Papers

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Electrical Engineering and Systems Science

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Friday 20 March

Systems and Control

Mixed-Integer vs. Continuous Model Predictive Control for Binary Thrusters: A Comparative Study

Franek Stark, Jakob Middelberg and Shubham Vyas

Binary on/off thrusters are commonly used for spacecraft attitude and position control during proximity operations. However, their discrete nature poses challenges for conventional continuous control methods. The control of these discrete actuators is either explicitly formulated as a mixed-integer optimization problem or handled in a two-layer approach, where a continuous controller's output is converted to binary commands using analog-to digital modulation techniques such as Delta-Sigma-modulation. This paper provides the first systematic comparison between these two paradigms for binary thruster control, contrasting continuous Model Predictive Control (MPC) with Delta-Sigma modulation against direct Mixed-Integer MPC (MIMPC) approaches. Furthermore, we propose a new variant of MPC for binary actuated systems, which is informed using the state of the Delta-Sigma Modulator. The two variations for the continuous MPC along with the MIMPC are evaluated through extensive simulations using ESA's REACSA platform. Results demonstrate that while all approaches perform similarly in high-thrust regimes, MIMPC achieves superior fuel efficiency in low-thrust conditions. Continuous MPC with modulation shows instabilities at higher thrust levels, while binary informed MPC, which incorporates modulator dynamics, improves robustness and reduces the efficiency gap to the MIMPC. It can be seen from the simulated and real-system experiments that MIMPC offers complete stability and fuel efficiency benefits, particularly for resource-constrained missions, while continuous control methods remain attractive for computationally limited applications.

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Signal Processing

Codebook-Based Self-Sustainable RIS: Optimal Splitting Schemes and Power Allocation

Friedemann Laue, Sebastian Lotter, Nikita Shani et al. (+1 more)

This paper studies the codebook-based configuration of a reconfigurable intelligent surface (RIS) that extends the coverage of a base station (BS) while utilizing energy harvesting to facilitate self-sustainable operation. For a given coverage area, we design a RIS codebook and propose a mathematical framework for analyzing the efficiency of three common energy harvesting schemes: power splitting (PS), element splitting (ES), and time splitting (TS). Thereby, we use a tile-based architecture at the RIS to exploit the advantages of both radio-frequency (RF) combining and direct-current (DC) combining. Moreover, we account for deterministic and random transmit signals for beam training and data transmission, respectively, and show their impact on the RF-DC conversion efficiencies at the rectifiers. Our main objective is to minimize the average transmit power at the BS by jointly optimizing the splitting ratio for the incident signal at the RIS and the power allocated to each RIS codeword. While the optimal power allocation is derived analytically, we show that the optimal splitting ratio can be determined by performing a grid search over a single optimization variable. Our performance evaluation reveals that the efficiency of the optimized splitting schemes depends on the adopted power consumption model and the number of tiles at the RIS. In particular, our results show that depending on the system parameters a different splitting scheme will achieve the lowest transmit power at the BS.

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All authors Friedemann Laue, Sebastian Lotter, Nikita Shani, Robert Schober

Signal Processing

LSTM-Based Power Delay Profile Predictions for Intra-Bus Wireless Propagation

Rajeev Shukla, Atharva Verma, Aniruddha Chandra et al. (+8 more)

Longlshort-term memory (LSTM) is a deep learning model that can capture long-term dependencies of wireless channel models and is highly adaptable to short-term changes in a wireless environment. This paper proposes a simple LSTM model to predict the channel transfer function (CTF) for a given transmitter-receiver location inside a bus for the 60 GHz millimetre wave band. The average error of the derived power delay profile (PDP) taps, obtained from the predicted CTFs, was less than 10% compared to the ground truth.

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All authors Rajeev Shukla, Atharva Verma, Aniruddha Chandra, Ondrej Zeleny, Radek Zavorka, Jiri Blumenstein, Ales Prokes, Jaroslaw Wojtun, Jan M. Kelner, Cezary Ziolkowski, Domenico Ciuonzo

Signal Processing

A Deep Learning Approach to Multipath Component Detection in Power Delay Profiles

Ondrej Zeleny, Radek Zavorka, Ales Prokes et al. (+5 more)

Power Delay Profile (PDP) plays a crucial role in wireless communications, providing information on multipath propagation and signal strength variations over time. Accurate detection of peaks within PDP is essential to identify dominant signal paths, which are critical for tasks such as channel estimation, localization, and interference management. Traditional approaches to PDP analysis often struggle with noise, low resolution, and the inherent complexity of wireless environments. In this paper, we evaluate the application of traditional and modern deep learning neural networks to reconstruction-based anomaly detection to detect multipath components within the PDP. To further refine detection and robustness, a framework is proposed that combines autoencoders and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering. To compare the performance of individual models, a relaxed F1 score strategy is defined. The experimental results show that the proposed framework with transformer-based autoencoder shows superior performance both in terms of reconstruction and anomaly detection.

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All authors Ondrej Zeleny, Radek Zavorka, Ales Prokes, Tomas Fryza, Jaroslaw Wojtun, Jan M. Kelner, Cezary Ziolkowski, Aniruddha Chandra

Audio and Speech Processing

Plug-and-Steer: Decoupling Separation and Selection in Audio-Visual Target Speaker Extraction

Doyeop Kwak, Suyeon Lee and Joon Son Chung

The goal of this paper is to provide a new perspective on audio-visual target speaker extraction (AV-TSE) by decoupling separation and target selection. Conventional AV-TSE systems typically integrate audio and visual features deeply to re-learn the entire separation process, which can act as a fidelity ceiling due to the noisy nature of in-the-wild audio-visual datasets. To address this, we propose Plug-and-Steer, which assigns high-fidelity separation to a frozen audio-only backbone and limits the role of the visual modality strictly to target selection. We introduce the Latent Steering Matrix (LSM), a minimalist linear transformation that re-routes latent features within the backbone to anchor the target speaker to a designated channel. Experiments across four representative architectures show that our method effectively preserves the acoustic priors of diverse backbones, achieving perceptual quality comparable to that of the original backbones. Audio samples are available at: https://plugandsteer.github.io

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Systems and Control · Cross-listed from cs.RO

Accurate Open-Loop Control of a Soft Continuum Robot Using Visually Learned Latent Dynamics

Henrik Krauss, Johann Licher, Naoya Takeishi et al. (+2 more)

This work addresses open-loop control of a soft continuum robot (SCR) from video-learned latent dynamics. Visual Oscillator Networks (VONs) from previous work are used, which provide mechanically interpretable 2D oscillator latents through an attention broadcast decoder (ABCD). Open-loop, single-shooting optimal control is performed in latent space to track image-specified waypoints without camera feedback. An interactive SCR live simulator enables design of static, dynamic, and extrapolated targets and maps them to model-specific latent waypoints. On a two-segment pneumatic SCR, Koopman, MLP, and oscillator dynamics, each with and without ABCD, are evaluated on setpoint and dynamic trajectories. ABCD-based models consistently reduce image-space tracking error. The VON and ABCD-based Koopman models attain the lowest mean squared errors (MSEs). Ablation and simulation stress tests confirm that training and architecture choices support open-loop performance, including static holding, stable extrapolated equilibria, and relaxation to rest. To the best of our knowledge, this is the first demonstration of reliable long-horizon open-loop control of a physical soft robot using interpretable latent dynamics models learned solely from video.

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All authors Henrik Krauss, Johann Licher, Naoya Takeishi, Annika Raatz, Takehisa Yairi

Systems and Control · Cross-listed from cs.LG

Heavy-Tailed and Long-Range Dependent Noise in Stochastic Approximation: A Finite-Time Analysis

Siddharth Chandak, Anuj Yadav, Ayfer Ozgur et al. (+1 more)

Stochastic approximation (SA) is a fundamental iterative framework with broad applications in reinforcement learning and optimization. Classical analyses typically rely on martingale difference or Markov noise with bounded second moments, but many practical settings, including finance and communications, frequently encounter heavy-tailed and long-range dependent (LRD) noise. In this work, we study SA for finding the root of a strongly monotone operator under these non-classical noise models. We establish the first finite-time moment bounds in both settings, providing explicit convergence rates that quantify the impact of heavy tails and temporal dependence. Our analysis employs a noise-averaging argument that regularizes the impact of noise without modifying the iteration. Finally, we apply our general framework to stochastic gradient descent (SGD) and gradient play, and corroborate our finite-time analysis through numerical experiments.

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All authors Siddharth Chandak, Anuj Yadav, Ayfer Ozgur, Nicholas Bambos

Systems and Control · Cross-listed from physics.soc-ph

On the existence of fair zero-determinant strategies in the periodic prisoner's dilemma game

Ken Nakamura and Masahiko Ueda

Stochastic games are a framework for investigating long-term interdependence of multi-agent systems with environmental feedback. When the number of environmental states is one, they are reduced to repeated games. In repeated games, zero-determinant (ZD) strategies attract much attention in evolutionary game theory, since they can unilaterally control payoffs. Especially, fair ZD strategies unilaterally equalize the payoff of the focal player and the average payoff of the opponents, and they were found in several games including the social dilemma games. Although the existence condition of ZD strategies in repeated games was specified, its extension to stochastic games remains largely unclear. Here, we investigate the existence condition of fair ZD strategies in the periodic prisoner's dilemma game, which is one of the simplest stochastic games. The periodic prisoner's dilemma game consists of two environmental states and the two states alternate deterministically. Whereas each stage game is not necessarily the prisoner's dilemma game, the whole game can be regarded as the prisoner's dilemma game on average. We show that fair ZD strategies do not necessarily exist in the periodic prisoner's dilemma game, in contrast to the repeated prisoner's dilemma game. Furthermore, we also prove that the Tit-for-Tat strategy, which imitates the opponent's action, is not necessarily a fair ZD strategy in the periodic prisoner's dilemma game, whereas the Tit-for-Tat strategy is always a fair ZD strategy in the repeated prisoner's dilemma game. Our results highlight difference between ZD strategies in the periodic prisoner's dilemma game and those in the standard repeated prisoner's dilemma game.

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Systems and Control · Cross-listed from cs.RO

ContractionPPO: Certified Reinforcement Learning via Differentiable Contraction Layers

Vrushabh Zinage, Narek Harutyunyan, Eric Verheyden et al. (+2 more)

Legged locomotion in unstructured environments demands not only high-performance control policies but also formal guarantees to ensure robustness under perturbations. Control methods often require carefully designed reference trajectories, which are challenging to construct in high-dimensional, contact-rich systems such as quadruped robots. In contrast, Reinforcement Learning (RL) directly learns policies that implicitly generate motion, and uniquely benefits from access to privileged information, such as full state and dynamics during training, that is not available at deployment. We present ContractionPPO, a framework for certified robust planning and control of legged robots by augmenting Proximal Policy Optimization (PPO) RL with a state-dependent contraction metric layer. This approach enables the policy to maximize performance while simultaneously producing a contraction metric that certifies incremental exponential stability of the simulated closed-loop system. The metric is parameterized as a Lipschitz neural network and trained jointly with the policy, either in parallel or as an auxiliary head of the PPO backbone. While the contraction metric is not deployed during real-world execution, we derive upper bounds on the worst-case contraction rate and show that these bounds ensure the learned contraction metric generalizes from simulation to real-world deployment. Our hardware experiments on quadruped locomotion demonstrate that ContractionPPO enables robust, certifiably stable control even under strong external perturbations.

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All authors Vrushabh Zinage, Narek Harutyunyan, Eric Verheyden, Fred Y. Hadaegh, Soon-Jo Chung

Systems and Control

Grid-following and Grid-forming Switching Control for Grid-connected Inverters Considering Small-signal Security Region

Qiping Lai, Yi Shen and Chen Shen

In high-penetration renewable power systems with complex and highly variable operating scenarios, grid-connected inverters (GCIs) may transition between different control modes to adapt to diverse grid conditions. Among these, the switching between grid-following (GFL) and grid-forming (GFM) control modes is particularly critical. Nevertheless, safe and robust GFL-GFM switching control strategies for GCIs remain largely unexplored. To overcome this challenge, this paper establishes a full-order small-signal state-space model for the GFL-GFM switched system, precisely reflecting all internal circuit and control dynamics. Subsequently, the small-signal security region (SSSR) of the switched system is defined and characterized, followed by an in-depth investigation into the multi-parameter impacts on the SSSRs and internal stability margin distributions (ISMDs). Furthermore, a novel comprehensive stability index (CSI) is proposed by integrating the stability margin, parameter sensitivity, and boundary distance. Based on this CSI, a multi-objective adaptive GFL-GFM switching control strategy is designed to guarantee the dynamic security and robustness of the system. Finally, the proposed SSSR analysis method for the GFL-GFM switched system and the designed CSI-based switching control mechanism are validated through electromagnetic transient (EMT) simulations.

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Systems and Control · Cross-listed from cs.AI

PowerLens: Taming LLM Agents for Safe and Personalized Mobile Power Management

Xingyu Feng, Chang Sun, Yuzhu Wang et al. (+5 more)

Battery life remains a critical challenge for mobile devices, yet existing power management mechanisms rely on static rules or coarse-grained heuristics that ignore user activities and personal preferences. We present PowerLens, a system that tames the reasoning power of Large Language Models (LLMs) for safe and personalized mobile power management on Android devices. The key idea is that LLMs' commonsense reasoning can bridge the semantic gap between user activities and system parameters, enabling zero-shot, context-aware policy generation that adapts to individual preferences through implicit feedback. PowerLens employs a multi-agent architecture that recognizes user context from UI semantics and generates holistic power policies across 18 device parameters. A PDL-based constraint framework verifies every action before execution, while a two-tier memory system learns individualized preferences from implicit user overrides through confidence-based distillation, requiring no explicit configuration and converging within 3–5 days. Extensive experiments on a rooted Android device show that PowerLens achieves 81.7% action accuracy and 38.8% energy saving over stock Android, outperforming rule-based and LLM-based baselines, with high user satisfaction, fast preference convergence, and strong safety guarantees, with the system itself consuming only 0.5% of daily battery capacity.

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All authors Xingyu Feng, Chang Sun, Yuzhu Wang, Zhangbing Zhou, Chengwen Luo, Zhuangzhuang Chen, Xiaomin Ouyang, Huanqi Yang

Systems and Control

Direct Digital-to-Physical Synthesis: From mmWave Transmitter to Qubit Control

Najme Ebrahimi, Haoling Li, Gun Suer et al. (+2 more)

The increasing demand for high-speed wireless connectivity and scalable quantum information processing has driven parallel advancements in millimeter-wave (MMW) communication transmitters and cryogenic qubit controllers. Despite serving different applications, both systems rely on the precise generation of radio frequency (RF) waveforms with stringent requirements on spectral purity, timing, and amplitude control. Recent architecture eliminates conventional methods by embedding digital signal generation and processing directly into the RF path, transforming digital bits into physical waveforms for either electromagnetic transmission or quantum state control. This article presents a unified analysis of direct-digital modulation techniques across both domains, showing the synergy and similarities between these two domains. The article also focuses on four core architectures: Cartesian I/Q, Polar, RF- Digital-to-Analog Converter (DAC), and harmonic/subharmonic modulation across both domains. We analyze their respective trade-offs in energy efficiency, signal integrity, waveform synthesis, error mitigations, and highlight how architectural innovations in one domain can accelerate progress in the other

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All authors Najme Ebrahimi, Haoling Li, Gun Suer, Kin Chung Fong, Leonardo Ranzani

Signal Processing

Multibeam Phased Arrays with Spherical Gold Spatio-temporal Coding for Fading-Resilient and Delay Robust Beam Isolations

Yuan Ma, Mike Ballou, Kyle Richard et al. (+2 more)

Future integrated sensing and communication (ISAC) systems require simultaneous multibeam operation with low-latency hardware and robust isolation under synchronization error and fading. Conventional code-division multiplexing using Walsh-Hadamard codes is extremely time-sensitive. This paper demonstrates that conventional temporal-only coded multibeam arrays suffer from inter-beam sidelobe level (SLL) collapse to within a few dB of the main lobe, with variations exceeding 10-20 dB over delay. By embedding moderate-length Gold sequences into a spherical spatial codebook, the proposed Spherical-Gold scheme leverages both temporal and spatial correlation bounds, achieving effective inter-beam isolation without increasing RF complexity. Measurement results and verifications are performed using an Analog Devices ADAR3002 Ka-band 256-element receiver with four simultaneous beams. The proposed scheme demonstrates at least 15 dB rejection with less than 2.5 dB variation in SLL under time error and fading, whereas temporal-only CDMA degrades to approximately -5 to -7 dB SLL with nearly 8 dB variation under time delay.

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All authors Yuan Ma, Mike Ballou, Kyle Richard, Hessam Mahdavifar, Najme Ebrahimi

Systems and Control

Verifiable Error Bounds for Physics-Informed Neural Network Solutions of Lyapunov and Hamilton-Jacobi-Bellman Equations

Jun Liu

Many core problems in nonlinear systems analysis and control can be recast as solving partial differential equations (PDEs) such as Lyapunov and Hamilton-Jacobi-Bellman (HJB) equations. Physics-informed neural networks (PINNs) have emerged as a promising mesh-free approach for approximating their solutions, but in most existing works there is no rigorous guarantee that a small PDE residual implies a small solution error. This paper develops verifiable error bounds for approximate solutions of Lyapunov and HJB equations, with particular emphasis on PINN-based approximations. For both the Lyapunov and HJB PDEs, we show that a verifiable residual bound yields relative error bounds with respect to the true solutions as well as computable a posteriori estimates in terms of the approximate solutions. For the HJB equation, this also yields certified upper and lower bounds on the optimal value function on compact sublevel sets and quantifies the optimality gap of the induced feedback policy. We further show that one-sided residual bounds already imply that the approximation itself defines a valid Lyapunov or control Lyapunov function. We illustrate the results with numerical examples.

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Thursday 19 March

Signal Processing

A Multi-Modal Dataset for Ground Reaction Force Estimation Using Consumer Wearable Sensors

Parvin Ghaffarzadeh, Debarati Chakraborty, Koorosh Aslansefat et al. (+2 more)

This Data Descriptor presents a fully open, multi-modal dataset for estimating vertical ground reaction force (vGRF) from consumer-grade Apple Watch sensors with laboratory force plate ground truth. Ten healthy adults aged 26–41 years performed five activities: walking, jogging, running, heel drops, and step drops, while wearing two Apple Watches positioned at the left wrist and waist. The dataset contains 492 validated trials with time-aligned inertial measurement unit (IMU) recordings (approximately 100 Hz) and force plate vGRF (Force\_Z, 1000 Hz). The release includes raw and processed time series, trial-level metadata, quality-control flags, and machine-readable data dictionaries. Trial-level matching manifests link recordings across modalities using stable identifiers. Of the 492 validated trials, 395 are triad-complete, containing wrist, waist, and force plate data, enabling cross-sensor analyses and reproducible model evaluation. Dataset quality is characterised through a three-phase cross-sensor plausibility and consistency framework, repeatability analysis of peak vGRF (intraclass correlation coefficient 0.871–0.990), and systematic checks of force ranges and trial completeness. Monte Carlo sensitivity analysis showed that correlation-based validation metrics were robust to single-sample timing perturbations at the IMU sampling resolution. All data are released under CC BY 4.0, with analysis scripts archived alongside the dataset and mirrored on GitHub. This resource supports reproducible research in wearable biomechanics, benchmarking of machine learning models for vGRF estimation, and investigation of sensor placement effects using widely available consumer wearables.

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All authors Parvin Ghaffarzadeh, Debarati Chakraborty, Koorosh Aslansefat, Ali Dostan, Yiannis Papadopoulos

Signal Processing

Stress Classification from ECG Signals Using Vision Transformer

Zeeshan Ahmad and Naimul Khan

Vision Transformers have shown tremendous success in numerous computer vision applications; however, they have not been exploited for stress assessment using physiological signals such as Electrocardiogram (ECG). In order to get the maximum benefit from the vision transformer for multilevel stress assessment, in this paper, we transform the raw ECG data into 2D spectrograms using short time Fourier transform (STFT). These spectrograms are divided into patches for feeding to the transformer encoder. We also perform experiments with 1D CNN and ResNet-18 (CNN model). We perform leave-onesubject-out cross validation (LOSOCV) experiments on WESAD and Ryerson Multimedia Lab (RML) dataset. One of the biggest challenges of LOSOCV based experiments is to tackle the problem of intersubject variability. In this research, we address the issue of intersubject variability and show our success using 2D spectrograms and the attention mechanism of transformer. Experiments show that vision transformer handles the effect of intersubject variability much better than CNN-based models and beats all previous state-of-the-art methods by a considerable margin. Moreover, our method is end-to-end, does not require handcrafted features, and can learn robust representations. The proposed method achieved 71.01% and 76.7% accuracies with RML dataset and WESAD dataset respectively for three class classification and 88.3% for binary classification on WESAD.

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Systems and Control

Remarks on Lipschitz-Minimal Interpolation: Generalization Bounds and Neural Network Implementation

Arthur C. B. de Oliveira, Ruigang Wang, Ian R. Manchester et al. (+1 more)

This note establishes a theoretical framework for finding (potentially overparameterized) approximations of a function on a compact set with a-priori bounds for the generalization error. The approximation method considered is to choose, among all functions that (approximately) interpolate a given data set, one with a minimal Lipschitz constant. The paper establishes rigorous generalization bounds over practically relevant classes of approximators, including deep neural networks. It also presents a neural network implementation based on Lipschitz-bounded network layers and an augmented Lagrangian method. The results are illustrated for a problem of learning the dynamics of an input-to-state stable system with certified bounds on simulation error.

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All authors Arthur C. B. de Oliveira, Ruigang Wang, Ian R. Manchester, Eduardo D. Sontag

Signal Processing · Cross-listed from cs.LG

Stochastic Sequential Decision Making over Expanding Networks with Graph Filtering

Zhan Gao, Bishwadeep Das and Elvin Isufi

Graph filters leverage topological information to process networked data with existing methods mainly studying fixed graphs, ignoring that graphs often expand as nodes continually attach with an unknown pattern. The latter requires developing filter-based decision-making paradigms that take evolution and uncertainty into account. Existing approaches rely on either pre-designed filters or online learning, limited to a myopic view considering only past or present information. To account for future impacts, we propose a stochastic sequential decision-making framework for filtering networked data with a policy that adapts filtering to expanding graphs. By representing filter shifts as agents, we model the filter as a multi-agent system and train the policy following multi-agent reinforcement learning. This accounts for long-term rewards and captures expansion dynamics through sequential decision-making. Moreover, we develop a context-aware graph neural network to parameterize the policy, which tunes filter parameters based on information of both the graph and agents. Experiments on synthetic and real datasets from cold-start recommendation to COVID prediction highlight the benefits of using a sequential decision-making perspective over batch and online filtering alternatives.

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Signal Processing

Geometric Performance Analysis of Doppler-Based Positioning with a Single LEO Satellite

Qi Liu, Marc Fernandez-Temprado, Antoni Reus-Bergas et al. (+2 more)

Low Earth Orbit (LEO) satellites have gained increasing attention as potential signal sources for Positioning, Navigation and Timing (PNT) applications. However, while most existing studies focus on multi-satellite LEO constellations, the fundamental positioning performance achievable with a single LEO satellite remains less extensively explored. This paper analyzes the geometric characteristics and positioning performance of single-satellite Doppler positioning through a theoretical analysis of the Dilution of Precision (DOP) and extensive numerical simulations. The results reveal a strong directional error behavior, with severe error in the cross-track direction but a significantly less error along the satellite track, reflecting an intrinsic geometric limitation of single-satellite LEO positioning. While these features were already identified at the early stages of satellite PNT missions, the present work provides an in-depth analysis and unveils the fundamental limitations and characteristics that could make LEO-based Doppler positioning feasible nowadays, using one single satellite only. In this way, the results of this work not only provide valuable insights into the role of observational geometry in Doppler navigation, but also offer guidance for optimizing geometric configurations in future small or single-satellite LEO constellations for strategic applications.

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All authors Qi Liu, Marc Fernandez-Temprado, Antoni Reus-Bergas, Gonzalo Seco-Granados, Jose A. Lopez-Salcedo

Systems and Control · Cross-listed from cs.SE

Coordinating Stakeholders in the Consideration of Performance Indicators and Respective Interface Requirements for Automated Vehicles

Richard Schubert, Marvin Loba, Alexander Blödel et al. (+3 more)

This paper presents a process for coordinating stakeholders in their consideration of performance indicators and respective interface requirements for automated vehicles. These performance indicators are obtained and processed based on the system's self-perception and enable the realization of self-aware and self-adaptive vehicles. This is necessary to allow SAE Level 4 vehicles to handle external disturbances as well as internal degradations and failures at runtime. Without such a systematic process for stakeholder coordination, architectural decisions on realizing self-perception become untraceable and effective communication between stakeholders may be compromised. Our process-oriented approach includes necessary ingredients, steps, and artifacts that explicitly address stakeholder communication, traceability, and knowledge transfer through clear documentation. Our approach is based on the experience gained from applying the process in the autotech.agil project, from which we further present lessons learned, identified gaps, and steps for future work.

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All authors Richard Schubert, Marvin Loba, Alexander Blödel, David Klüner, Alexandru Kampmann, Steven Peters

Audio and Speech Processing · Cross-listed from cs.SD

Listen First, Then Answer: Timestamp-Grounded Speech Reasoning

Jihoon Jeong, Pooneh Mousavi, Mirco Ravanelli et al. (+1 more)

Large audio-language models (LALMs) can generate reasoning chains for their predictions, but it remains unclear whether these reasoning chains remain grounded in the input audio. In this paper, we propose an RL-based strategy that grounds the reasoning outputs of LALMs with explicit timestamp annotations referring to relevant segments of the audio signal. Our analysis shows that timestamp grounding leads the model to attend more strongly to audio tokens during reasoning generation. Experiments on four speech-based benchmark datasets demonstrate that our approach improves performance compared to both zero-shot reasoning and fine-tuning without timestamp grounding. Additionally, grounding amplifies desirable reasoning behaviors, such as region exploration, audiology verification, and consistency, underscoring the importance of grounding mechanisms for faithful multimodal reasoning.

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All authors Jihoon Jeong, Pooneh Mousavi, Mirco Ravanelli, Cem Subakan

Systems and Control

Real-Time Regulation of Direct Ink Writing Using Model Reference Adaptive Control

Mandana Mohammadi Looey, Amrita Basak and Satadru Dey

Direct Ink Writing (DIW) has gained attention for its potential to reduce printing time and material waste. However, maintaining precise geometry and consistent print quality remains challenging under dynamically varying operating conditions. This paper presents a control-focused approach using a model reference adaptive control (MRAC) strategy based on a reduced-order model (ROM) of extrusion-based 3D printing for a candidate cementitious material system. The proposed controller actively compensates for uncertainties and disturbances by adjusting process parameters in real time, with the objective of minimizing reference-tracking errors. Stability and convergence are rigorously verified via Lyapunov analysis, demonstrating that tracking errors asymptotically approach zero. Performance evaluation under realistic simulation scenarios confirms the effectiveness of the adaptive control framework in maintaining accurate and robust extrusion behavior.

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Systems and Control

Exact and Approximate Convex Reformulation of Linear Stochastic Optimal Control with Chance Constraints

Tanmay Dokania and Yashwanth Kumar Nakka

In this paper, we present an equivalent convex optimization formulation for discrete-time stochastic linear systems subject to linear chance constraints, alongside a tight convex relaxation for quadratic chance constraints. By lifting the state vector to encode moment information explicitly, the formulation captures linear chance constraints on states and controls across multiple time steps exactly, without conservatism, yielding strict improvements in both feasibility and optimality. For quadratic chance constraints, we derive convex approximations that are provably less conservative than existing methods. We validate the framework on minimum-snap trajectory generation for a quadrotor, demonstrating that the proposed approach remains feasible at noise levels an order of magnitude beyond the operating range of prior formulations.

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Systems and Control

Variational Encrypted Model Predictive Control

Jihoon Suh, Yeongjun Jang, Junsoo Kim et al. (+1 more)

We develop a variational encrypted model predictive control (VEMPC) protocol whose online execution relies only on encrypted polynomial operations. The proposed approach reformulates the MPC problem into a sampling-based estimator, in which the computation of the quadratic cost is naturally handled by tilting the sampling distribution, thus reducing online encrypted computation. The resulting protocol requires no additional communication rounds or intermediate decryption, and scales efficiently through two complementary levels of parallelism. We analyze the effect of encryption-induced errors on optimality, and simulation results demonstrate the practical applicability of the proposed method.

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All authors Jihoon Suh, Yeongjun Jang, Junsoo Kim, Takashi Tanaka

Systems and Control

String stable platoons of all-electric aircraft with operating costs and airspace complexity trade-off

Lucas Souza e Silva and Luis Rodrigues

This paper formulates an optimal control framework for computing cruise airspeeds in predecessor-follower platoons of all-electric aircraft that balance operational cost and airspace complexity. To quantify controller workload and coordination effort, a novel pairwise dynamic workload (PDW) function is developed. Within this framework, the optimal airspeed solution is derived for all-electric aircraft under longitudinal wind disturbances. Moreover, an analytical suboptimal solution for heterogeneous platoons with nonlinear aircraft dynamics is determined, for which a general sufficient condition for string stability is formally established. The methodology is validated through case studies of all-electric aircraft operating in air corridors that are suitable for low-altitude advanced/urban air mobility (AAM/UAM) applications. Results show that the suboptimal solution closely approximates the optimal, while ensuring safe separations, maintaining string stability, and reducing operational cost and airspace complexity. These findings support the development of sustainable and more autonomous air traffic procedures that will enable the implementation of emerging air transportation technologies, such as AAM/UAM, and their integration to the air traffic system environment.

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Signal Processing · Cross-listed from stat.ML

Subspace Projection Methods for Fast Spectral Embeddings of Evolving Graphs

Mohammad Eini, Abdullah Karaaslanli, Vassilis Kalantzis et al. (+1 more)

Several graph data mining, signal processing, and machine learning downstream tasks rely on information related to the eigenvectors of the associated adjacency or Laplacian matrix. Classical eigendecomposition methods are powerful when the matrix remains static but cannot be applied to problems where the matrix entries are updated or the number of rows and columns increases frequently. Such scenarios occur routinely in graph analytics when the graph is changing dynamically and either edges and/or nodes are being added and removed. This paper puts forth a new algorithmic framework to update the eigenvectors associated with the leading eigenvalues of an initial adjacency or Laplacian matrix as the graph evolves dynamically. The proposed algorithm is based on Rayleigh-Ritz projections, in which the original eigenvalue problem is projected onto a restricted subspace which ideally encapsulates the invariant subspace associated with the sought eigenvectors. Following ideas from eigenvector perturbation analysis, we present a new methodology to build the projection subspace. The proposed framework features lower computational and memory complexity with respect to competitive alternatives while empirical results show strong qualitative performance, both in terms of eigenvector approximation and accuracy of downstream learning tasks of central node identification and node clustering.

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All authors Mohammad Eini, Abdullah Karaaslanli, Vassilis Kalantzis, Panagiotis A. Traganitis

Systems and Control · Cross-listed from nlin.AO

Operational tracking loss in nonautonomous second-order oscillator networks

Veronica Sanz

We study when a network of coupled oscillators with inertia ceases to follow a time-dependent driving protocol coherently, using a simplified graph-based model motivated by inverter-dominated energy systems. We show that this loss of tracking is diagnosed most clearly in the frequency dynamics, rather than in phase-based observables. Concretely, a tracking ratio built from the frequency-disagreement observable $E_\omega(t)$ and normalized by the instantaneous second-order modal decay rate yields a robust protocol-dependent freeze-out time whose relative dispersion decreases with system size. Graph topology matters substantially: the resulting freeze-out time is only partly captured by the algebraic connectivity $\lambda_2$, while additional structural descriptors, particularly Fiedler-mode localization and low-spectrum structure, improve the explanation of graph-to-graph variation. By contrast, phase-sector observables develop strong non-monotonic and underdamped structure, so simple diagonal low-mode relaxation closures are not quantitatively reliable in the same regime. These results identify the frequency sector as the natural operational sector for nonautonomous tracking loss in second-order oscillator networks and clarify both the usefulness and the limits of reduced spectral descriptions in this setting.

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Systems and Control

Bridging Conformal Prediction and Scenario Optimization: Discarded Constraints and Modular Risk Allocation

Giuseppe C. Calafiore

Scenario optimization and conformal prediction share a common goal, that is, turning finite samples into safety margins. Yet, different terminology often obscures the connection between their respective guarantees. This paper revisits that connection directly from a systems-and-control viewpoint. Building on the recent conformal/scenario bridge of \citet{OSullivanRomaoMargellos2026}, we extend the forward direction to feasible sample-and-discard scenario algorithms. Specifically, if the final decision is determined by a stable subset of the retained sampled constraints, the classical mean violation law admits a direct exchangeability-based derivation. In this view, discarded samples naturally appear as admissible exceptions. We also introduce a simple modular composition rule that combines several blockwise calibration certificates into a single joint guarantee. This rule proves particularly useful in multi-output prediction and finite-horizon control, where engineers must distribute risk across coordinates, constraints, or prediction steps. Finally, we provide numerical illustrations using a calibrated multi-step tube around an identified predictor. These examples compare alternative stage-wise risk allocations and highlight the resulting performance and safety trade-offs in a standard constraint-tightening problem.

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Image and Video Processing

TuLaBM: Tumor-Biased Latent Bridge Matching for Contrast-Enhanced MRI Synthesis

Atharva Rege, Adinath Madhavrao Dukre, Numan Balci et al. (+2 more)

Contrast-enhanced magnetic resonance imaging (CE-MRI) plays a crucial role in brain tumor assessment; however, its acquisition requires gadolinium-based contrast agents (GBCAs), which increase costs and raise safety concerns. Consequently, synthesizing CE-MRI from non-contrast MRI (NC-MRI) has emerged as a promising alternative. Early Generative Adversarial Network (GAN)-based approaches suffered from instability and mode collapse, while diffusion models, despite impressive synthesis quality, remain computationally expensive and often fail to faithfully reproduce critical tumor contrast patterns. To address these limitations, we propose Tumor-Biased Latent Bridge Matching (TuLaBM), which formulates NC-to-CE MRI translation as Brownian bridge transport between source and target distributions in a learned latent space, enabling efficient training and inference. To enhance tumor-region fidelity, we introduce a Tumor-Biased Attention Mechanism (TuBAM) that amplifies tumor-relevant latent features during bridge evolution, along with a boundary-aware loss that constrains tumor interfaces to improve margin sharpness. While bridge matching has been explored for medical image translation in pixel space, our latent formulation substantially reduces computational cost and inference time. Experiments on BraTS2023-GLI (BraSyn) and Cleveland Clinic (in-house) liver MRI dataset show that TuLaBM consistently outperforms state-of-the-art baselines on both whole-image and tumor-region metrics, generalizes effectively to unseen liver MRI data in zero-shot and fine-tuned settings, and achieves inference times under 0.097 seconds per image.

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All authors Atharva Rege, Adinath Madhavrao Dukre, Numan Balci, Dwarikanath Mahapatra, Imran Razzak

Signal Processing

Experimental Analysis of Microbubble Propagation for In-Body Data Transmission

Annika Tjabben, Lea Bergkemper, Carolin Conrad et al. (+2 more)

In-body communication is an upcoming field with significant implications for medical diagnostics and therapeutic interventions. Microbubbles have gained attention due to their distinct physical properties, making them promising candidates to facilitate communication within the human body. This work explores the use of microbubbles as communication carriers, with a particular focus on their detection and the application of a modulation scheme. Through experimental analysis the feasibility and effectiveness of microbubble-based communication is tested. Filtering and peak detection methods are applied to accurately identify the presence of microbubbles despite noise, demonstrating the feasibility of microbubble-based communication systems for future biomedical applications. The results offer insights into signal integrity, noise challenges, and the optimization of detection algorithms, providing a foundation for future advancements in this field.

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All authors Annika Tjabben, Lea Bergkemper, Carolin Conrad, Michael Gundall, Hans D. Schotten