Papers

Continual learning treats degradation on previously seen data as evidence of failure, a convention inherited from settings with a stationary prediction target, where a correct label remains correct indefinitely. World models do not satisfy this condition. Their prediction target is the environment, which changes, so knowledge that was accurate when acquired may later become false, and discarding it is required behavior rather than a defect. Non-stationary ground truth is well studied in the concept drift literature and in the temporal factuality of language models, but has not been formulated for world models, which are distinctive in that they also encode knowledge that must never be revised. We argue that continual world models require retention stratified by invariance timescale, separating invariants such as physics and object permanence, which must never be revised, from instance-level facts that should be revised as soon as the environment changes. Standard forgetting metrics cannot distinguish a world model that has correctly revised outdated knowledge from one that has suffered catastrophic forgetting, and consequently rank a frozen model highest, while existing physical-reasoning benchmarks evaluate only frozen checkpoints. We propose differential retention, which reports invariant regression testing across the adaptation stream jointly with revision latency, without aggregation.

Contactless radar sensing offers a promising approach to continuous vital-sign monitoring. However, indoor multipath can distort direction estimates and generate ghost responses. This paper presents a reconfigurable intelligent surface (RIS)-assisted monopulse radar whose RIS phase configuration is controlled by a soft actor-critic agent to improve angular localization. The proposed framework combines simultaneous sum and difference beams with a programmable reflected path, enabling a single radar node to adapt the propagation environment without relying on multiple synchronized sensors. A compact multipath model formulates RIS control as a continuous-action reinforcement-learning problem whose reward penalizes bearing errors and misleading multipath reinforcement. Numerical results compare the proposed method with conventional monopulse processing without an RIS, a frequency-modulated continuous-wave range-angle fast Fourier transform baseline, random RIS phase configurations, and deterministic RIS controllers. The evaluation considers different target separations, signal-to-noise ratio levels, and multipath conditions.

This paper proposes a learning-based beam adaptation framework for active/passive-aware monopulse integrated sensing and communication (ISAC) with spatial leakage suppression. A single gNB uses a shared array and orthogonal frequency division multiplexing dual-functional radar-communication signaling to jointly support downlink transmission and monopulse sensing. Radar detections and uplink spatial evidence are fused to distinguish cooperative active directions from passive directions, while a protected angular zone limits unintended illumination. Soft Actor-Critic deep reinforcement learning adapts the transmit beam using communication, sensing, leakage, and previous-action feedback. Numerical results show accurate active/passive spatial classification and robust leakage control as the number of passive targets increases from one to twelve. The proposed SAC controller maintains aggregate passive leakage below the prescribed budget across the full sweep and, from two passive targets onward, achieves the highest leakage-compliant sum rate among the considered baselines.

Wireless Internet-of-Things (IoT) networks can leverage locally observed channel state information (CSI) at base-stations (BSs) to perform region classification, i.e., infer the region of origin of a transmitter (TX), thereby enabling channel-based authentication. However, centralizing these measurements incurs substantial communication overhead and requires sharing location-dependent radio fingerprints. To address these difficulties, we propose an edge-native federated learning (FL) framework where BSs collaboratively train a shared classifier via a parameter server (PS) without exposing raw data, thereby supporting physical-layer security tasks like spoofing detection. To overcome the statistical heterogeneity arising from diverse BS locations and propagation conditions, we introduce FedSwitch, a personalized FL algorithm that automatically transitions edge nodes from collaborative training to local fine-tuning upon loss stagnation. We provide theoretical convergence guarantees for the proposed collaborative training phase and derive analytical bounds linking classification errors to statistical discrepancies. Extensive evaluations on synthetic channel measurements demonstrate that FedSwitch significantly improves region-classification accuracy over standard FL baselines.

Pinching-antenna systems (PASS) offer flexible radiation points for integrated sensing and communication (ISAC), but existing designs largely assume continuous antenna placement, which may be impractical with pre-installed hardware. We investigate a discrete-activation multi-waveguide PASS-ISAC system that selects sparse transmit and receive pinching antennas (TPAs/RPAs) from predefined locations. We formulate a communication-rate maximization problem by jointly optimizing beamforming and TPA/RPA activation under transmit-power, activation, and sensing signal-to-noise ratio (SNR) constraints. To solve this nonconvex problem, we develop a low-complexity joint discrete pinching-antenna activation and beamforming optimization (JDPABO) algorithm based on alternating optimization, successive convex approximation, and reweighted $\ell_1$ minimization. Simulations show that JDPABO approaches continuous-placement performance at low sensing SNR requirements and outperforms fixed activation schemes with sparse PA activation.

Multiple-Input-Multiple-Output (MIMO) technology is considered to be a viable method for spectral efficiency and data rate enhancement in wireless communications. This article presents a two-element capsule antenna with orthogonal current distributions for polarization multiplexing in a wireless capsule endoscopy (WCE) application at 915 MHz. The proposed antenna was evaluated in a MIMO link where it served as the transmitter and an existing on-body antenna was the receiver. The performance was assessed in two distinct environments: an anatomically accurate human body model using full-wave (FW) simulations, and a homogeneous liquid tissue phantom, using both FW simulations and measurements. The former was used to evaluate the performance in a realistic environment and to demonstrate the superiority of the proposed capsule antenna against existing two-element antennas in the literature, while the latter aimed at verifying the antenna design by comparison of simulated and measured MIMO links. We studied channel capacity and eigenvalues for the comparisons. The MIMO link with the proposed antenna provided nearly doubled median channel capacity relative to a baseline single-input-single-output link, consistently in the simulations and measurements. Our results prove the suitability of the proposed antenna for polarization multiplexing and, in terms of channel capacity, establish it as the best conformal two-element capsule antenna design at 915 MHz.

Future 6G systems share a central challenge with Physical AI: combining sensing, reasoning, and action on network-edge infrastructure in rapidly changing physical environments under strict latency, compute, and energy constraints. Addressing this challenge requires multi-timescale intelligence, combining fast perception that tracks wireless phenomena within milliseconds with slower reasoning that directs sensing and adapts network policies and resources over seconds. In this paper, we present a vision for Agentic RF Intelligence based on a dual-loop architecture that decouples fast, locally autonomous wireless sensing (e.g., detecting spectrum dynamics, interference, and signal sources) from slower agentic reasoning and orchestration. We provide an on-device prototype in which the full stack runs on a single NVIDIA Jetson Thor connected to a B200 Mini USRP. The fast loop processes IQ samples using signal-processing tools and wireless physical-layer foundation models (WPFM), while a local Large Language Model (LLM) asynchronously interprets RF events, invokes tools and steers sensing. Our experiments confirm the separation in timescales, as WPFMs operate at millisecond latency, while LLM interactions take seconds to minutes. The fast loop remains operational during agentic reasoning, providing initial evidence for the feasibility of this decoupled architecture. We conclude by outlining extensions toward memory-driven self-improvement, world models, network control, and multi-agent operation.

This paper investigates the secrecy performance of a cooperative non-orthogonal multiple access (NOMA) network under practical impairments. In the considered system, the source employs orthogonal space-time block coding (OSTBC) transmission, legitimate users adopt receive antenna selection (RAS), and the eavesdropper utilizes maximal-ratio combining (MRC) scheme. Moreover, an energy-constrained amplify-and-forward relay harvests energy from the received signal and allocates the harvested energy to information forwarding and artificial noise (AN) generation. To capture the realistic performance of a more practical transmission scenario, channel estimation errors (CEE), feedback delay (FBD), imperfect successive interference cancellation (SIC), and imperfect AN cancellation are taken into account. For this system, closed-form expressions for the legitimate-user signal-to-interference+noise ratios (SINR) cumulative distribution function (CDF) and the eavesdropper SINR probability density function (PDF) are derived, and a tractable analytical secrecy outage probability (SOP) expressions are obtained. In addition, the high-SNR secrecy behavior is characterized under ideal channel state information (CSI) conditions and practical impairments, revealing the achievable secrecy diversity order and the secrecy outage floor, respectively. Moreover, secrecy throughput and energy efficiency are analyzed based on the obtained SOP results. The numerical findings are in close agreement with the analytical results and further illustrate how the secrecy behavior of the proposed system is affected by key design parameters and practical impairments. The obtained results indicate that transmit and receive diversity and proper AN power allocation significantly improve the secrecy performance, whereas practical impairments lead to non-zero SOP floors in the high signal-to-noise ratio region.

Timing errors represent a fundamental challenge in high-speed analog-to-digital converters (ADCs). This paper presents a frequency-interleaved architecture with inherent robustness to timing errors. The frequency interleaving is achieved by an embedded analog Fourier transform (AFT), allowing accurate channel spacing. The charge-based passive AFT is able to share the capacitor array with the capacitive DAC of the subsequent data conversion. A circuit implementation for the complex-valued irrational coefficients in the AFT matrix is proposed, which eliminates the device-ratio-induced systematic errors. Measured at 10b 13GS/s, the calibration-free ADC achieves 45.8-dB SNDR and 56.1-dB SFDR at Nyquist. The ADC consumes 26.4mA from a single 1V supply, resulting in a Walden FoM of 12.7 fJ/conv.-step.

Radio-frequency interference threatens satellite navigation, wireless communications, radar, and industrial sensing. Reliable operation therefore requires spectrum situational awareness to detect interference and determine the number, time-frequency occupancy, and characteristics of active sources. In realistic environments, multiple sources may overlap. Source-specific segmentation and characterization are therefore particularly challenging. In this paper, we propose PI-SCAN (Permutation-Invariant Segmentation, Characterization, and Assignment Network), a joint set-based learning framework for source-specific segmentation and characterization of overlapping interference signals. A logarithmic power spectrogram derived from complex IQ samples is mapped to six unordered prediction slots. For each slot, the model estimates objectness, interference type, center frequency, bandwidth, received signal strength, binary offset carrier (BOC) modulation, and a source-specific segmentation mask. Permutation-invariant Hungarian assignment aligns the predicted slots with the unordered ground-truth sources, while a weighted multi-task objective jointly optimizes the individual prediction tasks. The framework is evaluated using a ray-traced industrial environment containing mixtures of one to six simultaneously active interferers drawn from 94 waveform configurations across six interference classes. The full seven-task configuration achieves a detection F1-score of 74.8%, type and BOC accuracies of 97.3% and 98.4%, and mean absolute errors of 1.29MHz, 2.54MHz, and 3.19dB for center frequency, bandwidth, and signal strength. Using only objectness, type, and mask prediction increases the F1-score to 91.1%, highlighting the trade-off between detection and comprehensive characterization.

We study the expected aperiodic autocorrelation sidelobes of random communication symbols transmitted through a unitary modulation matrix. We consider proper sub-Gaussian constellations, namely those with fourth absolute moment $\mu_4<2$ and zero pseudo-variance, including the commonly used uniform square quadrature amplitude modulation (QAM) and uniform phase-shift keying (PSK) constellations. For every prescribed lag, we determine the exact minimum expected sidelobe level (ESL) and a unitary modulation attaining it by applying a normalized inverse discrete Fourier transform (IDFT) separately on each chain of sample indices spaced by that lag. The full orthogonal frequency division modulation (OFDM) modulation attains this minimum if and only if the lag divides the block length $N$. Combining this criterion with the complete characterization of the minimizers at lag one, we show that a unitary modulation minimizes the ESL simultaneously over the first $r$ positive lags, called the prefix $\{1,\ldots,r\}$ with $1\le r<N$, if and only if every lag in that prefix divides $N$. In particular, simultaneous optimality over all positive lags is impossible for $N\ge 3$. We also prove that OFDM globally minimizes the partial expected integrated sidelobe level (EISL), defined as the sum of the ESLs over a selected subset of positive lags, for every such prefix. This also resolves the full EISL conjecture when $r=N-1$.

The rapid deployment of broadband low Earth orbit (LEO) constellations provides new opportunities for next-generation navigation. Compared with conventional global navigation satellite systems (GNSS), broadband LEO systems provide stronger received signals, wider bandwidths, rapidly varying satellite geometry, and larger constellation scales, offering new capabilities for positioning, navigation, and timing (PNT). Meanwhile, their communication-oriented waveforms, high dynamics, and resource-constrained architectures introduce new challenges that are fundamentally different from those in GNSS. This article provides a comprehensive overview of broadband LEO constellations for next-generation navigation, covering their fundamental characteristics, representative use cases, key enabling technologies, and open challenges. We first compare broadband LEO systems with conventional GNSS and summarize the main technical routes for LEO-based PNT. We then discussed the emerging use cases in GNSS-challenged, multipath-dominant, and integrated communication and navigation (ICAN) scenarios. Key enabling technologies including waveform design, high-dynamic signal processing, and constellation resource scheduling are further reviewed. As a representative case study, the navigation-oriented optimization of the Starlink primary synchronization sequence demonstrates that subcarrier power allocation can improve multipath resistance while maintaining ranging accuracy. Finally, several challenges and future directions of broadband LEO-based navigation are discussed, aiming to provide reference for future researches.

Speakers can differ not only in how much silence they produce but also in where interruptions fall, information that global pause counts obscure. We analyzed 115 publicly available Speech Accent Archive recordings of the same read passage: 57 speakers whose archive metadata listed Mandarin as their first language and a mainland-China birthplace, and 58 American English first-language speakers born in the U.S. Midwest. Word-level forced alignment was used to extract interword acoustic gaps and classify each position as punctuation-marked or unpunctuated. At a 250 ms threshold, punctuation-marked gaps were similar for the Mandarin and English groups (means 5.56 and 5.47), whereas unpunctuated-position gaps were more frequent in the Mandarin group (means 3.67 and 0.43; adjusted rate ratio 11.98, 95% confidence interval 6.53-22.00). A crossed-effects logistic model with random intercepts for speakers and passage positions confirmed that the contrast was disproportionately concentrated at unpunctuated positions (interaction odds ratio 9.44, 95% confidence interval 5.23-17.02). The pattern persisted at a 500 ms threshold, after removing positions adjacent to punctuation, after excluding severe transcript-deviation cases, and when analysis was restricted to acoustically confirmed silence. Nearly half of the Mandarin group's unpunctuated gaps occurred at positions classified as within-phrase in an exploratory single-coder annotation. In this fixed-passage corpus, acoustic gap placement relative to textual and syntactic structure distinguished the groups more clearly than punctuation-marked pausing. The results support placement-sensitive measurement as a reproducible dimension of breakdown fluency while not establishing a causal mechanism, proficiency difference, or perceptual consequence.

Most published neural-receiver studies train on synthetic channel models, although real over-the-air measurements can improve performance. We examine how much of this benefit transfers from other sites to a distinctly different deployment site and what adaptation to that site costs elsewhere. The study uses a dataset from a 5.88 GHz measurement campaign covering eight sites and four environment types. An industrial site measured entirely from a vehicle is withheld as a deliberate transfer stress test. One receiver architecture is trained under four regimes: simulation alone; seven other sites; all sites jointly; and other-site training followed by deployment-site finetuning. The last two use identical measurements through different optimization protocols. Across five independently trained seeds, sensitivity gains are evaluated at uncoded bit error rate (BER) targets of $10^{-1}$ and $10^{-2}$, representative of moderate- and higher-rate coded links, respectively. On the deployment site, training with other-site data rather than simulation improves sensitivity by 0.3 dB at $10^{-1}$ and 0.1 dB at $10^{-2}$. Adding deployment-site data contributes less than 0.1 dB at $10^{-1}$; at $10^{-2}$, total gains over simulation reach 0.4 dB with joint training and 0.6 dB with finetuning. Joint training shows no measurable aggregate loss on the other-site test set, whereas finetuning yields the larger deployment-site gain but reduces other-site sensitivity by about 0.1 dB at $10^{-2}$, indicating spatial forgetting.

Electromagnetic Fields (EMF) mapping is essential for wireless propagation modeling and are fundamental to a wide range of applications, including spectrum awareness, network planning, and integrated sensing and communication (ISAC). However, existing datasets are often limited to spatially separated 2D scenes without real base station (BS) information. To address this gap, we present UrbanEMF, a city-scale EMF mapping dataset constructed from real world urban geometry and physical BS deployments using ray tracing. Unlike conventional single scene-based datasets, UrbanEMF preserves continuous city-scale urban coverage and realistic transmitter location information. It further extends traditional 2D maps by incorporating multiple receiver heights. In addition, both aggregated received signal strength (RSS) maps and path loss maps are provided to characterize complementary aspects of the radio environment. This work provides a realistic and flexible benchmark for developing and evaluating learning-based methods in large-scale urban wireless environments. The code for this work is available at: https://github.com/lemonstudy/UrbanEMF

Constrained combinatorial optimisation provides a mathematical framework for modelling a wide range of practical decision making problems, including energy network operation, financial portfolio optimisation, logistics, routing, and resource allocation. This paper studies parameter optimisation for the multi angle quantum approximate optimisation algorithm (maQAOA) applied to the maximum independent set problem, a canonical constrained graph based optimisation task. We propose a coordinate wise training framework for maQAOA that updates one variational parameter at a time by solving a sequence of one dimensional optimisation subproblems. For each selected coordinate, the method reconstructs the objective dependence on that parameter and moves directly to the parameter value that minimises the training objective, or equivalently maximises the corresponding expected solution quality. While demonstrated specifically on maQAOA, the underlying theoretical principle, that the expectation value with respect to a given parameter can be analytically expressed as a finite Fourier series, is broadly applicable to general tasks solved via parameterised quantum circuits. Empirical evaluations on connected ErdHos Renyi graph benchmarks demonstrate that this analytic approach significantly reduces the computational training cost required to reach target approximation ratios and yields superior convergence reliability compared to standard gradient based, stochastic, and derivative free baselines.

The antenna-elements that make up a radio interferometer form a spatial filter that samples components of the Fourier transform of a target radio astronomical source brightness. Along the signal path, there are multiplicative and additive perturbation effects that alter the signal and that should be corrected. The process of mitigating these perturbation effects is called calibration. In this work, we develop a new and fast Maximum Likelihood based estimator of these perturbation effects using the Exponential Map and Lie groups. To evaluate its performance, we compare it to the Cramér-Rao Lower Bound and, to test the estimation time, we compare it with the Expectation Maximization algorithm, another fast Maximum Likelihood estimator. Finally, we apply our estimator to a real observation of the protoplanetary disk AS 209 made with the Submillimeter Array. We found that our proposed estimator meets the Cramér-Rao Lower Bound and it was approximately 40 times faster than the Expectation Maximization algorithm.

Collaborative embodied artificial intelligence (CEAI) enables multiple physical agents to perceive, reason, and act cooperatively in dynamic environments. Effective communication is essential for CEAI, yet CEAI agents must exchange not only large multimodal observations but also task-relevant insights, intents, and interactive information over long horizons. This article investigates token communication (TokCom) as a native intelligence interface for CEAI, in which tokens serve jointly as compact semantic carriers for communication and fundamental inference units for generative foundation models (GFMs). We first discuss how TokCom supports insight sharing, intent alignment, and interactive control among embodied agents. We then propose a TokCom-assisted CEAI framework driven by a task-adaptive communication protocol. Comprising a compact codebook, syntax rules, and contextual examples, this protocol guides GFM-based transceivers to distill messages into compact tokens and reconstruct them after wireless transmission. A case study on collaborative object transport demonstrates that the proposed TokCom framework substantially reduces the source payload bit consumption while preserving task efficiency and showing robustness under noisy channels. Finally, we outline future research directions.

Sensors deployed in real-world conditions often produce measurements corrupted by outliers due to model uncertainties, changes in the surrounding environment, and/or data loss. As a result, managing these outliers becomes crucial for state estimation to avoid inaccurate estimations and a reduction in the reliability of results. To address this issue, we introduce a novel form of extended Kalman filter (EKF) based on the maximum a posteriori (MAP) principle for scenarios where outliers simultaneously occur in multiple dimensions. For detecting outliers during the filtering process, we introduce a novel variant of the normalized innovation ratio (NIR) test and embed it within the EKF framework. Our approach enhances the estimation accuracy and computational efficiency of state estimation process even when data from several sensors simultaneously contain outliers.

Neural activity often exhibits multiple timescales that can vary with behavioral states and task conditions. Identifying these timescales from neural recordings is important for better understanding neural computation and function. However, traditional approaches based on autocorrelation fitting are difficult to scale to high-dimensional population recordings and can become unreliable when neural dynamics change with behavior. State-space models have been a powerful framework for modeling high-dimensional neural population activity through latent dynamical systems, but standard formulations and inference methods do not explicitly account for multiple timescales and therefore do not guarantee accurate recovery of the underlying temporal structure. Motivated by these questions, we introduce the Multi-Timescale Switching Linear Dynamical System (MTS-SLDS), a framework for identifying regime-specific latent timescales from continuous or spiking neural observations. MTS-SLDS combines a multi-lag moment initialization, which captures temporal structure across multiple observation lags, with regime-conditioned Laplace-EM inference, which reduces mixing of dynamical statistics across uncertain regimes. Characteristic timescales can then be extracted directly from the eigenvalues of the learned latent transition matrices. In synthetic and neural experiments with Gaussian and Poisson spike observations, MTS-SLDS accurately recovers timescales and switching structure over multiple datasets.

Wrist electrodermal activity (EDA) differs in amplitude from one person to the next, so affect-recognition models normalize their input before classification. Studies that test such models on held-out subjects seldom report where the normalization statistics come from, yet statistics computed from the held-out subject's own recording give the model information that a device does not have when it is first worn. We asked how this choice alters the measured benefit of pretraining. A compact convolutional network, SAFE-EDA, was pretrained on expert artifact annotations from 43 subjects and compared with the same network trained from scratch on the Wearable Stress and Affect Detection (WESAD) dataset (15 subjects, leave-one-subject-out), with two normalization sources crossed with four window hops. When the statistics came only from training subjects, pretraining raised macro-F1 by 0.078 to 0.227; when they came from the held-out user's full recording, the gain fell to between 0.020 and 0.050 and was no longer significant. Artifact supervision was far more useful than self-supervised pretraining on the same recordings (0.078 versus 0.008). Across 13 configurations in two datasets, the pretrained network was better in 12, but on the second dataset (26 subjects) per-user normalization increased the gain instead of reducing it, so the interaction depends on the data. Only five of 50 published WESAD studies state which data were used for normalization. Reporting this choice is necessary to separate first-use performance from performance after calibration.

Direct to device D2D connectivity from low Earth orbit LEO satellites is moving to Ku band, where a handheld terminal must obtain directional gain from several small phased array panels distributed around the chassis. Beam management then becomes a joint satellite panel beam selection problem with hundreds of candidates, and ordinary hand motion can change the best candidate within one decision interval. This paper proposes a low overhead predictive beam management scheme for a multiband LEO D2D downlink in which a low frequency anchor link carries control signalling and fallback traffic, and a Ku band link carries broadband data. The handset inertial measurement unit IMU reports attitude and angular rate with a known delay; the scheme extrapolates the delayed attitude to the current orientation, scores all satellite panel beam candidates analytically from the satellite ephemeris and panel geometry, and trains only a small candidate set built with a satellite panel diversity rule under a fixed pilot budget. The Ku band link is activated only when its predicted post training rate exceeds the anchor rate by a margin. Trace driven Monte Carlo simulations with a four panel handset and two visible satellites show that, with six pilots 0.3 percent training overhead, the proposed scheme improves mean goodput by 21.2 percent and reduces broadband outage by 57.6 percent at 90 degrees per second relative to an equal budget delayed attitude baseline, and operates within 3.8 percent of a zero overhead oracle.

Optical inter satellite links ISLs are becoming the backbone of modern LEO constellations offering high capacity and low latency but introducing stringent geometric and physical layer constraints Routing in such networks must therefore account for time varying topology jitter induced outage and the heterogeneous reliability of intra and inter plane optical links aspects that classical shortest path or existing learning based schemes do not fully capture This paper develops a joint geometric and QoS aware routing framework for optical LEO networks We derive a closed form outage expression under Gaussian beam propagation with pointing errors and obtain analytical maximum feasible link ranges for different ISL classes These relations remove beam divergence from the optimization variables and embed optical feasibility directly into the routing layer leading to a latency reliability capacity constrained routing formulation that is proved to be NP hard To enable scalable decision making we cast snapshot routing as a Markov decision process and introduce an angle constrained masked deep Q network AC MDQN that integrates optical feasibility masks potential based latency shaping and a geometry aware corridor filter around the source destination great circle path This design significantly reduces the effective action space complexity while preserving near optimal routing choices Simulations on a Starlink like constellation demonstrate that AC MDQN achieves end to end latency within 1 to 2 percent of constrained shortest path solutions remains robust under varying pointing jitter and supports controllable hop latency trade offs through reward design The results confirm that the proposed framework provides an efficient and physically consistent routing solution for large scale optical LEO networks

Wearable ultrasound probes must transfer increasingly large acquisition payloads while maintaining compact, low-power electronics. In TinyProbe, the current bottleneck in data transfer occurs between the acquisition FPGA and the wireless system controller. This work presents an open-source, multi-wire SPI readout interface that uses serial command and address phases followed by a build-time-selectable dual- or quad-lane payload phase that is intended to address this bottleneck by increasing the potential bandwidth over the wifi limit while retaining compatibility with the Microcontroller-centric wearable US architecture. The interface emulates a serial flash memory, enabling compatibility with a broad range of microcontroller families and their existing peripheral interfaces. On the FPGA, the data path connects the existing acquisition FIFOs to the SPI interface through clock-domain crossing, sample reshaping, and packing into 32-bit words. Dual-SPI readout is integrated into the existing IGLOO2/SiWG917 TinyProbe architecture and verified at an SCLK frequency of 5 MHz. A separate Kria K26 testbed is used to characterize the FPGA SPI interface independently of the acquisition and wireless subsystems, demonstrating error-free transfers at SCLK frequencies up to 66 MHz. These measurements identify the SiWG917 multi-lane SPI implementation as the next bandwidth-limiting component and motivate a future upgrade of the system controller. The HDL and MCU implementations are released under a permissive open-source license.

Miniaturized ultrasound (US) probes require programmable and synchronized transmit (TX) excitation across multiple elements, while existing compact platforms often rely on limited microcontroller (MCU) pulse generators or closed-source fixed-function pulser devices. We present U-Sonic, an open-source digital US TX peripheral integrated into a 32-bit RISC-V system-on-chip (SoC). The implemented SoC integrates 8 pulser cores, while the parameterized architecture supports up to 16 channels. Each core generates single- or dual-tone bursts with programmable period, duty cycle, pulse count, polarity, and idle level, together with optional inverted stop pulses for active damping. A shared memory-mapped Open Bus Interface (OBI) enables synchronous start and stop of arbitrary channel subsets and supports composite bipolar, gated, and three-level excitation schemes. Functional correctness was verified in Verilator against a Python golden model over 4379 checked cycles across directed and randomized configurations, and confirmed on a Terasic DE10-Lite field-programmable gate array (FPGA). The design was synthesized and placed-and-routed in IHP 130 nm. The post-layout area in kilo gate equivalents (kGE), scales as 1.65 kGE plus 1.66 kGE per channel. The 8-channel instance occupies 14.9 kGE, corresponding to approximately 14.3% of the 104 kGE SoC. The register-transfer level (RTL), register descriptions, verification collateral, and software support are released as open source.

Wearable ultrasound enables continuous deep-tissue monitoring, and a single programmable probe can operate in multiple complementary modes, such as structural A-mode and Doppler flow measurement. However, each operating mode requires dedicated measurement parameters and peripheral states, with no single configuration serving all modes on resource-constrained devices. Time multiplexing of operating modes introduces reconfiguration latency that lowers the effective mode repetition rate. To address this limitation, we present an open-source, transition-aware control stack for low-latency, in-session reconfiguration of the 32-channel TinyProbe wearable platform. Operating modes are described as hardware configurations, and host-side shadow registers track the peripheral states, enabling transition-specific register updates. Transition sequences are executed either by the host (over Wi-Fi 6) or by a firmware loop on the probe MCU. We validate the stack on a pulsatile-flow phantom by interleaving blocks of 25 to 100 pulsed-wave Doppler shots at 1.43 kHz PRF with single 16-channel A-mode acquisitions, changing channel configurations at every transition. Compared to full reconfiguration, the overhead per transition decreases from 30.2 ms to 11.6 ms (host-scheduled) and 3.1 ms (MCU-scheduled). For 75-shot Doppler blocks, the multi-mode repetition rate reaches 16.0 Hz (MCU-scheduled), 90.1% of the theoretical maximum of 17.7 Hz. Concurrent reconstruction of a Doppler spectrogram and a lumen-diameter trace demonstrates the functionality of time-multiplexed flow and structural monitoring.

This paper introduces the integration of dual-band reconfigurable intelligent surfaces (DBRIS) in wireless system design, and investigates the ensuing performance gains in different application scenarios. This type of metasurfaces offers a compelling technology due to their reduced material usage, compact design, and multi-functional operation across multiple frequency bands compared to single-band RISs. Two representative use cases are considered to assess the achievable performance gains with the said integration, namely, DBRIS-assisted wireless communication, and DBRIS-assisted simultaneous wireless information and power transfer (SWIPT). For the first design, where the DBRIS is deployed as a relay, we focus on the energy efficiency (EE) maximization of the communication system. An alternating manifold method is proposed for the joint optimization of the source transmit power and the DBRIS phase shifting. For the second design, we investigate a DBRIS-assisted SWIPT system using a novel power splitting approach. An EE metric is defined, and an alternating optimization algorithm is developed to maximize it. Numerical results validate the advantages of the proposed design architectures as well as the proposed optimization framework. In particular, we highlight the energy efficiency gains enabled by the DBRIS as compared to operations with conventional single-frequency surfaces, in both application scenarios.

Integrated Sensing and Communication (ISAC) is a foundation of 6G networks, demanding architectures that simultaneously enhance sensing accuracy and communication reliability. This paper presents a Reconfigurable Distributed Antenna and Reflecting Surface (RDARS) aided ISAC framework, where an RDARS overcomes the limitations of conventional passive Reconfigurable Intelligent Surfaces (RIS) and Distributed Antenna Systems (DAS). By enabling each element to dynamically operate in either reflection or connection mode, RDARS synergistically harnesses reflection gain, distribution gain, and an additional mode-selection gain. We investigate the joint optimization of transmit beamforming at the base station and dynamic mode selection at the RDARS to minimize the sensing performance metric, namely the Cramér-Rao Bound (CRB) for target localization, while guaranteeing a minimum required Signal-to-Interference-plus-Noise Ratio (SINR) for multiple communication users. To solve the resulting non-convex and mixed-integer problem, we develop an efficient iterative algorithm based on the Alternating Optimization (AO) framework, effectively leveraging Majorization-Minimization (MM) and Penalty methods. Comprehensive simulations validate the proposed design, demonstrating that the dynamic RDARS configuration achieves a superior trade-off between the Position Error Bound (PEB) and communication SINR, significantly outperforming benchmark passive RIS and DAS systems.

Studying brain lateralization and the resulting hemispheric asymmetry has been a topic of interest for many researchers. Very few studies have focused on differences in brain lateralization and hemispheric asymmetry associated with neurodegeneration. Additionally, Quantitative Susceptibility Mapping (QSM) has not been extensively used to investigate these differences. This article aims to study differences in hemispheric asymmetry between healthy and Alzheimer's disease (AD)-affected brains by calculating the Myelin Hemispheric Ratio (MHR) and Oxygen Extraction Fraction Hemispheric Ratio (OEFHR) between the two hemispheres using QSM as an intermediate marker of neurodegeneration. The estimations and corresponding results show marginal yet promising differences between healthy subjects and AD patients. These encouraging initial results emphasize the need to further investigate structural brain asymmetry and its alteration as a potential marker of AD-related neurodegeneration.

Smartphones rely on Global Navigation Satellite System (GNSS)-based positioning for many of the functions they execute everyday. The GNSS receivers embedded in smartphones are susceptible to anthropogenic radio frequency interference attacks in the forms of jamming and spoofing due to the low-power and open-architecture signals they receive from the satellite constellations. While jamming is a practice that denies a GNSS receiver the ability to form a position, velocity, and time (PVT) solution, spoofing represents a more insidious threat by using forged satellite signals that aim at causing the victim receiver to compute a false PVT solution. The ubiquity of smartphones and the sensitive geolocation data they hold make them a primary target for malicious spoofing. However, their hardware constraints and the lack of deep visibility into the GNSS receiver processing chain create significant hurdles for effective countermeasures. Existing surveys comprehensively explore general spoofing countermeasures but fail to address these mobile-specific limitations. This article fills that gap with a novel survey focused on techniques viable within the unique constraints of smartphone architectures. Specifically, we establish a taxonomy for defining GNSS spoofing attack effects and countermeasures, provide a historical review of smartphone vulnerability characterization, and provide an overview of techniques proposed to detect and counteract smartphone spoofing threats, offering a comparative framework to weigh their respective pros and cons on mobile platforms.