Audio and Speech Processing · Cross-listed from math.CO
Tonnetz Theory, Classical Harmony, and the Combinatorial Geometry of Abstract Musical Resources
Jeffrey R. Boland and Lane P. Hughston
In a previous submission, we established a fundamental relation between tone networks and configurations. It was shown that the Eulerian tonnetz can be represented by a $\{12_3\}$ of Daublebsky von Sterneck type D222. We also constructed a tonnetz for Tristan-genus chords (dominant sevenths and half-diminished sevenths) and showed that this tonnetz can be represented by a $\{12_3\}$ of type D228. In both constructions the associated Levi graphs play an important role. Here we look at the tonnetze associated with some other musical systems, thereby offering concrete examples of an abstract view of music as combinatorial geometry. First, we look at the tonal harmonies of the classical period. In the case of diatonic triads, we show the existence of a bipartite graph of type $\{7_3\}$ and girth four that represents the relations between the seven diatonic degrees and their pitch classes. In the case of diatonic seventh chords, we obtain a Fano configuration $\{7_3\}$, which gives a characterization of the voice-leading relations that hold between such chords. Next, we construct a tonnetz for pentatonic music based on the Desargues configuration $\{10_3\}$ and we construct a tonnetz for the 12-tone system based on the Cremona-Richmond configuration $\{15_3\}$. Both can be used as resources for compositions. Finally, we show that the relation between the chromatic pitch class set and the major triad set is represented by a D222. The minor triads are in one-to-one correspondence with the members of a certain class of hexacycles in the Levi graph of this configuration. In this way, the characteristic duality between major and minor triads in the tonnetz can be broken.
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Systems and Control · Cross-listed from cs.DC
Equinox: Decentralized Scheduling for Hardware-Aware Orbital Intelligence
Ansel Kaplan Erol and Divya Mahajan
Earth-observation satellites are emerging as distributed edge platforms for time-critical tasks, yet orbital scheduling remains challenged by intermittent energy harvesting and temporal coupling where eager execution risks future battery depletion. Existing schedulers rely on static priorities and lack mechanisms to adaptively shed work. We present Equinox, a lightweight, decentralized runtime for resource-constrained orbital systems. Equinox enables adaptive scheduling by compressing time-varying constraints, including battery charge, thermal headroom, and queue backlog, into a single state-dependent marginal cost of execution. Derived from a barrier function that rises sharply near safety limits, this cost encodes both instantaneous pressure and future risk. This local signal serves as a constellation-wide coordination primitive. Tasks execute only when their value exceeds the current cost, enabling value-ordered load shedding without explicit policies. If local costs exceed a neighbor's, tasks are dynamically offloaded over inter-satellite links, achieving distributed load balancing without routing protocols or global state. We evaluate Equinox using a multi-day simulation of a 143-satellite constellation grounded in physical Jetson Orin Nano measurements. Equinox improves scientific goodput by 20% and image-processing throughput by 31% over priority-based scheduling while maintaining 2.2x higher mean battery reserves. Under high demand, Equinox achieves 5.2x the execution rate of static scheduling by gracefully shedding work rather than collapsing under contention.
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Audio and Speech Processing
Indic-CodecFake meets SATYAM: Towards Detecting Neural Audio Codec Synthesized Speech Deepfakes in Indic Languages
Girish, Mohd Mujtaba Akhtar, Orchid Chetia Phukan et al. (+1 more)
The rapid advancement of Audio Large
Language Models (ALMs), driven by Neural Audio Codecs (NACs), has led to the emergence of highly realistic
speech deepfakes, commonly referred to as CodecFakes (CFs). Consequently, CF detection has attracted increasing
attention from the research community. However, existing studies predominantly focus on English or Chinese, leaving the vulnerability of Indic
languages largely unexplored. To bridge this gap, we introduce Indic-CodecFake (ICF)
dataset, the first large-scale
benchmark comprising real and NAC-synthesized
speech across multiple Indic
languages, diverse speaker profiles, and multiple NAC types. We use IndicSUPERB as the real
speech corpus for generation of ICF
dataset. Our experiments demonstrate that state-of-the-art (SOTA) CF detectors trained on English-centric
datasets fail to generalize to ICF, underscoring the challenges posed by phonetic diversity and prosodic variability in Indic
speech. Further, we present systematic evaluation of SOTA ALMs in a zero-shot setting on ICF
dataset. We evaluate these ALMs as they have shown effectiveness for different
speech tasks. However, our findings reveal that current ALMs exhibit consistently poor performance. To address this, we propose SATYAM, a novel
hyperbolic ALM tailored for CF detection in Indic
languages. SATYAM integrates semantic representations from Whisper and prosodic representations from TRILLsson using through Bhattacharya distance in
hyperbolic space and subsequently performs the same
alignment procedure between the fused
speech representation and an input conditioning
prompt. This dual-stage fusion framework enables SATYAM to effectively model hierarchical relationships both within
speech (semantic-prosodic) and across modalities (
speech-text). Extensive evaluations show that SATYAM consistently outperforms competitive end-to-end and ALM-based baselines on the ICF
benchmark.
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All authors Girish, Mohd Mujtaba Akhtar, Orchid Chetia Phukan, Arun Balaji Buduru
Signal Processing
A Hybrid Gauss Markov LSTM Mobility Model for Indoor OWC
Walter Zibusiso Ncube, Ahmad Adnan Qidan, Taisir El-Gorashi et al. (+1 more)
Optical wireless communication (OWC) has emerged as a promising candidate for future high-capacity indoor wireless networks, driven by its large unregulated spectrum, high spatial reuse, and ability to support multi-gigabit data rates. However, OWC systems are highly sensitive to user mobility, as link performance depends strongly on the spatial
alignment between transmitter and receiver. Accurate modelling of user position and device orientation is therefore essential for reliable channel estimation and system evaluation. To that effect, this paper proposes a hybrid Gauss–Markov and long short-term
memory (GM–LSTM) mobility model for indoor OWC environments. The Gauss–Markov component captures the temporal correlation of user motion, while the LSTM learns residual behaviour to model non-linear movement patterns and orientation dynamics. The proposed model jointly predicts user position and device orientation, enabling improved representation of mobility in OWC channels. Performance is evaluated using prediction accuracy and per-user data rate
evolution. Results show that the proposed hybrid GM–LSTM model outperforms conventional Random Waypoint and Gauss–Markov models, providing more accurate mobility prediction and more stable communication performance in dynamic indoor environments.
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All authors Walter Zibusiso Ncube, Ahmad Adnan Qidan, Taisir El-Gorashi, Jaafar M. H. Elmirghani
Systems and Control
Cross-Atlantic Research Agenda for Scalable Grid Architectures and Distributed Flexibility
Mads R. Almassalkhi, Dakota Hamilton, Hasan Giray Oral et al. (+10 more)
Electric power systems are rapidly evolving into deeply digital, cyber-physical infrastructures in which large fleets of distributed energy resources must be coordinated as system-level flexibility across multiple spatial and temporal scales. Despite growing distributed energy resource deployment, existing grid and
market architectures lack scalable, interoperable mechanisms to reliably translate device-level flexibility into grid-aware services, creating risks to reliability, affordability, and resilience at high penetration. We propose that scalable and reliable coordination of distributed energy resource-based flexibility in future power systems is fundamentally an architectural problem that can be addressed through laminar cyber-physical design using minimal, standardized interoperability interfaces that link device autonomy with system-level objectives. To assess this claim, we present and discuss a layered cyber-physical systems architecture and explicate its implementation through standards-based interfaces, Flexibility Functions, hierarchical control, and case studies spanning U.S. and Danish regulatory,
market, and operational contexts. Empirical evidence from New York's Grid of the Future proceedings, Danish Smart Energy Operating System pilots, and operational aggregator deployments demonstrates that such architecture enables predictable, grid-aware flexibility while preserving device autonomy, interoperability, reliability, and quality of service. These results support a cross-Atlantic research agenda centered on joint testbeds, harmonized interoperability mechanisms, and coordinated policy experiments to accelerate the deployment of resilient, scalable, and flexible clean energy systems.
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All authors Mads R. Almassalkhi, Dakota Hamilton, Hasan Giray Oral, Yury Dvorkin, Dennice Gayme, Bri-Mathias Hodge, Brian Vad Mathiesen, Jakob Stoustrup, Tobias Ritschel, Rune G. Junker, Shahab Tohidi, Razgar Ebrahimy, Henrik Madsen
Signal Processing · Cross-listed from cs.IT
Finite-Length Empirical Comparison of Polar, PAC, and Invertible-Extractor Secrecy Codes over the Wiretap BSC
Jaswanthi Mandalapu, Tyler Kann, Andrew Thangaraj et al. (+1 more)
We compare three secrecy-coding schemes for the degraded wiretap binary symmetric channel (BSC) in the finite-blocklength regime: (i) polar wiretap coset codes, (ii) PAC codes used as wiretap coset codes, and (iii) the invertible-extractor (IE) framework of Bellare–Tessaro. Our comparison is empirical and uses a common semantic-secrecy metric (distinguishing advantage). For polar coset codes, we compute Eve's polarized bit-channel capacities (via the Tal–Vardy construction) to obtain explicit finite-length upper bounds on mutual-information leakage, yielding strong secrecy bounds. For PAC coset codes, we prove that Eve's synthesized bit-channels are equivalent to those of polar codes (up to a permutation), so the same leakage bounds apply; we then convert these strong-secrecy bounds into semantic-secrecy guarantees for symmetric wiretap channels. For the IE scheme, we use the closed-form semantic-secrecy bounds already established. Finally, we report finite-length results that jointly characterize (a) semantic-secrecy guarantees against Eve and (b) frame-error-rate performance at Bob. Our simulations indicate that Polar and PAC codes exhibit comparable reliability performance under the considered finite-length settings, while PAC codes retain the same secrecy bounds as Polar codes. More broadly, our results highlight that non-universal designs of wiretap codes provide tighter secrecy guarantees than universal design based on invertible extractors.
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All authors Jaswanthi Mandalapu, Tyler Kann, Andrew Thangaraj, Matthieu R. Bloch
Signal Processing
Grassmannian-Coded Beamforming for mmWave Channel Sensing with Unknown Complex Path Gain
Parthasarathi Khirwadkar, Robin Rajamäki and Piya Pal
This paper introduces a subspace-coding perspective to millimeter-wave channel sensing with a single RF chain when the complex channel gain is unknown. We show that in this case, candidate directions-of-arrival (DoAs) map naturally to subspaces through their beamspace responses, revealing an intrinsic Grassmannian geometry. This motivates beamspace Grassmannian codes (BGCs), designed to reduce DoA error by maximizing the minimum subspace distance of the joint beamformer-array response. We identify two regimes: one in which existing Grassmannian packings are exactly realizable as BGCs when the angular grid matches the array size, and another in which realizability for finer grids is constrained by the array geometry. Our analysis establishes the joint roles of subspace distance and beamforming gain in sensing performance and motivates two complementary beamformer designs. Without prior DoA information, we develop spatially isotropic beamformers based on algebraic Grassmannian packings and modulation-based channel codes. With a known DoA region of interest, we design convolutional beamspaces that combine directional gain with favorable subspace distance. Numerical results demonstrate robust BGC performance for both on-grid and off-grid DoAs, supporting the effectiveness of the proposed Grassmannian framework for mmWave channel sensing.
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Systems and Control
Output Feedback Backup Control Barrier Functions: Safety Guarantees Under Input Bounds and State Estimation Error
David E. J. van Wijk, Tamas G. Molnar, Samuel Coogan et al. (+3 more)
Guaranteeing the safety of controllers is vital for real-world applications, but is markedly difficult when the states are not perfectly known and when the control inputs are bounded. Backup control barrier functions (bCBFs) use predictions of the flow under a prescribed controller to achieve safety in the presence of bounded inputs and perfect state information. However, when only an estimate of the true state is known, this flow may not be precisely computed, as the initial condition is unknown. Furthermore, the true flow evolves using feedback from the estimated state, thus introducing coupling between known and unknown flows. To address these challenges, we propose a technique that leverages an uncertainty envelope centered around the estimated flow and show that ensuring the safety of this envelope guarantees that the true state satisfies the safety constraints. Additionally, we show that in the presence of state uncertainty, using the resulting Output Feedback Backup Control Barrier Functions (O-bCBFs), there always exists a feasible control input that can guarantee the safety of the true state, even in the presence of input constraints.
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All authors David E. J. van Wijk, Tamas G. Molnar, Samuel Coogan, Manoranjan Majji, Aaron D. Ames, Joel W. Burdick
Signal Processing · Cross-listed from cs.LG
FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning
Abdulmoneam Ali and Ahmed Arafa
Personalized
Federated Learning (PFL) aims to learn multiple task-specific models rather than a single global model across heterogeneous data distributions. Existing PFL approaches typically rely on iterative optimization-such as model update trajectories-to cluster users that need to accomplish the same tasks together. However, these learning-dynamics-based methods are inherently vulnerable to low-quality data and noisy labels, as corrupted updates distort clustering decisions and degrade personalization performance. To tackle this, we propose FB-NLL, a feature-centric framework that decouples user clustering from iterative training dynamics. By exploiting the intrinsic heterogeneity of local feature spaces, FB-NLL characterizes each user through the spectral structure of the covariances of their feature representations and leverages subspace similarity to identify task-consistent user groupings. This geometry-aware clustering is label-agnostic and is performed in a one-shot manner prior to training, significantly reducing communication overhead and computational costs compared to iterative baselines. Complementing this, we introduce a feature-consistency-based detection and correction strategy to address noisy labels within clusters. By leveraging directional
alignment in the learned feature space and assigning labels based on class-specific feature subspaces, our method mitigates corrupted supervision without requiring estimation of
stochastic noise transition
matrices. In addition, FB-NLL is model-independent and integrates seamlessly with existing noise-robust training techniques. Extensive experiments across diverse
datasets and noise regimes demonstrate that our framework consistently outperforms state-of-the-art baselines in terms of average accuracy and performance stability.
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Signal Processing
Soft-Coherent Direct Multipath SLAM
Benjamin J. B. Deutschmann, Klaus Witrisal and Erik Leitinger
Challenging indoor and urban environments with severe multipath propagation and obstructed line-of-sight degrade classical
radio positioning. Multipath-based simultaneous localization and mapping (MP-SLAM) addresses this by building and exploiting propagation maps for robust localization. Emerging distributed multiple-input multiple-output (D-
MIMO)/extremely large-scale
MIMO (XL-
MIMO) infrastructures provide large spatial apertures and high-resolution sensing, especially when phase coherence is maintained across base stations, subarrays, or distributed arrays. We propose a scalable
Bayesian direct MP-SLAM method for coherent data fusion in D-
MIMO/XL-
MIMO systems that jointly infers the environment while performing robust, high-accuracy localization directly from raw
radio signals. While commonly used zero-mean Type-II likelihood functions inherently lead to noncoherent processing across distributed arrays and thus to aperture loss, the proposed phase-preserving nonzero-mean Type-II likelihood shares a complex mean across distributed arrays. This enables coherent fusion and preserves the distributed aperture gain, while the variance captures noncoherent signal power. The method is combined with a surface model that enables map-feature fusion across the distributed infrastructure and supports near-field propagation and visibility effects.
Bayesian inference is performed using belief propagation by means of the sum-product
algorithm on a factor graph with particle-based messages. Parallelizing over particles and arrays, the GPU-accelerated implementation achieves millisecond-level runtimes even in large or distributed infrastructures. Simulation results show that the proposed method achieves performance gains over existing noncoherent methods and approaches the corresponding posterior CRLB, highlighting the potential of coherent processing for high-resolution sensing and localization.
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Systems and Control · Cross-listed from cs.CV
A Network-Aware Evaluation of Distributed Energy Resource Control in Smart Distribution Systems
Houchao Gan
Distribution networks with high penetration of Distributed Energy Resources (DERs) increasingly rely on communication networks to coordinate grid-interactive control. While many distributed control schemes have been proposed, they are often evaluated under idealized communication assumptions, making it difficult to assess their performance under realistic network conditions. This work presents an implementation-driven evaluation of a representative virtual power plant (VPP) dispatch
algorithm using a co-simulation framework that couples a linearized distribution-system model with packet-level downlink emulation in ns-3. The study considers a modified IEEE~37-node feeder with high photovoltaic penetration and a primal–dual VPP dispatch that simultaneously targets feeder-head active power tracking and voltage regulation. Communication effects are introduced only on the downlink path carrying dual-variable updates, where per-DER packet delays and a hold-last-value strategy are modeled. Results show that, under ideal communication, the dispatch achieves close tracking of the feeder-head power reference while maintaining voltages within the prescribed limits at selected buses. When realistic downlink delay is introduced, the same controller exhibits large oscillations in feeder-head power and more frequent voltage limit violations. These findings highlight that distributed DER control performance can be strongly influenced by communication behavior and motivate evaluation frameworks that explicitly incorporate network dynamics into the assessment of grid-interactive control schemes.
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Signal Processing
Networked Tracking of Multiple Moving Targets in 6G Network
Yanmo Hu, Weifeng Zhu, Chenshu Wu et al. (+3 more)
This paper considers a networked tracking architecture in 6G integrated sensing and communication (
ISAC) systems, where multiple base stations (BSs) cooperatively transmit
radio signals and process received echo signals to track multiple moving targets. Compared to the single-BS counterpart, networked tracking allows the moving targets to be associated with different BSs over time such that the wireless resources can be dynamically allocated among BSs based on target locations. However, networked tracking imposes new challenges for
algorithm design and resource allocation. In this paper, we first design the networked Kalman Filter (NKF) that is suitable for multi-BS based tracking, then characterize the posterior Cramer-Rao bound (PCRB) under this NKF, and last design the beamforming vectors of all the BSs to minimize the tracking PCRB. Numerical results show that our dynamic beamforming design can properly associate the targets to the suitable BSs at various sensing blocks and reduce the tracking mean-squared error (MSE).
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All authors Yanmo Hu, Weifeng Zhu, Chenshu Wu, Shuowen Zhang, J. Andrew Zhang, Liang Liu
Systems and Control
Towards Reproducible Test Annotation for Cyber-Physical Energy Systems using Ontology-driven Dataspaces
Kai Heussen, Jawad Kazmi, Narges Mehran et al. (+3 more)
Reproducibility, traceability, and transparency in testing cyber-physical energy systems are crucial for scientific advancement and cross-laboratory collaboration. Current experimentation and test documentation practices lack formal semantics, making it difficult to reproduce experiments, share data, and apply, for example, the artificial intelligence-driven analysis. A dataspace that relies on structured ontologies aims to address these gaps by providing machine-actionable descriptions. In this work, we outline an ontology-driven approach for reproducibility of cyber-physical energy systems testing and illustrate its applicability through representative cross-laboratory use cases, demonstrating feasibility while identifying remaining semantic and metadata gaps that limit reproducibility. Based on these observations, we propose an open three-viewpoint ontology framework to guide future ontology extensions.
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All authors Kai Heussen, Jawad Kazmi, Narges Mehran, Artjoms Obushevs, Terence O'Donnell, Thomas I. Strasser
Signal Processing
Pilot-Free Predictive Multi-User Beamforming via Sensing Management in Cell-Free Networks
Eren Berk Kama, Murat Babek Salman, Isaac Skog et al. (+1 more)
This paper presents a sensing management frame- work for integrated sensing and communications (
ISAC) within
cell-free massive multiple-input multiple-output (
MIMO) systems to reduce pilot-based channel state information (CSI) acquisition overhead. Conventional communication systems rely on frequent channel estimation procedures that impose significant signaling overhead, consuming valuable time-frequency resources. To ad- dress this inefficiency, we propose a state-based architecture that partitions users into communication and sensing groups based on service requirements. When users are not requesting data, the system utilizes sensing capabilities to track their location. Upon receiving a communication request, the system transitions to communication mode, leveraging the tracked state for predictive beamforming to eliminate the need for uplink pilot training. We develop an extended Kalman filter (EKF) based tracking
algorithm coupled with adaptive resource allocation strategies. Furthermore, we analyze the impact of inter-target interference and design a sensing management protocol that performs sensing operations only when necessary to maintain the accuracy of user location estimates. Simulation results demonstrate that the pro- posed EKF-based tracking and sensing management can support predictive beamforming with downlink spectral efficiency close to the perfect-CSI case, while requiring sensing only occasionally after an initial convergence period. The results also indicate that this performance is robust in a
cell-free massive
MIMO setup and can be achieved with practical sensing waveforms.
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All authors Eren Berk Kama, Murat Babek Salman, Isaac Skog, Emil Björnson
Systems and Control
Safety-Critical Contextual Control via Online Riemannian Optimization with World Models
Tongxin Li
Modern world models are becoming too complex to admit explicit dynamical descriptions. We study safety-critical contextual control, where a Planner must optimize a task objective using only feasibility samples from a black-box Simulator, conditioned on a context signal $\xi_t$. We develop a sample-based Penalized Predictive Control (PPC) framework grounded in online Riemannian optimization, in which the Simulator compresses the feasibility
manifold into a score-based density $\hat{p}(u \mid \xi_t)$ that endows the action space with a Riemannian geometry guiding the Planner's gradient descent. The barrier
curvature $\kappa(\xi_t)$, the minimum
curvature of the conditional log-density $-\ln\hat{p}(\cdot\mid\xi_t)$, governs both convergence rate and safety margin, replacing the Lipschitz constant of the unknown dynamics. Our main result is a contextual safety bound showing that the distance from the true feasibility
manifold is controlled by the score estimation error and a ratio that depends on $\kappa(\xi_t)$, both of which improve with richer context. Simulations on a dynamic
navigation task confirm that contextual PPC substantially outperforms marginal and frozen density models, with the advantage growing after environment shifts.
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Signal Processing
Odour sensing in turbulent plumes with high-speed electronic nose and non-invasive ground truth
Nik Dennler, Elle Stark, Saimon Collaku et al. (+6 more)
Chemical sensing in real-world environments requires resolving rapidly fluctuating and spatially heterogeneous concentration fields. However, these dynamics are strongly distorted by widely used, low-cost metal-
oxide (MOx) gas sensors, whose thermal and surface-kinetic response acts as a low-pass filter on the underlying concentration signal. Quantifying and compensating for these effects remains challenging, largely due to the lack of
benchmark datasets that simultaneously capture the spatiotemporal structure of turbulent odour fields and the time-resolved response of point sensors. Here, we present a
dataset combining planar
laser-induced fluorescence (PLIF) measurements of an acetone tracer plume with synchronised recordings from a custom, kilohertz-rate microelectromechanical (MEMS) MOx electronic nose deployed in a laboratory wind tunnel. The PLIF system provides quantitative, two-dimensional concentration fields at high spatial and temporal resolution, while the co-located e-nose records film resistance, heater currents, and environmental parameters with aligned timestamps. The
dataset enables quantitative assessment of sensor dynamics, development and benchmarking of reconstruction and deconvolution
algorithms, and data-driven modelling of plume structure. All recordings, metadata, calibration files, and example analysis scripts are released in open, platform-independent formats. Together, these provide a valuable reference for researchers working in odour-guided robotics, environmental monitoring, computational fluid dynamics, and neuromorphic sensing, supporting the design and evaluation of high-speed odour-sensing systems.
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All authors Nik Dennler, Elle Stark, Saimon Collaku, Lars Larson, André van Schaik, Michael Schmuker, John Crimaldi, Andreas T. Güntner, Aaron True
Signal Processing · Cross-listed from cs.LG
SAGE: Training-Free Semantic Evidence Composition for Edge-Cloud Inference under Hard Uplink Budgets
Inhyeok Choi and Hyuncheol Park
Edge-cloud hybrid inference offloads difficult inputs to a powerful remote model, but the uplink channel imposes hard per-request constraints on the number of bits that can be transmitted. We show that selecting transmitted content based solely on
attention-based importance, the standard approach in collaborative inference, is inherently limited under hard budgets. Two findings support this claim. First, replacing high-importance units with low-importance but complementary ones improves server accuracy. This shows that what matters is not individual importance but how well the transmitted set covers diverse aspects of the input. Second, spatially uniform selection without any content information achieves competitive accuracy at moderate budgets. This confirms that spatial coverage alone carries independent value. Based on this analysis, we propose SAGE (Semantic
Attention-Guided Evidence), a principled, training-free method that combines importance filtering with
embedding-diversity sampling. SAGE achieves 93% of the server ceiling in offloaded accuracy while transmitting fewer than half of the available evidence units on ImageNet-1K, substantially outperforming importance-only composition.
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Signal Processing
Positivity of a Hadamard Product
Roger A. Horn, Shengxuan Luo, Hongwei Xu et al. (+1 more)
A notable difference between the ordinary and Hadamard products is that the Hadamard product of two singular positive semidefinite
matrices can be nonsingular, and one of the factors can even be indefinite. We present an
eigenvalue lower bound for a Hadamard product that depends on the rank, effective condition number, and diagonal entries of one factor, and the smallest
eigenvalues of certain principal submatrices of the other factor. We give numerical examples and discuss its applications in array signal processing and
matrix time series analysis.
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All authors Roger A. Horn, Shengxuan Luo, Hongwei Xu, Zai Yang
Signal Processing
Active Inference-Enabled Agentic Closed-Loop ISAC with Long-Horizon Planning
Guangjin Pan, Zhuojun Tian, Mehdi Bennis et al. (+1 more)
Wireless agentic systems enable
agents to autonomously perceive, reason, and act. However, existing works neglect the tight coupling between sensing and control in closed-loop integrated sensing and communication (
ISAC) systems. In this paper, we propose an active inference (AIF)-driven wireless agentic system for closed-loop
ISAC, which jointly optimizes control and sensing resource allocation via backward–forward message passing on a factor graph. The AIF
agent maintains a generative model as a digital twin by integrating a localization model for uncertainty-aware state inference and a localization channel knowledge map (CKM) for approximating observation quality during planning. Simulation results demonstrate that the AIF-enabled
agent adaptively allocates sensing resources based on spatially varying channel conditions, achieving superior balance among tracking accuracy, control effort, and sensing resource consumption over baseline strategies.
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All authors Guangjin Pan, Zhuojun Tian, Mehdi Bennis, Henk Wymeersch
Systems and Control
Simulation of Switching Converters Using Linear Capacitor Voltage and Inductor Current Prediction and Correction
Aleksandra Lekić and Vujo Drndarević
In this paper an
algorithm for transient simulation of switching converters using prediction and correction to calculate duty ratio is proposed. It provides large signal simulation on the level of averaged currents and voltages in the
circuit. Calculation of duty ratio using inductor current and capacitor voltage prediction and correction do not require their priori knowledge. Number of
circuit solving per switching period is fixed and equal to two. Using this
algorithm various of constant frequency regulated switching converters can be simulated. Due to predetermined
circuit values convergence problems are avoided. This
algorithm results in very fast and accurate large signal simulation.
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Systems and Control · Cross-listed from cs.LG
Switching Theory for Q-Learning
Donghwan Lee
Q-learning is a fundamental algorithmic primitive in reinforcement learning. This paper develops a new framework for analyzing constant step-size tabular Q-learning from a switching linear system (SLS) viewpoint. In particular, we derive a
stochastic SLS representation of the Q-learning error, and a finite-time error analysis through the joint spectral radius (JSR) of the corresponding SLS model, where the JSR is the exact worst-case exponential rate of the associated SLS. To the best of our knowledge, this is the first convergence rate analysis of standard Q-learning whose leading exponential rate is expressed through the JSR. The resulting rate is tied to the intrinsic worst-case exponential rate of the direct SLS representation and can be sharper than row-sum upper bounds when those bounds are conservative. We further prove that the JSR of Q-learning equals the largest spectral radius among the deterministic-policy modes and give an exact linear programming characterization that can be evaluated to any prescribed accuracy.
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Systems and Control · Cross-listed from cs.LG
Accelerating Optimization and Machine Learning through Decentralization
Ziqin Chen, Zuang Wang and Yongqiang Wang
Decentralized optimization enables multiple devices to learn a global machine learning model while each individual device only has access to its local
dataset. By avoiding the need for training data to leave individual users' devices, it enhances
privacy and scalability compared to conventional centralized learning, where all data has to be aggregated to a central server. However, decentralized optimization has traditionally been viewed as a necessary compromise, used only when centralized processing is impractical due to communication constraints or data
privacy concerns. In this study, we show that decentralization can paradoxically accelerate convergence, outperforming centralized methods in the number of iterations needed to reach optimal solutions. Through examples in logistic regression and neural network training, we demonstrate that distributing data and computation across multiple
agents can lead to faster learning than centralized approaches, even when each iteration is assumed to take the same amount of time, whether performed centrally on the full
dataset or decentrally on local subsets. This finding challenges longstanding assumptions and reveals decentralization as a strategic advantage, offering new opportunities for more efficient optimization and machine learning.
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Image and Video Processing
Defining Robust Ultrasound Quality Metrics via an Ultrasound Foundation Model
Ziyang Huang, Bingyan Li, Chen Ma et al. (+6 more)
Clinicians lack a principled framework to quantify diagnostic utility in ultrasound reconstructions. Existing standards like PSNR and VGG-LPIPS are inadequate, failing to account for modality-specific physics or the structural nuances of acoustic imaging. We close this gap with a TinyUSFM-based evaluation framework featuring two distinct metrics: TinyUSFM-uLPIPS, a full-reference perceptual distance based on multi-layer
token relations, and TinyUSFM-NRQ, a deployable no-reference quality score utilizing clean-
manifold modeling and worst-region aggregation to detect localized harmful artifacts. We demonstrate that the presented metrics have four unique advantages: 1) Task-linked quality, where TinyUSFM-uLPIPS achieves superior calibration with semantic task damage, accurately reflecting Dice-score drops in
segmentation where VGG-based metrics fail; 2) Cross-organ comparability, maintaining stable scoring scales and consistent severity rankings across diverse anatomical sites and domain-shifted data; 3) PSNR-consistent sensitivity, with TinyUSFM-NRQ providing a reliable quality score without ground-truth images that remains consistent with traditional fidelity
benchmarks (i.e. PSNR); and 4) Clinical utility, improving the prediction of expert preference from 47.2$\%$ to 72.8$\%$ accuracy and producing super-resolution reconstructions preferred by sonographers. By integrating these advantages into a unified assessment and optimization loop, this work establishes a modality-aligned standard that finally bridges the gap between algorithmic performance and diagnostic utility. Our code is available at https://github.com/sextant-fable/US-Metrics.
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All authors Ziyang Huang, Bingyan Li, Chen Ma, Tianyi Liu, Yihui Zhai, Hong Xu, Yi Guo, Zeju Li, Yuanyuan Wang
Image and Video Processing
Harmonizing MR Images Across 100+ Scanners: Multi-site Validation with Traveling Subjects and Real-world Protocols
Savannah P. Hays, Lianrui Zuo, Muhammad Faizyab Ali Chaudhary et al. (+13 more)
Reliable harmonization of heterogeneous
magnetic resonance~(MR) image
datasets, especially those acquired in pragmatic clinical trials, is critical to advance multi-center neuroimaging studies and translational machine learning in healthcare. We present an enhanced and rigorously validated version of the HACA3 harmonization
algorithm, which we refer to as HACA3$^+$, incorporating key methodological enhancements: (1)~an improved artifact encoder to better isolate and mitigate image artifacts, (2)~background and foreground-sensitive
attention mechanisms to increase harmonization specificity, and (3)~extensive training using data spanning 100+ scanners from 64 independent sites, providing a broader diversity of scanners than other harmonization methods. Our study focuses on four commonly acquired MR image contrasts (T1-weighted, T2-weighted,
proton density, & fluid-attenuated inversion recovery), reflecting realistic clinical protocols. We perform inter-site harmonization experiments using traveling subjects to assess the generalization and robustness of the harmonization model. We compare the results of the publicly available version of HACA3 and our implementation, HACA3$^+$. Downstream relevance is further established through whole
brain segmentation and image imputation. Finally, we justify each enhancement through an ablation experiment. Pre-trained weights and code for HACA3$^+$ are made publicly available at https://github.com/shays15/haca3-plus.
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All authors Savannah P. Hays, Lianrui Zuo, Muhammad Faizyab Ali Chaudhary, Kathleen M. Bartz, Samuel W. Remedios, Jinwei Zhang, Jiachen Zhuo, Murat Bilgel, Shiv Saidha, Carolyn Koch, Sandra D. Cassard, Ellen M. Mowry, Scott D. Newsome, Jerry L. Prince, Blake E. Dewey, Aaron Carass
Systems and Control · Cross-listed from cs.RO
Wrench-Aware Admittance Control for Unknown-Payload Manipulation
Hossein Gholampour and Logan E. Beaver
Unknown payloads can strongly affect compliant robotic manipulation, especially when the payload center of mass is not aligned with the tool center point. In this case, the payload generates an offset wrench at the
robot wrist. During motion, this wrench is not only related to payload weight, but also to payload inertia. If it is not modeled, the compliant controller can interpret it as an external interaction wrench, which causes unintended compliant motion, larger tracking error, and reduced transport accuracy. This paper presents a wrench-aware admittance control framework for unknown-payload pick-and-place using a UR5e
robot. The method uses force-torque measurements in two different roles. First, a three-axis translational excitation term is used to reduce payload-induced force effects during transport without making the
robot excessively stiff. Second, after grasping, the controller first estimates payload mass for transport compensation and then estimates the payload CoM offset relative to the TCP using wrist force-torque measurements collected during the subsequent translational motion. This helps improve object placement and stacking behavior. Experimental results show improved transport and placement performance compared with uncorrected placement while preserving compliant motion.
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Signal Processing
Optimal Multispectral Imaging using RGB Cameras
Tomislav Matulić, Ivan Škrabo, Dubravko Babić et al. (+1 more)
We present a physics-driven framework for accurate evaluation of discrete spectral bands using a low-cost multispectral setup built from off-the-shelf RGB cameras and narrow multi-band optical filters. The approach starts by explicitly formulating a linear measurement model. The camera responses are expressed as linear mixtures of unknown spectral components, with mixing coefficients determined by the overlap between the camera spectral sensitivities and the filter transmittances. For a multi-camera configuration, the per-camera models are stacked into a single global system whose structure is fully determined by the allocation of target wavelengths across the camera–filter units. We pose wavelength allocation as a deterministic design problem and select the configuration that minimizes the spectral condition number of the resulting system
matrix. Guided by a frame-theoretic interpretation, this criterion promotes numerical stability, maximizes worst-case output signal-to-noise ratio, and improves the robustness of spectral reconstruction. The design space is finite, enabling the evaluation of all feasible configurations under practical constraints. We demonstrate the method on a representative example with 12 target wavelengths and four triband filters, and identify the wavelength allocation that yields the most stable and noise-robust recovery. The proposed framework includes redundant configurations, in which individual wavelengths are measured by multiple cameras, thereby providing additional degrees of freedom that further improve noise robustness.
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All authors Tomislav Matulić, Ivan Škrabo, Dubravko Babić, Damir Seršić
Systems and Control
Robust Nonlinear Trajectory Tracking Control for Autonomous Racing on Three-Dimensional Tracks
Joscha F. Bongard, Georg Jank, Simon Sagmeister et al. (+1 more)
We propose a robust nonlinear model predictive control (MPC) scheme for trajectory-tracking control of autonomous vehicles at the limits of handling on non-planar road surfaces. We derive the dynamics from first principles and selectively omit terms with negligible dynamic influence to maintain real-time capability. The resulting MPC with a three-dimensional (3D) dynamic single-track model integrates relevant dynamic effects directly into the prediction model and leverages them to improve prediction accuracy and therefore control performance. Even if the influence of terrain-induced vertical loads on the total acceleration potential is modeled, tire-road interactions are subject to uncertainty and disturbance. The uncertainty-aware constraint tightening scheme introduces a margin to constraint bounds to keep the vehicle controllable and stable in this environment. To validate our proposed approach, we perform high-fidelity dynamic double-track vehicle dynamics simulations on a model of a real
circuit. We find that our
algorithm can improve trajectory-tracking accuracy while maintaining low computation times.
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All authors Joscha F. Bongard, Georg Jank, Simon Sagmeister, Boris Lohmann
Systems and Control
State Forecasting in an Estimation Framework with Surrogate Sensor Modeling
Sriram Narayanan, Mohamed Naveed Gul Mohamed, Ishan Paranjape et al. (+3 more)
In recent years, computational power and data availability breakthroughs have revolutionized our ability to analyze complex physical systems through the inverse problem approach. Data-driven techniques like system identification and machine learning play an important role in this field, allowing us to gain insights into previously inaccessible phenomena. However, a major hurdle remains: How can meaningful information from partial measurements be extracted? In the aerospace domain, the challenge of state estimation is particularly pronounced due to the limited availability of observational data and the constraints imposed by sensor capabilities for tracking resident space objects (RSOs). To address these limitations, advanced compensation methodologies are required. Currently, range and bearing measurements obtained from radar and optical systems constitute the primary observational tools in the space situational awareness (SSA) community. In this work, we propose a novel framework that integrates a simplified reference dynamics model with a data-driven surrogate measurement model. This fusion process leverages the strengths of both models to estimate complex dynamical behaviors under conditions of partial observability. Extensive numerical experiments were conducted across multiple
datasets to validate the proposed framework. The results demonstrate its efficacy in accurately reconstructing system dynamics from incomplete measurement data. Furthermore, to ensure the robustness of the framework, an initial consistency analysis of the surrogate modeling approach is presented. By addressing the current challenges and refining the integration of data-driven techniques with traditional physics-based modeling, this framework aims to advance state estimation methodologies in the aerospace sector.
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All authors Sriram Narayanan, Mohamed Naveed Gul Mohamed, Ishan Paranjape, Indranil Nayak, Suman Chakravorty, Mrinal Kumar
Systems and Control · Cross-listed from cs.NI
Direction-Dependent Path Loss Modeling in Olive Orchards for Precision Agriculture
Mohammad Rowhani Sistani, Katarzyna Kosek-Szott and Pierluigi Gallo
Wireless links deployed in orchards often exhibit significant variability in the strength of the received signal that is not adequately captured by classical distance-based propagation models. In row-structured olive groves, signal attenuation differs markedly between along-row and cross-row propagation directions, leading to discrepancies when using omnidirectional propagation assumptions such as those adopted in the Free Space Path Loss (FSPL) model or ITU-R vegetation loss formulations. This paper proposes a
topology-based propagation model that explicitly accounts for orchard layout and the relative positions of
radio devices within the plantation structure. Experimental validation was conducted using LoRa technology operating at 868 MHz, and the results were compared with established models from the literature and with the proposed two-dimensional model. The proposed approach achieves a closer fit to measured RSSI data than conventional models, providing a more reliable basis for link budgeting and network planning in structured agricultural environments.
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Signal Processing · Cross-listed from cs.IT
On the Practical Performance of Noise Modulation for Ultra-Low-Power IoT: Limitations, Capacity, and Energy Trade-offs
Felipe A. P. de Figueiredo, Pedro M. R. Pereira, Evandro C. Vilas Boas et al. (+3 more)
Ultra-low-power (ULP) Internet of Things (IoT) applications demand communication architectures with minimal energy consumption. Noise Modulation (NoiseMod) addresses this by encoding data through the statistical variance of a noise-like signal, eliminating the need for a coherent carrier. To bridge the gap between theoretical potential and practical deployment, this paper
benchmarks NoiseMod against standard modulations like BPSK and NC-FSK. We analytically derive the optimal detection threshold and Bit Error Rate (BER) for AWGN and Rayleigh fading channels. Our results show that non-coherent NoiseMod suffers a catastrophic error floor in fading environments, making architectural additions like channel state information (CSI) estimation and 2-
antenna selection diversity desirable. Using an ADC-aware energy model, we reveal that NoiseMod's oversampling severely bottlenecks capacity and imposes an 8 dB SNR penalty compared to NC-FSK for a $10^{-3}$ BER in AWGN. Despite its oscillator-free design drastically reducing baseline
circuit power, these limitations establish a critical energy crossover distance, which decreases with frequency. Below this distance, NoiseMod offers superior energy efficiency; beyond it, the radiated power needed to overcome its SNR penalty makes coherent schemes like BPSK vastly superior.
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All authors Felipe A. P. de Figueiredo, Pedro M. R. Pereira, Evandro C. Vilas Boas, Fernando D. A. Garcia, Hadi Zayyani, Rausley A. A. de Souza