shape. Furthermore, cross-brand analysis proves that utilizing generic transformer models introduces significant prediction errors, confirming that accurate simulation requires manufacturer- and tap-specific data. Validated against in-situ field measurements from three transmitters, this digital twin replicates the path loss and dominant frequency-selective fading of the physical grid. Yielding a Root Mean Square Error (RMSE) between 4.65 dB and 9.73 dB across the three transmit paths, the model provides a practically reliable framework for deploying NB-PLC across rural SWER infrastructure.

While the recursive least square (RLS) algorithm is widely used in adaptive filtering applications like acoustic echo cancellation (AEC) due to its fast convergence rate, its high computational complexity severely limit its practical deployment for long filters. In this paper, a regularized block-diagonal RLS (RBD-RLS) algorithm is proposed to address these challenges. By approximating the inverse covariance matrix as a block-diagonal structure, RBD-RLS simplifies the update process into independent parallel computations of sub-blocks, effectively reducing the computational complexity. Additionally, Tikhonov regularization is applied to each sub-blocks for enhance numerical stability. A series of experimental results demonstrate that RBD-RLS maintains good convergence while significantly reducing computational complexity. Moreover, it still exhibits relative robustness in real-world scenarios.

Surface hopping is an attractive mobility mode for small-body exploration, but designing fuel-optimal hops on asteroid 433~Eros requires solving a nonconvex optimal control problem with an irregular polyhedral gravity field, thrust–mass coupling, and collision-avoidance constraints. We show that a physics-informed Bayesian Optimization (BO) warm-start—a Gaussian-process search over a single Bézier control point that requires no offline training—provides a more reliable initialization for Sequential Convex Programming (SCP) than the standard straight-line guess. The straight chord penetrates the asteroid on every inter-site transfer considered here and degrades convergence. The physics-informed BO reference feeds an SCP stage in which a log-mass change of variables convexifies the thrust–mass coupling and nearest-facet half-spaces enforce collision avoidance, and an automated Pareto time-of-flight sweep selects fuel-priority solutions without introducing bilinear terms. Applied to all 20 ordered transfers among five representative surface sites and validated under 10 random seeds, the framework tracks every trajectory to meter-level terminal accuracy in closed-loop Monte Carlo simulation, completes a five-site tour for one fifth of the propellant budget, and reduces mean $\Delta V$ by roughly one third relative to an idealized two-impulse ballistic trajectory estimate. A straight-line ablation credits the warm-start with cutting the mean SCP iteration count from 8.8 to 5.9 and removing the one convergence failure. A target-perturbation analysis further shows that the solutions vary smoothly with the landing target, with no jumps between local basins.

Unsupervised segmentation methods that embed an image as the potential of a Schrödinger-type Hamiltonian and read out object structure from its low-lying eigenstates have demonstrated strong empirical performance across natural and volumetric imagery, yet the robustness of this spectral representation to sensor noise and geometric transformation has never been established theoretically. This paper closes that gap. A discrete graph-Hamiltonian formulation is adopted, consistent with existing quantum-inspired segmentation frameworks, and an explicit finite-sample perturbation bound is derived that links the drift of its eigenvalues and ground-state eigenvector under additive noise of known variance to the operator's spectral gap, extending a one-dimensional noise-error analysis originally developed for semiclassical signal analysis to the two-dimensional graph setting used in image segmentation. The eigenvector-level bound is further propagated to the induced binary segmentation mask, giving, for the first time, a quantitative guarantee on segmentation stability under noise rather than an empirical observation of it. A complementary equivariance analysis characterizes the conditions under which the operator's spectrum transforms exactly under the planar rigid-motion group, and quantifies the equivariance gap introduced by anisotropic potential terms commonly used in practice. Theoretical predictions are validated numerically on natural-image and noise-perturbed benchmarks, showing close agreement between the derived bounds and observed spectral and segmentation-mask deviation, and offering practitioners a principled criterion for selecting operator parameters under known noise conditions.

In the field of Human-Robot Interaction (HRI), achieving flexibility in human-accompanying within real-world environments holds great potential for various applications but also poses significant challenges. Traditional methods typically restrict robots to fixed positions relative to humans, such as tracking from behind, in front, or side-by-side, which limits robot adaptability in dynamic workspaces. This study introduces a novel human-companioning strategy that uses Reinforcement Learning (DRL) to enable mobile robots to dynamically adjust their tracking positions according to varying conditions. An interaction space is defined to capture the relationship between the human and the robot while considering the environment, which serves as the basis for state spaces in DRL to assist the robot in adapting to environmental changes. A human-robot companion controller is developed by integrating Model Predictive Path Integral (MPPI) control with Control Barrier Functions (CBF), ensuring that the robot accurately follows the target's movement in both position and orientation while avoiding obstacles and enhancing social acceptance and safety. The proposed approach is evaluated in real-world scenarios, both indoors and outdoors, and compared with other studies. The results show that the proposed method improves the success rate and tracking accuracy by at least 24% and 47%, respectively, while enhancing human comfort. Experiments demonstrate the robot's ability to flexibly accompany a person walking at speeds of up to 1.7 m/s, dynamically adjusting its strategy without being confined to a fixed position. Additionally, the robot respects the human's intimate space to ensure safety, comfort, and effective obstacle avoidance.

Communication intelligence is undergoing two linked transitions: algorithm development is moving from manual model-based design toward foundation-model-assisted generation and evaluation, while deployed systems are moving from offline optimization toward agent-driven online decision and guarded deployment. Existing data-driven and LLM-assisted methods remain primarily design-time tools and cannot cover every future combination of service intent, channel, spectrum, and hardware state. We propose the Agent-Native Metamorphic Communication Fabric, a closed-loop architecture in which an agent observes operating state, selects or generates an explicit communication candidate, evaluates it in a digital twin, applies hard feasibility gates, and deploys it with monitoring and fallback. Three levels bound the scale of change: Level 1 adjusts parameters while preserving algorithm topology; Level 2 switches and configures receiver algorithms while preserving protocol and waveform; and Level 3 reconfigures the waveform or waveform-multiple-access chain while preserving the service contract and safety interface. Simulations validate all three levels. Level 1 improves rate, channel tracking, or quantization energy under fixed topologies. Level 2 selects three-iteration weighted Jacobi, five-iteration diagonally preconditioned conjugate gradient, and direct MMSE in favorable, intermediate, and harsh MIMO regimes; its hardware proxy predicts up to 67.1% energy and 73.3% latency reduction relative to direct MMSE. Level 3 selects CP-OFDM, SC-FDMA, OTFS, filtered OFDM, and SCMA-over-OFDM across five operating regimes and forms continuous switching boundaries under Doppler, spectrum contiguity, load, and RF-power sweeps. These results establish a minimum viable mechanism for verifiable runtime communication adaptation without unconstrained end-to-end learning or arbitrary online code mutation.

Many consumer smartphones, stereo cameras, and light field cameras record multiple synchronized viewpoints in a single exposure event. However, novel view synthesis pipelines commonly use only a monocular stream and rely on camera motion or learned priors to obtain angular coverage. In this paper, we ask: why do we use only one viewpoint? We analyze sensor-limited multi-view, where one sensor trades off spatial and angular resolution, and exposure-limited multi-view, where multiple sensors on one commodity device observe each event simultaneously. We introduce a new dataset incorporating three types of commodity multi-view cameras, and evaluate sparse-view 3DGS and 4DGS baselines measuring reconstruction quality as a function of number of exposures and angle between extreme views. Our results demonstrate that using multiple cameras, even with a low baseline, significantly improves reconstruction quality in single-shot, few-shot, and casual video settings. In addition, under a fixed sensor budget, angular sampling improves reconstruction when exposures are scarce despite lower spatial resolution. The gains are most pronounced for single-shot and dynamic scenes, where a stationary monocular camera lacks the angular diversity to recover scene geometry and motion.

Diffusion models (DMs) have emerged as powerful generative priors for MRI reconstruction with promising results. Yet DM-based methods require extensive iterative refinement, limiting their practical deployment. Consistency models (CMs) provide a compelling alternative, aiming to map out the diffusion trajectory in a single pass, enabling faster generation. In this work, we propose CM-RED, a novel MRI reconstruction method that integrates a pretrained CM into the regularization by denoising (RED) scheme. Our method builds on accelerated proximal gradient RED (RED-APG), and further incorporates controlled noise injection during the update steps to enhance generative diversity and accelerate convergence. Extensive experiments on the fastMRI knee and brain datasets demonstrate that CM-RED achieves high-quality reconstructions across multiple anatomies, contrast weights, acceleration factors, and undersampling patterns, using only 4 network function evaluations (NFEs). The proposed method consistently outperforms existing DM- and CM-based approaches in both quantitative metrics and visual fidelity, and exhibits strong robustness to hyperparameter variations, highlighting CM-RED as an efficient and effective generative framework for accelerated MRI reconstruction. The source code and pretrained models are publicly available at https://github.com/MerveGulle/CM-RED.

Vision-language-action (VLA) models can follow natural-language (NL) task instructions, but such instructions may not precisely specify safety-critical or spatiotemporal requirements on the resulting behavior. We introduce Logic-VLA, a formal-requirement-aware VLA that conditions on Signal Temporal Logic (STL) specifications supplied at inference time. Logic-VLA uses a syntax-graph-based STL encoder pre-trained to capture temporal logic semantics. Policy adaptation proceeds in two stages: STL-conditioned supervised fine-tuning on satisfying demonstrations is followed by trajectory-level preference optimization over matched satisfying-violating rollout pairs using a flow-matching surrogate for Identity Preference Optimization. This formulation improves formal requirement satisfaction while preserving the nominal NL task. We evaluate Logic-VLA in closed-loop quadcopter navigation simulation across randomized photorealistic environments and test generalization to STL formulas unseen during training. Across the evaluation benchmarks, Logic-VLA improves STL satisfaction rate over an STL-blind base policy by 24.8 to 40.7 percentage points (pp) while reducing nominal NL task success by at most 1.8 pp, showing that a single VLA can adapt its behavior to varying formal requirements without requiring a separate policy for each specification.

This work addresses the problem of applications that require wireless power transfer to devices embedded within conductive blocks. The emergence of eddy currents tends to generate a magnetic field opposing that of the transmitter, drastically reducing the coupling coefficient and, consequently, the efficiency of the process. A case study involving a titanium implant is presented and analyzed with a load designed to ensure a constant current of 1 mA. An experimental setup is assembled and evaluated to compare differences in the measured electrical parameters when the receiver is embedded in the implant, considering several solutions that mitigate or avoid the effects of eddy currents. The results show that such a system may be technically unfeasible if careful attention is not paid to the geometry of the cavity in which the receiver is positioned.

As a promising technique, fluid antennas enable adaptive radio environment management and interference mitigation through reconfigurable fluid port selections. In this work, to explore the benefits of fluid antennas for data freshness enhancement, we consider a fluid-antenna assisted status update system supported by multiple source nodes monitoring the same environmental status. We assume a subset of the source nodes are activated to report status updates with different periods. Each user is assigned to one source node and equipped with an fluid antenna to adaptively enhance the channel gain to assigned source node while mitigating interference from other activated source nodes. With maximal signal-to-interference-noise ratio (SINR)-based fluid port selection at all users, we formulate an optimization problem to minimize the maximum average age of information (AoI), where source node activation and assignment, i.e., source scheduling, is jointly optimized with the update periods of all activated source nodes. To optimally solve the resulting mixed-integer nonlinear problem, we first consider given source scheduling decision and apply performance achievability analysis. Aided by fixpoint theory, we propose an efficient bisection algorithm for optimal update scheduling. Based on these characterizations, we further propose a filtering algorithm which efficiently eliminates all non-optimal source scheduling decisions. The globally optimal joint solution is then obtained by combining the resulting optimal source scheduling with its corresponding optimal update scheduling. Numerical results validate the optimality of proposed solution and demonstrate the effectiveness of fluid antennas in enhancing data freshness.

Self-supervised learning for imaging inverse problems is increasingly important in photon-limited settings, where acquiring clean ground truth is impractical and reconstruction must remain stable under dataset and acquisition shifts. This challenge is amplified under Poisson noise, whose signal-dependent statistics interact with sampling operators (e.g., CFA mosaicing). Meanwhile, foundation vision encoders trained at web scale offer distortion-invariant, content-related representations that generalize well across domains, suggesting a promising route to build priors that transfer beyond the training distribution without expensive fine-tuning. This paper proposes an ADMM-inspired unrolled plug-and-play solver for Poisson inverse problems that decouples a closed-form data-consistency update from a parameter-efficient prior. The prior is implemented as a lightweight decoder operating on frozen CLIP RN50 dense multi-scale features, adapting foundation representations with less trainable parameters. For self-supervision, the method integrates GR2R measurement-domain re-corruption with an Equivariant Imaging regularizer via virtual acquisitions. Experiments on Poisson CFA demosaicing and deblurring show competitive quality, improved robustness under shifts, and self-supervised performance approaching supervised training.

Foundation models, i.e., large neural networks pretrained on broad unlabeled data and adapted to many downstream tasks, have reshaped natural language processing and computer vision and are now being explored for the wireless physical layer. Wireless Physical-Layer Foundation Models (WPFMs) aim to learn transferable representations of signals such as channel state information (CSI), in-phase and quadrature (IQ) samples, and spectrograms so that a single pretrained backbone can support tasks ranging from channel estimation and prediction to localization and sensing while using limited task-specific data. This paper provides a dedicated review of WPFMs from learning design to practical deployment. We first establish the theoretical background, covering the neural architectures used for wireless signals, the self-supervised pretraining paradigms of masked modeling, contrastive learning, and generative pretraining, and the fine-tuning strategies that adapt pretrained models to downstream tasks. We then introduce a taxonomy that organizes existing models along five dimensions: architecture family, input modality and tokenization, pretraining objective, model scale and deployment target, and generalization capability. Building on this basis, we review applications across telecommunications, localization, and sensing, and, for each domain, analyze deployment feasibility by mapping model size to the memory, compute, and latency budgets of representative wireless hardware. Finally, we discuss model compression and efficient deployment, summarize the cross-cutting challenges, and outline open research directions. Our goal is to provide a reference that connects pretraining, architecture, and fine-tuning with the practical constraints of wireless systems.

Safety filters are an effective tool for enforcing constraints in safety-critical systems, but most existing methods assume perfect state information, which is rarely available in practice. Recent work has begun to close this gap by developing filtering mechanisms that are robust to state estimation error, but these methods can still exhibit safety violations or overly conservative behavior as estimation error grows. Focusing on obstacle avoidance, we develop two new control barrier function (CBF) formulations: drift-measurement-robust (DMR)-CBFs and neural measurement-robust (NMR)-CBFs. The DMR-CBF augments the standard CBF condition with an inner optimization over the worst-case uncertainty in the drift dynamics, improving robustness to estimation error. This DMR-CBF then supervises a pretraining phase for the NMR-CBF, which replaces the inner optimization with a learned term. The NMR-CBF is subsequently finetuned through differentiable trajectory rollouts, yielding a filter that achieves empirical safety comparable to the DMR-CBF while reducing both conservativeness and computational cost. We provide theoretical analysis of the DMR-CBF along with numerical results on a planar double integrator and a 12D quadrotor, where both proposed approaches prevent collisions while other robust methods either fail or are overly conservative. Finally, we deployed the NMR-CBF on a Unitree Go2, enabling successful navigation of an obstacle field under odometry errors that caused a standard CBF to collide.

Deep neural networks are often overconfident, assigning high confidence even to incorrect predictions. Consequently, users lack a reliable signal for deciding when a prediction can be trusted. Post-hoc confidence estimation addresses this by training a lightweight auxiliary head over a frozen classifier. Existing targets, however, suffer from inherent ambiguity: they assign overlapping confidence values to correct and incorrect predictions, while errors near the decision boundary receive confidence scores indistinguishable from correct predictions. In this work, we propose $TCP_\alpha$, a novel confidence target that resolves these limitations by introducing a margin-controlled penalty for misclassified samples. We prove that $TCP_\alpha$ guarantees complete separation between the target values of correct and incorrect predictions, with a separation margin that is independent of the number of classes and increases monotonically with the penalty parameter. Since accurate classifiers naturally produce very few errors, learning these targets results in a severely imbalanced regression problem. We therefore present a systematic study of training strategies for learning under this imbalance and identify an effective training configuration through extensive ablation studies. We evaluate the proposed approach on rāga identification, investigate its robustness under domain shift, and further validate it on frame-wise ornamentation detection without modifying the selected configuration. Across all settings, $TCP_\alpha$ consistently outperforms existing confidence targets for failure prediction. Rejecting only the least-confident 8% of predictions improves the base model's macro-F1 from 0.89 to 0.98, while fine-tuning the confidence head with only 5% labeled samples from a new corpus effectively restores performance under domain shift.

Transformational electric vertical take-off and landing (eVTOL) vehicles have gained significant attention over the past decade due to their efficient wing-borne cruise capabilities and reduced reliance on ground-based infrastructure. However, control system design for these vehicles remains challenging, as they must operate across multiple flight phases, each with distinct dominant dynamics. If left unaddressed, this complexity would significantly increase pilot workload, thus motivating the development of pilot control systems for multi-phase flight operations. The Simplified Vehicle Operations concept presents a promising strategy for reducing pilot workload. This study presents the design and implementation of a novel pilot control concept for eVTOL aircraft, validated through a tandem tilt-wing aircraft simulation on a full-motion simulator equipped with an active, force-feedback side stick. The system provides pilots with tactile feedback during specific flight phases, supporting intuitive control. The proposed approach enables seamless transitions and multi-phase flight maneuvers by leveraging the available degrees of freedom. Furthermore, an optimal-control-based methodology is proposed as a metric to evaluate command-filter-induced performance penalties and inceptor activities. The results show that the proposed command filter does not significantly increase the mission duration compared to the closed-loop system, while the active side stick helps reduce inceptor activity.

Variable active power injections arising from device behavior or compromised dispatch complicate voltage regulation in electric power networks with distributed energy resources (DERs). An operator can limit the resulting voltage deviations by remotely selecting DER reactive power parameters before observing the active power injections. IEEE Standard 1547-2018 specifies constant power factor as one such control mode, coupling each device's reactive power to its realized active power. Using a linear voltage model, we formulate the operator's decision as a robust minimax problem in which the operator minimizes the largest feasible aggregate voltage deviation. We solve this problem by expressing the power factor decisions through continuous reactive to active power ratios and exactly decomposing the payoff according to the signs of the voltage deviations. When every feasible voltage residual remains on its initial side of nominal, the resulting ratios cancel each injection's contribution and yield a closed form minimax strategy. We identify realistic DER ratings for which this strategy applies and quantify the regulation capacity lost under restricted power factor ranges. Numerical tests check the cancellation computation, solve the complete minimax problem directly at a representative DER rating, and compare the linear voltage predictions with nonlinear AC power flow.

Sparse pursuit after dictionary learning can yield a precise atom support even when its physical interpretation is not justified by the calibration data, especially for highly coherent dictionaries where alternative calibration-compatible dictionaries may assign different physical meanings to the same selected support. We develop resolution-aware physical-support inference that jointly accounts for uncertainty in the learned dictionary and in the representation of a deployment signal. Our cross-dictionary confidence correspondence retains calibration-compatible dictionaries and deployment-compatible sparse representations, then projects the surviving explanations onto physical-support space. For local coherent-atom classes with separation scale s, once the deployment data resolve the coherent-block explanation and its atom support, the minimax physical resolution from N calibration signals satisfies $\delta_{\mathrm{opt}}(N,s)\asymp\min\{s,\frac{1}{\sqrt{N}s^2}\}$, with relative resolution governed by the orientation-information scale $Ns^6$. Deployment replication improves physical localization only when orientation changes cannot be absorbed by adjusting the active coefficients. For computation, we introduce active endpoint bracketing (AEB), an adaptive finite-bank procedure that evaluates only candidates that can still affect the physical report and otherwise safely coarsens or abstains. Finite-bank experiments, including a four-region synthetic application, show that a point-valued plug-in selector can be physically overprecise, whereas AEB avoids unsupported refinement with fewer candidate evaluations.

Integrated sensing and communication (ISAC) is a key enabling technology for 6G wireless networks, but its broadcast nature raises a physical-layer security concern when the sensing target can act as a potential eavesdropper. Although intelligent reflecting surfaces (IRSs) can enhance wireless propagation and improve secrecy, existing secure IRS-assisted ISAC designs are mostly limited to fixed deployments and passive phase control, which offer limited spatial adaptability in line-of-sight-dominated low-altitude scenarios. To address this limitation, we investigate an unmanned aerial vehicle (UAV)-mounted six-dimensional movable IRS-assisted secure ISAC system, where the IRS location, orientation, and reflection coefficients are jointly optimized with the BS beamformer to maximize the secrecy rate under communication quality-of-service (QoS), power, unit-modulus, and visibility constraints. The resulting problem is highly non-convex due to the coupled active/passive beamforming variables and the location-and-orientation-dependent (pose-dependent) channel responses. To solve it efficiently, we develop a three-block alternating optimization (AO) framework, in which the active beamformer, IRS pose, and passive reflection vector are updated via linearized ADMM, warm-started particle swarm optimization, and Riemannian gradient descent, respectively. Simulation results show that the proposed design significantly outperforms fixed-location and orientation-only baselines, highlighting the importance of joint translation, rotation, and phase control for secure ISAC.

Orthogonal Time Frequency Space (OTFS) modulation is a promising waveform for future wireless networks. However, its resilience to RF impairments remains relatively understudied. Low-cost RF front ends are crucial for next-generation wireless systems, yet their performance is often degraded by RF impairments such as transmit IQ imbalance (IQI), phase noise (PN), timing offset (TO), and carrier frequency offset (CFO). Hence, this paper addresses the estimation and compensation of these RF impairments in OTFS systems under high mobility. A unified system model is developed that incorporates TO, CFO, PN, IQI and other channel effects into an effective channel representation. Using the pilot with cyclic prefix (PCP), a low-peak to average power ratio (PAPR) pilot suitable for OTFS, we propose a pilot-aided synchronization and estimation framework. The dual periodicity of PCP is exploited in our proposed TO estimation technique. A maximum-likelihood-based technique is also proposed to jointly estimate the CFO and effective channel using a complex exponential basis expansion model (CE-BEM). Finally, a linear detection model is formulated in the delay-Doppler domain to mitigate residual interference caused by RF impairments. Our simulation results corroborate the efficacy of our proposed synchronization and channel estimation techniques.

Multiple-Input Multiple-Output (MIMO) arrays transmit unique waveforms on each of their elements increasing the degrees of freedom available to synthesize novel transmit beampatterns. The wideband MIMO beampattern shape is determined by the structure of the MIMO Cross Spectral Density Matrix (CSDM) whose elements are the cross-spectra between each waveform in the set as a function of frequency. This paper demonstrates a model for synthesizing wideband MIMO beampatterns using Multi-Tone Sinusoidal Frequency Modulated (MTSFM) waveform sets. The MTSFM waveform's instantaneous phase is a finite Fourier series. The Fourier coefficients are modified to synthesize constant modulus and spectrally compact waveforms whose CSDM closely approximates a desired wideband MIMO transmit beampattern. An optimization routine is formulated that synthesizes MTSFM waveform sets that possess the necessary CSDM structure and is demonstrated using an illustrative design example.

Modern speech synthesizers can produce highly realistic speech, making source tracing (i.e. identifying the generator behind a spoofed utterance) increasingly important for forensics, online content provenance, and platform accountability. Building on our prior work on transparent probabilistic attributes, which represent utterances as probability distributions over synthesizer sub-components, we extend speech deepfake source tracing with two key ingredients: multi-task training of the probabilistic attribute extractors and a structured Kolmogorov–Arnold Network (KAN) for attack classification. The probabilistic features are estimated jointly with a multi-task learning module built on a shared AASIST or SSL-AASIST countermeasure backbone. The resulting probabilistic feature embedding is classified by a structured KAN whose topology follows known attribute-to-attack relationships. This provides interpretability by construction: the architecture reflects the generative hierarchy of attacks, while KAN feature-importance scores quantify each probabilistic feature's contribution without post-hoc explainers such as SHAP. On ASVspoof2019-attr-17, the extended framework achieves balanced accuracies above 99% for all seven probabilistic feature extractors, with EERs of 0.16% to 0.07%, and 99.64% balanced accuracy with 0.11% EER for 17-class attack classification. Our revised model outperforms the earlier two-stage baselines, in addition to demonstrating reliable interpretability, with importance scores consistent with SHAP values, and stable results across batch sizes. These findings highlight the potential of structured KAN for speech deepfake source tracing that is both accurate and interpretable by design. For transparency and reproducibility, our codebase is publicly available: https://github.com/HoangHPham/KAN-Probabilistic-Deepfake-Attribution.

Active sonar target tracking in shallow-water environments is challenging when weak target echoes are embedded in a time-varying background containing structured multipath components. Conventional detect-before-track methods rely on thresholded detections, which may discard weak target evidence or generate false tracks from multipath-induced detections. At low signal-to-noise ratios, track-before-detect filtering can improve tracking performance by exploiting weak target information directly from raw sensor measurements, but directly accounting for the time-varying background leads to a joint target–background inference problem, which is computationally demanding. To address this, this paper develops a physics-motivated and computationally efficient approximation of the raw sensor measurement likelihood for Bernoulli track-before-detect filtering. The multipath components in the background are modeled in the raw sensor measurement domain and recursively tracked using an extended Kalman filter. By neglecting the posterior dependence between the target and background state, the predicted background statistics are used to construct approximate target-present and target-absent likelihoods for the Bernoulli filter. Evaluations using measurements generated from the statistical model and BELLHOP show improved target-confirmation and localization performance over constant-false-alarm-rate-based tracking. These results indicate that explicitly accounting for the background through a computationally tractable approximate likelihood can exploit weak target information without requiring full joint target–background inference.

Following a target speaker in a noisy environment, commonly known as the cocktail party problem, remains particularly challenging for cochlear implant (CI) users. Recent studies have explored EEG-based auditory attention decoding (AAD) using neural networks to enhance hearing assistance. This paper presents a resource-efficient ASIC for real-time EEG-based auditory attention decoding by integrating a quantized CNN inference engine and a Pearson-correlation classifier. The proposed architecture employs streaming execution, on-chip buffering, and memory-efficient dataflow to reduce hardware cost while maintaining real-time performance. The proposed ASIC has been fully implemented in GF22FDX 22-nm CMOS technology, occupying a total silicon area of 2.09 mm$^2$(1264$\mu$m x 1654$\mu$m), with the CNN inference engine and streaming classification engine requiring only 0.076 mm$^2$. Operating at a core voltage of 0.55 V, the design achieves a power consumption of 0.4941 mW and an inference latency of 7.34 ms, providing an energy-efficient hardware platform for EEG-based auditory attention decoding in hearing-assistance applications.

Bilateral teleoperation requires stability despite uncertain master and slave dynamics and delayed communication channels. Existing radial basis function neural network (RBFNN) controllers mainly differ in uncertainty decomposition, while online adaptive parameters often increase with network size. This paper proposes a compact two-sided adaptive controller for a nonlinear bilateral teleoperator with constant forward and backward delays. Operator and environment impedances are incorporated into the manipulator dynamics, and each side uses only two scalar adaptive estimates: one for the ideal RBF weight norm and another for the combined effects of friction, approximation error, and disturbances. Both estimates are updated through sigma-modification, resulting in only two adaptive parameters regardless of the number of RBF nodes. A Lyapunov-Krasovskii functional integrating sliding-variable energy, estimation errors, and delay-dependent integral terms is developed. Free-weighting matrices based on sliding-surface identities provide delay-dependent matrix conditions that guarantee uniform ultimate boundedness of synchronization errors, sliding variables, and adaptive estimates. Simulations on two 2-degree-of-freedom (2-DOF) revolute manipulators with friction, external disturbance, and stepwise operator inputs demonstrate synchronization within approximately 2 s and reveal the trade-off between accuracy and control chattering.

Studies of machine-learning-based power-system protection increasingly report near-perfect scores, yet the meaning of those scores depends strongly on the evaluation setting. Protection task, physical scope, measurements, timing, targets, preprocessing, and validation often vary jointly and remain incompletely specified. This paper proposes a standardization-oriented framework that treats evaluation design as part of the scientific contribution. It defines seven required study dimensions: protection objective, physical scope, observability, timing and decision windows, targets and sample validity, validation protocol, and evaluation outputs. The framework is instantiated in a bounded case study on the public PROTECT-90 electromagnetic-transient benchmark, comprising 9022 simulated episodes from a 90 kV double-line topology, for onset-conditioned fault classification and localization. Under centralized sensing, simulation-metadata-aligned 20 ms windows, and episode-grouped validation, a multi-layer perceptron (MLP) achieved a five-fold mean macro-averaged F1 score of 0.991 +/- 0.001 for classification and a localization mean absolute error of 10.20 +/- 0.25% of line length (mean +/- std across episode-grouped folds). Extending the decision horizon to 50 ms preserved this task-dependent performance asymmetry, while reduced observability approximately doubled the MLP localization error but had little effect on classification. A synchronized two-ended conventional locator outperformed the learning locators under its richer clean information set, and measurement degradation showed that clean predictive performance did not determine robustness. The framework turns evaluation assumptions into explicit, reproducible evidence and provides a basis for more comparable, auditable evaluation and future certification-oriented assessment of machine-learning protection functions.

Modern stormwater infrastructure faces increased demands that require a corresponding increase in capacity. Traditionally, these demands have been met by constructing new infrastructure assets, which is a costly endeavor. More recently, many system operators have achieved great success in employing feedback control techniques to improve system performance. However, the resulting closed-loop system exhibits power directionality constraints that introduce nonlinear constraints in feedback synthesis. In this work, we develop a stochastic control synthesis procedure with provable performance bounds on mean-square reference tracking for a general class of problems in which power directionality constraints arise. The proposed method is then applied to a flood mitigation example using a numerical model of a real-world smart water system. The key result is an extension of the performance-guaranteed control (PGC) framework, which was originally designed for disturbance rejection, to accommodate reference tracking control objectives.