Systems and Control
Experimental Validation of Combined Imaging and Vibration Mitigation for High-Altitude Platforms
Ákos Radványi, Anita Keszler, Dániel Balogh et al. (+6 more)
The demand for continuous, high-resolution aerial monitoring is driving interest in High- Altitude Pseudo-Satellites (HAPS), which offer a cost-effective alternative to satellites with greater flexibility. While HAPS platforms provide valuable Earth observation potential, se- lecting an optical sensor requires balancing resolution and payload constraints, given their lightweight structures. We propose a multidisciplinary concept to enhance the Earth obser- vation capabilities of HAPS using a commercially available, weight-efficient distributed camera network, combined with advanced image processing and active vibration control. A super- resolution pipeline is introduced, where images from individual cameras are preprocessed and fed into a super-resolution
algorithm. The low structural rigidity of HAPS platforms increases their sensitivity to low-frequency vibrations, which must be mitigated through active control to preserve imaging performance. A nominal H-infinity mixed-sensitivity controller is designed to suppress resonance peaks at camera locations. The methodology is experimentally validated on a simplified wing platform designed for real-time testing. Quantitative evaluation based on nine metrics demonstrates significant image quality improvements, highlighting the effectiveness of H-infinity control in mitigating low-frequency elastic modes and stabilizing the platform for super- resolution. These results show that lightweight commercial sensors, combined with advanced image processing and control, can deliver high-quality imaging for future HAPS remote sensing missions.
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All authors Ákos Radványi, Anita Keszler, Dániel Balogh, Béla Takarics, András Majdik, Andrej Tokarjev, Gábor Kovács, Tamás Szirányi, Bálint Vanek
Systems and Control · Cross-listed from math.OC
Communication-efficient ADMM over Hierarchical Networks
Binh Nguyen, Shuangqing Wei and Truong X. Nghiem
This paper develops a novel distributed optimization
algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve hierarchical optimization problems over tree-structured networks, termed hierarchical ADMM (hADMM), with a particular focus on enhancing communication efficiency across the network. By rearranging the augmented Lagrangian to establish a query-response communication mechanism between nodes that explicitly exploits the hierarchical tree structure, the proposed
algorithm significantly reduces communication costs compared to existing ADMM-based methods for hierarchical optimization. Furthermore, hADMM guarantees asymptotic convergence under convexity assumptions. We also present a convergence rate analysis based on linear
matrix inequalities to characterize the maximum theoretically achievable convergence rates across different network
topologies, showing that the proposed hADMM attains linear convergence under mild conditions. Three numerical experiments demonstrate that hADMM is compatible with arbitrary tree network structures and outperforms existing approaches in terms of communication efficiency.
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Signal Processing
3D Protraction Technique for Anchor-Free, Range-Free Wireless Localization in Industrial Warehouses via Multi-Level Binary Reachability
Peyman Moeini and Mark Coates
Three-dimensional (3D) localization is difficult in industrial facilities because fixed-node placement is constrained by hardware, power, networking, installation, and maintenance costs. This paper presents the 3D Protraction Technique (3D-PT), an anchor-free, range-free method that converts reciprocal packet detections at ordered transmit-power levels into reliability-weighted ordinal distance relations. It estimates a relative 3D configuration without Received Signal Strength Indicator (RSSI) magnitudes, estimated ranges, surveyed anchors, or prior coordinates. In a simulated multi-height warehouse, four transmit-power levels reduce mean network-diameter-normalized RMSE by 16.0% relative to MDS-MAP(P) in both small- and medium-network regimes, and by 23.9% and 34.0% relative to CATL-reproduction, respectively. Accuracy largely saturates beyond four power levels. The results show that multi-level binary reachability provides useful
geometric information beyond a single maximum-power connectivity graph.
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Systems and Control
From Learning to Control: Data-Driven Multi-Agent Reinforcement Learning for Multivariable Control in a Microalgae Bioprocess
Juan D. Gil, Ehecatl Antonio Del Rio Chanona, José Luis Guzmán et al. (+1 more)
Effective control of bioprocesses is particularly challenging due to the intrinsic nonlinearity and dynamic variability of living-
cell systems. In microalgae-based photobioreactors (PBRs), maintaining stable pH and dissolved oxygen (DO) levels is critical for optimal growth and productivity, yet their strong coupling and sensitivity to environmental fluctuations make multivariable control difficult. This study proposes a novel hybrid offline-online Multi-
Agent Reinforcement Learning (MARL) framework for simultaneous pH and DO regulation, leveraging Deep Deterministic Policy Gradient (DDPG)
agents to achieve a fully data-driven and model-free control solution. The
agents are trained using historical data generated by an expert system, eliminating the need for direct experimentation with the environment. After deployment, the
agents operate autonomously, continuously fine-tuning their policies daily to adapt to evolving process dynamics and reject fast transient disturbances. Experimental validation in an open, industrial-scale PBR at the University of Almeria demonstrated the framework's capability to maintain stable operation under realistic conditions. The results confirm that model-free MARL control provides a robust and adaptive alternative for complex bioprocess environments.
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All authors Juan D. Gil, Ehecatl Antonio Del Rio Chanona, José Luis Guzmán, Manuel Berenguel
Systems and Control · Cross-listed from cs.RO
No Free Checker: A Survey of Verifiers for Robot Policies
Yang Wan, Xihang Yue, Zhirui Liu et al. (+7 more)
A verifier for
robot policies reads a candidate behavior and returns a score for how well it did, used both to evaluate
vision-
language-action policies and to train them. Verifiers range from success detectors and reward models to runtime monitors, safety filters, and temporal-logic specifications. We survey roughly 150 verifiers and compare them along two properties. Availability is how much a verdict costs, how early in a rollout the verdict arrives, and how often a verdict can be asked for. Availability rises as verdicts get cheaper, earlier, and denser. Credibility is how much a high score tells us about the task. Credibility falls as the judgment becomes gameable and self-serving. We group the verifiers by who supplies the judgment: human verifiers, rule-based and formal verifiers, learned and pretrained verifiers, and model-intrinsic verifiers. Across the four families, we find that credibility falls as availability rises. Regardless of who supplies the judgment, there is no free checker. We then examine what validates a verifier itself, and how much a high score tells us. Three measures appear in the literature: agreement with human labels, the performance of the policy it trains, and behavior under reward hacking. We close with nine metrics that make a verifier claim checkable, and coordinates for the verifiers still to be built.
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All authors Yang Wan, Xihang Yue, Zhirui Liu, Ziyuan Chu, Shuxun Wang, Yuhan Chen, Xiaonan Jiang, Xukun Zhu, Yubo Dong, Linchao Zhu
Signal Processing
Omnidirectional UMi Path Loss Models for 3GPP Extension Above 100 GHz Using Extensive Empirical Data Sets from 6.75 to 142 GHz
Xingchen Liu, Homa Nikbakht, Mingjun Ying et al. (+5 more)
Extending standardized urban microcell (UMi) path loss (PL) models from the upper mid-band (FR3) to sub-terahertz (sub-THz) frequencies requires measurement-based comparisons spanning a wide spectral range within a unified framework. In this paper, we derive single- and multi-frequency omnidirectional PL models for UMi environments based on extensive NYU WIRELESS measurements at 6.75, 16.95, 28, 73, and 142 GHz in line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. Single-frequency close-in (CI) and floating-intercept (FI) models for each band show that CI yields more stable and physically interpretable parameters, with shadow fading standard deviations within 0.98 dB of FI across all five frequencies. We then extend the multi-frequency PL analysis to the 7-24 GHz, 0.5-100 GHz, and 0.5-150 GHz frequency ranges using CI, close-in with frequency weighting (CIF), and alpha-beta-gamma (ABG) models. Across progressively wider frequency spans, CI and CIF maintain stable distance exponents that remain closely aligned with the 3rd Generation Partnership Project (3GPP) UMi interpretations, whereas ABG offers only very modest reductions in fitting error at the cost of much greater parameter sensitivity. These results support physically anchored CI/CIF formulations with a close-in free space path loss anchor point at 1 m in order to extend 3GPP-oriented UMi PL models over the entire 0.5 to 150 GHz frequency range.
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All authors Xingchen Liu, Homa Nikbakht, Mingjun Ying, Dipankar Shakya, Hitesh Poddar, Art Ishii, Daniel Abraham, Theodore S. Rappaport
Systems and Control · Cross-listed from cs.RO
Online, Reachability-Aware, Sampling-Based Motion Planning
Brendan Gould, Zhiyuan Zhang, Panagiotis Tsiotras et al. (+1 more)
Sampling-Based Model-Predictive Control (MPC)
algorithms are a flexible class of controllers used for navigation on a wide range of robotic systems. Historically, such approaches have lacked hard safety guarantees, a shortcoming which we remedy in this work by computing guaranteed reachable-set overapproximations online with a fast, interval-based pipeline. We show that our method achieves similar performance to a state-of-the-art reachability-based planner without the need for the expensive pre-computation step, and can be scaled to systems that are infeasible using existing approaches. Finally, we demonstrate that our technique reduces safety violations by over 99% in a racing simulation and successfully controls a model racecar on real hardware experiments without crashes.
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All authors Brendan Gould, Zhiyuan Zhang, Panagiotis Tsiotras, Samuel Coogan
Systems and Control
Efficient Quantile-Resolved Hosting Capacity Assessment on Nodal Level for Low-Voltage Grids
Maximilian Köhler, Edwin Mora, Mathias Duckheim et al. (+1 more)
Hosting capacity - the maximum additional capacity a network can accommodate without violating operational limits - is a key metric in distribution system planning and operation. Decisions on grid reinforcements and the deployment of flexibility management require not only the worst-case HC but also an understanding of the distribution of HC under different likelihoods in load and generation patterns.
Quantile-resolved HC distributions provide this view by expressing HC as a function of an acceptable operational limit exceedance likelihood. Monte Carlo sampling is the established approach for computing such distributions but demands large computational resources. Approximations sacrifice either accuracy, the ability to capture uncertainty correlations, or scalability when assessing real-world networks. This paper introduces a computationally efficient method for calculating distributions for
quantile-resolved HC. It uses a representation of load samples as multivariate normal distribution, propagated through a linearized power flow model. This allows for leveraging a re-parametrized AC-OPF problem for each hosting capacity
quantile. Benchmarking against Monte Carlo-based methods on realistic LV networks demonstrates that the proposed method achieves comparable accuracy with a mean deviation of approx. 3%, while reducing computational time by orders of magnitude. For the exemplary networks the computational time decreases from 11 min to 2 s, and 38 h to 50 s, respectively. The method's scalability is also suitable for recalculation in 15-minute cycles encountered in DSO practice for e.g., real-time grid management.
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All authors Maximilian Köhler, Edwin Mora, Mathias Duckheim, Stefan Niessen
Systems and Control · Cross-listed from math.CT
Time-Varying Data as Sheaves: an Invitation to Narratives
Wilmer Leal, Benjamin Merlin Bumpus, Jana K. Nickel et al. (+3 more)
Modern science and engineering increasingly rely on time-varying data, yet the mathematical tools used to model temporal phenomena are often developed within separate disciplines, obscuring common principles and limiting the transfer of ideas across fields. This chapter presents the theory of narratives, an abstract framework for time-varying objects of any mathematical kind that supports both theoretical investigations and applications. To illustrate this perspective, the chapter develops three vignettes, each illustrating a different research direction. The first addresses a general concern: What information loss can occur when switching between different representations of temporal data? The second concerns structural and algorithmic approaches: How can we systematically decompose time-varying data into simple pieces and obtain invariants describing its structural complexity? The third is an application to control theory: How can we model multi-
agent systems with switching communication
topologies? More important than any individual vignette, the central message of this invitation is that a suitable abstract perspective can organize and guide research across remarkably diverse mathematical and scientific domains.
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All authors Wilmer Leal, Benjamin Merlin Bumpus, Jana K. Nickel, Johan García, James Fairbanks, Warren Dixon
Image and Video Processing · Cross-listed from cs.CV
Spheriverse: 3D Scene Understanding from Spherical Observations in the Wild
Fei Teng, Sheng Wu, Mengfei Duan et al. (+7 more)
Spherical observations provide global visual context for 3D scene understanding. However, visual information is encoded in an angular domain, whereas the physical world is represented in Cartesian coordinates. This cross-space representation gap complicates
geometric correspondence and semantic evidence aggregation. To delve into this challenge, we introduce Spheriverse, comprising 64,400 temporally aligned spherical image-
LiDAR pairs organized into 644 sequences. The
dataset spans diverse scenes, illumination, and weather conditions, with fine-grained semantic classes. We further establish
benchmarks for semantic occupancy prediction, semantic mapping, and 3D object detection, evaluating 30+ methods through overall and scene-wise comparisons. For dense prediction, we propose SphereOcc, an occupancy framework that couples spherical geometry modeling with semantic evidence
retrieval. Cartesian-Spherical Representation Remodeling (CSRR) incorporates spherical range-azimuth geometry into Cartesian voxel features through region-wise modulation. Spherical Evidence Re-querying (SER) then conditions queries on voxel content and range-height-azimuth geometry to adaptively retrieve relevant semantic evidence from source spherical image features. SphereOcc achieves 13.91% mIoU and 24.65% GeoIoU, yielding relative improvements of 13.9% and 9.3% over the respective best-performing methods, TPVFormer and SurroundOcc. It also ranks first in both metrics across all five scene categories, with consistent advantages across the evaluated spatial partitions and reduced fields of view. The established
benchmark and source code will be available at https://feit-feiteng.github.io/Spheriverse.
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All authors Fei Teng, Sheng Wu, Mengfei Duan, Guoqiang Zhao, Junhui Ma, Kai Luo, Siyu Li, Hao Shi, Zhiyong Li, Kailun Yang
Systems and Control
Intelligent Fault and Lightning Detection Algorithm for VSC-MTDC grids based on ResNet with Hybrid Attention Mechanism
Yunqi Zhang
To address the existing challenges in fault detection for voltage source converter-based multi-terminal DC (VSC-MTDC) grids, this paper proposes an intelligent fault and lightning detection
algorithm based on S transform and Residual Network with hybrid
attention mechanism (RWHAM).The DC line double-ended initial current traveling waves (ICTWs) are first converted into time-frequency
matrices by performing S transform and then visualized as a two-dimensional image. The image effectively characterizes the time-frequency features of ICTWs under different fault and lightning conditions, making it easier to extract critical features for the models. Next, the RWHAM model is constructed and the images are fed into the model to detect internal and external faults and lightning interference. The weight of the key information in the image is increased by hybrid
attention mechanism, which in turn improves the fault detection ability of the RWHAM model. Extensive simulations involving 21,620 distinct cases on PSCAD/EMTDC validate the
algorithm's high accuracy and rapid response across different types of faults and lightning interference. Furthermore, it exhibits excellent generalization ability when the parameters of VSC-MTDC grids change.
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Signal Processing · Cross-listed from cs.LG
Physics-Informed Deep Learning for False Ventricular Tachycardia Alarm Reduction in the ICU
Athanasios Papastathopoulos-Katsaros, Alexandra Stavrianidi and Zhandong Liu
False ventricular tachycardia (VT) alarms are a leading contributor to alarm fatigue in intensive care units. We propose a deep learning framework combining a 1D SE-ResNet with ICU-realistic data augmentations and a physics-informed auxiliary reconstruction task based on the three-element Windkessel hemodynamic model, implemented as a differentiable forward simulation. By requiring the network's latent representation to produce physiologically plausible arterial pressure waveforms, artifact-driven ECG patterns are penalized while true VT remains coherent across modalities. Evaluated on the VTaC
benchmark under a strict real-time protocol (10-second pre-alarm window), our method achieves a 5-point Challenge Score improvement over prior state-of-the-art. Ablation studies confirm that the physics-informed objective is the primary performance driver, providing gains in accuracy, 2x label efficiency, and more localized and clinically meaningful ECG segments.
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Audio and Speech Processing
Orantqing, Shengpeng Ji, Junlong Tong et al. (+19 more)
In this work, we present Gander, a native
multimodal duplex interaction model that builds on MiniCPM-o 4.5 and is further adapted for realtime interaction with an asynchronous
agent loop. In contrast to conventional turn based systems, Gander continuously processes streaming user inputs, enabling full-duplex interaction in both everyday conversations and complex workflow
agent scenarios. Users can interrupt an ongoing response, while the model can proactively provide intermediate feedback or ask follow up questions. To natively support these capabilities, Gander adopts two key architectural designs: 1) a Cerebellum-
Brain collaborative framework, Cerebellum is responsible for realtime interaction while the
Brain handles complex
reasoning and higher level agentic tasks. The two components interact continuously through tool calling and the
agent orchestration runtime. 2) The Cerebellum is built upon a streaming Thinker-Talker architecture, where user inputs and model outputs are flattened into an ordered
token stream at the chunk level. We evaluate Gander across conversational ability, interactive capability, understanding, and tool assisted task execution. Internal human evaluations show that Gander maintains natural and expressive spoken dialogue, while
benchmark results demonstrate effective turn taking capability and encouraging results on spoken question answering and related understanding tasks. Gander also supports a range of challenging interaction settings, including background noise interference, multi-party interactions, and backchannel communication. While our current evaluation focuses on tool assisted settings, broader long horizon
agent tasks and more diverse deployment conditions remain promising directions for further study. We release Gander together with its models, code, and data to facilitate further research and development in the community.
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All authors Orantqing, Shengpeng Ji, Junlong Tong, Jialong Zuo, Dongjie Fu, Di Cao, Yangzhuo Li, Shangda Wu, Franz, Evan, Theron Veyra, Changhao Pan, Jingyu Lu, Dongchao Yang, Zhifei Xie, Yang Tan, Xiaoyu Shen, Xiaoda Yang, Wenfu Wang, Teddy Sun, Steve Yves, Zhou Zhao
Systems and Control
Unifying Power Flow and Electromagnetic Transient Modeling
Tina Gao, Aayushya Agarwala, Timothy McNamara et al. (+1 more)
Grid tools are separated by timescales: steady-state analysis is performed by power flow (PF), whereas the fastest dynamics are captured by electromagnetic transient (EMT) simulation. Although operating at varying timescales, different tools should produce consistent results when analyzing the same grid conditions. However, the PF steady-state solution does not match the time-to-infinity EMT response. This mismatch exists because simplified device models in PF are inconsistent with the "ground truth" EMT models derived from first principles. As inverter-based resources (IBRs) and data centers strip away inertia and operating reserves, approximations used in PF risk grid security by missing violations. To address this, we introduce a steady-state framework that uses full-physics EMT models for high-fidelity steady-state grid analysis. The challenge lies in systematically formulating algebraic frequency-domain representations of dynamic power devices that are naturally described in state-space form. Our tool, SALT (Steady-state After Last Transients), constructs steady-state representations that exactly capture grid device physics when embedded into transmission networks. The approach exploits the structural properties of power device models and the single-harmonic, balanced nature of the transmission system. Results demonstrate SALT achieves EMT steady-state accuracy while delivering a 1x10^6 times speedup. We demonstrate that SALT can capture security threats in contingency scenarios without exception — whereas PF-based analyses reported 75% fewer rated line violations, 18% fewer Q-limit violations, and 7% fewer voltage violations.
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All authors Tina Gao, Aayushya Agarwala, Timothy McNamara, Lawrence Pileggi
Systems and Control
The consequences of high SMR operating costs in electricity markets
Pradyumna Rao, Daniel T. Kaffine and Bri-Mathias Hodge
As US power
markets contend with growing demand for firm generation, the nuclear industry has offered Small Modular Reactors (SMRs). However, how these concepts would fare in a rapidly evolving power grid is unclear, given the paucity of operational examples. Current literature, informed by substantial cost escalations for traditional nuclear plants, focuses on the investment costs SMRs need to achieve for private investment feasibility. However, this work finds that the operating and marginal costs of SMRs are more critical to economic feasibility in
market environments. This work dispatches SMRs using a flexible operations model, considering revenue from two main electric
markets, capacity and wholesale energy
markets, with and without policy support. Manufacturer advertised costs for investment and operating costs are used, with fuel costs calculated from manufacturer provided design parameters. Results indicate that SMRs are uneconomical primarily because investment cost reductions are offset by increased marginal costs. As such, an environment of prices and subsidies beyond historic norms are necessary to attract private investment at manufacturer advertised cost
benchmarks. Current SMRs are as profitable as advanced estimates of the AP1000 traditional nuclear reactor, and if investment costs escalate at the average rate for nuclear projects, they are similar to Vogtle 3 & 4. In projected future power
markets, reductions in marginal cost may be more beneficial than those in investment costs.
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Audio and Speech Processing · Cross-listed from cs.SD
From Scores to Evidence: Auditable Decisions Can Improve Speech Deepfake Detection
Mengzhe Geng, Yujia Lu, Manuela Kunz et al. (+1 more)
Speech deepfake detectors usually emit one score per utterance, but a borderline score does not reveal why two examples differ during retrospective error analysis. We ask whether a final score can be calibrated from component fields while keeping those fields visible for inspection. We build a decision record with a passive detector score and a score from a probe applied to a marked copy. It also includes
retrieval support held out of the evaluated family, a margin from a support-set profile, and raw neighbor closeness. A cross-fit calibrator combines these fields and two differences between raw scores into one final score. On matched ASVspoof development data, the calibrated record reduces equal error rate (EER) by 3.48 percentage points relative to the fixed
retrieval-augmented rule. It reaches 8.43% EER, whereas a passive WavLM baseline reaches 6.71% on the same subset. The record is therefore not the strongest detector in this comparison. Its value is to retain inspectable component fields while producing one scalar score for retrospective diagnosis.
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All authors Mengzhe Geng, Yujia Lu, Manuela Kunz, Patrick Littell
Signal Processing
A Material-Aware Channel Model for Efficient CKM Generation via Environment Reconstruction
Xuancheng Zhu, Zhenjun Dong, Yong Zeng et al. (+1 more)
Channel knowledge map (CKM) is a promising technology for environment-aware wireless communication, sensing, and localization in 6G networks. Accurate CKM generation requires precise reconstruction of the environment, including 3D geometries and scatterer materials, typically from multi-modal sensory observations such as
LiDAR point clouds and sparse channel measurements. While the former is relatively easy to acquire, materials remain difficult to obtain directly from sparse channel measurements due to the lack of an explicit channel model linking them. To fill this gap, this paper proposes a material-aware channel model that explicitly characterizes the influence of scatterer materials on the wireless channel. Based on this model, an iterative gradient descent based material reconstruction
algorithm is proposed. Full wave simulation results validate the developed model and the proposed
algorithm, demonstrating their potentials for efficient CKM generation via environment reconstruction.
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All authors Xuancheng Zhu, Zhenjun Dong, Yong Zeng, Cheng-Xiang Wang
Systems and Control
Asynchronous Model Predictive Control Under Model Mismatch: Stability and Performance Guarantees
Changrui Liu, Anil Alan, Shengling Shi et al. (+1 more)
Certainty-equivalence model predictive control (CE-MPC) is widely used for its simplicity and efficiency, but theoretical guarantees under asynchronous feedback remain limited. This paper establishes stability and performance guarantees for asynchronous CE-MPC of input-constrained nonlinear systems. We first derive a nominal stability condition and competitive-ratio bound that explicitly account for inter-execution intervals without prescribing a feedback mechanism. A value-function perturbation analysis for quadratic stage costs then accommodates additive, potentially non-smooth model mismatch without constraint qualification conditions. Combining these results yields stability criteria and competitive-ratio bounds for CE-MPC under general asynchronous feedback, including event/self-triggered and multi-step MPC. The guarantees explicitly relate prediction horizon, inter-execution time, and uncertainty magnitude, quantifying performance degradation relative to an ideal infinite-horizon controller. These results clarify tradeoffs between feedback frequency, model accuracy, and horizon length, guiding asynchronous MPC design using approximate or learned models.
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All authors Changrui Liu, Anil Alan, Shengling Shi, Bart De Schutter
Audio and Speech Processing
Interpreting Dolphin Vocal Sequences via Multiple Sequence Alignment
Daniel Kohlsdorf, Denise Herzing and Thad Starner
Dolphin communication understanding is essential for uncovering the linguistic complexity and social structures of wild pods. We adapt the ClustalW bioinformatics
algorithm to analyze continuous acoustic data, treating vocalizations as high-dimensional spectral feature vectors. By replacing discrete scoring with a continuous Gaussian kernel similarity measure, our framework generates Multiple Sequence
Alignment (MSA) visualizations that reveal shared structural patterns. These
alignments highlight temporal motifs such as synchronized burst pulses in aggressive contexts that are difficult to detect through standard spectrogram inspection.
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Systems and Control · Cross-listed from math.OC
Optimal input design via Frank-Wolfe
Fethi Bencherki, Bruce Lee, Nikolai Matni et al. (+1 more)
We study optimal input design over a finite horizon for linear dynamical systems. The goal is to minimize a weighted inverse-covariance (information) criterion subject to an energy budget. The set of covariances achievable by causal policies is convex but lacks a tractable explicit description, ruling out projection-based methods. We show that Frank–Wolfe applies naturally: each linear minimization subproblem is a budget-constrained finite-horizon linear quadratic (LQ) problem, solvable by a Riccati recursion and one-dimensional bisection over a Lagrange multiplier. Using smoothness of the objective over the feasible set, we establish an $\mathcal{O}(1/M)$ convergence rate for the objective value, while strong convexity yields an $\mathcal{O}(1/\sqrt{M})$ rate for the iterates. We further extend the framework to input design for system identification with unknown dynamics and adaptive online LQR, and illustrate the approach numerically.
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All authors Fethi Bencherki, Bruce Lee, Nikolai Matni, Anders Rantzer
Systems and Control
NL-FRA: A MATLAB package for nonlinear frequency response analysis
Rajintha Gunawardena, Zi-Qiang Lang and Fei He
Nonlinear Output Frequency Response Functions (NOFRFs) provide a one-dimensional frequency-domain representation of nonlinear dynamics, enabling direct decomposition of an output spectrum into contributions from different orders of nonlinearity. NL-FRA (NonLinear Frequency Response Analysis) is an open-source MATLAB package implementing a data-driven Least Squares (LS) method for estimating NOFRFs directly from system input-output data. It provides an integrated workflow for NOFRF estimation, validation, and visualisation, together with nonlinear transmissibility analysis. The package supports different types of inputs, i.e. general band-limited, multi-one and harmonic inputs, and includes routines for identifying the frequency ranges over which each nonlinear order contributes to the output. Furthermore, since the LS approach estimates NOFRFs directly from input-output data, it can be applied to experimental measurements or data generated using any suitable dynamic model of the system. The software facilitates interpretable nonlinear frequency-domain analysis for applications including fault diagnosis, condition and structural health monitoring, and biomedical engineering.
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Systems and Control
Model Predictive Control for Dynamic Hydraulic Balancing in Building Radiator Heating Systems: Modeling, Design, and Experimental Validation
Erfan Shakhesi, Maurice Heemels and Paula Chanfreut
Hydronic radiator systems are among the most widely used heating systems in buildings. In the literature, radiator heat outputs are often assumed to be independent of one another and arbitrarily adjustable in time when designing controllers. However, in practice, radiators are supplied by one or multiple common heat sources and are hydraulically coupled through the water circulation system. Maintaining occupant comfort in multi-zone buildings therefore requires not only an appropriate supply temperature but also proper hydraulic balancing to distribute the available water flow according to the heating demand of each zone. To this end, we develop a grey-box thermal model that captures the hydraulic interactions among radiators and the effects of radiator valves, circulation pumps, and heat sources. In a real building, we show that accounting for hydraulic interactions reduces the root mean square error (RMSE) between the measured and modeled zone temperatures by approximately 11% compared with a model that neglects these interactions. Additionally, we integrate the developed model into a model predictive control (MPC) framework for dynamic hydraulic balancing that jointly optimizes valve openings and the supply temperature to maintain thermal comfort in each zone while reducing energy consumption. Through both real-world experiments and numerical case studies, we demonstrate that, compared with existing MPC formulations that neglect hydraulic interactions or do not control radiator valves, the proposed MPC reduces comfort-range violations by at least 27% while requiring a similar or even lower cumulative supply temperature.
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Signal Processing · Cross-listed from astro-ph.IM
Full Characterisation of the Polarisation Primary Beam of the GRAO 32-m Telescope
Theophilus Ansah-Narh, Nia Imara, Benedicta Woode et al. (+4 more)
Direction-dependent instrumental polarisation is a major systematic limitation in high-fidelity single-dish
radio polarimetry, yet a unified characterisation of beam leakage and the conditioning of polarisation recovery is lacking for the Ghana
Radio Astronomy Observatory (GRAO) 32-m
telescope. We aim to establish a quantitative, direction-dependent polarimetric beam model at 5.0 and 6.7 GHz. High-resolution GRASP electromagnetic simulations of the nominal
telescope configuration are used to derive the full complex Jones response and corresponding Mueller
matrices, from which beam shape, beam squint, instrumental Stokes leakage, and intrinsic cross-polarisation ratio (IXR) are evaluated across the primary beam. The Stokes $I$ beams have half-power beam widths of $396.4$ and $295.3$ arcsec at 5.0 and 6.7 GHz, respectively, with main-beam efficiencies of $56.4$ and $55.6$ per cent. At 5.0 GHz, the circular-polarisation beam squint is $9.96$ arcsec ($2.5$ per cent of the HPBW), whereas no statistically significant squint is detected at 6.7 GHz. Leakage from Stokes $I$ into linear polarisation remains below $0.1$ per cent within the half-power beam but increases substantially towards the sidelobes. IXR reaches approximately $80$ dB on axis and decreases with angular offset. These results establish the intrinsic electromagnetic polarimetric response of the GRAO 32-m
telescope and provide a quantitative baseline for direction-dependent calibration and subsequent observational validation.
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All authors Theophilus Ansah-Narh, Nia Imara, Benedicta Woode, Joseph Bremang Tandoh, Emmanuel Proven Adzri, Diana Klutse, Evaristus Uzochukwu Iyida
Systems and Control
Entropic Risk-Sensitive Evolutionary Learning and Equilibrium Selection in Coordination Games
Solaleh Mohammadi, Xiang Gao and Kaiqing Zhang
We study risk-sensitive evolutionary learning dynamics and their long-run
equilibrium selection behaviors in coordination games.
Agents' risk attitudes enter through the classical entropic risk measure, which evaluates opponent-induced payoff uncertainty and feeds into noisy best responses under two standard revision protocols: best response with mutations and logit choice. We first analyze $2\times 2$ coordination games in both single-population symmetric and two-population asymmetric settings. In the single-population setting, unlike the risk-neutral case where the dynamics are known to favor the risk-dominant
equilibrium, we show that risk sensitivity can change the stochastically stable outcome: a greater risk-seeking attitude favors the payoff-dominant
equilibrium, while a greater risk-averse attitude favors the maximin
equilibrium. Thus, the population's risk attitude may act as a control knob for long-run
equilibrium selection. In both population settings, we also identify a robust regime: any super-dominant
equilibrium is stochastically stable for all risk attitudes, under both protocols, and across populations. We further extend the single-population analysis to symmetric $k$-action games, which include symmetric $k$-action coordination games as a special case, under risk-sensitive best response with mutations. In this setting, we show that, for sufficiently large populations, sufficiently risk-seeking
agents uniquely select the strongly payoff-dominant
equilibrium when it exists, whereas sufficiently risk-averse
agents uniquely select the strongly maximin
equilibrium when it exists. These results show that entropic risk sensitivity may serve as a systematic mechanism for steering
equilibrium selection in evolutionary games, beyond the classical risk-neutral
benchmark.
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Signal Processing
Partial Superimposed Pilot-Aided Sparse Vector Transmission for High-Mobility URLLC
Yanfeng Zhang, Jinkai Zheng, Hui Liang et al. (+4 more)
A partial superimposed pilot-aided sparse vector transmission (PSP-SVT) scheme is proposed for short-packet ultra-reliable and low-latency communications in high-mobility scenarios. Unlike conventional full superimposed pilot-aided SVT schemes, the proposed PSP-SVT scheme deploys only a few pilots over a subset of subcarriers. This sparse pilot structure is sufficient for basis expansion model based channel tracking while effectively reducing pilot-data interference. Based on the PSP pattern, an iterative receiver is developed to jointly perform channel estimation and data decoding. The reduced pilot interference in PSP-SVT provides more accurate initial channel estimation and data detection, thereby improving the subsequent iterative refinement and mitigating their error propagation. Moreover, the impacts of the number of PSPs and power allocation ratio on block error rate (BLER) performance are investigated to reveal the near-optimal pilot configuration. Simulation results show that the proposed PSP-SVT scheme outperforms existing full superimposed pilot-aided SVT schemes in terms of BLER with fast convergence speed.
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All authors Yanfeng Zhang, Jinkai Zheng, Hui Liang, Wen Wu, Tom H. Luan, Di Zhang, Xu Zhu
Signal Processing
CrossRAFT: Cross-Domain Complex-Valued Feature Extraction for Ultrasound Motion Estimation
Yang Leng, Yuchen Tang, Kai-Hang Yiu et al. (+2 more)
Accurate multi-dimensional motion estimation is fundamental to broad biomedical ultrasound applications, primarily performed using real-valued
radio-frequency (RF), analytic, and in-phase and quadrature (IQ) signals. Real-valued RF and analytic signals offer high subsample accuracy but are computationally intensive. Regardless of the signal domain used, traditional
algorithms suffer from interframe decorrelation, resolution tradeoffs, and a lack of a carrier and low sampling in the lateral direction. Emerging deep learning (DL) models demonstrate improved performance and faster inference. However, existing architectures are real-valued and disrupt inherent phase coupling crucial for accurate motion estimation by treating complex components as independent channels. To address this, we propose CrossRAFT, an end-to-end trainable network designed for subsample motion estimation directly from complex-valued ultrasound signals. Built on the RAFT architecture, CrossRAFT features complex-valued encoders and a custom correlation module to explicitly extract and use phase information. We evaluated CrossRAFT across real RF, analytic, and IQ signal presentations on simulated phantoms, synthetic echocardiographic, and private in vivo echocardiographic
datasets. Experimental results show that CrossRAFT estimates displacements accurately under complex tissue motion and in the challenging lateral direction. Compared to real RF-based RAFT, analytic-based CrossRAFT reduces vertical and horizontal displacement errors by 69% and 51% on the synthetic echocardiographic data, respectively. These findings demonstrate that leveraging phase information via complex-valued neural networks provides a powerful framework for ultrasound motion estimation, showing potential for clinical elastography and functional imaging.
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All authors Yang Leng, Yuchen Tang, Kai-Hang Yiu, Yik-Chung Wu, Wei-Ning Lee
Signal Processing
Efficient Deep Learning Adaptation for Cross-Environment RSS-Based Indoor Localization
Cien Zhang and Jiaming Zhang
Received signal strength (RSS)-based indoor localization has attracted increasing
attention due to its low cost and compatibility with existing wireless infrastructures. However, RSS measurements are highly sensitive to environmental variations, making it challenging for deep learning-based localization models to generalize across different physical configurations. Driven by these challenges, this paper proposes a scenario-adaptive RSS localization framework based on backbone reuse and efficient adaptation. The proposed architecture consists of a lightweight extractor and a shared backbone, where the extractor projects heterogeneous RSS inputs into a unified feature space and the backbone captures transferable localization knowledge. During adaptation, the backbone trained from the source
dataset is reused, while the extractor is optimized to adapt to the new
dataset configuration. A short optimization stage is further introduced to slightly refine the backbone with a lower learning rate. Experimental results on four
datasets demonstrate that the proposed training strategy enables faster convergence and improved adaptation compared with training from scratch. In addition, the proposed framework reduces training time under different
dataset configurations, verifying its effectiveness and efficiency for adaptive RSS-based indoor localization.
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Signal Processing · Cross-listed from cs.IT
Sequential Lossy Compression With Causal Conditional Perception
Photios A. Stavrou and Zixuan He
In this paper, we study sequential lossy compression under a causal conditional perception criterion comparing source and reconstruction distributions given the same reconstruction history. For first-order Markov sources, we formulate the finite-horizon nonanticipative rate-distortion-perception function (NRDPF) with stagewise constraints and establish one-shot lower and upper bounds on the minimum variable-length sum rate using a strengthened strong functional-representation lemma (SFRL) and common randomness. For time-varying scalar Gauss–Markov sources under pointwise mean-squared error (MSE) and conditional squared Wasserstein-$2$ fidelity, we prove Gaussian optimality, derive a log-variance characterization, and obtain a closed-form solution that recovers the classical Gaussian nonanticipative rate-distortion function (NRDF) when perception is unconstrained and the classical Gaussian RDPF when the source is stationary and memoryless.
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Signal Processing
FedGenSC: Federated Generative Semantic Communication with Channel-Aware Adaptation
Rita Abou Fares, Razan Al Kakoun, Maher Nouiehed et al. (+1 more)
Integrating generative
adversarial networks (GANs) into
federated semantic communication (SemCom) is a natural progression, as generative priors can recover semantic fidelity under channel distortion that discriminative decoders cannot. However, naive GAN federation introduces three failure modes that prior work has, to the best of our knowledge, neither identified nor resolved: discriminator aggregation instability under non-independent and identically distributed (non-IID) data, semantic drift caused by divergent local
embedding spaces, and channel-agnostic generation that cannot adapt to heterogeneous link conditions. We propose
federated generative semantic communication (FedGenSC), which mitigates all three by employing a global generator with local-only discriminators, providing cross-client semantic information through a semantic prototype bank, and conditioning generation on the instantaneous signal-to-noise ratio (SNR). Experiments on the Europarl
dataset over Rayleigh fading channels (K=10 clients, Dirichlet {\alpha}=0.5) show that FedGenSC under non-IID data outperforms the FedDeepSC baseline across the tested SNR range, achieving up to a 58.2% relative improvement in bilingual evaluation understudy (BLEU)-1 at 18 dB. Ablation studies confirm the independent contribution of each component.
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All authors Rita Abou Fares, Razan Al Kakoun, Maher Nouiehed, Hadi Sarieddeen
Audio and Speech Processing · Cross-listed from cs.HC
Rescuing Performance from the Demo: Co-Designing Drum Gesture Mappings with a Percussionist
Jordie Shier, Teresa Pelinski, Charalampos Saitis et al. (+2 more)
Augmenting instruments with sensors and neural network mappings is a well-explored digital musical instrument design approach. While augmentations can create new expressive opportunities, they also exert aesthetic influence and can constrain musicians' gestural
language, which, if left unchecked, can lead to technological capture. To examine this, we conducted a study with a professional percussionist, co-developing a gesture mapping toolkit and recording a ten-track album. Drawing on the concept of productive dissonance, our study aimed to hold the musician's aesthetic in tension with technological constraints. This, along with a practice-based reflective approach, supported the development of a continuous gesture recognition method for percussive mapping and surfaced insights into the design process. We identify knowing-when as a form of tacit knowledge that supported productive dissonance, and raise an open question: absent a musician's broader social context, how do we know whether a technology's influence is genuinely supporting their practice?
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All authors Jordie Shier, Teresa Pelinski, Charalampos Saitis, Andrew Robertson, Andrew McPherson