Image and Video Processing · Cross-listed from cs.CV
A$^3$PreCore: Robust Rigid Registration for Native FLAIR-Space Brain-Mask Transfer
Elmira Mirzabeigi, Zahra Khalilzad, Taylor Snowden-Richardson et al. (+4 more)
Reliable
brain-mask transfer into native fluid-attenuated inversion recovery (FLAIR) space can fail when T1-to-FLAIR rigid registration is unstable. We developed A$^3$PreCore, a six-degree-of-freedom rigid-registration strategy that aligns the full bias-corrected T1-weighted image to native FLAIR using normalized mutual information while preserving the established T1-space anatomical mask. We also introduced extended quantitative quality control (E-QC), which characterizes mask extent, field-of-view-relative position, FLAIR intensity support, boundary support, inferior morphology, and pairwise
geometric disagreement. In 78 participants with paired T1-weighted and FLAIR imaging, A$^3$PreCore was compared with fsl_anat and the
Brain Extraction Tool (BET). Gross rigid-body misregistration occurred in 21/78 fsl_anat outputs (26.9%) and 0/78 A$^3$PreCore outputs (exact two-sided McNemar p=9.54 x 10^-7). Among 57 participants without gross fsl_anat misregistration, A$^3$PreCore preserved close agreement (median Dice=0.993; median paired Delta NMI=-0.00007, p=0.429). A$^3$PreCore remained broadly concordant with BET (median Dice=0.955), with less inferior and extracranial extension in representative comparisons. Method-blinded neuroscientists ranked A$^3$PreCore first in 35/40 participants (87.5%). E-QC additionally exposed severe orientation-related disagreement not evident from global descriptors or centroid displacement alone. These findings support A$^3$PreCore as a robust native-FLAIR
brain-mask transfer strategy and E-QC as a complementary framework for multidimensional mask characterization and participant-specific
geometric quality control.
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All authors Elmira Mirzabeigi, Zahra Khalilzad, Taylor Snowden-Richardson, Jesus Eduardo Lugo Arce, Jodie R.Gawryluk, Brian R. Christie, Jocelyn Faubert
Systems and Control
FedRIC: Heterogeneity-Aware Federated Learning with Hard Deadline Constraints in Open RAN
Adhwaa Al-Chaab, Ayman Younis and Dario Pompili
Open
Radio Access Networks (O-RANs) bring intelligence to disaggregated RANs through virtualized platforms and open interfaces, but the stringent latency budgets of the RAN Intelligent Controller (RIC) and heterogeneous traffic conditions make real-time deployment costly. Centralized learning is ill-suited to the RIC due to its computational burden, timing constraints, and communication overhead, motivating
Federated Learning (FL) as a distributed alternative. We formulate FL in O-RAN as a Mixed-Integer Nonlinear Program (MINLP) that minimizes communication cost and learning time under heterogeneity in User Equipment (UE) compute, traffic, and channel conditions, and decompose it into a UE Assignment (UA) subproblem and a Computation and Bandwidth Allocation (CBA) subproblem. We then propose FedRIC, a heterogeneity-aware FL framework combining compressed gradient descent, slice-aware resource allocation, and multilevel hierarchical aggregation. Experiments show that FedRIC outperforms FedCOM, FedAvg, and QFedAvg in convergence rate, communication cost, and latency.
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Systems and Control · Cross-listed from cs.LG
The Physics Is the Network: Fast, Training-Free Neural Emulation
Luka Todorovic, Kaushal Gianchandani and Sai Ravela
Neural emulation need not require learning a substitute for known physics. We investigate PolyNets that couple a continuous-time neural representation of
polynomial dynamics with a simple recurrent network for discrete-time integration. When the governing coefficients are known, the resulting tensor graph requires no training and preserves the prescribed numerical scheme while enabling efficient hardware execution and parallel simulation. Using the one-dimensional Korteweg-de Vries equation, we compare PolyNet with CPU and GPU finite-difference Runge-Kutta solvers and three trained neural models: a state-map emulator, a physics-informed predictor, and a learned-tendency integrator. Analytical
soliton solutions provide a reference for amplitude and phase errors across grid resolutions, 10^6 dispersively scaled time steps, ensembles of up to 10^4 trajectories, and varying initial amplitudes. PolyNet achieves accuracy comparable to the numerical solvers, including at amplitudes outside the learned models' training conditions. It combines this fidelity with high GPU throughput, approximately constant over the tested resolution range, and favorable ensemble scaling.
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Systems and Control
Controller- and Communication-Aware Flexibility Margins for Electric Distribution Networks
Saif R. Kazi and Kaarthik Sundar
This paper develops a framework for quantifying operational flexibility in radial electric distribution systems under coupled active- and reactive-load uncertainty. Flexibility is defined as the largest scaling of a prescribed uncertainty set that can be accommodated while satisfying network and actuator limits under a given control architecture. We consider a one-sided Euclidean set for robust demand-growth capability, with a direct extension to bidirectional variations. Under LinDistFlow, controllable distributed resources and feeder-head exchange with the upstream grid jointly balance active-power disturbances, while local Volt/VAR feedback regulates reactive power. The resulting affine closed-loop disturbance response admits exact second-order-cone robust counterparts. For fixed Volt/VAR gains, a convex reformulation jointly optimizes the flexibility margin and active-power participation factors, including feeder-head participation. A sparse load-to-generator feedforward map further captures communication constraints. Numerical studies on the Iowa-240 feeder demonstrate how the amount of control authority and communication structure shape the achievable flexibility margin.
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Audio and Speech Processing · Cross-listed from cs.SD
Quantizer Redundancy and Exact Index Recoding in a Frozen Neural Audio Codec
Youssef Hemimy
Residual scalar quantization can produce highly dependent indices when successive stages reuse identical, unscaled grids. This case study examines that behavior in a frozen neural audio codec and develops an exact recoding procedure that reduces storage while preserving its decoder inputs. Analysis of the repeated-grid recurrence identifies predictable stage sequences; finite-precision checks motivate preserving every original index through explicit exception records. The model weights and reconstruction graph remain fixed. On the studied 62- and 512-file cohorts, a predictor, adaptive coding and explicit file selection reduce complete stored bytes. The final 512-file rate is 129.591 kbps including headers, 25.943% below an original-index XZ control. Original recorded CPU runs establish bitwise latent and waveform preservation; independent readers verify exact index and metadata recovery in subsequent saved-stream experiments. Later coding choices use previously studied cohorts, and fresh encoding requires generating three alternatives. The engineering result is implementation-specific storage reduction with exact index recovery; the underlying lossy reconstruction quality remains unchanged.
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Audio and Speech Processing · Cross-listed from cs.SD
vst-bench-ml: Auditable Dataset Generation for Audio Plugins
Youssef Hemimy
Generating
datasets with audio plugins requires preserving how each audio observation was produced. vst-bench-ml is a Python package that samples plugin parameters, coordinates rendering through a separately installed host, and records software identities, rendering settings, and output hashes for later verification. A sealed manifest binds this evidence and supports offline integrity checks without loading the plugin. A companion repository provides a sample-free VST3 test plugin, a deterministic offline host, and additional checks linking rendering attempts to recorded outputs. On a pinned macOS arm64 and Python 3.12 stack, functional validation of the pre-release versions reproduced package and host tests. Two runs in fresh processes reproduced the same render-plan hash and all eight expected output hashes, covering four sampled parameter configurations and two fixed MIDI signals; 12 negative cases also passed. Later licensed 0.1.0 release candidates passed producer-run continuous integration and local validation. Version 0.1.1 adds public Git source repositories, package-index installation, and user documentation; a separate release check reproduced the expected plan and output hashes. These checks establish integrity and repeatability within the tested setup; they do not establish portability, scientific suitability, or perceptual quality.
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Systems and Control
A Practical Framework for Incremental Testing and Technology Maturation of Spacecraft Autonomy
Kerianne Hobbs, Ben Bycroft, Pamela Wood et al. (+19 more)
This paper addresses the "trust gap" in deploying AI-enabled spacecraft autonomy through an integrated verification framework combining offline ground testing with online flight monitoring. The offline phase progresses systematically through simulation studies, processor-in-the-loop (PIL), hardware-in-the-loop (HIL), and robotic emulation testing, culminating in flight validation. The online phase employs AI watchdog systems for performance monitoring and safety guardrails for real-time constraint enforcement. Key contributions include: (1) systematic organization of verification techniques emphasizing closed-loop behavioral testing at each phase, (2) integration of AI watchdogs with runtime assurance, and (3) demonstration of the framework through a reinforcement learning-based attitude control case study. This approach integrates mature technologies to provide a viable pathway for safe deployment of AI-enabled autonomy in high-consequence space operations while addressing simulation-to-reality transfer challenges.
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All authors Kerianne Hobbs, Ben Bycroft, Pamela Wood, Mathew W. Bissonnette, M.J. Durst, Kimberly Sover, Alexandra Mangel, Gabriel Behrendt, Upneet Singh, Juan Rodriguez, Lukas Dillingham, Zeve Akerling, Nicholas Oune, Daniel Thomlinson, Evan N. Sperber, Denise Burrell, Phaedrus Leeds, Brian Hardy, Nathaniel Hamilton, Raul Santos, Arturo de la Barcena, Kyle Dunlap
Signal Processing
Modeling Diffuse Scattering in Mobility Scenarios: Ray Tracing Model and Measurement Validation
Milica Djordjevic, Thomas Wilding, Anja Dakić et al. (+3 more)
Open-source ray tracing (RT) frameworks are increasingly used for site-specific channel modeling due to their reproducibility and extensibility. In non-stationary mobility scenarios, repeated RT evaluations can generate different diffuse propagation paths without preserving their correspondence, limiting a consistent representation of their temporal
evolution. To address this limitation, we extend diffuse scattering modeling in Sionna RT using persistent surface-associated scattering points. Their positions remain fixed, while their contributions evolve according to changes in the propagation geometry and visibility. The proposed approach is validated using empirical
radio channel impulse response measurements in an urban vehicular scenario at 5.9 GHz. Compared with the standard Sionna RT baseline, the proposed model shows improved agreement with measurements in the delay and Doppler domains.
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All authors Milica Djordjevic, Thomas Wilding, Anja Dakić, Markus Hofer, Benjamin Rainer, Thomas Zemen
Signal Processing
To Stack or Not to Stack? Jacobian Conditioning and the Benefits of Depth in Metasurface-Based Linear Transformation Synthesis
Andrea Abrardo and Andrea Garulli
This paper investigates when and why stacking programmable metasurfaces into Stacked Intelligent Metasurfaces (SIMs) improves linear transformation synthesis at a fixed phase-control budget. The analysis shows that algebraic Jacobian rank alone is insufficient to characterize practical synthesis, which also depends on the strength and balance of the sensitivity directions generated by the available controls. A layer-resolved analysis of the cascade model explains how inter-layer propagation can generate complementary sensitivity directions. The relevance of this mechanism is then assessed using an S-parameter multiport model that includes electromagnetic coupling and multiple scattering. Numerical results in near- and far-field sensing scenarios show that distributing the same control resources across multiple layers can strengthen weak transformation directions, improve Jacobian conditioning, and substantially enhance synthesis accuracy. These findings explain why increasing depth can offer benefits that are not obtained by simply enlarging a single programmable surface, and identify layer complementarity and Jacobian conditioning as useful criteria for SIM architecture design.
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Audio and Speech Processing
Continued fractions and passive ladder networks for acoustic impedances: the baffled piston and the horn
Ronald M. Aarts
Lumped-element networks give transparent, passive models of acoustic impedances whose low- and high-frequency limits can be read from the
circuit, but a systematic, arbitrarily accurate construction for non-rational impedances is lacking. Continued fractions, one- and two-point Padé approximants and their identity with ladder networks and Cauer synthesis are introduced in tutorial form and applied to two examples: the radiation impedance of a baffled circular piston and the throat impedance of a horn with a piston mouth. The even contraction of the piston's continued fraction has positive coefficients, certified by interval arithmetic for the first 100 pairs, and yields a ladder of positive resistances, masses and compliances; with 14 elements the error is 4e-4 up to ka = 5. Beranek's four-element network is, to about 2%, the order-two two-point Padé approximant. A horn modelled as a cascade of waveguide sections is a continued fraction whose tail is the mouth impedance; the infinite exponential horn is a periodic continued fraction, an exact high-pass ladder. The piston networks are passive at every certified order (up to 202 elements) and are extended without changing existing elements; for a finite horn the error of the mouth network passes one to one to the throat impedance.
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Signal Processing
Dual-Polarized Rate-Splitting Multiple Access for mmWave Communications: A Practical Design and Prototype Implementation
Álvaro Pendás-Recondo, Álvaro F. Vaquero, Jesús Alberto López-Fernández et al. (+1 more)
This work presents a dual-polarized rate-splitting multiple access (RSMA) design for millimeter-wave (mmWave) or frequency range 2 (FR2) communications. Focusing on a practical implementation, we consider the specific scenario of a single transmitter serving two receivers (users) within the same time, frequency, and code resource block. The transmitter and each receiver are equipped with a pair of dual-polarized
antennas. We first present the system formulation and compute the achievable rate expressions for discrete constellations. Based on these expressions, we propose and evaluate two receiver architectures: one based on successive interference cancellation (SIC) and a non-SIC decoder. Both designs exhibit comparable performance, and the combination of polarization diversity and RSMA provides the system with significant flexibility to adapt to different channel conditions when serving both users, while the non-SIC decoder enables a simpler hardware implementation. We then present a prototype implementation of the proposed design, operating at a carrier frequency of 25 GHz, and evaluate it by measuring the symbol error rate (SER) of the different data streams under various signal-to-noise ratio (SNR) and polarization diversity conditions. The obtained measurements show good agreement with software simulations, thereby validating the system implementation. To the best of our knowledge, this is the first mmWave RSMA prototype reported in the literature and the first to exploit polarization diversity. Furthermore, given the selected carrier frequency, the proposed design may also find applicability in the upper portion of the envisioned FR3 band (7-24 GHz).
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All authors Álvaro Pendás-Recondo, Álvaro F. Vaquero, Jesús Alberto López-Fernández, Marcos R. Pino
Systems and Control · Cross-listed from cs.RO
VIGOR-MPC: Vision-Guided Objective Regulation MPC
Jelena Trisovic, Rahel Rickenbach, Marek Landert et al. (+2 more)
Controlling an
agent in an environment solely perceived through onboard
vision is challenging: perception yields noisy, variable-length, and partially occluded context, while onboard compute is shared between perception and control, limiting the planning horizon executable in real time. We propose VIGOR-MPC, a modular student-teacher framework, agnostic to the perception pipeline, which addresses reference tracking across navigation tasks by distilling a privileged, field-of-view-aware long-horizon MPC into a computationally lightweight short-horizon MPC whose cost parameters are determined online from
vision. The teacher operates in simulation with full state knowledge and a multi-trajectory formulation that guarantees the
agent can stay within its field of view. The student observes only the noisy, occluded context produced by a perception module and learns, via a multi-level recurrent mapping, to infer cost parameters that aim to recover the teacher's behavior despite missing information. We evaluate VIGOR-MPC on
vision-based autonomous racing in simulation and on hardware. Using only onboard perception of the track, VIGOR-MPC generalizes to tracks unseen during training and approaches the lap times of the privileged long-horizon teacher while reducing per-step computation by over 90%.
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All authors Jelena Trisovic, Rahel Rickenbach, Marek Landert, Panagiotis Minos, Melanie N. Zeilinger
Systems and Control
Delay-Enabled Adaptive Learning of Time-Varying Parameters
Chengzhi Yuan and Mingxi Zhou
This paper proposes a novel adaptive learning method for unknown time-varying parameters by deliberately incorporating a delay into the parameter update. Specifically, a conventional adaptive
estimator is augmented with only one additional dynamic state, which estimates the parameter variation rate from the difference between current and previous parameter estimates. The method fundamentally changes the influence of time variation on estimation performance: the resulting estimation error is governed by the second derivative of the unknown parameter rather than its first derivative. Under persistent excitation, a small-gain analysis proves exponential stability of the complete estimation-error system and establishes explicit bounds for the parameter, state-prediction, and parameter-rate errors. In particular, accurate learning of linearly time-varying parameters and their unknown rates can be guaranteed with errors converging exponentially fast to zero. Numerical comparisons with conventional adaptive estimation and a recent timevarying parameter
estimator demonstrate superior learning performance in the considered examples. The proposed delay-enabled adaptive
estimator requires neither state derivatives nor any assumed model of parameter variation, and its implementation consists of one delayed difference and a first-order filter. These results thus provide a direct extension of adaptive learning from constant to time-varying parameters without sacrificing the simplicity of the conventional adaptive estimation mechanism.
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Signal Processing
Opportunities for Direct-to-Cell Polarization MIMO in the S-band Downlink of a LEO Satellite Footprint
Rainer Bachl, Matteo Ferro, Bingguang Peng et al. (+1 more)
A single low Earth orbit (LEO) satellite offers almost no spatial degrees of freedom. Its two polarizations, however, can be exploited to carry a second layer to a handset, which 5G New
Radio (NR) non-terrestrial networks do not use yet. The gain of this layer in downlink spectral efficiency (SE) is mapped over the footprint of a satellite at 330 km altitude above Munich transmitting at 2 GHz. The channel is modeled as a cascade of Jones factors for the phased-array payload, Faraday rotation, a dual-polarized land mobile satellite (LMS) channel with a line of sight, and a standardized handset
antenna element at three attitudes. For a handset with boresight pointing to zenith, ideal precoding selects rank 2 with a probability of at least 0.95 within about 340 km of the nadir point at a reference signal-to-noise ratio (SNR) of 6 dB. With closed-loop precoding, such a handset on the nadir track is in rank 2 during 99% of a pass. At 13 dB, rank selection with closed-loop precoding raises the mean SE over the footprint by 49%, and upright handsets gain about 31%. Their gain is limited to the positions where they face the satellite, since the element gain is low behind the handset. At 3 dB, these gains fall to 15% and 7%.
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All authors Rainer Bachl, Matteo Ferro, Bingguang Peng, Muhammad Nabeel
Systems and Control
Asynchronous Distributed Multi-Sensor Fusion Over Sensor Track Hypotheses
Ryan Draves, Nisar Ahmed, Eric Borcherding et al. (+1 more)
This work proposes a multi-target tracking solution that reasons over combinations of sensor-level tracks to perform distributed multiple hypothesis tracking (MHT) in an asynchronous multi-sensor network. This approach handles clustered target tracking and allows for efficient information pedigree summarization for distributed data fusion (DDF). By decomposing the association problem into partitioned hypothesis sets and fusing across sensor track combinations, the method reduces combinatorial complexity compared to traditional MHT approaches. Evaluation on a satellite constellation-based tracking scenario demonstrates improved estimation accuracy and data association quality compared to a reference target tracker with global nearest neighbor association.
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All authors Ryan Draves, Nisar Ahmed, Eric Borcherding, Jonathan Chan
Signal Processing
Beam-STeP: Generative Site-Specific Beam Prediction with Spatio-Temporal Priors
Ziyue Wang, Zongyao Zhao, Zhaolin Wang et al. (+1 more)
A generative site-specific beam prediction (Beam-STeP) method is proposed. Spatial priors from site-specific propagation and mobility information are jointly exploited with temporal priors from historical beam observations for beam prediction. To address the beam prediction ambiguity from
multimodal spatio-temporal priors, the generative model is adopted, where a history-conditioned
diffusion model is proposed to learn the
multimodal conditional distribution of future beamforming vectors. Multiple beam candidates are sampled from the distribution using a denoising
diffusion implicit model (DDIM). The candidate with the highest received power is then selected for transmission. Simulation results across three urban scenarios demonstrate that: i) Beam-STeP generally achieves higher normalized beamforming gains than the baselines under the same probing budget, particularly as beam prediction progresses; ii) site-specific propagation and mobility priors improve recursive beam prediction performance; and iii) DDIM sampling enables a flexible gain-latency trade-off.
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All authors Ziyue Wang, Zongyao Zhao, Zhaolin Wang, Yuanwei Liu
Signal Processing
FlowSep-RF: Rectified Flow Matching for Single-Channel Radio-Frequency Signal Separation
Mohamed Kamel and Tamer Nadeem
Single-channel
radio-frequency (RF) signal separation is important for interference mitigation when a signal of interest overlaps with a co-channel interferer in time and frequency. Classical matched filtering and LMMSE receivers are limited under structured non-Gaussian interference, while
diffusion and score-based methods often require iterative sampling or optimization. We propose FlowSep-RF, a rectified flow matching framework that learns a clean-endpoint transport from the received mixture to the signal of interest. The model combines time-domain IQ samples with frequency-domain features and recovers the separated waveform using one endpoint velocity evaluation. We evaluate FlowSep-RF on QPSK mixtures with OFDM-BPSK, OFDM-QPSK, and RFChallenge CommSignal2 interference against classical, discriminative, and generative baselines. FlowSep-RF achieves the lowest average BER and MSE among evaluated baselines across all three settings with one-step inference.
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Audio and Speech Processing · Cross-listed from cs.CL
Repairing Speech Access After Text Updates with Frozen Affine Fields
Ming-Hao Hsu, Yuxiang Wang, Yingda Shen et al. (+1 more)
Speech-capable
language models inherit most of their knowledge from text-rich
language backbones, and continued model maintenance is likewise dominated by text updates. Knowledge acquired through a text update can nevertheless remain difficult to access when the same query is spoken. We formulate this mismatch as post-update
speech access repair, where the knowledge-bearing checkpoint stays frozen and a detachable operator repairs
speech-only access in one model pass. We introduce Affine Fields for Interface Repair across Modalities (AFFIRM), which first learns and freezes a base correction for
speech hidden states, then adds query-specific corrections with zero mean over the operator-training queries. Paired
speech and text states provide the coordinates that condition these corrections on each query. On balanced synthetic-
speech PopQA, paired comparisons do not establish a clear advantage for AFFIRM over global SALSA steering or low-rank ReFT interventions under the same frozen single-state constraint. On recorded SLUE-SQA-5 questions with text passages, a full-scale PopQA-fitted AFFIRM operator transfers without refitting and improves ROUGE-L by 0.0941 over its paired No Repair baseline. Low-rank ReFT interventions also transfer positively, whereas the matched SALSA variant falls 0.0707 below its paired baseline. These results show that text acquisition and
speech access can diverge after an update and should be verified and repaired separately.
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All authors Ming-Hao Hsu, Yuxiang Wang, Yingda Shen, Zhizheng Wu
Systems and Control · Cross-listed from cs.LG
Adversarial Reinforcement Learning for Robust Planetary Landing
Miguel Leiva-Vélez, Nicolas Gaston-Rozado, Andrea Scorsoglio et al. (+2 more)
Deep reinforcement learning (DRL) policies for planetary landing and other aerospace guidance, navigation, and control tasks are commonly trained under randomized disturbances to improve robustness. However, typical
stochastic perturbation sampling assumes independent variations, whereas real mission disturbances often exhibit spatial and temporal correlation and cannot be reproduced through independent random draws. This work investigates
adversarial reinforcement learning to discover challenging off-nominal conditions. A secondary
agent generates physically constrained perturbations that degrade the performance of a landing controller, forming an asymmetric sequential zero-sum game. Training proceeds in three stages: baseline controller learning, robustness enhancement with
stochastic perturbations, and an iterative
adversarial phase in which the controller and disturbance generator are alternately optimized. Preliminary experiments in a three-degree-of-freedom landing environment show that
adversarial disturbances reveal vulnerabilities missed by conventional randomization and provide early indications that, with a more extensive training campaign, the approach can yield improved robustness and stronger generalization under previously unseen perturbation regimes.
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All authors Miguel Leiva-Vélez, Nicolas Gaston-Rozado, Andrea Scorsoglio, Hodei Urrutxua, Víctor Rodríguez-Fernández
Systems and Control
Robust Data-Driven Controller Synthesis from Sampled and Uncertain Frequency Response Estimates using Integral Quadratic Constraints
Luuk Marceau Spin, Maurice W. P. M. H. Heemels, Nathan van de Wouw et al. (+1 more)
In this paper, we develop a novel
MIMO controller synthesis method that relies solely on frequency-domain data of discrete-time systems. The approach is based on an iterative design procedure using integral quadratic constraints (IQCs). It generalizes existing data-driven methods that approximate semi-infinite IQC conditions by enforcing them on a finite set of measured frequency points. Such approximations do not provide guarantees on the continuous frequency axis and may fail to capture inter-frequency behavior. We address this limitation by formulating a convex optimization problem that preserves robustness guarantees over the entire frequency range. In particular, the proposed method explicitly accounts for inter-frequency behavior of both plant and controller, leading to certified robust stability and performance. The effectiveness and computational efficiency of the approach are demonstrated using experimentally obtained frequency response data from industrial-grade
semiconductor equipment. The synthesized controller is implemented on the physical setup, and its performance is validated in real-time experiments.
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All authors Luuk Marceau Spin, Maurice W. P. M. H. Heemels, Nathan van de Wouw, Roland Toth
Signal Processing · Cross-listed from cs.IT
Exact Cap Response and Aperture Selection for Molecular Communication under Aligned Drift
Yen-Chi Lee, Chun-An Yang and Nan Yang
Spatial reception under directed molecule transport requires balancing useful molecule capture against intersymbol interference (ISI). To characterize this balance, we study a fully absorbing spherical receiver that counts absorption events only within a prescribed cap. Using the known joint first-hitting law under aligned drift, we derive exact cap impulse, cumulative, and eventual responses. We perform stable cumulative evaluation. We then assess the derived responses through particle simulations and time-step comparisons, and demonstrate that our conditional-timing analysis explains when opposite drifts of equal magnitude can favor the same wider aperture. We also show that for the studied geometry and a symbol duration of $0.15$ s, the aperture that maximizes the signal-to-interference difference (SID) increases from $29.13^\circ$ at zero drift to $61.86^\circ$ at a drift magnitude of $40$ $\mu$m/s. These results (i) connect joint arrival-time and location statistics to spatial reception design, (ii) characterize the tradeoff between useful molecule capture and ISI, (iii) support SID-based aperture selection for a given drift and symbol duration, and (iv) quantify the SID loss incurred by retaining the zero-drift optimal aperture for the same symbol duration.
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Signal Processing
TRP-UE Bistatic Sensing for 6G Performance Analysis and Insights
Necati Kagan Erkek, Marco Di Renzo, Arman Shojaeifard et al. (+4 more)
Integrated sensing and communication (
ISAC) equips cellular networks with the dual capability of data transmission and environmental sensing by sharing spectrum, waveforms, and
radio hardware. While network-side sensing via transmission-reception points (TRPs) has been widely investigated, the use of user equipment (UEs) as sensing receivers introduces additional bistatic viewpoints and can improve target observability. UE-assisted operation is nevertheless constrained by small UE apertures and by waveform processing, especially when data-bearing communication signals are reused for sensing. The present paper investigates theoretical limits and operating conditions for UE-assisted passive target positioning, with emphasis on hybrid operation that fuses TRP–TRP and TRP–UE measurements. A unified OFDM-based signal-and-noise model is developed to cover both positioning reference signals (PRS) and communication waveform reuse with modulation-dependent noise enhancement. Based on this model, closed-form Fisher-information expressions and Cramer-Rao lower bounds are derived for delay and angular parameters and are mapped to positioning error bounds (PEBs) for two
estimator classes: time-angle positioning based on TDOA + DoA and delay-only 3D ellipsoid positioning based on excess-delay/TDOA measurements. Monte-Carlo simulations across reference SNR, subcarrier spacing and occupied bandwidth, modulation order, radar cross section (RCS), sensing mode, and positioning
algorithm identify regimes where UE participation and hybrid sensing provide significant gains and quantify the associated
estimator-to-bound trade-offs.
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All authors Necati Kagan Erkek, Marco Di Renzo, Arman Shojaeifard, Yasser Mestrah, Remun Koirala, Mohammad Heggo, Kunjan Shah
Signal Processing
Sensing-Aided Vehicular Beam Tracking: System Gain Over Link Feedback and Probe-Verified Sensing Invocation
Jeongwan Kang, Miguel Bordallo López, Nhan Thanh Nguyen et al. (+2 more)
Sensor-based beam prediction is usually evaluated against exhaustive beam training, leaving open when sensing adds useful information beyond a deployed tracker's link feedback and when it should be withheld. We separate system gain from the marginal value of sensing in action-restricted causal replay of recorded 64-beam sweeps from four vehicular DeepSense 6G scenarios. The replay exposes only serving-beam and explicitly probed candidate powers and counts probes and model invocations separately. The proposed controller combines a scheduled post-setup re-sweep with probe-verified sensing admission and blacklist backoff. In three scenarios where feedback-only tracking is within 0.25 dB of the recorded-sweep oracle, the controller stays close to feedback-only in mean loss while rarely invoking the model, whereas always-on sensing performs worse. In the sole high-innovation scenario, the controller's system gain is 0.82 dB (95% confidence interval [0.30, 1.40]) over the plain feedback-only tracker and 0.15 dB over a probe-limited re-sweeping reference, with fewer probes than either. Sensing and the re-sweep both correct setup lock loss. At the native decision interval, enabling sensing in the same controller yields no statistically resolved loss reduction but cuts probing by 21%. In this scenario, accurate sensing reduces loss relative to the sensor-disabled controller under emulated link noise, staleness or longer decision intervals. Weak or delayed sensing can instead increase loss despite probe verification. These exploratory results characterize sensing as a conditional substitute for RF probing or a source of loss reduction, depending on the limitations of link feedback and on the accuracy and timeliness of sensing.
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All authors Jeongwan Kang, Miguel Bordallo López, Nhan Thanh Nguyen, Junhyeong Kim, Sunwoo Kim
Signal Processing
Radar-Only Long-Term Beam Prediction in mmWave Vehicular Networks: An Empirical Study of Doppler Input and Cross-Modal Distillation
Jeongwan Kang, Miguel Bordallo López, Nhan Thanh Nguyen et al. (+2 more)
Sensing-aided beam prediction could cut the beam-training overhead of millimeter-wave vehicular links, and two design choices could strengthen a radar-only predictor: a Doppler-resolved input and knowledge distilled from a camera during training. We measure what each is worth in received power rather than in top-1 accuracy, whose differences of a few points amount to only tenths of a decibel. We use real DeepSense 6G measurements, mainly at one roadside site, and one convolutional-recurrent predictor family evaluated open-loop up to 0.67 s ahead. Adding a range-Doppler map to the range-angle map lowers the mean beam-selection loss by 0.28 dB (95% bootstrap interval [0.14, 0.41]). The gain comes from the Doppler content, not from the extra input, and replicates, as pre-specified, at a second base station, although holding the last measured beam beats both predictors there. Cross-modal knowledge
distillation from a motion-mask
vision-radar teacher, by contrast, changes received power by -0.01 dB ([-0.07, +0.06]). The tested teacher, student, fusion and
distillation variants show the same null in the metric each was scored on. Where
distillation helps, in an exploratory run with few training passes, a radar-only teacher trained on all training passes reproduces most of the gain. For this predictor family and the full training set, the measurable gain at the link comes from Doppler-resolved radar content; no tested route for transferring training-only visual information adds a resolved gain.
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All authors Jeongwan Kang, Miguel Bordallo López, Nhan Thanh Nguyen, Junhyeong Kim, Sunwoo Kim
Systems and Control · Cross-listed from cs.RO
Enabling Tidying Tasks in Robotic Vacuum Cleaners via a Geometry-Induced Self-Locking Deployable Linkage
Eunseok Song, inwoong Kim, Sohee Lim et al. (+5 more)
Robotic
vacuum cleaners must extend their functionality within tight limits on size, cost, and durability. Deployable linkages with few actuators meet these limits, but their backdrivability makes it difficult to hold a load-bearing posture without continuous motor torque. We introduce a two-stage deployable linkage that is self-locking, in the sense used for toggle and singularity-based linkages, through its own geometry: at full deployment an internal three-link subchain approaches a collinear, near-singular configuration where the input–output transmission ratio approaches zero, so that plate loads produce a small residual back-driving torque, absorbed by drive-train friction and a lightweight
magnetic detent. A single actuator performs deployment and locking, a passive chassis-mounted trigger releases the lock for retraction, and a second actuator adjusts only the deployment height. We integrate the mechanism into a commercial robotic
vacuum cleaner and validate it through a 100-cycle repeatability test, a quasi-static load test in which the locked plate held 50\,N with the motor de-energized, and 140 pushing-type tidying trials on seven household objects against a commercial arm-equipped
vacuum robot under a documented protocol. We derive the analytical holding condition and discuss its limits as an intermediate design point between passive compliance and articulated manipulation.
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All authors Eunseok Song, inwoong Kim, Sohee Lim, Taehyeong Kim, Yongkook Kim, Hyeontaek Lim, Hyungmin Son, Seungyong Hyung
Signal Processing
Movable-Antenna Relay Enhanced Multiuser Communications: Reinforcement Learning-Based Antenna Positioning and Power Allocation
Yuanjun Zhang, Lipeng Zhu, Haobin Mao et al. (+2 more)
Movable
antenna (MA) has emerged as a promising technology for enhancing wireless communication performance by flexibly reconfiguring wireless channels via
antenna movement. In this paper, we propose an MA relay-enhanced multiuser communication system that adopts a half-duplex decode-and-forward (DF) relay scheme. The relay node is equipped with an MA array, and a buffer is employed to enhance the end-to-end transmission performance. Specifically, we formulate an optimization problem to maximize the minimum average achievable rate among multiple users over a given transmission period by dynamically optimizing the positions of MAs, transmit power, and beamforming
matrices, subject to the constraints on
antenna movement, inter-
antenna spacing, and total energy budget for
antenna movement and transmission. To address this non-convex problem with highly coupled variables, we propose a reinforcement learning (RL)-based
algorithm, where a twin delayed deep deterministic policy gradient (TD3)
algorithm is utilized to optimize the
antenna positions and power allocation, while the transmit and receive beamforming solutions are obtained through closed-form expressions in each time slot. Numerical results validate the efficacy of the proposed RL-based
algorithm and demonstrate the superiority of the proposed MA relay-enhanced multiuser communication system over the conventional fixed-position
antenna (FPA)-based relay system and other MA-based
benchmarks.
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All authors Yuanjun Zhang, Lipeng Zhu, Haobin Mao, Zhe Li, Zhenyu Xiao
Systems and Control
The Cost of Executability: Temporal Margins for Continuous-Time Multi-Agent Path Finding under Closed-Loop Control
Sabino Roselli, Alvin Combrink and Ze Zhang
Multi-
agent path finding (MAPF) solvers are evaluated on the schedules they produce, not on how well those schedules can be executed. Specifically, continuous-time optimal solvers can achieve even lower cost than their discrete counterparts by scheduling
agents with near-zero temporal/spatial separation, and by exploiting graphs with higher connectedness; however, such schedules may prove hard to execute under a realistic controller. We integrate AOC-CBS, an anytime-optimal continuous-time MAPF solver, into a fleet coordination framework in which each
agent tracks its timed path using model predictive control, and we evaluate the resulting system on congested MovingAI
benchmark instances with 21 to 40
agents. Without intervention, only a fraction of the instances complete execution. We then apply T-robustness by adding a time-margin, i.e., a minimum temporal gap between
agents at shared locations, as well as spatial inflation of the
agent footprint. Increasing either margin raises execution success, for a relatively small increase in the schedule's sum-of-costs; it also improves how closely
agents adhere to the schedule: when tracking each
agent's position throughout execution and comparing it against the position it should occupy according to the schedule, the mean discrepancy falls significantly as the margin increases. In general, the two margins have different effects; temporal margin decreases deviation, giving the controller room to stay on schedule. Spatial margin affects deviation less and instead increases the clearance between
agents, so that the same deviation no longer leads to a blocking stall. They are nonetheless substitutes rather than complements: spatial inflation is almost free in schedule cost, but its effect on execution success is small and does not compound with a temporal margin already in place.
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Audio and Speech Processing
CochlearMark: Audio Watermarking via Phase Locked Auditory Nerve Representations
Qin Liu, Strahinja Nikolic and Milos Cernak
Modern audio watermarks achieve strong robustness with learned encoder-decoder models, but their behavior under new processing is established empirically. CochlearMark is introduced as a training-free audio watermark that uses auditory-nerve phase-locking as its feature representation. A small waveform perturbation is optimized against a keyed random projection of this representation, and the same projection is used for detection, with no trained encoder or decoder. Because phase-locking represents temporal fine structure, the resulting robustness has a direct physiological interpretation: the mark is retained when this structure is preserved and lost when it is disrupted or rebuilt. Across
speech, music, and environmental audio, CochlearMark achieves signal-edit robustness and imperceptibility comparable to trained neural baselines while requiring only a lightweight computational footprint. Among the evaluated training-free methods, CochlearMark is the only one to retain substantial detectability under neural-codec re-encoding.
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Signal Processing
Radial-Angular Resolution Framework for Near-Field Communications
Viviana Centritto, Sotiris Droulias and Angeliki Alexiou
Near-field beamfocusing confines radiated power into a localized three-dimensional focal spot, making the analytical characterization of radial and angular resolution critical for sixth-generation (6G) multi-user systems, yet existing models treat the focal resolution as a fixed
geometric outcome rather than a tunable design variable. Based on the Angular Representation (ASR) approach, this paper presents a resolution framework to design contiguous focal regions across both the radial and angular domain for steered multi-user systems. By extending the definition of a tunable inter-beam overlap $\rho$, we derive closed-form expressions for the radial and angular focal boundaries and extents, alongside the steering-dependent minimum focal distance $d_{0,\text{min}}$ and maximum steering angle $\theta_{\text{max}}$. Using these boundaries, iterative synthesis
algorithms are developed to construct uniform and non-uniform radial and angular beam arrangements. Finally, by analyzing both isolated and joint radial-angular configurations, we quantify the fundamental trade-offs between $\rho$, achievable user capacity, inter-user interference, and system sum-rate across varying steering angles.
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Audio and Speech Processing · Cross-listed from cs.AI
Designing Textual Interfaces To Enhance Llm Reasoning On Audio-Visual Question Answering
Yi Wang, Tianzi Wang, Tong Lei et al. (+2 more)
Automatic
prompt optimization (APO) has shown strong effectiveness for text-based LLMs and LLM systems, but its application to audio-visual understanding systems requires suitable textual interfaces through which
multimodal reasoning can be exposed to text-based optimizers. We investigate how the design of textual interfaces affects
prompt optimization for
multimodal reasoning, on the audio-visual question answering task. We compare three interface designs, including a direct inference system consisting of a single omni-modal large
language model (OmniLLM), a captioner-reasoner system, and an agentic architecture in which a primary
agent orchestrates subagent OmniLLMs. Experiments with Qwen3-Omni and Gemini show that direct
prompt optimization provides model-dependent gains; the captioner-reasoner system and the agentic system expose more interpretable optimization interfaces but are harder to optimize, where error analysis of cases affected by optimization is conducted. Our study highlights textual interface design as an important factor in making
prompt optimization effective for omni-modal
reasoning systems. Our implementation details are specified in GitHub repo.
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All authors Yi Wang, Tianzi Wang, Tong Lei, Dahan Wang, Peter Bell