Audio and Speech Processing · Cross-listed from cs.CL
The Dynamics of Human and AI-Generated Language: How Semantics Fluctuates across Different Timescales
Han-Jen Chang, Yasir Çatal, Angelika Wolman et al. (+5 more)
Spoken
language, whether produced by humans or large
language models (LLM), unfolds over time with varying semantic content. However, we still lack simple, interpretable time-series features that capture how generic versus specific content is distributed over time, and that can be used to compare human and AI-generated
speech. We introduce a semantic-timescale analysis pipeline that turns word-level transcripts with timestamps into semantic time-series. For each spoken narrative, we compute (i) semantic specificity using WordNet-based word depth and (ii) contextual similarity using SBERT
embeddings and quantify their temporal dependence using autocorrelation-window measures (ACW-0 and related metrics). We then compare original
speech to multiple shuffled controls that selectively disrupt lexical identity, temporal order, and word duration. Across human-read autobiographical narratives, TTS readings, and LLM-generated texts rendered with TTS, we find that segments with longer ACW-0 in the semantic time-series tend to contain more generic vocabulary, whereas segments with shorter ACW-0 are enriched in more specific words. These associations are strongly attenuated or abolished when word order and timing are randomized, indicating that ACW-based measures capture non-trivial temporal organization of semantic content beyond static lexical distributions. Our results suggest that ACW-based semantic timescales are a useful family of features for analyzing and comparing the temporal structure of human and AI-generated
speech.
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All authors Han-Jen Chang, Yasir Çatal, Angelika Wolman, Agustín Ibáñez, David Smith, I-Wen Su, Kai-Yuan Cheng, Georg Northoff
Systems and Control
Probabilistic Repair Logistics Modeling for Utility-Scale PV Inverter Fleets Using Event-Driven Simulation
Jinlei Wei, Yongxin Zhang and Guanyu Tian
As renewable energy systems expand, inverter availability becomes increasingly important for grid reliability and economics, yet photovoltaic inverter repair logistics remain under-modeled. This paper presents an event-driven Monte Carlo framework for a centralized repair facility with parallel production lines, capturing the full repair cycle from administrative pre-wait and transport to health-driven repair and return-to-inventory. The model incorporates opportunistic scheduling that uses mandatory hold periods to insert additional units onto temporarily idle lines, improving throughput without added capacity. Stage durations are represented by a two-component VaR-style mixture distribution for routine and heavy-tailed delays, while a continuous health score determines repair completion. Calibrated by minimizing the one-dimensional Wasserstein distance between simulated and empirical repair-duration distributions, the model is applied to 43 field-observed repairs, reproducing the empirical bimodal structure with a Wasserstein distance of 53.3 days. Results show that 51.2% of units are accommodated through opportunistic insertion, indicating that hold periods provide a significant recoverable scheduling resource.
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Systems and Control
An Admittance-Based Inverter Connection Screening Tool for Small-Signal System Strength
Andreas Hadjileonidas, Debargha Brahma, Yue Zhu et al. (+1 more)
The increasing occurrence of small-signal instability, particularly sub-synchronous oscillations (SSOs), in power systems with a high penetration of inverter-based resources (IBRs) has made the planning of new IBR connections increasingly important and challenging. The impact of such connections on small-signal stability is not always straightforward, as it strongly depends on the connection location, inverter operating mode, control configuration, parametrisation, and operating conditions. This paper proposes an inverter connection screening tool (ICST) that enables efficient and accurate assessment of the impact of prospective inverter configurations on small-signal system strength. It can identify, among the candidates considered, the most suitable inverter configuration for a given connection location that avoids degrading small-signal system strength and can also enhance it. As a result, higher IBR penetration can be supported while maintaining small-signal stability. The ICST evaluates candidate inverter configurations using their admittances at critical modal frequencies, along with the system's admittance spectrum, thereby avoiding the need for analytical models. The ICST-based planning procedure, which can support system operators, asset owners, and IBR developers in decision-making across different stages of planning studies, is demonstrated using a modified IEEE 57-bus system. Comparisons with model-based studies demonstrate the accuracy of the ICST in predicting the modal impact of inverter connections and its effectiveness in selecting suitable inverter control configurations.
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All authors Andreas Hadjileonidas, Debargha Brahma, Yue Zhu, Timothy C. Green
Signal Processing
Beamforming Gain with Single-RF Movable Arrays
Zhenqiao Cheng, Chongjun Ouyang, Hao Jiang et al. (+2 more)
A single-
radio-frequency (RF) movable array is investigated, in which all movable elements are driven by a single RF chain with equal amplitude and equal phase. The achievable beamforming gain enabled by
antenna placement is analyzed. Linear beamforming gain scaling with the number of
antennas is shown to be achievable in single-path channels, while coherent-combining conditions and aperture requirements are established for multipath channels. For multiuser transmission, the optimal max-min power allocation is derived in closed form, based on which an element-wise coordinate-search
algorithm is developed for
antenna placement design. Numerical results validate the analysis and reveal a fundamental tradeoff: beamforming gains can be achieved through
antenna placement alone, but only at the expense of increased aperture resources.
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All authors Zhenqiao Cheng, Chongjun Ouyang, Hao Jiang, Xingqi Zhang, Arumugam Nallanathan
Systems and Control · Cross-listed from math.OC
Quantized Stochastic Primal-Dual Methods for Distributed Optimization under Relaxed Global Geometry
Susmit Sarkar, Abhinav Raghuvanshi, Kushal Chakrabarti et al. (+1 more)
We study distributed optimization with
stochastic gradients and finite-bit communication modeled by random (unbiased) quantization. We propose q-PDGD, a quantized
stochastic primal-dual method, and analyze it under relaxed global geometry. Under restricted secant inequality (RSI), a constant step-size yields linear contraction to an explicit neighborhood determined by gradient noise, quantization distortion, and network connectivity, while a diminishing step-size achieves O(1/k) convergence without shared-minimizer assumptions. Under Polyak-Lojasiewicz (PL) inequality, we obtain linear-to-neighborhood convergence in the same
stochastic quantized setting. Our results match the best-known centralized
stochastic rates in oracle complexity, and are supported by experiments demonstrating the predicted tradeoffs between quantization level, step-size choice, and graph structure.
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All authors Susmit Sarkar, Abhinav Raghuvanshi, Kushal Chakrabarti, Mayank Baranwal
Systems and Control · Cross-listed from cs.HC
Towards a Joint Understanding of Remote Operation for Vehicles in Public Road Traffic
Elisabeth Shi, Maria-Magdalena Wolf, Nina Theobald et al. (+9 more)
Sustained driving automation systems are envisioned to be used as the foundation for driverless mobility services. However, both researchers and practitioners acknowledge that current driving automation systems are not yet able to handle all traffic situations that a human driver can handle. To bridge this gap and enable mobility services without an in-vehicle human driver or fallback, remote operation (or teleoperation) is increasingly discussed. Recently, first legal actions have been taken to enable some forms of remote operation on public roads. Remote operation encompasses a broad spectrum of methods to support a driving automation system, ranging from remote assistance, which includes providing information or releasing a maneuver, to remote driving, which includes driving the vehicle from a remote location. As such, safe implementation of remote operation in public road traffic challenges the collaboration of multiple academic disciplines (e.g. engineering, psychology, informatics, law, etc.) and stakeholders (e.g. remote operation service providers, remote operators, vehicle manufacturers, regulatory authorities, etc.). At the same time, the interdisciplinary discourse is often challenging due to differing expectations and
language. To build a common ground, this article traces terminology back to the original differences in information processing both on human and vehicle side. This framework aims to help further discourse by directly specifying what is needed to engage a diverse audience including researchers and stakeholders of different backgrounds and interests. Recently discussed forms of teleoperation are integrated into this framework.
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All authors Elisabeth Shi, Maria-Magdalena Wolf, Nina Theobald, Bettina Abendroth, Eugen Wige, Johannes Springer, Katharina Hottelart, Andreas Schrank, Thorben Brandt, Michael Oehl, Frank Diermeyer, Lena Plum
Signal Processing
Learning-Based Phase Estimation for Multi-Frequency Carrier Phase Ranging under Structured Multipath Conditions
Jakub Bonczyk, Jakub Nikonowicz and Łukasz Matuszewski
Carrier-phase (CP) ranging is a key enabler of high-precision positioning in modern wireless systems. In multi-frequency OFDM-based sensing, phase observations across subcarriers provide information about the underlying propagation geometry. However, in realistic industrial and urban environments, these observations exhibit non-Gaussian and asymmetric characteristics due to deterministic multipath components, violating standard circular statistical assumptions. In this work, we analyze CP-based ranging as an estimation problem over circular phase observations. We show that conventional model-based
estimators, such as circular averaging under von Mises assumptions, become biased under 3GPP-compliant propagation conditions. Using a QuaDRiGa-based simulation framework, we evaluate empirical phase distributions in Industrial Factory (InF) and Urban Microcell (UMi) scenarios and quantify their deviation from classical statistical models. To address these limitations, we propose a learning-based
estimator that operates directly on empirical phase distributions without assuming a predefined statistical model. Experimental results show improved accuracy compared to classical
estimators, particularly under multipath conditions.
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Systems and Control
Shared Renewable Allocation and Hydrogen Flexibility in Local Energy Markets: A Market Design Perspective
Pratik Mochi and Magnus Korpås
The integration of green hydrogen in local energy
markets is often analyzed from a technical flexibility perspective, while the effect of
market design rules remains less explored. This paper proposes a coordinated local electricity-hydrogen
market framework in which hydrogen participation is regulated by explicit renewable access mechanisms. A mixed-integer linear programming model is developed to co-optimize electricity trading, battery operation, wind allocation and hydrogen production under centralized coordination. Six regulatory cases are examined including hydrogen supply options and access of local wind. Results are obtained for representative seasonal weeks for Norwegian energy community. Electrolyzer, when connected as rigid load, increases grid dependence, but also improves system cost when price-based participation is activated. Direct renewable access reduces grid imports, enhances wind allocation and introduces competition with households for energy distribution and system cost optimization. Furthermore, findings show that (i) hydrogen integration in local energy systems is essentially a
market design problem and (ii) renewable access rules critically determine system behaviour, flexibility interactions and seasonal performance.
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Image and Video Processing
Intelligent Skin Cancer Detection Using a Multispectral Metasurface and a Hybrid
Afsane Saee Arezoomand
Skin cancer is among the most prevalent malignancies worldwiAdbe satnradcitts early detection is essential for improving patient survival and reducing treatment costs Conventional dermoscopic and visual imaging techniques are primarily limited to the visible spectrum and often fail to capture subtle spectral signatures associated with early stage malignancies This study proposes an innovative framework that integrates a multispectral metasurface for imaging with a hybrid deep learning architecture based on Convolutional Neural Networks and
Vision Transformers The designed metasurface enables noninvasive acquisition of rich spectral information highly sensitive to tissue alterations while the hybrid CNN ViT model simultaneously extracts local and global features to robustly classify skin lesions Simulation-based evaluations demonstrate that the proposed method achieves approximately 98 accuracy 95 percentages sensitivity and 99 perentage specificity surpassing conventional RGB-based and single-architecture approaches Qualitative analyses using
attention maps reveal that the model focuses on clinically relevant lesion regions improving interpretability Overall the results indicate that combining metasurface based multispectral imaging with hybrid deep learning can introduce a new generation of diagnostic tools in dermatology and pave the way for portable fast and highly accurate clinical systems
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Signal Processing · Cross-listed from cs.IT
Designed-Source Reductions and a Dual-Purpose Feasibility Band for Semantic Rate-Distortion
Joss Armstrong
The joint rate-distortion framework of Stavrou and Kountouris (IEEE Transactions on Communications 2023) characterises dual-fidelity tradeoffs for semantic communication on
stochastic semantic sources. Many task-oriented communication systems instead use designed sources, where the semantic object is a deterministic oracle allocation $\phi^(t)$ rather than a
stochastic quantity given by nature. We isolate the subclass of designed sources under smooth concave utility with assumptions A1, A2 and Euclidean allocation codomain, and restrict the encoder class to deterministic common-category mappings. Within this subclass the SK exponential-tilting decoder and generalised Blahut–Arimoto iteration specialise to conditional-mean decoding and Lloyd–Max stationarity on $\phi^(t)$. When the second fidelity is a monotone single-letter distortion, the joint problem stays inside the SK admissible class; the common-category SK rate is lower-bounded by the max of the corresponding Shannon rate-distortion functions, with equality only when the common-category reconstruction is compatible and RDF-optimal. When the second fidelity is aggregate verification, the joint problem leaves the SK single-letter class and admits a constrained-design feasibility band $R_{\min}(\varepsilon^) \leq R \leq R_{\max}(\beta^)$ of width $\log_2(K_{\max}/K_{\min})$ bits in partition cardinality. The reduction and the band are scope statements on the SK apparatus, not modifications to it. A smart-grid economic-dispatch example with a non-technical-loss-detection contrast illustrates the band.
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Audio and Speech Processing
Massive Open-Vocabulary Keyword Spotting
Leonor Barreiros, Raul Monteiro, Afonso Mendes et al. (+1 more)
Automatic
speech recognition systems have been shown to under-perform when it comes to transcribing words rarely seen in the training data, namely specialized terminology. Open-vocabulary keyword spotting, combined with contextual biasing, has been shown to mitigate this issue. However, existing systems can only handle glossaries of a few hundred terms without becoming an infeasible bottleneck. We propose a system that stores features with a
memory footprint up to 128 times smaller than a comparable baseline and allows users to process massive databases while remaining open-vocabulary. Without fine-tuning the
speech recognition model, our system achieves a comparable entity recall as uncompressed solutions, even in
languages not seen during training.
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All authors Leonor Barreiros, Raul Monteiro, Afonso Mendes, Gonçalo M. Correia
Audio and Speech Processing · Cross-listed from cs.CL
Multi-Faceted Interactivity Alignment in Full-Duplex Speech Models
Atsumoto Ohashi, Neil Zeghidour, Alexandre Défossez et al. (+1 more)
Full-duplex spoken dialogue models can listen and speak simultaneously, making them a promising architecture for natural conversation. However, current models are trained solely with supervised learning through
token-level likelihood maximization, which does not directly optimize interaction-level behaviors, causing interactivity issues such as excessive silence and ill-timed turn-taking. Recent work has applied reinforcement learning (RL) to improve interactivity, but existing methods address only a limited set of interactive behaviors in their rewards. In this work, we propose a post-training
alignment method that comprehensively improves the interactivity of full-duplex spoken dialogue models through RL. We address the four canonical axes of interactivity: pause handling, turn-taking, backchanneling, and user interruption. For each axis, we extract short audio segments from human conversation corpora and optimize the model with axis-specific reward functions. An extra LLM-based reward for response quality prevents semantic degradation. We apply our method to two open-source models, Moshi and PersonaPlex, demonstrating consistent improvements in interactivity on both offline evaluation with pre-recorded audio and real-time multi-turn dialogue evaluation.
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All authors Atsumoto Ohashi, Neil Zeghidour, Alexandre Défossez, Eugene Kharitonov
Signal Processing
Pre-Fault Voltage Discrimination and Time-Domain Protection for Distribution Networks with Inverter-Based Resources
Junyuan Zhao, François Bouffard and Géza Joós
The increasing proliferation of inverter-based resources (IBRs) in distribution networks is presenting a major challenge for phasor-based overcurrent protection. This challenge stems from IBRs' lack of short-
circuit current sourcing capacity. As a result, traditional overcurrent protection functions (e.g., ANSI 51) are inadequate in such scenarios, and warrant alternative approaches. Time-domain protection, for example, shows promise in overcoming this challenge. In this paper we propose a pre-fault voltage discrimination (PVD) strategy whose role is to detect faults and discriminate normal switching and
transformer inrush disturbances from actual faults. The use of PVD allows for the design of a simple, yet effective fault detection
algorithm by using time-domain protection principles for distribution networks containing IBRs. The introduction of PVD provides for faster fault detection without reducing security and dependability. Offline simulation experiments and controller hardware-in-the-loop real-time simulation validate the effectiveness of the proposed
algorithm against various fault and normal switching events.
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Signal Processing
DMT: Demographic Conditioning, Morphology-Enhanced Transformer for Cuffless Blood Pressure Estimation from PPG Signals
Yidan Shen, Neville Mathew, Maham Rahimi et al. (+4 more)
Blood pressure (BP) is a key marker for cardiovascular risk assessment and therapeutic decision-making, and Photoplethysmography (PPG) enables low-cost, wearable-friendly cuffless BP estimation. However, even with recent progress, many PPG-based models are trained with BP regression alone and may rely on amplitude-dominated shortcuts. In addition, demographic
covariates that systematically modulate vascular compliance are often incorporated only via late fusion, limiting subject-specific representation learning. We propose a
Transformer-based network for cuffless BP estimation from PPG signal, leveraging self-
attention to capture long-range dependencies across multiple cardiac cycles. To account for subject-specific vascular differences, the model is conditioned on demographics via FiLM-style feature modulation applied through the
attention and feed-forward sublayers of
Transformer blocks. In addition, we add an auxiliary morphology head to guide the model to attend to BP-relevant waveform morphology associated with arterial stiffness and wave reflection. Under calibration-based evaluation protocols on the large-scale PulseDB
dataset, the proposed method achieves MAE of 4.56 mmHg for systolic BP and 2.62 mmHg for diastolic BP, reducing errors by 47% and 50% compared with prior demographic-enhanced PPG baselines. The resulting lightweight, single-sensor model supports scalable and clinically grounded cuffless BP estimation in calibration-enabled deployment settings.
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All authors Yidan Shen, Neville Mathew, Maham Rahimi, Deependra Dhakal, George Zouridakis, Xin Fu, Renjie Hu
Image and Video Processing
Multimodal Brain Tumour Classification Using Feature Fusion
Wajih ul Islam, Muhammad Yaqoob, Javed Ali Khan et al. (+1 more)
Clinicians diagnose
brain tumors by synthesizing patient symptoms, medical history, and quantitative imaging data from modalities such as MRI and CT scans into a unified clinical judgement. However, most deep learning models rely on MRI/CT images alone, failing to replicate the clinicians
multimodal reasoning. We explore a two-branch
multimodal network combining raw MRI scans with 91 extracted radiomic features (intensity, texture, shape, and boundary descriptors) to classify
brain tumors into glioma, meningioma, pituitary, and no-tumor. A pre-trained CNN backbone encodes the image stream, whereas a dedicated MLP encodes the radiomic stream. Both streams are fused via concatenation, gated, or bidirectional cross-modal
attention strategies. Across nine experimental runs on a balanced 7,200 image
dataset, all
multimodal configurations outperform unimodal baselines with gated fusion achieving the best accuracy of 96.13%.
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All authors Wajih ul Islam, Muhammad Yaqoob, Javed Ali Khan, Volker Steuber
Systems and Control
QUIET: Quantifying Underutilized Influential Edges for Targeted Synchronization
Sovesh Mohapatra, Christoffer G. Alexandersen, Panagiotis Fotiadis et al. (+4 more)
Network control theory can be used to model intrinsic and extrinsic strategies to steer neural dynamics. Standard approaches are node-centric, structural, and focused on achieving desired instantaneous states. Here, we develop an edge-centric approach which incorporates both structure and function to achieve extended patterns of neural dynamics characterized by desired synchronization states. Our method, Quantifying Underutilized Influential Edges for Targeted Synchronization (QUIET), is an edge-centric framework that integrates structural controllability of individual white matter connections and mutual information between pairwise functional timeseries to identify energy-efficient synchronization pathways. QUIET identifies quiet highways, edges that are structurally influential but functionally underutilized, to optimize regional synchronization. We validated QUIET across 75 synthetic configurations, where QUIET-ranked edge sets significantly outperformed random selection in 93% of cases (p<0.01). The framework, tested on Human Connectome Project participants, revealed that the control energy required for synchronization of the salience network correlates with fluid intelligence. QUIET, applied to healthy adults undergoing dexmedetomidine-induced unresponsiveness, showed that the frontoparietal and default-mode networks exhibited the largest control energy required for synchronization in both awake and sedated states. QUIET is released as a stand-alone software to be used to study theoretically-defined synchronization pathways, which in turn could inform testable hypotheses in perturbative studies.
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All authors Sovesh Mohapatra, Christoffer G. Alexandersen, Panagiotis Fotiadis, Max B. Kelz, John A. Detre, Fabio Pasqualetti, Dani S. Bassett
Systems and Control · Cross-listed from cs.MA
LLM-Mediated Demand Response Coordination in Smart Microgrids
J. de Curtò and I. de Zarzà
Effective demand response in smart microgrids requires prosumers to cooperate voluntarily under strategic self-interest, a coordination problem structurally equivalent to a repeated Prisoner's Dilemma on a social network. This paper presents a multi-
agent simulation in which a Large
Language Model (LLM) Influence Compiler issues structured demand-response directives to a population of heterogeneous prosumer
agents, each governed by a hybrid decision architecture combining game-theoretic base probability (derived from payoff history, neighbour imitation, and exploitation
memory) with LLM narrative evaluation of incoming coordination signals. The hybrid architecture resolves a key methodological challenge: LLMs aligned via Reinforcement Learning from Human Feedback (RLHF) exhibit strong cooperation bias when used as direct decision-makers, producing flat dynamics regardless of grid conditions. By separating strategic
reasoning from grounded narrative evaluation, the model generates realistic prosumer behaviour across six personality archetypes, with baseline cooperation near 50% and clear differentiation under influence. Compiled structured directives achieve 33.3% demand-curtailment cooperation versus 27.0% for unstructured messaging and 28.0% for a no-intervention baseline ($\Delta_\mathrm{comp} = +0.063$), with the advantage preserved across both grounded and idealized
agent substrates ($\Delta = +0.083$) and across all resistance levels ($R = 0.1$ to $0.7$). Hub-targeted dissemination via high-centrality network nodes outperforms peripheral or random targeting, confirming that grid
topology provides mechanistic amplification independent of message content. These results suggest that structured LLM compilation, grounded
agent reasoning, and network-aware targeting are complementary design principles for scalable, interpretable demand-response coordination in smart-city energy systems.
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Systems and Control
Free Parametrization of L_2-Bounded Structured State-Space Controllers for Nonlinear Control with Stability Guarantees
Muhammad Zakwan, Leonardo Massai, Efe C. Balta et al. (+1 more)
Designing stabilizing control policies for nonlinear systems while optimizing complex objectives remains a formidable challenge. Neural networks (NNs), despite their expressive power, can be highly sensitive to small input perturbations and can easily destabilize the closed-loop system. Existing approaches often impose explicit constraints on the controller's parameters to ensure stability, but this typically leads to additional computational overhead. To address this issue, we leverage recently proposed structured state-space models (SSMs) to parametrize discrete-time control policies for nonlinear systems. Our key contribution is a new free parametrization of linear time-invariant (LTI) systems with a prescribed L2 gain. We use this result to construct the L2-Recurrent Unit (L2RU), an SSM layer that enforces the desired L2 bound by design. The resulting architecture can be used to guarantee closed-loop stability via the small-gain
theorem or the so-called performance-boosting framework, independently of the controller's optimization parameters, thereby enabling fully unconstrained optimization of general nonlinear objectives. Furthermore, the structure induced by the proposed parametrization enables the efficient processing of long input sequences, as it is highly parallelizable through
algorithms such as parallel scan. We demonstrate the effectiveness of this approach on a formation-control task for mobile
robots, where the L2RU-based controller ensures collision and obstacle avoidance while maintaining stability and performance.
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All authors Muhammad Zakwan, Leonardo Massai, Efe C. Balta, Giancarlo Ferrari-Trecate
Signal Processing
Multi-Channel Soil Moisture Measurement: High Accuracy and Low Crosstalk Through Optical-Semiconductor Based Differential Sensing
Thomas Maier, Charlotte Rohleder, Lukas Kamm et al. (+3 more)
Soil moisture measurement plays a key role in irrigation and environmental management. Yet it remains unreliable due to heterogeneous soils, limited sensing volumes, temperature drift, and parasitic inter-channel coupling. This work presents a compact multi-depth capacitive probe that extends a parallel-plate geometry from previous work with differential activation to suppress stray capacitances and improve accuracy. An equivalent-
circuit model quantifies parasitic effects, and optically coupled transistor bridges isolate each sensing layer. Raw capacitance is converted to volumetric water content and plant-available water using established calibration models. Laboratory results show a fourfold reduction in temperature sensitivity, strong confinement of the sensing volume, and improved repeatability in heterogeneous soils. Field validation against reference sensors demonstrates high accuracy and precision comparable to widely used instruments, enabling a practical and scalable solution for agricultural and urban soil-moisture monitoring.
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All authors Thomas Maier, Charlotte Rohleder, Lukas Kamm, Philipp Dauner, Jan Pinski, Bjoern M. Eskofier
Audio and Speech Processing · Cross-listed from cs.LG
Data-Driven Runway and Taxiway Exits Prediction of Landing Aircraft: A Case Study at Hartsfield-Jackson Atlanta International Airport
Alex Porcayo, Yutian Pang, Maria Thomas et al. (+1 more)
Airport surface operations increasingly constrain performance at high-throughput hubs. This study examines arrival taxi-in decisions at Hartsfield-Jackson Atlanta International Airport (KATL) and proposes a two-stage, data-driven decision aid that mirrors controller workflow. Stage I predicts the runway exit selected by an arriving aircraft. Stage II predicts whether, given that exit, the aircraft will cross the active departure runway at a designated point or use the end-around taxiway. Models are trained using ASDE-X surface trajectories, aircraft characteristics, ramp destinations, short-horizon traffic rates, and weather across multiple look-back windows. We
benchmark nine classifiers, including Random Forest, XGBoost, LightGBM, and CatBoost, and evaluate accuracy, macro-F1, precision-recall behavior, confusion
matrices, Brier score, and Expected Calibration Error. Across east and west flows, XGBoost and LightGBM outperform Random Forest. Stage I achieves 0.86-0.89 accuracy with macro-F1 scores of 0.40-0.50, while Stage II achieves 0.70-0.74 accuracy with macro-F1 scores of 0.28-0.55. Feature-importance analysis shows that approach speed is the main driver of exit choice. Departure rate, crossing rate, ramp destination, and, for west flow, the selected exit are the strongest predictors of crossing versus end-around routing. Minority classes remain harder to predict because of feature-space overlap, as shown by t-SNE and UMAP analyses. The proposed framework supports controller situational awareness through calibrated, explainable predictions while preserving human responsibility for final routing decisions.
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All authors Alex Porcayo, Yutian Pang, Maria Thomas, John-Paul Clarke
Systems and Control
A Companion App for an Autonomous Family Vehicle: Identification of Values for an Autonomous Mobility System
Leon Johann Brettin, Tobias Schräder, Kerstin Kuhlmann et al. (+2 more)
In this paper, we present a companion app for an autonomous vehicle aimed at user groups who would normally require an accompanying person to drive them. Two aspects of a companion app are presented in this paper: First, the possibility for a trusted person to track the ride of the person in need of support and second, to put the settings of the vehicle for persons in need of support in the hands of a trusted person. In addition, this article describes the requirements and addressed values and discusses the safety-relevant aspects of such a companion app. We also discuss and identify the values that influence passengers and trusted persons using the companion app. Overall, a companion app can provide new perspectives and opportunities for people in need of support, allowing them to take advantage of the features offered by autonomous vehicles. It enables trusted individuals to configure the vehicle according to the passengers needs. Also such an app can be a mechanism to involve trusted persons in the options given by the vehicle and give them the possibility to adapt the vehicle to the needs of the person in need of support.
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All authors Leon Johann Brettin, Tobias Schräder, Kerstin Kuhlmann, Vanessa Schmidt, Markus Maurer
Systems and Control · Cross-listed from cs.RO
Multi-UAV Active Sensing with Information Gain-based Planning and Belief Fusion
S. Habibi and L. Marques
Unmanned aerial vehicles (
UAVs) are increasingly used for active sensing and information gathering in spatially distributed environments. Their performance, however, is constrained by limited flight time, sensing uncertainty, and the trade-off between spatial coverage and observation accuracy. This paper presents a real-world validation of a multi-
UAV active sensing framework for probabilistic binary terrain mapping, with precision agriculture used as the application case. The environment is represented as a probabilistic belief map, where spatial dependencies are modeled through a factor-graph formulation.
UAV decision making is guided by Information Gain based Informative Path Planning (IGbIPP), and the approach is compared with Random Walk and Sweep coverage path planning baselines using both synthetic terrains and real
UAV-derived agricultural imagery. The study also evaluates spatial correlation weights and several probabilistic belief-fusion rules for multi-
UAV information sharing. Results show that IGbIPP reduces
entropy and mapping error more effectively than the baselines, while a wider field of view improves real-world coverage and map accuracy. The results further show that simple equal or biased spatial weights can be more robust than adaptive weights, and that
Bayesian, log-odds, and Dempster–Shafer fusion achieve the best cooperative mapping performance. These findings highlight the importance of uncertainty-driven planning, sensing geometry, spatial modeling, and probabilistic fusion for real-world
UAV-based active sensing.
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Signal Processing · Cross-listed from math.OC
Learning Doubly Sparse Explicitly Conditioned Transforms
Tudor Pistol
Finding convenient spaces in which certain hypotheses regarding an assumed sparse structure of natural signals hold true has become a desirable result in recent research, its implications being reflected in areas such as data compression, noise reduction and feature extraction. While the extensively used analytical transforms, such as DFT or DCT, already provide efficient
algorithms and robust sparse representations, they assume a fixed prior about the data, failing to accurately capture the specific structure of more restrictive classes of signals. To address this, the concept of a data-adaptive, learnt transform has been introduced in the literature, allowing for the reduction of a residual term in the transform domain. More recent studies have shown that the condition number serves as a good metric in this context, where the desired outcome alternates between a generalizing tendency and one that achieves minimal approximation error. Motivated by these considerations, we introduce the learning of a structured, explicitly conditioned transform formulated as the product of a fixed canonical
matrix and a refining data-adaptive sparse component. This approach seeks to preserve the advantages of fast and stable analytical transforms, while introducing controllable adaptivity to the data. No references that concern this specific formulation have been identified so far, indicating its novelty. The proposed
algorithm is motivated within the framework of inexact proximal methods, leveraging a newly derived closed-form projection operator. Empirical observations demonstrate state-of-the-art results on the doubly sparse transform learning problem and comparable performance with its dense variant at significantly lower computational costs and sometimes faster convergence and better avoidance of bad local minima.
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Audio and Speech Processing
Optimizing 2D Input Representations and Sub-phase Fusion Strategies for Differential Diagnosis of Asthma and COPD Using CNN- and GRU-Based Networks
Ipek Sen, Ozgur Ozdemir and Elena Battini Sonmez
This study aims to explore the performance of the VAR model in comparison with mel-frequency cepstral coefficient (MFCC)
matrices and log-mel spectrograms using deep learning. In pulmonary sound classification, spectrogram-based representations suffer from inconsistent temporal dimensions due to varying respiratory cycle durations. Along with traditional trimming/zero-padding, adaptive-length windowing was presented to fix their temporal dimensions. Their spectral and temporal dimensions were optimized by testing a range of parameters. Different convolutional neural network (CNN) architectures were employed to extract features from the two-dimensional representations obtained over the sub-phases. The extracted sub-phase features were then fused using various strategies including direct concatenation, gated recurrent unit (GRU) network and GRU with
attention mechanism. Model performances were assessed through respiratory cycle-based evaluation and subject-based evaluation comprising multiple respiratory cycles. Several data augmentation techniques were also studied to cope with limitations in data size. The best cycle-based F1-score (0.877) was obtained using the MFCC
matrices with thirteen coefficients and 64-point time resolution per sub-phase representation followed by direct feature concatenation, and the best subject-based F1-score (0.855) was obtained using the MFCC
matrices with thirteen coefficients and 256-point time resolution per full-cycle representation, both obtained by adaptive-length windowing. Augmentation degraded the performance of models overall, yet mixup augmentation was the best among the methods tested. MFCC outperformed log-mel spectrogram and VAR model in differentiation of asthma and COPD. Sophisticated fusion strategies did not improve the diagnosis. Augmentation did not contribute, demonstrating the significance of authentic data in pulmonary sound studies.
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Systems and Control · Cross-listed from cs.RO
Resilient Navigation for Autonomous Farm Robots by Leveraging Jerk-Augmented Models with IMU-Only Disturbance Rejection
Batu Candan, Mohammed Atallah, Simone Servadio et al. (+1 more)
Precise state estimation for
navigation of autonomous agricultural
robots is often compromised by sensor outages (GNSS/
LiDAR/Visual) and high-frequency vibrations inherent in off-road environments. This paper proposes a robust
navigation algorithm based on a jerk-augmented Extended Kalman Filter (EKF) integrated with a Multiple Tuning Factor (MTF) adaptation method. Unlike standard EKF approaches that assume constant measurement noise, our method dynamically adjusts the measurement covariance
matrix in real-time, allowing the system to cope with sudden disturbances and sensor outliers. We evaluate the
algorithm using real-world data from a Salin247 autonomous
robot. Results demonstrate that jerk-augmentation combined with MTF adaptation significantly reduces 3D position Root Mean Square Error (RMSE) compared to baseline EKF models, providing superior dead-reckoning capabilities.
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All authors Batu Candan, Mohammed Atallah, Simone Servadio, Saeed Arabi
Systems and Control · Cross-listed from math.DS
Spectral Koopman Approach for Reconstructing State-space Geometry of Cislunar Restricted 3-Body Problem
Subhrajit Sinha, Sriram S. K. S. Narayanan, Raktim Bhattacharya et al. (+1 more)
In this work, we propose a novel approach, based on the path integral formulation of Koopman spectrum, to discover the phase-space geometry of the planar Cislunar Restricted 3 Body Problem (CR3BP). In contrast to existing techniques, which use trajectory-based (usually) local analysis, we leverage the Koopman operator framework, which generates a global linear representation of the system, to reconstruct the global phase space geometry of the CR3BP. In particular, we compute the principal eigenfunctions of the Koopman operator via the path integral approach and show how the zero level curves of these eigenfunctions encode the phase space characteristics of the planar CR3BP.
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All authors Subhrajit Sinha, Sriram S. K. S. Narayanan, Raktim Bhattacharya, Umesh Vaidya
Systems and Control
Robust Current Regulation of MMC-based MTDC Power Systems based on Lyapunov Inequality
Victor Daniel Reyes Dreke, Rahul Rane and Aleksandra Lekić
Multi-terminal DC (MTDC) transmission systems based on modular multilevel converters (MMCs) are a key component of the envisioned future energy sector, where sustainability and efficiency are increasingly prioritized. To ensure their reliable operation, MMC currents must be regulated safely and rapidly under a wide range of uncertain operating conditions. Consequently, the design of current controllers faces a fundamental challenge: achieving fast transient response while maintaining robustness against uncertainties. This paper addresses this challenge by proposing a linear
matrix inequality (LMI)-based design framework that leverages Lyapunov stability conditions to synthesize a less conservative static state-feedback controller. The proposed design method explicitly accounts for system constraints, including input saturation and overcurrent limits. The proposed method effectiveness is assessed on the CIGRE MT-HVDC
benchmark, simulated in RTDS, and compared with existing methods.
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Signal Processing
Personalized Deep Learning for Short-Term Forecasting of Impending Atrial Fibrillation from Continuous Wearable ECG Signals
Jangwon Suh, Soonil Kwon, Jungmin Ko et al. (+4 more)
Background and Objective: Continuous wearable electrocardiogram (ECG) monitoring is increasingly used for ambulatory arrhythmia surveillance, yet
forecasting impending atrial fibrillation (AF) is challenged by inter-patient ECG variability. This study investigated whether personalizing a global model via fine-tuning on an individual's ECG signals improves short-term
forecasting of impending AF. Methods: A global model trained on the ICENTIA11K
dataset was compared against personalized models fine-tuned across three cohorts: ICENTIA11K, IRIDIA-AF, and MobiCARE. Following preprocessing, models processed 60-second ECG segments for a five-minute forecast horizon. We evaluated the impact of adaptation data volume and analyzed ECG features, such as heart rate and RMSSD. Results: Personalized models significantly outperformed the global model, achieving AUROCs of 0.711 vs. 0.614 in ICENTIA11K and 0.686 vs. 0.585 in MobiCARE. Personalization benefits increased with the amount of patient-specific fine-tuning data. While the global model's accuracy rose as AF onset approached, personalized models in the two external cohorts exhibited distinct temporal dynamics, which may indicate the capture of patient-specific characteristics less dependent on proximity to the AF event. Pre-AF episodes showed elevated heart rates and RMSSD. Feature attributions highlighted clinically relevant precursors, including frequent premature atrial complexes (PACs) and short supraventricular tachycardias (SVTs). Conclusions: Adapting deep learning models with patient-specific wearable ECG data significantly enhances short-term
forecasting of impending AF. This personalized framework supports timely preventive interventions and improved AF management in ambulatory monitoring environments.
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All authors Jangwon Suh, Soonil Kwon, Jungmin Ko, Yun Kwan Kim, Hee Seok Song, Eue-Keun Choi, Wonjong Rhee
Image and Video Processing
Low-Dose 3D Bonding Mapping Through "Soft" Core-Loss EELS Tomography and Unsupervised Deep Learning
Mario Pelaez-Fernandez, Daniel del-Pozo-Bueno, Adrien Teurtrie et al. (+10 more)
Resolving the 3D chemical configuration of beam-sensitive nanomaterials at high spatial resolution remains a persistent frontier in scanning transmission electron microscopy (STEM). The main limitation lies in the trade-off between high electron dose required for analytical signals and the large number of projections needed for tomographic reconstruction. Here, we achieve dose-efficient 3D bonding mapping of FeO/Fe$_3$O$_4$ core-shell nanocubes with high resolution via electron energy loss spectroscopy (EELS). Our approach relies on two developments. First, a standardless "soft" core-loss EELS methodology exploiting Fe-M$_{2,3}$ edges provides ${\sim}50\times$ higher dose efficiency than conventional Fe-L$_{2,3}$ edges, using the latter only as a source of FeO and Fe$_3$O$_4$ standards. Second, we introduce multi-channel deep image prior with total variation regularization (DIPm-TV), an unsupervised method for spectroscopic
tomography that jointly reconstructs multiple channels by exploiting spatial correlations under sparse-view and low-dose conditions. Using simulated
datasets, high-quality reconstructions are obtained from as few as nine projections over $-70^\circ$ to $+70^\circ$, without HAADF-STEM signal or
symmetry constraints. Applied to FeO/Fe$_3$O$_4$ nanocubes, Fe-M$_{2,3}$ EELS maps show improved SNR and spatial resolution, revealing a thin outer FeO shell surrounding the magnetite shell. DIPm-TV yields ${\sim}1$ nm isotropic resolution oxidation-state volumes preserving cubic morphology, recovering the outer FeO shell, and revealing a small internal void, features not accessible with conventional reconstruction methods. This work establishes a pathway for low-dose 2D and 3D analytical mapping of beam-sensitive materials using shallow core-loss edges, enabling orders-of-magnitude dose reduction while maintaining spectral fidelity and reliable 3D information.
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All authors Mario Pelaez-Fernandez, Daniel del-Pozo-Bueno, Adrien Teurtrie, Serge Brosset, Maya Marinova, Phillipe Ciuciu, Marta Estrader, German Salazar-Alvarez, Francesca Peiró, Raul Arenal, Sonia Estradé, Zineb Saghi, Francisco De la Peña
Systems and Control
Temperature-Aware Heat Pump Modeling for Large-Scale Energy System Optimization
Simon Malacek, Sonja Wogrin and Yannick Werner
Heat pumps are expected to dominate the heating sector, substantially increasing peak electricity demand. At the same time, building thermal inertia enables operational strategies, providing temporal flexibility in heat pump operation and short-term demand response. However, this dynamic behavior is not yet represented in large-scale energy system optimization models. To address this gap, we present an innovative formulation of building thermal inertia. The resulting temperature variable is integrated into a novel conic temperature-aware heat pump efficiency formulation, enabling a more precise emulation of smart control strategies. In a case study of the European energy system, we show that the approach captures operational heating flexibility while remaining computationally efficient. The results indicate substantial untapped flexibility potential, enabling up to a 22% reduction in heating-related electricity costs. This potential can be realized through a suitable energy
market design that incentivizes coordinated heat pump control, individually or via aggregators.
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