Systems and Control
Dynamic Operational Reserve Margin Assessment from Risk-Constrained Unit Commitment States
Pratishtha Shukla, Shaked Regev, Evan J. R. Brody et al. (+2 more)
We propose Dynamic Reserve Margin (DRM) as a time-varying operational adequacy metric derived from risk-constrained unit commitment (RCUC) states. DRM quantifies reserve adequacy using the additional generation capacity that committed generators can provide within a 5-minute response window relative to uncertainty and contingency reserve requirements. We introduce a complementary Reserve Risk Envelope (RRE) metric to quantify operational reserve headroom in directly interpretable MW terms. A low-margin duration metric is further developed to quantify the persistence of reserve stress over an operating horizon. We use an IEEE 14-bus example to illustrate these concepts, followed by large-scale RCUC case studies under multiple operating scenarios. Results demonstrate that reserve requirements and ramp-accessible reserve capability can vary substantially across operating conditions, and that commitment decisions adapt to maintain reserve adequacy under changing system conditions. The proposed DRM and RRE metrics provide an interpretable operational characterization of reserve adequacy, reveal reserve accessibility and stress persistence that are not captured by conventional reserve margin metrics.
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All authors Pratishtha Shukla, Shaked Regev, Evan J. R. Brody, Charles Foltz, Teja Kuruganti
Systems and Control
Hosting Capacity Assessment of Data Centers with Voltage Ride-Through Capability in Power Systems
Pengyu Ren, Wei Sun and Fei Teng
Large data centers are emerging as concentrated, power-electronic grid loads whose abrupt disconnection or transfer to on-site backup supply during voltage disturbances can remove large demand from the power system, and may create a system-level stability problem. Their interconnection feasibility therefore depends not only on steady-state thermal and voltage limits, but also on whether internal power-conditioning systems can maintain IT service while limiting customer-initiated load reduction. This paper presents a voltage ride-through (VRT)-aware data center and grid co-planning framework that couples transmission-level fault simulation with an internal data center ride-through model. Python-based dynamic simulations generate point-of-interconnection (POI) voltage trajectories under selected network faults, and the resulting waveforms drive an internal model incorporating IT and cooling-load dynamics, DC-link, Uninterruptible Power Supply (UPS) response, and converter apparent power limits. The IEEE 118-bus case study shows that internal VRT capability can become a binding interconnection constraint: steady-state planning alone can overestimate feasible data center capacity, whereas increased UPS converter headroom progressively restores hosting capacity. Under the reduced-order response models studied, the grid-forming mode provides greater ride-through margin than the current-limited grid-following mode under the same network fault conditions. The results further show that VRT constraints can materially change both the total hosting capacity of data centers and its spatial allocation across candidate interconnection buses.
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Image and Video Processing · Cross-listed from q-bio.QM
Sparse concept attribution for histomorphological hypothesis generation from whole-slide classifiers
Tristan Lazard, Kenza Bouzid, Julius Hense et al. (+6 more)
Histology images contain rich morphological information and can provide insights into pathological processes. However, deriving hypotheses relating morphological phenotypes to clinical attributes is bottlenecked by a manual image interpretation step. Here, we demonstrate that this process can be automated through interpretable deep learning. We present SCOPE, a method to interpret slide-level classifiers by combining pathology-specific
vision–
language models with sparse concept attribution onto a generalist histomorphological concept bank. To measure whether such explanations recover known morphology, we introduce MorphoRecoveryBench, a
benchmark of seven tasks with pathologist-curated reference descriptions. On this
benchmark, dense concept attribution is indistinguishable from a random baseline, whereas sparse attribution recovers substantial known morphology; decomposing the pooled slide
embedding reaches similar explanation correctness at a fraction of the computational cost. Post-hoc interpretation of whole-slide classifiers can thus generate morphological hypotheses at scale, for expert validation.
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All authors Tristan Lazard, Kenza Bouzid, Julius Hense, Shruthi Bannur, Daniel Coelho de Castro, Daniel Shao, Rajesh Jena, Drew Williamson, Stephanie Hyland
Image and Video Processing
Learning from Scarce Labels: Multi-View Echocardiography for Ejection Fraction Prediction
Zhiyuan Gao, Dominic Yurk and Yaser S. Abu-Mostafa
We present, to the best of our knowledge, the first publicly available resource for predicting left ventricular ejection fraction (EF) from parasternal long-axis (PLAX) echocardiography. Because no PLAX-EF
datasets previously existed, our work focuses on an innovative data generation strategy to overcome this scarcity. By leveraging a time-based correlation between clinical notes and echocardiographic
videos, combined with fine-tuning view classifiers and proxy labeling, we created a labeled
dataset of over 25,000 PLAX
videos. This enables us to train the first reproducible PLAX EF model, achieving a mean absolute error (MAE) of 6.86%. Given that apical four-chamber (A4C) methods, the clinical standard, report MAE values of 6%-7%, our results demonstrate that EF estimation from PLAX views is both feasible and clinically relevant. This surpasses the performance of existing methods and provides a clinically relevant solution for situations where apical views may not be feasible. Going further, we demonstrate that combining PLAX and A4C predictions via simple unweighted late fusion improves both single-view baselines to a 6.37% MAE, underscoring the value of multi-view integration. To promote continued research, we release the
dataset labels, trained models, and runnable demos on GitHub, Hugging Face, and Google Colab: https://github.com/Jeffrey4899/PLAX_EF_Labels_202509
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Signal Processing · Cross-listed from cs.IT
Direct Satellite-to-Device Communications: From Cooperative Task Offloading to Non-Cooperative Access Monitoring
Sai Huang, Wanli Ni, Ke Lv et al. (+5 more)
Direct satellite-to-device (DS2D) communication is emerging as a transformative paradigm for extending ubiquitous connectivity and edge computing capabilities to remote and underserved regions within 6G non-terrestrial networks. However, practical deployment faces dual critical challenges: i) dynamic satellite channel conditions (e.g., severe Doppler shifts, fast fading) and constrained satellite computing resources in cooperative scenarios; and ii) unauthorized satellite access introduces significant spectrum security threats in non-cooperative scenarios. To address these challenges, we propose a versatile DS2D system that supports cooperative task offloading and non-cooperative access monitoring. For cooperative DS2D communications, we integrate a channel estimation module with a dueling double deep Q-network (D3QN) to dynamically optimize task offloading strategy. For non-cooperative DS2D communications, we propose
Transformer-based models to enable blind signal detection and automatic modulation classification (AMC). Simulation results show that: 1) The D3QN
algorithm reduces average latency by up to 225% compared to static association policies. 2) Our signal detection model achieves an average presence detection probability of 90.5% for DS2D signals. 3) The proposed AMC
algorithm achieves superior performance across different signal-to-noise ratios (SNRs), with a 9.4% accuracy gain in low-SNR regimes compared to existing methods.
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All authors Sai Huang, Wanli Ni, Ke Lv, Pengcheng Zhang, Yurui Zheng, Menghan Zhang, Zihui Gong, Zhiyong Feng
Systems and Control
Tests on the POD-P controller of INELFE Spain-France VSC-HVDC interconnector
Javier Renedo, Agustín Diaz-García, Gilles Torresan et al. (+11 more)
INELFE interconnector consists of a 2x1000 MW high voltage direct current system based on voltage source converters (VSC-HVDC) interconnecting France and Spain. INELFE VSC-HVDC link is embedded into the high voltage alternating current (HVAC) system. Electromechanical oscillations, also known as power oscillations, are a major concern worldwide. INELFE VSC-HVDC link has specific controllers to damp power oscillations by modulating active (P)- and reactive (Q)-power injections of the VSC converters (POD-P and POD-Q controllers, respectively). The Spanish and French Transmission System Operators (TSOs) carried out a join task force to - Increase the gain of POD-P controller of INELFE VSC-HVDC link. - Make it possible to use the POD-P controller together with angle difference control (ADC) of INELFE VSC-HVDC interconnector. The objective of these modifications is to improve the effectiveness of POD-P controller and, therefore, to increase its contribution to the damping of inter-area oscillations in the Continental Europe (CE) power system. Such changes require (a) extensive simulation studies and (b) extensive tests in different operation modes, in order to ensure the correct behavior of the system. This paper presents simulation studies and field tests on the POD-P controller of INELFE VSC-HVDC interconnector in different modes of operation.
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All authors Javier Renedo, Agustín Diaz-García, Gilles Torresan, Eduardo Lorenzo Cabrera, Antonio Cordón, Silvia Sanz Verdugo, Juan Peiró, Javier Pérez Castro, Gorka Calvo, Aitor Hernández Sautua, Ambroise Petit, Hamza Oukhayi, Samy Akkari, Hani Saad
Signal Processing · Cross-listed from cs.SD
Understanding Automatic Mixing: A Subtask-Oriented Analysis of Two-Stage Mixing System
Jinjie Shi, Wei Hua, Kunzhu Xie et al. (+3 more)
Automatic mixing transforms multitrack recordings into perceptually coherent, balanced, and aesthetically consistent mixes. In real-world production, this task is challenging due to large track counts, diverse instrumentation, and strong inter-track dependencies. Two-stage systems address this complexity by separating intra-group processing from inter-group mixing, yet it remains unclear whether their gains arise from stronger component models or from explicit task decomposition. We present a subtask-oriented analysis of automatic mixing through three controlled listening experiments. We investigate whether full-mix models transfer to intra-group mixing, whether downstream models compensate for grouping and loudness errors, and whether two-stage decomposition improves full-mix quality. Across three dense pop and rock excerpts, transfer differs between the evaluated models; inappropriate grouping causes clear downstream degradation, while altered loudness relationships have weaker and model-dependent effects. Both two-stage variants significantly outperform their corresponding single-stage baselines. These findings support explicit separation of local balance and global mix coordination as a useful design principle for automatic mixing. Code and audio examples are available online.
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All authors Jinjie Shi, Wei Hua, Kunzhu Xie, Make Li, Yuchen Liu, Joshua Reiss
Audio and Speech Processing
VibeVoice-ASR-Streaming Technical Report
Yujie Tu, Zhiliang Peng, Jianwei Yu et al. (+11 more)
Traditional speaker-attributed ASR systems treated ASR and speaker diarization as two separate tasks. Recently, end-to-end models such as VibeVoice-ASR have unified the two tasks within a single model. However, existing unified models still mainly support offline recognition, making it difficult to meet the low-latency requirements of real-time voice assistants and
agents. To tackle this issue, we present VibeVoice-ASR-Streaming, one of the first LLM-based end-to-end approaches to streaming speaker-attributed ASR. It interleaves fixed-size audio chunks, a small amount of lookahead audio and previous text. This allows the model to produce ”who said what” as
speech arrives, without a separate diarization stage. For transcription accuracy, our 7B model achieves the lowest average WER/CER across five evaluation sets. For speaker attribution, it achieves the best or tied-best on 12 of 13 evaluation settings. We release the 1.5B and 7B model weights together with inference code.
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All authors Yujie Tu, Zhiliang Peng, Jianwei Yu, Li Dong, Songchen Xu, Yaoyao Chang, Wenhui Wang, Zilong Wang, Zehua Wang, Yan Xia, Ruibin Yuan, Jiajun Zhang, Xie Chen, Furu Wei
Systems and Control
Guidelines for the implementation of power oscillation damping controllers in power converters
Javier Renedo, Macarena Martín Almenta, Sergio Martínez Villanueva et al. (+3 more)
One of the most effective ways to damp electromechanical oscillations in power systems is by means of supplementary controllers attached to the different devices in the power system is by means of power system stabilizers (PSS) in synchronous machines or by means of power oscillation damping (POD) controllers in facilities with power converters. In the recent years, the use of POD controllers in voltage source converters (VSCs) with grid-following (GFL) control has been investigated. Although the potential of POD controllers to help to damp inter-area oscillations in power systems is enormous, their correct implementation is not trivial, because their effectiveness is strongly linked to their settings. This paper provides guidelines for the implementation of POD controllers in power converters for application in real-world power systems. The paper proposes compliance criteria for POD controllers using a synthetic test system and a systematic methodology used in Spanish technical standard for monitoring compliance (NTS), considering practical considerations. The paper also includes numerical examples to illustrate compliance criteria for POD controllers in a synthetic test system. A generic power converter with grid-following (GFL) control is used for the analysis by simulation and POD controllers using modulation of active-power injection (POD-P), reactive-power injection (POD-Q) or both simultaneously (POD-PQ) will be analysed. Results were validated in a large-scale power system. The paper concludes that by using appropriate synthetic systems, methodologies and compliance criteria, POD controllers in power converters could be effective to damp electromechanical oscillation in large-scale power systems.
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All authors Javier Renedo, Macarena Martín Almenta, Sergio Martínez Villanueva, Agustín Díaz-García, Antonio Cordón, Davide Gotti
Signal Processing
A Dry-Contact Ear-EEG System With Continuous Electrode-Skin Impedance Mismatch Monitoring for Motion Artifact Cancellation Using DRL Stimulus
Lohan Atapattu, Sajitha Madugalle, Imasha Nethmal et al. (+6 more)
Dry-contact ear-electroencephalography (Ear-
EEG) enables wearable neural monitoring. However, motion induced electrode-skin impedance (ESI) mismatches between electrodes can severely degrade signal quality. To the best of our knowledge, this paper presents the first proof-of-concept dry-contact EarEEG system that uses a driven-right-leg (DRL) stimulus for continuous ESI mismatch monitoring, enabling online adaptive motion artifact cancellation. A 1 kHz sinusoidal stimulus is injected through the DRL electrode. The resulting response to the injected carrier is separated from the
EEG using bandpass filtering and demodulation, and then used to extract the ESI mismatch information as the reference input for a normalized least-mean-square adaptive filter followed by a Hampel filtering stage. To evaluate artifact suppression and preservation of neural activity, alpha-band
EEG activity was analyzed involving four healthy participants performing head nodding, electrode tapping, and jaw clenching. The system achieved artifact power reductions of 6.5, 12.6, and 9.0 dB (77.6%, 92.8%, and 86.4%, respectively) while alpha-band modulation remained clearly observable after processing. This demonstrates the feasibility of DRL-stimulusbased ESI mismatch monitoring for motion artifact cancellation in wearable dry-contact Ear-
EEG.
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All authors Lohan Atapattu, Sajitha Madugalle, Imasha Nethmal, Erandee Jayathilaka, Avishka Herath, Kithmin Wickremasinghe, Simon L. Kappel, Nilan Udayanga, Chamira U. S. Edussooriya
Image and Video Processing
LoFi RADIO: A Distilled In-Domain Backbone Applied for Artifact-Severity Grading of Ultra-Low-Field Neonatal Brain MR
Jonathan B. Martin, Yashwant Kurmi and Charlotte R. Sappo
Ultra-low-field MRI makes neonatal
brain imaging deploy- able in low-resource settings, but its low SNR, lack of shielding, and long scan duration make it especially prone to acquisition artifacts, motivating automated quality control. We address the LISA 2026 Task 1a challenge: multi-label severity grading (0/1/2) of seven common image artifacts on ULF T2 weighted volumes. We identify that a number of backbones may be successfully paired with a classification MLP, but that no single backbone is uniformly best across artifacts. To improve performance, we evaluate routing complementary foundation model teachers through a per-artifact gate, as well as distilling the teachers into a single in-domain ViT-S student (LoFi
RADIO) over an unlabeled low-field MRI corpus. Both of these strategies improve the weighted composite. The distilled backbone matches or exceeds the gate and has the added advantage of not requiring deployment of multiple large foundation models at infer- ence.
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Signal Processing
ARFT: A Synchronized Multimodal RF-Acoustic Dataset for Positioning in Distributed Environments
Daan Delabie, Jarne Van Mulders, Bert Pyck et al. (+2 more)
This paper documents the acoustic-
radio fusion in Techtile (ARFT)
dataset, a synchronized measurement campaign for distributed wireless sensing and positioning in the Techtile testbed. Ultrasonic and
radio frequency (RF) signals are simultaneously transmitted and captured at multiple positions in a 2D spatial grid inside the Techtile testbed. Each acquisition cycle corresponds to one rover stop, one position sample, one acoustic recording and one RF snapshot. The RF modality is recorded as per-host pilot measurements and released as channel state information (CSI) tensors for 42
antennas mounted at the ceiling of the room. The acoustic chirp is recorded with 91 synchronized microphones and transmitted by a synchronized quasi omni-directional speaker. The campaign spans 5011 spatial position samples spanning a 5.57 m by 2.89 m area with complete RF and acoustic data. We detail how the measurements are recorded, quantify per-experiment coverage, describe the RF and acoustic data contents, and explain how the aligned modalities support RF-only, acoustic-only, and joint positioning workflows. Furthermore, a static acoustic positioning pipeline that performs anchor selection, pulse-compression ranging, and least squares (LS) localization is elaborated.
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All authors Daan Delabie, Jarne Van Mulders, Bert Pyck, Gustav Nilsson Gisleskog, Gilles Callebaut
Signal Processing
Active Inference for Joint Port Selection and Pilot Allocation in Fluid Antenna Systems Under Partial CSI
Kian Fotovat, Kamran Fotovat and Zijun Wang
Fluid
antenna systems (FAS) obtain spatial degrees of freedom by activating a few radiating ports from a dense grid of candidates, but deciding which ports to activate presupposes channel knowledge at ports that are never measured. Prior port-selection work assumes full or near-full channel state information (CSI) and treats the pilot overhead as a fixed cost. We instead formulate port selection and pilot allocation jointly as active inference. A spatio-temporal generative model – sub-wavelength spatial correlation together with an order-p autoregressive temporal prior – maintains a Gaussian belief over all N port channels, and the transmitter chooses both the ports it serves and the subset of them it pilots by minimizing an expected free energy that trades achievable rate against information gain and port-switching cost. Selection is greedy and submodular, at O(NM) per slot. On a 441-port grid with a hybrid front end at n_RF = 2K, the
agent attains 91% of a full-CSI genie's sum rate while measuring only 2.3% of the candidate ports; pilots can then be withdrawn from ports that are still served, one fifth of them for 2% of the sum rate and two fifths for 8%. No training is required.
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Systems and Control
Economic Model Predictive Control with Policy-Guided Terminal Ingredients
Salim Msaad and Robert D. McAllister
Conventional designs for model predictive control typically rely on terminal costs and constraints derived from a steady state to guarantee closed-loop stability and performance. However, this dependence on a steady-state assumption limits the applicability of this control method to systems in which such a fixed operating point is either not available or not desirable. This work introduces a novel framework, termed policy-guided MPC, to address this limitation. Our approach constructs terminal costs and constraints using a known sub-optimal control policy. Specifically, the terminal region is defined around a center determined by a rollout of the policy, and a penalty on deviation from this center is used to define the terminal cost. This method obviates the need for a steady state or reference trajectory. Closed-loop performance guarantees are established relative to the guiding policy, for both finite and infinite horizon problems. The effectiveness of the proposed framework is demonstrated through numerical simulations on an energy management example.
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Systems and Control
Zonotope-Based Active Exposure of Stealthy Deception Attacks in Sensor-Fusion Systems
Meiqi Tian, Shuo Li and Bingzhuo Zhong
This paper investigates the stealthy attack detection for sensor-fusion cyber-physical systems with unknown-but-bounded noises through the control channel. The detection framework is particularly applicable to sensor-fusion scenarios in which multiple suspicious sensors contributing to the fused estimate may be compromised simultaneously. First, we construct an admissible output set using secure sensors and an attack output set for each attack hypothesis. Then, we introduce a receding-horizon optimization framework to design exposure inputs, namely bounded auxiliary control perturbations injected through the control channel, so as to enlarge the separation between the admissible output set and the attack output sets according to the separation tendency. A sufficient detection condition is further derived, showing that set separation guarantees detectability of the compromised sensors. Moreover, an offline exposure budget guidance is developed to support budget selection before online exposure starts. Simulations on a
UAV navigation system under stealthy GNSS and
LiDAR attacks validate the proposed method.
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Systems and Control · Cross-listed from cs.RO
Pre-Lane-change Signal in Transitional Autonomous Vehicles: Results from Controlled Experiments
Zeyu Mu, Danjue Chen, Abhinav Sharma et al. (+1 more)
This paper investigates how a production transitional autonomous vehicle (tAV) develops and executes mandatory lane-change decisions. Using 150 controlled mandatory lane changes from the NC-tALC experiments, the study examines whether the eventual target gap is observable before lateral movement begins and how the tAV progresses longitudinally from that pre-lane-change state to lane-change start. Signal time (SigT) is defined as an operational pre-lane-change-start reference point. A Firth logistic regression predicts whether the tAV eventually merges in front of or behind its nearest target-lane vehicle using relative position and relative speed at SigT. Longitudinal progression from SigT to lane-change start is then examined separately for in-position and repositioning cases. The traffic state at SigT contains substantial information about eventual target-gap choice and provides meaningful lead time before lateral movement begins. The proposed formulation predicts whether the tAV remains with its current gap or repositions to a neighboring gap by moving forward or dropping back, including cases with longitudinal overlap and ambiguous current-gap geometry. The model achieves an average five-fold cross-validated accuracy of 0.89. Results also provide preliminary evidence that in-position and repositioning cases follow different longitudinal pathways from SigT to lane-change start. These findings support a two-stage
conjecture of the observable lane-change process: longitudinal preparation from SigT to lane-change start, followed by lateral maneuver execution. The formulation applies to in-position, repositioning, and longitudinally overlapping cases, and can support lane-change models that distinguish target-gap choice from lateral-onset timing while representing longitudinal preparation before lateral movement begins.
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All authors Zeyu Mu, Danjue Chen, Abhinav Sharma, George F. List
Image and Video Processing · Cross-listed from cs.GR
LightBridge: Feed-Forward Generative Relighting for 3D Gaussian Splatting
Hezhi Cao, Panhao Cheng, huangsheng du et al. (+3 more)
3D Gaussian Splatting (3DGS) achieves high-quality, real-time novel view synthesis, but the resulting assets have baked-in illumination and cannot be easily relit. Inverse rendering methods optimize simplified reflectance and illumination models for each scene, limiting efficiency and relighting quality. Recent generative approaches leverage large
diffusion models for realistic lighting edits, but applying them to 3DGS typically requires an additional per-scene optimization stage to bake the edited appearance into the representation. We present LightBridge, a feed-forward generative framework for controllable relighting of complete 3DGS assets in a single pass. To enable feed-forward training, we construct a large-scale Multi-Illumination Relighting
Dataset with paired source and target observations of the same scenes. Latent Bridge Relighting
Diffusion models relighting as source-to-target transport in latent space, enabling one-step extraction of 2D visual
tokens without iterative
diffusion sampling. A Gaussian Propagation
Transformer uses a point
transformer with sparse image-to-point self-
attention followed by point-to-image cross-
attention to efficiently propagate these cues across the complete 3DGS, while avoiding full
attention over all image and Gaussian
tokens. Experiments validate these designs, demonstrating competitive relighting quality and efficient single-pass prediction of complete relit 3DGS assets without scene-specific optimization. The code and
dataset will be made publicly available upon acceptance.
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All authors Hezhi Cao, Panhao Cheng, huangsheng du, Qibiao Li, Youcheng Cai, Ligang Liu
Systems and Control
Physics-based Online Adaptive Koopman Model Predictive Attitude Control for Combined Spacecraft with Dynamic Uncertainties
Yicheng Sun, Yueyong Lyu, Yuhan Liu et al. (+2 more)
This paper proposes a physics-based adaptive Koopman Model Predictive Control (MPC) strategy for combined spacecraft attitude stabilization under inertia uncertainties and active target maneuverability. A novel, quaternion-based Koopman model is constructed from a set of analytical lifting functions derived from the quaternion kinematics, which provides a more compact and physically interpretable linear representation of the nonlinear dynamics compared with the conventional black-box EDMD and higher-dimensional DCM-based model. Leveraging the linear structure of this nominal model, a gradient descent-based update law is employed to efficiently identify time-varying inertial uncertainties from real-time input/output data. By integrating this adaptive linear model into the MPC framework, the optimal control problem reduces to a computationally efficient Quadratic Program (QP), thereby significantly lowering the online computational burden compared to nonlinear adaptive MPC. Recursive feasibility and regional input-to-state stability are formally established through the design of terminal ingredients for the MPC. The effectiveness and superiority of the proposed strategy are validated through comparative simulations of an attitude stabilization task for combined spacecraft in a high-fidelity 3D simulator.
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All authors Yicheng Sun, Yueyong Lyu, Yuhan Liu, Yanning Guo, Wei Pan
Systems and Control
Effective Range and Optimal Frequency of Through-the-Earth Magnetic Induction Communication
Honglei Ma, Erwu Liu, Wei Ni et al. (+4 more)
Magnetic induction communication (MIC) is a promising technology for through-the-earth (TTE) communication. Previous studies on the MIC range have often overlooked the impact of eddy losses caused by underground materials. For TTE MIC, significant eddy losses complicate the analysis of the effective MIC range, which is vital for optimizing performance but has never been addressed in the literature. Accounting for the conductivity and permittivity of the underground medium, this paper derives the effective MIC range in TTE MIC, along with a closed-from expression that predicts the optimal carrier frequency to maximize this range. Finite element simulations validate the analysis, demonstrating that the optimal carrier frequency can significantly enhance the MIC range. It is also revealed that optimizing the
antenna radius is effective in extending the MIC range for TTE and vehicle MIC applications.
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All authors Honglei Ma, Erwu Liu, Wei Ni, Jun Zhu, Zhijun Fang, Rui Wang, Xinyu Qu
Audio and Speech Processing
VAANI Noise Event Dataset: A curated spontaneous speech dataset annotated with timestamps for noise events
Pavan Kumar J, Agneedh Basu, Pranav Bhat et al. (+3 more)
Most public sound-event corpora are optimized either for general audio tagging or for clean
speech separation, and comparatively few provide strong timestamped noise annotations layered directly on top of spontaneous, real-world
speech. We present the VAANI Noise Event Timestamp
Dataset, a derived annotation layer built on Project VAANI field recordings of spontaneous
speech collected across 165 Indian districts in 105
languages. Unlike synthetically mixed corpora, VAANI captures
speech and ambient noise in situ and simultaneously, and annotates each recording with fine-grained start/end timestamps for overlapping background noise events organized into a compact seven-class semantic taxonomy: animal, traffic, baby/child, music, signal/alarm, appliance, and non-
speech human. This combination of spontaneous multilingual Indic
speech, authentic regional soundscapes, and span-level noise tags that may overlap with
speech targets tasks that existing
datasets address only partially: noise-robust Automatic
Speech Recognition (ASR), sound event detection (SED), and
speech enhancement. We position VAANI against nine widely used corpora and
benchmarks, including WHAM!, AVA-
Speech, MUSAN, FSD50K, CHiME-6, AudioSet, DESED, the India-specific iNoise noise database, and the Kathbath-Noisy noisy-ASR
benchmarks, and describe the annotation protocol and quality-control procedure used to produce the timestamped tags.
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All authors Pavan Kumar J, Agneedh Basu, Pranav Bhat, Sujith Pulikodan, Suryansh Shukla, Nihar Desai Prasanta K. Ghosh
Systems and Control
Multivariable Geometric Laplace Transform and Fault Detection in Distributed-Converter Lines
Francisco Manuel Arrabal-Campos, Francisco G. Montoya, Santiago Sánchez-Acevedo et al. (+2 more)
Monitoring a DC line with many distributed power converters is a genuinely spatio-temporal problem: the information about a localized fault travels along the whole conductor and reaches a few measurement points mixed with the dynamics of the line itself. This paper develops a two-dimensional
geometric Laplace transform (t,x) -> (s_t,s_x) over a commutative subalgebra of the
geometric algebra Cl(4,0), isomorphic to Segre's bicomplex numbers, in which two bivectors B_t and B_x act as independent imaginary units for the temporal and the spatial phase. Because the two phases live in algebraically distinguishable planes, a fault at position x_f leaves a transformed residual that factorizes as F_f(s_t) e^{-s_x x_f}: its temporal nature stays in the first factor and its location can be read as a
geometric argument of the second. On this representation we build a transmission-line model of the converter line and its space-time dispersion relation, a distributed control by admittance shaping, including an exact treatment of discrete converter sites (spatial sampling, aliasing, and a per-converter droop realization that is exact on the sub-Nyquist band), and a fault diagnosis chain that detects, localizes and classifies injection-loss, shunt, sensor and local-controller faults, extends to multiple simultaneous faults with automatic order selection, and distinguishes the outage of a plant from a cable defect. As an integral object the transform is known in bicomplex analysis, and with a single independent variable it reduces to the complex Laplace transform; the contribution lies in its
geometric embedding and in its operational use for fault diagnosis in distributed-converter networks. All results are reproduced by an accompanying open implementation.
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All authors Francisco Manuel Arrabal-Campos, Francisco G. Montoya, Santiago Sánchez-Acevedo, Raymundo E. Torres-Olguin, Alfredo Alcayde
Systems and Control
Leveraging Time-Causal State Variable Aggregation for Real-Time Schedule of Massive Air Conditioners
Jingguan Liu, Xiaomeng Ai, Shichang Cui et al. (+5 more)
Air conditioner (AC) loads offer promising flexibility for active distribution networks to manage uncertainties, such as those in renewable energy generation, electricity prices, and load demand. However, real-time scheduling of ACs is challenging due to their massive temporal coupling constraints and time-causal uncertainties. To address this, a novel time-causal aggregation-based approximate dynamic programming (TCA-ADP)
algorithm is proposed for efficient scheduling. The time-causality requirements for aggregating state variables are first analyzed to align with the real-time sequential decision-making process. Subsequently, an enhanced aggregation model is developed to ensure both high accuracy and adherence to time causality. The aggregation process is further reformulated as a linear program to optimize aggregation parameters and enable tractable computation. Accordingly, the TCA-ADP leverages aggregated state variables to approximate the value function as a new way, balancing computational efficiency and economy against the large value function space of massive ACs. By training the value function offline using historical data, the TCA-ADP efficiently achieves near-optimal real-time scheduling of massive ACs through parallel and closed-form disaggregation. Case studies demonstrate the effectiveness and scalability of the TCA-ADP, highlighting its aggregation accuracy, uncertainty handling, and the trade-off between economy and tractability.
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All authors Jingguan Liu, Xiaomeng Ai, Shichang Cui, Xizhen Xue, Shengshi Wang, Jiakun Fang, Jinyu Wen, Yang Shi
Systems and Control
Continuous-Time Aggregation of Massive Flexible HVAC Loads Considering Uncertainty for Reserve Provision in Power System Dispatch
Jingguan Liu, Xiaomeng Ai, Jiakun Fang et al. (+4 more)
Heating, ventilation, and air conditioning (HVAC) loads, with their rapid response capabilities, can provide considerable intra-hour flexibility on the demand side for reserve provision in order to follow the fast variations of renewables. However, scheduling massive HVACs is challenging due to computation complexity and the uncertainty of outdoor temperature. In this paper, we first introduce a novel continuous-time (CT) aggregation model to reveal the potential intra-hour flexibility of HVACs. For accurate aggregation, a new affine transformation is designed to handle the heterogeneity in high-dimensional feasible region. Further, for reliable aggregation in practical environment, the outdoor temperature uncertainty is constructed by distributionally robust chance constrains and integrated into the aggregation model. Secondly, for the tractable calculation of the proposed CT aggregation model, a cascade of tailored reformulation techniques is proposed, including the Bernstein
polynomial spline, polytope projection, and linearization transformation. Thirdly, a customized hierarchical dispatch framework is proposed via incorporating the proposed CT aggregation model into reserve provision in power system dispatch, so as to efficiently schedule massive HVACs to cope with the renewable uncertainty. Case studies verify the effectiveness and scalability of the proposed CT aggregation model in aggregation accuracy, intra-hour flexibility utilization, and uncertainty handling.
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All authors Jingguan Liu, Xiaomeng Ai, Jiakun Fang, Shichang Cui, Shengshi Wang, Wei Yao, Jinyu Wen
Image and Video Processing
Seeing Beyond the Lesion: Disease Recognition from Reactive CNS Tissue
Jan Schnorrenberg, Jan Ernsting, Enrico Küllenberg et al. (+3 more)
Sampling error yields exclusively reactive, non-lesional
brain parenchyma in a significant proportion of intracranial biopsies, leaving the underlying disease undiagnosed. We
benchmark four pathology foundation models (UNI2-h, Virchow2, Prov-GigaPath, H-optimus-0) as frozen patch encoders within a shared
attention-based multiple-instance learning framework using 245 whole-slide images from 186 patients with confirmed downstream diagnoses. We first show that coarse disease-category prediction can be reproduced largely from slide size alone. After restricting classification to three finer diagnostic distinctions within common tissue categories, this confound no longer explains performance, yet disease labels remain predictable above chance under permutation testing (p $\le 10^{-4}$ throughout). Surprisingly, performance is statistically indistinguishable across all foundation-model encoders, suggesting that recovering these weak morphological signatures is not limited by current patch representations. Signed instance-contribution maps and expert review further test whether predictive evidence localizes to reactive parenchyma rather than sampling-induced bias like blood introduced during tissue sampling. These results position acquisition-shortcut auditing via a provenance-only baseline as a necessary control in computational-pathology
benchmarks, and show, once that confound is removed, that weakly supervised models still recover disease signal from tissue conventionally regarded as non-diagnostic.
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All authors Jan Schnorrenberg, Jan Ernsting, Enrico Küllenberg, Tim Hahn, Benjamin Risse, Christian Thomas
Systems and Control · Cross-listed from math.OC
On the invariance of risk-sensitive LQR gain under input randomization
Yeongjun Jang
This paper shows that the optimal gain of the risk-sensitive linear quadratic regulator (LQR) problem is invariant under input randomization, i.e., when the controller deliberately injects noise into the nominal control input. This appears counterintuitive at first glance because certainty equivalence does not hold for risk-sensitive LQR and input randomization inflates the effective process noise. Nonetheless, the gain is preserved because the input noise enters not only the system dynamics but also the cost functional, and its total effect on the gain eventually vanishes. Consequently, the optimal gain and its associated Riccati recursion need not be recomputed, and the increment in the optimal cost can be readily evaluated in closed form. This result facilitates the use of risk-sensitive LQR in applications that employ input randomization for
privacy or exploration, such as watermarking for replay attack detection, differential
privacy, and path integral control.
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Systems and Control · Cross-listed from cs.RO
Humanoid Safe Stop via Learned Stoppability Value
Junfeng Long, Pieter Abbeel, Koushil Sreenath et al. (+3 more)
Humanoid
robots responding to emergency stop commands typically execute a fixed maneuver, without
reasoning about whether a safe stop is actually feasible from the current state. We cast emergency stopping as a reach-avoid problem and propose Safe-Stop, a task-agnostic framework that pairs a learned stop policy with learned stoppability
estimators. The
estimators are complementary: a stop-probability
estimator supervised by the actual outcomes of the fixed stop policy, and a reach-avoidance
estimator supervised by a Hamilton-Jacobi backup over physical state. The first captures emergent stopping behavior of the learned controller; the second provides a complementary recoverability signal. Because the stop policy and
estimators do not depend on the behavior policy that preceded the stop command, they transfer across diverse upstream tasks without retraining. At deployment, the two estimates are combined: Safe-Stop commits to the stop only when both
estimators indicate that stopping remains feasible, otherwise it hands off to a fall policy, instantiated as a damping fallback. This agreement check yields decisions that are robust without sacrificing reactivity.
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All authors Junfeng Long, Pieter Abbeel, Koushil Sreenath, Roberto Horowitz, Guanya Shi, C. Karen Liu
Audio and Speech Processing · Cross-listed from cs.SD
SonicCaps: Large-Scale Diverse and Fine-Grained Captioning for Improved Audio-Retrieval
Zineb Lahrichi, Marc Ferras, Gaël Richard et al. (+1 more)
Recent advances in audio-
language modeling have been driven by large-scale audio captioning
datasets. However, existing
datasets remain limited by low semantic diversity, generic descriptions lacking acoustic details, and one-to-one audio-caption mappings that poorly reflect the inherent ambiguity of auditory perception. We introduce SonicCaps, a large-scale audio captioning
dataset comprising ~15M captions paired with ~700k audio clips, generated using a multi-modal large
language model (Qwen3-Omni) conditioned on both audio and text. To explicitly promote diversity, we generate around 24 captions per audio via structured
prompt engineering and few- shot generation, spanning main descriptions, rephrased variants (verbosity, style) and semantic tags. Human evaluation shows that SonicCaps is rated significantly higher than existing captioning
datasets, with fine-grained analyses indicating that our captions are perceived as more descriptive and precise, which strongly correlates with quality judgments. Finally, training CLAP models on SonicCaps with a multi-caption sampling strategy consistently improves audio
retrieval and zero-shot classification, with stronger generalization across public and commercial
benchmarks. We release both SonicCaps and two specialized CLAP models on hugging face: https://huggingface.co/
datasets/Zineb/SonicCaps.
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All authors Zineb Lahrichi, Marc Ferras, Gaël Richard, Geoffroy Peeters
Systems and Control · Cross-listed from math.OC
A simple derivation of the Kalman filter
Marco Chiani, Giovanni Petris and Moe Z. Win
In this lecture note, we present a concise and self-contained derivation of the discrete-time Kalman filter equations that requires only a basic understanding of least squares estimation. The treatment is designed to minimize mathematical overhead while preserving both rigor and generality.
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Signal Processing
Enhancing Vehicular Network Performance Through Integrated RSU and UAV Deployment
Muhammad Ismail, Syed Luqman Shah, Fazal Muhammad et al. (+1 more)
The increasing density of connected vehicles can place substantial pressure on fixed roadside infrastructure, particularly when the available communication resources become insufficient to accommodate temporary traffic surges. This paper investigates auxiliary unmanned aerial vehicle (
UAV) assistance as a flexible mechanism for improving service availability and throughput in vehicular networks. Two representative network configurations are considered. In the first, an auxiliary
UAV (UAVa) supplements two fixed roadside units (RSUs), whereas in the second, UAVa assists a heterogeneous infrastructure comprising one RSU and one
UAV. Vehicle service is determined according to node coverage, the line-of-sight (LoS) probability of aerial links, and a prescribed signal-to-interference-plus-noise ratio (SINR) requirement. The resulting framework enables UAVa to accommodate eligible vehicles that cannot be adequately served by the primary infrastructure as the network load increases. Simulation results show that, under the considered configurations, the aerial nodes benefit from more favorable propagation conditions and achieve higher throughput than the fixed terrestrial RSU. Moreover, the introduction of UAVa increases the available service capacity under high vehicular loads, both for a purely terrestrial baseline and for a network already supported by an aerial node. These results demonstrate the potential of auxiliary
UAV assistance as a flexible load-relief mechanism for capacity-constrained vehicular networks.
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All authors Muhammad Ismail, Syed Luqman Shah, Fazal Muhammad, Zeeshan Shafiq
Signal Processing · Cross-listed from stat.ML
From topology learning to graph generation: A unifying perspective
Xiaowen Dong, Hoi-To Wai, Siheng Chen et al. (+2 more)
Learning graph structures from data is a fundamental problem that spans a wide range of signal processing and machine learning tasks. While significant effort has been made to tackle the problem, existing research has largely evolved along two parallel directions. The first seeks to infer the
topology of an individual graph from observations supported on it, whereas the second seeks to learn a generative distribution from observed graph instances, enabling the sampling of new graphs. This review presents a unified framework that connects these formulations by viewing them as inverse problems of a common generation process for graph data. We review the major methodologies within this framework, highlight their relationships, strengths, and limitations, and identify opportunities for integrating ideas across paradigms. By bridging graph
topology learning and graph generation, this review provides a broader cross-disciplinary perspective on the field and outlines promising directions for future research.
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All authors Xiaowen Dong, Hoi-To Wai, Siheng Chen, Laura Toni, Dorina Thanou