Papers

Automated seizure detection from scalp electroencephalography (EEG) is difficult because seizure morphology varies among patients and seizure samples are substantially outnumbered by non-seizure samples. This paper presents an attention-enhanced dual-branch network that jointly learns time–frequency and temporal representations from the same EEG segment. A continuous wavelet transform converts each segment into a scalogram processed by an ImageNet-pretrained ConvNeXt-Tiny backbone and squeeze-and-excitation attention. In parallel, a bidirectional long short-term memory network followed by multi-head self-attention models the raw signal. The two feature vectors are concatenated and classified by a weighted multilayer perceptron. Experiments use 14 subjects from the CHB-MIT scalp EEG database with subject-wise partitioning performed before overlapping segmentation. The model obtains $97.88\%$ accuracy and $97.51\%$ F1-score over ten across-subject splits, and $97.51\%$ accuracy, $96.59\%$ F1-score, and $98.02\%$ area under the ROC curve under 14-fold leave-one-subject-out validation. Removing temporal attention causes the largest ablation loss. The model requires 28.26 million parameters and 4.56 GFLOPs, with a measured network-only inference latency of 4.64 ms per segment.

Distributed Acoustic Sensing (DAS) utilizes fiber-optic cables to create virtual sensor arrays that monitor vibrations and strain over long distances. DAS is particularly useful in remote seismic sensing and infrastructure protection but generates large data volumes that pose processing challenges. The paper presents Orthogonal Representation for Compression of distributed Acoustic Sensing (ORCAS), a framework designed for efficient compression of DAS signals. ORCAS employs a learned orthogonal transform for swift sparse encoding, delivering over 250 MB/s throughput. It also includes a DAS-specific coding scheme, optimizing quantization thresholds with the Lloyd-Max algorithm for improved data fidelity. The system achieves real-time throughput exceeding 90 MB/s and demonstrates superior rate-distortion performance compared to existing standards like JPEG2000 and ZFP. Compression ratios reach around 13, with SNR and PSNR values supporting 13 dB and 28 dB. Evaluations are based on a diverse, publicly sourced DAS dataset.

Automatic modulation classification (AMC) of received radio signals is prudent for further signal processing tasks such as communication monitoring, cognitive radio operation, and interference mitigation in the electromagnetic spectrum. Traditional methods often rely on handcrafted features and struggle under complex channel conditions, whereas deep learning (DL) architectures can learn discriminative representations directly from raw signals. In this work, we propose HFEMCNet, a novel compact hybrid frequency-enriched multi-channel network that jointly exploits spatiotemporal dependencies and frequency domain information derived via Fast Fourier Transform (FFT). HFEMCNet integrates raw IQ samples with hierarchical spectral features to produce a rich signal representation, which is processed by convolutional layers for spatial feature extraction and Long Short-Term Memory (LSTM) units for temporal modeling. Experimental evaluation on benchmark datasets RML2016.10a, RML2016.10b, and over-the-air RML2018.01a demonstrates that HFEMCNet significantly outperform contemporary state-of-theart DL models in classification accuracy while achieving a reduced parameter count, smaller memory footprint, and lower tail-latency, making it suitable for real-time deployment on resource-constrained platforms. These results highlight the effectiveness of combining hybrid architectures with frequencydomain enrichment for robust and efficient AMC.

Objective: Recent studies demonstrate that functional connectomes contain subject-specific signatures, or fingerprints, that can identify individuals across repeated sessions and tasks. Existing methods mostly rely on edge-level features that are sensitive to noise, difficult to interpret, and limited in their ability to generalize across tasks and datasets. Methods: We propose a multiscale community-based functional connectome fingerprinting framework that characterizes each individual by the mesoscale structure of their functional networks. We introduce a signed multilayer community detection framework that incorporates both correlated and anti-correlated brain activity to identify subject-specific community structures across tasks and sessions. Graph-theoretic metrics are then computed from the resulting joint community structures to derive low-dimensional community-level fingerprint representations. Results: The proposed framework is evaluated on 810 healthy control subjects from the Human Connectome Project (HCP). The results show that community-based fingerprints provide a reliable and interpretable substrate for individualized brain characterization across sessions and tasks. Conclusion: Mesoscale community structure provides meaningful and discriminative subject-specific fingerprints. Significance: The proposed framework offers a promising foundation for precision neuroimaging and personalized neuroscience applications.

AI-based structured interference rejection has grown more popular because deep learning approaches can outperform traditional methods by jointly considering the signal of interest (SOI) and the signal mixture (SOI plus interference). This work builds on a previous AI-enabled approach utilizing autoregressive transformer-based models by adding a Finite Scalar Quantization (FSQ) tokenizer layer which aims to improve the interference rejection performance while keeping overall latency to a minimum. Additionally, we experiment with other inference optimization techniques with the goal of speeding up inference without much accuracy loss. We explore this space with an experiment where the SOI is a digitally modulated radio frequency (RF) signal and the structured interference is a digital television signal, an extremely common type of Orthogonal Frequency-Division Multiplexing (OFDM) transmission. Our results achieve low latency and increased interference rejection over traditional techniques and prior work with other AI-enabled methods. We demonstrate the benefits of the AI-enabled approaches via audio metrics such as Perceptual Evaluation of Speech Quality (PESQ). Additionally, we explore a variety of applications and detail how our interference rejection algorithm may be used in operationally-relevant scenarios.

Distributed multiple-input multiple-output (D-MIMO) offers favorable geometry for localization and sensing, with its greatest potential arising from joint coherent processing across panels. However, stringent frequency-synchronization and phase-calibration requirements, together with multimodal likelihood functions, complicate the estimation problem. Consequently, most existing methods process the panels noncoherently, potentially sacrificing localization accuracy. We present a unified family of Bayesian state filters based on concentrated Type-I and marginal Type-II likelihoods for wideband near-field D-MIMO systems. The Type-I filters realize (i) noncoherent, (ii) coherent, and (iii) carrier-phase-based processing. We show that a zero-mean Type-II model is inherently noncoherent under distributed processing, whereas observation stacking restores coherence. A recently proposed nonzero-mean Type-II model adapts to the coherence available in the data, a property termed “soft coherence.” We derive posterior Cramér–Rao lower bounds (PCRLBs) for all three coherence levels and show that each level is fundamentally tied to the number of phase parameters used for positioning or treated as nuisance parameters. Numerical results show that the filters closely approach their respective PCRLBs and that coherent processing substantially outperforms noncoherent processing. Our particle-based implementations parallelize over particles and panels, scale linearly with the observed data, and achieve runtimes of tens of milliseconds per time step under GPU acceleration.

X-ray speckle-based dark-field imaging offers high sensitivity to sub-pixel structural features, yet its quantitative reliability in clinical and preclinical settings remains constrained by low photon flux and finite focal spot sizes. However, how hardware-induced noise and source blurring propagate through retrieval algorithms to degrade signal integrity is not fully understood. Here, we systematically evaluate algorithm robustness quantified by signal linearity, sensitivity, and bias under photon starvation and source blurring across two mathematically distinct frameworks: differential-based intrinsic tracking (Low-Coherence System, LCS) and patch-wise explicit tracking (X-ray Speckle-Tracking Speckle-Vector-Tracking, XST-XSVT). Our experimental results demonstrate that input speckle pattern distortions propagate through retrieval algorithms in fundamentally different ways depending on algorithm architecture. As an example, using our setup, under severe photon starvation, derivative noise amplification in LCS causes its dark-field signal linearity and sensitivity to drop precipitously, while sharply elevating baseline bias. In contrast, XST-XSVT restricts these losses for sensitivity while maintaining a stable baseline, as its patch-wise variance calculation inherently suppresses stochastic noise. Similarly, under blur-limited conditions , source blurring washes out the speckle pattern, directly reducing dark-field sensitivity for both LCS and XST-XSVT. This characterization establishes operational boundaries for low-power and low-coherence X-ray systems, guiding algorithm selection and framework optimization to realize quantitative dark-field imaging in preclinical and clinical applications.

This paper investigates a rotatable antenna (RA)-enhanced half-duplex amplify-and-forward relaying system, where a multi-antenna base station (BS) serves multiple single-antenna users via a multi-antenna relay. The BS and users employ isotropic antennas, whereas the relay employs directional RAs whose pointing matrix is shared by both hops to avoid inter-hop reorientation delay and control overhead. We aim to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all users by jointly optimizing the BS precoding, relay precoding, and RA pointing matrices. To tackle this non-convex problem, we first investigate the single-user scenario and reduce the joint design to RA pointing optimization using the optimal relay precoding matrix available in closed form. A manifold-aware Frank–Wolfe (MFW) method is then employed to obtain a suboptimal pointing solution. Under a symmetric far-field line-of-sight geometry, we further characterize the globally optimal pointing structure and derive a directivity threshold separating common pointing from antenna splitting. Building on this MFW procedure, we next address the general multiuser scenario. Specifically, the quadratic transform is first applied to obtain an equivalent auxiliary-variable formulation, which is subsequently solved suboptimally via alternating optimization (AO). In particular, we employ a safeguarded extension of the MFW method based on log-sum-exp smoothing to update the RA pointing matrix in each AO iteration. Simulation results demonstrate that the proposed algorithms consistently achieve the best signal-to-noise ratio (SNR) and SINR performance among all considered schemes. It is further shown that the optimized RA pointing adapts to the two-hop geometry, the preferred directivity factor depends on user load, and relay placement with relatively balanced two-hop propagation conditions is generally preferable.

This letter investigates range and velocity estimation under relativistic motion for device-based (DB) and device-free (DF) sensing. By deriving the exact time-scaling and time-shift relations induced by one-way and two-way propagation, both sensing modes are cast into a unified affine signal model. Closed-form Cramér–Rao bounds (CRBs) are obtained as explicit functions of normalized velocity, root-mean-squared (RMS) bandwidth, and RMS duration. The bounds recover the classical low-speed results but exhibit distinct velocity scaling in the ultrarelativistic regime. For rapidly receding motion, the range CRB diverges while the velocity CRB vanishes. For rapidly approaching motion, both CRBs vanish. The DB and DF modes further exhibit different asymptotic orders in the two directions, showing that relativistic motion changes not only the signal model but also the fundamental scaling laws governing sensing accuracy.

This paper presents a wideband power amplifier (PA) designed and implemented in Infineon Technologies' 130-nm SiGe BiCMOS process for upper W-band and lower D-band applications. A complete load-pull simulation methodology is carried out, and a band pass filter (BPF)-based matching strategy is employed for the design of the output and inter-stage matching networks. The fabricated PA prototype achieves a small-signal gain 3-dB bandwidth of 71-133 GHz. Moreover, it maintains a relatively flat gain of approximately 15.7 dB over 75-128 GHz, with less than 1-dB fluctuation. The measured saturated output power is 8.8-11.7 dBm, while the measured peak power-added efficiency (PAE) is 7.2-11.1%. These results demonstrate the potential of SiGe BiCMOS technology for wideband and integrated transmitter front ends operating across the W-/D-band frequency range.

This paper presents a compact, fully differential, two-stage millimeter-wave (mm-wave) cascode power amplifier (PA) designed and implemented in a 22nm FD-SOI CMOS process (22FDX+). The PA employs the newly introduced extended-drain MOS (EDMOS) device in 22FDX+, together with a carefully engineered device core and transformer baluns. At 50 GHz, the prototype achieves 18.8 dBm saturated output power (PSAT), 18.1 dBm 1-dB compression output power P1dB, and 21% power-added efficiency (PAE) at P1dB. To the best of our knowledge, this work achieves the highest reported power density of 2.6 W/mm2 among single-way, two-stage CMOS cascode PAs.

Deep neural network based deepfake detection systems have achieved high levels of accuracy on benchmark datasets and competitions. However, most models lack interpretability. It is challenging to extract reasoning from the network that can convince the human evaluator to trust the decision. Humans often rely on acoustic cues like unnatural pitch jitter, robotic intonation, acoustic artifacts, and unnatural sounding fricatives to judge the quality of the synthetic audio. This study explores the role played by the voiced and unvoiced regions of speech in discriminating synthetic from bonafide speech. A measure of signal periodicity is used to analyze speech into voiced and unvoiced components. Then, the graph attention based AASIST detection system is trained independently on each component. This work compares the accuracy of deepfake detection system using voiced and unvoiced components and analyzes the results on the MLAAD dataset. Our results show that unvoiced regions are particularly more effective in distinguishing synthetic (deepfake) speech from bonafide, and achieves an equal error rate of 6.62%. When combined with voice regions through score-level fusion, the overall performance improves further, yielding a 5.82% EER, a relative improvement of 49% over the baseline system that uses the full audio.

In this paper we discuss the development of a fully pilotless multi-access-point Wi-Fi receiver. This is based on a self-attention Transformer that operates on per-(access point, subcarrier) tokens and outputs bit-wise logits for a standard low-density parity-check decoder. In realistic ray-traced indoor channels, pilotless decoding becomes feasible as soon as some spatial diversity is available. While a single-access point configuration does not attain practical error rates, when compared to an ideal maximum-ratio-combining baseline with perfect channel knowledge, cooperative configurations with three access points achieve coded bit error rates of approximately $10^{-5}$. This is achieved while benefiting from higher spectral efficiency by eliminating pilots.

Spatial audio enhances user immersion by reproducing 3D sound fields, with Ambisonics being a widely adopted representation. While Ambisonics is theoretically independent of the recording setup, practical microphone arrays introduce hardware-dependent encoding artifacts. Moreover, existing data-driven solutions lack flexibility, as they are typically restricted to fixed array geometries. To overcome these limitations, we propose ADEPS, a generative framework that explicitly embeds the physical acquisition model into the inference process. By leveraging this formulation, ADEPS effectively compensates for array-specific distortions while enabling zero-shot encoding across arbitrary array topologies. We train the underlying generative prior in an unsupervised manner solely on target Ambisonic representations. Extensive evaluations across diverse simulated and real microphone arrays demonstrate that ADEPS consistently outperforms both traditional linear and parametric baselines in spatial fidelity and spectral quality.

We present a high-performance channelizer for Microwave Kinetic Inductance Detectors (MKIDs), designed to mitigate spectral leakage and scalloping loss through a 50% overlapping polyphase filter bank (PFB). The design is implemented on a Xilinx Zynq UltraScale+ RFSoC ZCU111 using a custom design - primarily in Vitis High-Level Synthesis (HLS), with the time-critical output serializer and glue logic in VHDL - that processes a 4.096 GSPS input stream with a per-branch super-sample rate (SSR) of 16. Rather than relying on aggressive 512 MHz clocking and vendor IP cores, our split-path architecture computes the delayed and non-delayed polyphase branches concurrently, so that the bulk of the channelizer operates at a robust 256 MHz while a single 512 MHz subdomain is confined to the BRAM-to-FFT section. This architectural parallelism enables a deep 16-tap prototype filter that doubles the filter depth of comparable high-speed systems and substantially improves channel isolation. We report the complete internal architecture of the reordering engine that implements the overlap, including two implementation hazards not documented in prior art: a pipeline-stage skew affecting state toggles in HLS, and the state-preservation requirements of a restartable design. The 2/1 overlapped channel response is validated in simulation - recovering the ~3.9 dB scalloping loss of a critically sampled channelizer to below 0.1 dB across the full 2 MHz channel - and characterized systematically with a 100-tone batch frequency sweep, while the parent readout system has been operated cryogenically with an MKID array in an adiabatic demagnetization refrigerator (ADR), identifying individual resonators in total darkness. The result is a resource-efficient, high-isolation benchmark for wideband frequency-division-multiplexed readouts.

Vibration and impact measurements in mechanical systems are invariably corrupted by noise, which degrades every quantity derived from them, such as modal parameters and contact forces. Classical filters attenuate noise only when its statistics are known a priori and often fail at very low signal-to-noise ratio (SNR) or under spectrally coloured noise. This work presents Time-Window Noise2Noise (TWN2N), a self-supervised method for blind denoising of signals produced by deterministic mechanical systems. The method requires neither a clean reference signal nor paired noisy observations, nor any prior characterization of the noise: by reconstructing each time instant from its temporal neighbourhood while masking the instant itself, a compact autoencoder is forced to learn the underlying dynamics rather than the noise. The formulation is general for any deterministic dynamical system, is computationally lightweight (a single feed-forward pass per sample at inference), and is assessed on two benchmarks with exactly known ground truth: the impulse response of a linear 3-degree-of-freedom system and a nonlinear Hunt-Crossley impact. Across white, pink, brown and quantization noise, and over repeated realizations, TWN2N yields statistically significant SNR gains (p < 0.001) without any prior noise information, and it is benchmarked against optimally-tuned Savitzky-Golay and wavelet (VisuShrink) filters. The benefit propagates to identification: at 15 dB input SNR, modal parameters that are unidentifiable from the raw signal are recovered within 4% of their reference values after denoising.

State estimation in partially known state space (SS) models is challenging when the dynamics or observation model varies across short data blocks. Classical model-based approaches, such as the expectation-maximization (EM) Kalman filter, jointly recover the latent states and the unknown model parameters, but rely on linear-Gaussian assumptions that should accurately describe the system and require numerous forward-backward passes. Consequently, their performance and computational efficiency may deteriorate under complex and non-stationary real-world conditions. On the other hand, learned Kalman smoothers are robust to model mismatch yet cannot adapt at inference to unseen model variations without labeled data. In this work, we propose EM-KalmanNet, an AI-aided tracking algorithm for adaptive smoothing in blockwise time-varying SS models. The method unfolds a fixed, small number of EM-like iterations into a trainable architecture: a parameter-aware RTSNet implements a learned E-step conditioned on the current model-parameter estimate, while a lightweight M-Net implements a learned M-step that updates the state-transition or the observation matrix using empirical moments, residuals, and gradient-related statistics. The two modules are shared across the unfolded iterations and are trained offline via a dedicated three-stage procedure. During deployment, the per-block parameter estimate is propagated between consecutive blocks, enabling observation-driven adaptation without labeled online data or knowledge of the noise statistics. Experiments involving linear and nonlinear models, Gaussian and non-Gaussian noise, Lorenz attractor tracking, and acoustic source localization demonstrate that EM-KalmanNet consistently outperforms model-based and data-driven benchmarks while substantially reducing inference latency relative to the EM-KF.

Pretrained EEG backbones improve transfer performance, but downstream diagnosis heads remain hard to audit: predictions are made from unrestricted hidden states, whereas explanations are usually produced only after the decision. We introduce tPoE-EIB, an evidence-information bottleneck head for adapting EEG backbones under an evidence-only prediction constraint. tPoE-EIB selects temporal and channel evidence, maps the selected summaries to Gaussian experts over a shared latent variable, and fuses them with a tempered product-of-experts posterior. The classifier observes only this latent, so the decision path is explicit and rate-limited by the expected posterior KL. This gives a tractable supervised objective with an information-rate penalty, while the closed-form tempered posterior mitigates overconfident fusion from correlated evidence axes. We evaluate tPoE-EIB on pretrained EEG foundation-model backbones across six diagnosis settings: event-type classification, abnormality detection, seizure detection, cognitive-decline staging, depression screening, and cerebrovascular-disease classification. The evaluation spans public benchmarks and in-house clinical cohorts, binary screening and fine-grained staging, and sparse and dense montages. tPoE-EIB preserves competitive balanced accuracy and improves over representative post-hoc explanations on selection-faithfulness audits, including insertion-deletion and gate-causality tests. Its structured posterior further enables integration-faithfulness audits, including expert-drop, posterior-reliance, and expert-disagreement tests. Overall, these results suggest that evidence-only, rate-limited fusion is a practical route to auditable diagnosis on top of frozen EEG foundation backbones.

Despite the promising applications of electronic noses (e-Noses) in medical diagnosis and industrial process control, sensor drift remains a critical challenge that degrades long-term sensing reliability by inducing gradual shifts in sensor responses. Conventional drift compensation methods are typically designed for batch learning and lack the ability to support continuous online learning in non-stationary environments. Although several online drift compensation methods have recently been proposed, they are mainly based on quasi-online mini-batch learning for distribution adaptation, while true sample-wise online learning without buffering remains largely unexplored. To address these issues, this paper proposes a sample-wise online drift compensation method based on spiking neural networks (SNNs) for feature adaptation and spiking reservoir computing (SRC) for classification. By exploiting spike-timing-dependent plasticity (STDP), the SNN self-organizes spatiotemporal attractor dynamics for label-free feature adaptation (STDP-FA), and the adapted features are classified by SRC with self-supervised adaptation driven by winner-take-all (WTA) competition. The proposed method addresses various concept drift patterns, including gradual drift, random drift, sensor failures, and abrupt changes. Simulations on a real-world sensor drift dataset demonstrate a clear improvement in classification accuracy over baseline methods.

Visible light communication (VLC) offers wireless data connectivity indoors, yet its performance is severely limited by link blockages. While optical reconfigurable intelligent surfaces (ORISs) have been proposed to overcome these blockages, existing literature predominantly relies on the unrealistic assumption of ideal channel state information. In this paper we investigate the contribution of ORIS elements in VLC systems under practical, non-ideal channel estimation scenarios. We analyze the communication performance using two pilot-based estimation techniques: pilot symbol assisted modulation (PSAM) and superimposed training (ST), evaluating the impact of data power allocation factors and averaged orthogonal frequency division multiplexing (OFDM) symbols. Our results show that PSAM with an ORIS deployment enables optimal large data power allocation factors, resulting in spectral efficiency improvements that surpass scenarios with ideal channel estimates but without ORIS deployment. While ST performs well with a large number of averaged OFDM symbols, it is unable to overcome the performance of PSAM when ORIS elements are deployed. We also find the system robust to imperfect ORIS orientation; even with only half of the elements accurately aligned, performance remains acceptable. Ultimately, we show that spectral efficiency values of approximately 5 bits/s/Hz are achievable using ORIS with PSAM, a performance comparable to, or even exceeding, that of unobstructed line-of-sight links.

Device-free human pose estimation using commodity WiFi signals has emerged as a promising paradigm for pervasive sensing in mobile computing systems. However, existing approaches often suffer from severe performance degradation when deployed across heterogeneous environments due to complex multipath propagation and domain shifts in wireless signals. In this paper, we present a physics-informed WiFi sensing framework for robust 3D human pose estimation under mobile and cross-environment settings. Our approach explicitly models wireless signal propagation characteristics and incorporates multipath-aware attention to capture environment-dependent signal variations. To further improve generalization, we introduce a disentangled representation learning scheme that separates pose-related features from environment-specific factors, enabling effective cross-domain adaptation without requiring extensive retraining. We implement our system using commodity WiFi devices and evaluate it on multiple public benchmarks, including Person-in-WiFi-3D and MM-Fi, as well as real-world deployments across diverse indoor environments. Experimental results demonstrate that our framework significantly improves robustness and generalization performance compared to state-of-the-art methods, particularly under cross-environment scenarios. These results highlight the potential of physics-informed wireless sensing for enabling reliable, scalable, and infrastructure-free human-centric applications in mobile computing systems.

Differential binary noise (DBN) modulation encodes information in the polarity transition of a reused noise realization, dispensing channel state information and carrier-phase recovery. This letter proposes single-input multiple-output (SIMO) reception for DBN via post-correlation decision-statistic combining, followed by one zero-threshold decision. The combined statistic is a Hermitian quadratic form in complex Gaussian vectors, yielding an exact conditional bit error probability (BEP) with no Gaussian approximation. Averaging it over $\kappa$-$\mu$ fading and the reused energy gives the exact BEP for arbitrary weights. Because that energy is shared, its deep fades are common to every branch, and the observation length caps the diversity order at $\min(M\mu,N)$. The deflection-optimal weights follow in closed form and interpolate soft combining and power weighting. Measured 65 GHz indoor non-line-of-sight (NLoS) results quantify the trade-off between spatial resources and observation length.

Open, cloud-native, and AI-enabled 6G architectures make network functions easier to deploy, observe, and replace, often implemented with separate software modules. However, each explicit interface can also create an entry point, trust transition, and lateral-movement route from a cybersecurity perspective. This paper formulates architectural disaggregation as a graph-based Stackelberg design game for 6G system security. A deployment graph specifies candidate 6G system seams, while an architecture-dependent directed attack graph specifies externally reachable functions and multistage paths. The defender commits to seams and allocates hardening and monitoring; an informed attacker then chooses a path and effort. Because edge traversal and evasion factors compose multiplicatively, the attacker's path response becomes a shortest-path problem after a logarithmic transformation. Convex effort costs admit a conjugate representation, and, for each fixed architecture, the defender's control problem is a convex exponential-epigraph program solvable by path generation. We derive an architecture-comparison criterion, a threshold for AI transducers within 6G systems that jointly add visibility and exposure, and comparative statics for attacker entry and bypass innovation. Experiments with stylized standards-anchored O-RAN/6G systems over RAN, RIC, core, cloud, and edge-AI functions show that neither full integration nor maximal disaggregation is generally best. Uncontrolled seams can increase risk, whereas selectively monitored and hardened seams can improve the Stackelberg objective. We find a boundary is worthwhile only when its detection and containment gains exceed its added attack opportunities and coordination cost.

Reconstructing the full-aperture channel of a fluid antenna system (FAS) from a few activated ports requires inferring unobserved responses from limited spatial samples. This paper proposes a covariance-domain FAS channel-reconstruction method based on nonnegative least squares (NNLS). A nonuniform port arrangement constructs a difference coarray with an enlarged aperture. Vectorizing the sample covariance maps the physical array to a virtual steering dictionary, and the resulting complex covariance-fitting model is recast as a strictly equivalent real-valued NNLS problem. A projected-gradient iteration initialized at zero estimates the nonnegative angular-occurrence coefficients, and the L_s strongest well-separated peaks yield the angles of arrival (AoAs). Simulations show that the proposed FAS-NNLS estimator exploits the enlarged virtual aperture and achieves lower AoA mean squared errors than uniform linear array (ULA) multiple signal classification (ULA-MUSIC) and ULA estimation of signal parameters via rotational invariance techniques (ULA-ESPRIT). Its non-line-of-sight (NLoS) path accuracy matches FAS covariance matching pursuit, while its line-of-sight (LoS) estimation is significantly improved.

Walsh-sequence-based receiver architectures offer an alternative approach for the reception and reconstruction of multiple simultaneous RF signals over wide bandwidths. While previous works have focused on the architecture and theoretical operation of Walsh-domain processing, limited experimental results have been reported so far on hardware operation under realistic conditions. This paper presents the experimental evaluation of a Walsh-sequence-based receiver prototype. A measurement campaign was conducted under various signal placement, interference, and receiver configuration scenarios. Reconstructed spectra, Walsh-lane power spectral densities (PSDs), and Error Vector Magnitude (EVM) measurements were used to investigate Walsh-domain signal representation, interference effects, and receiver performance. Finally, dense- spectrum experiments involving up to ten simultaneously active signals demonstrate the potential of the architecture for low power wideband spectrum sensing applications.

Traffic sensor placement in large urban networks must balance traffic monitoring needs with limited installation, maintenance, and data-management budgets. This study develops a framework for preserving network-wide traffic information while reconstructing flow and density at unobserved links and the macroscopic fundamental diagram (MFD). The framework combines normalized mutual information (NMI) with Inductive Graph Neural Network Kriging (IGNNK). NMI quantifies lag-aware dependence between joint flow-density states, while a greedy algorithm selects sensors by balancing weighted information coverage against redundancy. An IGNNK-based bidirectional graph-diffusion model reconstructs unobserved link states and generates the MFD. The framework was evaluated against a principal component analysis (PCA) benchmark using seven hours of dynamic traffic assignment simulation data for the Chicago network, with 4,805 directed links and 420 one-minute intervals split into training, validation, and independent testing subsets. Reconstruction performance for both PCA and NMI-IGNNK varied substantially over the sensor ratios of 0.5% to 30%. PCA generally yielded lower root mean squared error (RMSE) over the 0.5%-3% range, but its errors were nonmonotonic and reached their minimum at 2%. At 3%, normalized joint RMSE was similar for PCA and NMI-IGNNK (0.100 and 0.105, respectively). NMI-IGNNK remained applicable up to 30% coverage and generally improved as coverage increased, reducing testing-subset normalized joint RMSE from 0.212 at 0.5% to 0.043 at 30%, with consistent trends over the complete analysis period. The framework integrates lag-aware, information-theoretic sensor placement based on joint flow-density states with directed graph-based reconstruction, enabling a broad range of sensor budgets in large-scale networks without strict limits on the number of sensors.

Ballistocardiography (BCG) is promising for unobtrusive long-term blood pressure (BP) monitoring in laboratory settings, but traditional BCG signals are vulnerable to the variations in body-bed interaction with shifted fiducial points in temporal or amplitude axis, and BP varies with personal hemodynamic changes, causing misaligned representations that affect model generalizability and robustness. In this work, we propose Phy-BP, a physics-constrained BP estimation framework based on triaxial bodyseismography (BSG) acquired using bed-mounted sensors as an extension of single-axis BCG. Firstly, we design an adaptive quality-control algorithm combining neighboring beat patterns and universal cardiogenic templates to retain reliable cardiac components. Secondly, we propose a physical model of wave propagation through the body-bed system to constrain latent feature evolution, aligning triaxial representations generated by a shared cardiogenic excitation. These mechanical constraints connect multi-axis feature learning to body-bed dynamics and complement data-driven regression from vibration signals. Evaluation on a 162-hour hospital dataset from 21 subjects against invasive arterial BP yields a mean absolute error of 5.07 mmHg, a prediction-error standard deviation of 7.24 mmHg, and a Pearson correlation coefficient of 0.86 for mean arterial pressure. This framework is intended for unobtrusive monitoring during rest and sleep, with potential applications in overnight hospital and home monitoring.

A machine learning-assisted framework for the inverse design of pixelated millimetre-wave patch antennas targeting the 22–30 GHz band is presented. The antenna surface is represented as a 19x23 binary pixel grid on a Rogers RT/duroid 5880 substrate, where each pixel is either metal or empty, with a continuous electrical path from the feed enforced by design. An initial dataset of approximately 6,000 full-wave CST simulations was collected from structured random pixel patterns, of which only around 40% achieved a resonance with |S11| <= -10 dB anywhere in the band, resulting in an imbalanced dataset. To improve simulation efficiency, an XGBoost binary classifier was trained on this data to distinguish resonant from non-resonant patterns before simulation. Using the classifier as a pre-simulation filter, an additional 4,000 patterns were selected and simulated, raising the overall proportion of resonant designs in the combined 10,000-sample dataset from approximately 40% to 52%. A hybrid CNN-BiLSTM forward surrogate was then trained on this augmented dataset to predict the full complex S11 response across 801 frequency points, using a physics-guided composite loss that explicitly emphasises resonance dip accuracy. Finally, an inverse design model was developed that optimises in a compact 64-dimensional latent space using gradient descent to generate pixel patterns matching a desired S11 specification. The results show good agreement between the surrogate-predicted and CST-simulated |S11| responses for the generated designs and demonstrate the feasibility of automatically designing and reconfiguring antenna structures.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for improving communication reliability in radar-communication coexistence (RCC) scenarios, particularly for communication users (CUs) located inside radar exclusion zones, where only the radar is permitted to operate and the two systems remain uncoordinated. In such settings, CUs may suffer from strong radar interference whose phase is random and difficult to track while also facing difficulty in obtaining accurate CSI from the base station. These challenges become even more critical in the presence of RIS phase errors, which limit the applicability of conventional coherent detection methods. Motivated by these practical limitations, this paper develops an RIS-assisted RCC framework for a CU operating in an uncoordinated RCC setting. Within this framework, we derive two practical maximum-likelihood (ML)-based detectors, both of which avoid tracking the radar interference phase: 1) a non-coherent detector that does not require instantaneous CSI and incorporates RIS phase uncertainty, and 2) a mismatched coherent detector that relies on imperfect CSI. For the non-coherent case, we derive an exact likelihood expression and a closed-form detector in the low to moderate SINR regime. For the imperfect CSI case, we derive the corresponding closed-form detector in the low-to-moderate SINR regime, and analyze performance through pairwise error probability (PEP), yielding a tractable approximation based on Gauss-Chebyshev quadrature. Numerical and analytical results show that the proposed non-coherent detector closely matches the optimal ML detector, validating its practical usefulness.

This paper presents a fully reconfigurable RF vector modulator (RFVM)-based phase shifter for wideband beamforming phased array of 6G/NextG satellite communication (SATCOM). It covers the frequency range from S-band up to Ku-band. Specifically, the proposed RFVM features a novel vector modulation approach that relaxes the frequency constraint in conventional scenario while avoiding applying a quarter all-pass filtering (QAF) network. It achieves a topology that RF input signal is divided into multi-branch transversal sections where each branch enables individual gain weightings and phase delays. By resumming the signals from all branches, the output signal of proposed RFVM can realize a full 360 degree phase shifting while it preserves magnitude response in wideband scenario.