Systems and Control · Cross-listed from cs.RO
RED: Adaptive Real-Time DAG Scheduling for Robotic Inference under Environmental Dynamics
Zexin Li, Tao Ren, Johnathan Liu et al. (+2 more)
Robots deployed in dynamic environments must contend with environment-driven changes that reshape computation at runtime: new tasks may appear, precedence relations can shift, and overall workload structure evolves, all of which degrade performance, especially when multi-task inference is required under tight resource and real-time budgets. We present RED, a real-time scheduling framework for multi-task deep neural network workloads on resource-constrained robotic platforms that adapts to Robotic Environmental Dynamics (RED) while preserving end-to-end timing guarantees under modeling assumptions. The core of RED is a deadline-aware scheduler that assigns intermediate sub-deadlines, allowing it to accommodate evolving computation graphs and asynchronous inference induced by unpredictable conditions. The framework also supports flexible deployment of MIMONet (multi-input multi-output neural networks), commonly used in multi-tasking
robots to alleviate
memory pressure through weight sharing. RED explicitly leverages this shared-parameter property via a workload refinement and graph-reconstruction procedure that aligns MIMONet structure with schedulability requirements, improving compatibility and efficiency. We implement RED on NVIDIA Jetson family platforms and on an Apple M-series MacBook and evaluate it on
navigation-oriented workloads representative of real robotic scenarios. Experiments show consistent gains over existing methods in throughput, deadline satisfaction, robustness to interference, adaptability, and runtime overhead.
Read the full abstract ▾
All authors Zexin Li, Tao Ren, Johnathan Liu, Xiaoxi He, Cong Liu
Image and Video Processing
Do Synthetic Brain MRIs Reliably Improve Tumour Classification? A StyleGAN2-ADA Class-Plane Augmentation Study on BRISC 2025
José Rafael Noriega Cedeño
Generative augmentation is often proposed as a remedy for small medical-image
datasets, but synthetic images are only useful when they improve downstream task performance. "Augmentation" here means synthetic supplementation: GAN-generated samples added to the real training pool, not
geometric or photometric transforms of existing images. Twelve class-plane StyleGAN2-ADA generators were trained on constrained BRISC 2025 partitions to test whether their output, with or without InceptionV3 feature-space filtering, improves held-out tumour classification across three classifier families: a random forest (RF) on InceptionV3 features, a compact two-headed convolutional neural network (CNN), and MobileViTV2, a mobile hybrid convolutional-
transformer. Each was evaluated at 1:1 and 1:2 real-to-synthetic ratios. An independent GPT-5.5 blind test placed gated real-versus-synthetic discrimination at 57.73% (95% CI: 54.48–60.92%) on the model-legible subset – modestly above chance. The RF classifier did not benefit from the synthetic MRIs. The CNN showed consistent mean gains that did not survive Holm correction. MobileViTV2 showed the clearest benefit: filtered 1:1 augmentation improved tumour classification accuracy by 1.02% absolute (95% CI: 0.54–1.54%; Holm-corrected p = 0.0104). A secondary efficiency analysis found that every augmented CNN condition selected its checkpoint 42–64% earlier than baseline, while compute-matched MobileViTV2 runs reached selection after 50–67% fewer real-data epochs. Overall, augmentation utility was found to be architecture- and ratio-dependent, not guaranteed by visual fidelity alone.
Read the full abstract ▾
Signal Processing
AFDM as a Software Upgrade of OFDM: One Firmware Patch, a New Frontier
Hyeon Seok Rou and Giuseppe Thadeu Freitas de Abreu
In this white paper, we summarize for the benefit of the wider research community on wireless communications, the two key results that we shared with the attendees of the 2026 IEEE Communication Theory Workshop in Azores, Portugal, about affine frequency division multiplexing (AFDM). Firstly, we show that in contrast to the wide perception by most researchers, AFDM can be implemented at marginal costs by means of a simple software upgrade (firmware patch) of conventional orthogonal frequency division multiplexing (OFDM), indicating that its adoption can potentially be achieved across a wide range of OFDM-based wireless infrastructure and systems. The most crucial relevance of this finding is that such an upgrade would enable, under the specific conditions of the corresponding systems and their applications, exploiting various advantageous features of AFDM, including robustness to doubly dispersive channels (i.e., to support high-mobility use-cases in 6G), inherent integrated sensing and communications (
ISAC) compatibility (i.e., to support sensing use-cases in 802.11bf), and the straightforward introduction of low-complexity physical-layer security at the waveform level (as needed in next-generation IoT systems). Secondly, we also show that the same mathematical principles underpinning the aforementioned finding, also imply an inherent capability of AFDM to reap the full uncoded diversity of static linear time-invariant (LTI) channels, demonstrating that this simple upgrade taps into previously undiscovered strengths of multicarrier waveforms.
Read the full abstract ▾
Signal Processing
Copula-Induced Correntropy for Robust Conjugate Gradient Learning
Farshad Rostami Ghadi, F. Javier Lopez-Martinez, David Morales-Jimenez et al. (+2 more)
Robust learning in the presence of non-Gaussian and statistically dependent noise remains a fundamental challenge in signal processing and adaptive systems. Although information-theoretic learning criteria such as correntropy offer strong robustness against impulsive and heavy-tailed disturbances, existing formulations are commonly applied componentwise and therefore do not explicitly exploit the dependence structures inherent in multivariate, multi-sensor, and temporal signals. In this paper, we propose a learning framework, termed copula-induced information-theoretic learning (CITL), which extends correntropy by
embedding a copula space representation of residual dependence into the similarity measure. Unlike conventional correntropy-based approaches that operate pointwise on raw residuals, the proposed criterion is defined in a copula-transformed residual space, thus separating marginal robustness from dependence weighting. We derive a copula-induced correntropy (CIC) objective and a mixed marginal–dependence objective used in the implementation, provide information-theoretic and
Bayesian interpretations, and develop a robust conjugate gradient (CG) learning
algorithm tailored to this criterion. For fixed smooth marginal
estimators, a fixed copula-space metric, and a regularized radial penalty, we establish sufficient descent and global stationarity guarantees for the corresponding fixed-
estimator subproblem under standard line-search conditions. Experiments on synthetic multivariate signal processing regression problems demonstrate that the proposed method consistently outperforms mean squared error (MSE), Huber, Student's-$t$, and classical correntropy-based approaches, particularly in the presence of dependent heavy-tailed noise.
Read the full abstract ▾
All authors Farshad Rostami Ghadi, F. Javier Lopez-Martinez, David Morales-Jimenez, Kai-Kit Wong, Marios Kountouris
Systems and Control
Open-Source METANET Calibration for Reproducible Freeway Traffic Macroscopic Simulation
Monica Chan, Shreyaa Raghavan and Cathy Wu
METANET is a widely used second-order macroscopic traffic flow model for freeway networks, supporting applications across traffic simulation, ramp metering, and variable speed limit control. The predictive accuracy of any traffic model, however, hinges on careful calibration to real-world conditions. Despite its widespread use, there have not been open-source tools for calibrating METANET's parameters. Without open-source calibration, results cannot be easily reproduced or extended to other networks. This work provides an open-source METANET calibration, simulation, and data visualization tool. The calibration is formulated as a nonlinear program (NLP) solved via the interior-point method (IPOPT), with joint ramp flow estimation. We validate our calibration on real-world freeway data from two widely used traffic monitoring systems: Interstate-24 MObility Technology Interstate Observation Network (I-24 MOTION), one of the largest open-road trajectory instruments in the country, and loop detector data from the Caltrans Performance Measurement System (PeMS), which spans nearly 40,000 detectors across California freeways and serves as a standard
benchmark in traffic research. Models calibrated using our method are able to reproduce these
datasets' observed traffic patterns across diverse network geometries and traffic conditions including complex stop-and-go congestion waves. As large-scale traffic monitoring infrastructure continues to expand, open-source calibration tools are essential for translating growing volumes of sensor data into validated models that can support real-world traffic control. The complete code is publicly available at https://github.com/woxsao/metanet-calibration to support reproducible research in freeway traffic modeling and control.
Read the full abstract ▾
Systems and Control
Holistic Grid-Forming Control to Enhance the Frequency Support from HVDC-Connected Offshore Wind Power Plants
Zhenghua XU, Dominic Groß, George Alin Raducu et al. (+3 more)
To address the frequency stability challenges posed by the rising penetration of power electronics in power systems, HVDC-connected offshore wind power plants (OWPPs) are increasingly expected to provide inertial response and frequency containment reserve (FCR). In this paper, an improved holistic grid-forming (GFM) control is proposed, aiming to enhance the frequency support by coordinating the GFM controls implemented at all AC and DC terminals of an HVDC-OWPP system, without requiring communication. Firstly, the model of a typical HVDC-OWPP system is developed for control design. Accordingly, the proposed controllers are formulated, followed by an analytical tuning method, where the upper bound of the bandwidth at each AC or DC terminal is identified. Finally, simulations are conducted to verify the functionality and compare the performance with that of representative control configurations. The results show that the proposed holistic GFM control achieves faster response and thus more effective frequency support, while the utilization of the inherent energy storage of each converter is minimized, thereby supporting a new design philosophy for converter control in converter-dominated systems.
Read the full abstract ▾
All authors Zhenghua XU, Dominic Groß, George Alin Raducu, Behnam Nouri, Oscar Saborío-Romano, Nicolaos A. Cutululis
Systems and Control
A Methodology for Impedance-based Stability Margin Analysis for Interconnected Offshore Wind Clusters
Germano Rugendo Mugambi, Behnam Nouri, Oscar Saborío-Romano et al. (+2 more)
With recent developments in offshore grid architectures, power park modules (PPMs) such as clusters of offshore wind power plants (OWPPs) are increasingly interconnected offshore. Consequently, it is necessary to assess how integrating a new OWPP affects the stability margins of an existing OWPP at the point of connection. Although impedance-based methods are widely used for small-signal stability assessment of interconnected converter-based systems, many studies rely primarily on Nyquist encirclements and do not explicitly quantify stability margins. Thus, this paper proposes a general impedance-based methodology to (i) evaluate the stability margins of an existing connection after a new PPM is integrated and (ii) derive a maximum allowable impedance for the new connection such that the minimum stability margin requirements specified by system operators are satisfied and stable operation is maintained. In addition, new Nyquist-based stability regions are introduced to complement the generalized Nyquist criterion, providing analytical indications of margin compliance and headroom. The proposed method is validated through case studies using vendor-based frequency-domain models of two interconnected OWPPs and HVDC system.
Read the full abstract ▾
All authors Germano Rugendo Mugambi, Behnam Nouri, Oscar Saborío-Romano, George Alin Raducu, Nicolaos A. Cutululis
Systems and Control · Cross-listed from q-bio.NC
Active Sensing Subserves Task-Level Control
Andrew Lamperski, Debojyoti Biswas, Eric S. Fortune et al. (+3 more)
Active sensing is traditionally defined as the expenditure of energy, typically in the form of movement, for obtaining information. Here, we propose that the combination of reliance on adaptive sensors, the linkage between movement and sensing, and task-level control inevitably gives rise to the emergence of active sensing movements. In this way, active sensing is not driven by sensory goals, such as minimizing uncertainty about the state, but rather is necessary for task-level control. This hypothesis, that active sensing subserves control, is supported by both empirical data from organisms and mathematical theory. Interestingly, active sensing behaviors often occur in discrete epochs, interspersed with goal-oriented behavior. This suggests that animals switch between two behavioral modes with distinct control policies, an `explore' mode in which animals produce dynamic movements to shape sensory feedback, and an `exploit' mode in which animals produce slower compensatory movements that are directly related to achieving task goals. This strategy for feedback control that relies on adaptive sensors, active sensing, and mode switching is not commonly used in engineered systems despite being ubiquitous in biology. Engineered systems comprising state-of-the-art sensors, actuators, and mechanical designs can outperform animals with respect to “cost functions” such as maximum force generation, precision, and speed. Nevertheless, animals routinely achieve robust, graceful behaviors that are currently unmatched by engineered systems, suggesting that current control systems are insufficient. These insights, expressed in the
language of control theory, may be critical for improving robotic sensing and control.
Read the full abstract ▾
All authors Andrew Lamperski, Debojyoti Biswas, Eric S. Fortune, John Guckenheimer, Kathleen Hoffman, Noah J. Cowan
Systems and Control · Cross-listed from math.OC
Occupation-Weighted Performance Bounds for Rollout Policies in Stochastic Shortest Path Problems
Anders Hansson and Bo Wahlberg
We study rollout policies constructed from an approximate value function for
stochastic shortest path problems with an absorbing terminal state. Our main bound weights how much the selected action exceeds the optimal one-step value by expected state visits before termination; the uniform hitting-time estimate follows as a corollary. The bound separates value-approximation and numerical-optimization errors from the error caused by replacing an expectation with a nominal disturbance, and uses approximation errors only at states that can occur after one transition from states visited by the implemented policy. A positive lower bound on nonterminal stage costs also gives conditions under which the generated policy reaches the terminal state almost surely with finite expected total cost. In the minimum expected hitting-time problem, a uniform value-approximation error smaller than one half guarantees almost-sure termination, finite expected cost, and a multiplicative hitting-time bound, and the value one half cannot be increased. A deterministic construction shows that the factor two in the uniform rollout bound is asymptotically sharp. Certainty-equivalent rollout is covered by the same analysis.
Read the full abstract ▾
Signal Processing
OctCGS: Octree-Contextual Gaussian Splatting with Explicit Multi-Order Propagation Modeling for Channel Knowledge Map Construction
Jinghan Zhang, Xitao Gong, Qi Wang et al. (+2 more)
Channel knowledge maps (CKMs) learn the relation between transmitter (Tx) and receiver (Rx) positions and channel knowledge to support environment-aware wireless communications. Implicit neural methods can model continuous channel variation but often incur high training and inference cost, while existing Gaussian-splatting-based CKM methods improve efficiency yet still compress wireless multipath interactions into aggregated scattering representations. Consequently, explicit modeling of multi-bounce wireless propagation remains absent from CKM construction. We propose OctCGS, an octree-contextual Gaussian splatting framework that explicitly models the order of bounce jointly over Tx/Rx positions and carrier frequencies. OctCGS partitions the environment into a multi-resolution octree and anchors one Gaussian primitive to each leaf node. Rather than having each Gaussian independently encode all multi-path propagations, it models complex electromagnetic interactions among scatterers through tree
attention over the octree hierarchy with controlled complexity. Experiments on simulated
benchmarks show that OctCGS achieves a 2.99 dB channel-gain mean absolute error (MAE) and 0.065 channel gain normalized mean absolute error (NMAE), outperforming the strongest baseline by 0.88 dB MAE and 0.021 NMAE.
Read the full abstract ▾
All authors Jinghan Zhang, Xitao Gong, Qi Wang, Richard A. Stirling-Gallacher, Giuseppe Caire
Image and Video Processing · Cross-listed from q-bio.TO
ROI Extraction in Thermographic Breast Images Using Genetic Algorithms
LC Mendes, EO Rodrigues, Sandro C Izidoro et al. (+2 more)
This work proposes the use of Genetic
Algorithms (GA) to identify the area of the breast from the background in thermographic breast images. The proposed method uses color information, a fitness function based on cardioids, and GA. This is the first work in the literature to propose a Region of Interest (ROI) extraction based on GA and cariods. ROI extraction can improve the accuracy of cancer detection and assist with the standardization of acquisition protocols. The method is able to successfully separate the breast region in 52 out of 58 images, while being fully automatic, and not requiring manual selection of seed points.
Read the full abstract ▾
All authors LC Mendes, EO Rodrigues, Sandro C Izidoro, Aura Conci, Panos Liatsis
Signal Processing
Angqi Li, Ab Basit Rafi Syed, Hamzeh Alzweri et al. (+3 more)
We introduce a novel Longitudinal Focused
Attention Meditation Electroencephalography (L-FAME)
dataset and an accompanying
benchmark, designed to foster research into the neural effects of various meditation practices and the
evolution of these effects over a six-week training period. The
dataset contains
EEG recordings and psychological assessments from 74 healthy college participants, collected at two distinct time points: pre-intervention and post-intervention. Participants were randomly assigned to one of three distinct meditation groups: two mantra-based techniques (SA-TA-NA-MA and Hare Krishna) and one Breath Focus practice. Leveraging this unique longitudinal and comparative
dataset, we propose a
benchmark suite comprising three distinct classification tasks: (1) cognitive state decoding to distinguish between resting and meditation states, (2) fine-grained classification of the specific meditation techniques, and (3) cross-session adaptation to evaluate model generalization across the longitudinal time gap. We provide comprehensive baseline results for these tasks utilizing a range of classical machine learning
algorithms and deep learning architectures. The complete
dataset, preprocessing pipelines, and
benchmark evaluation code will be publicly released, offering a valuable resource and a standardized framework for the development and comparison of new analytical methods in computational meditation research and
EEG-based machine learning. The
dataset is available at https://huggingface.co/
datasets/L-FAME-
Dataset-
Benchmark/L-FAME
Read the full abstract ▾
All authors Angqi Li, Ab Basit Rafi Syed, Hamzeh Alzweri, Taosheng Liu, Barry H. Cohen, Saiprasad Ravishankar
Audio and Speech Processing · Cross-listed from cs.LG
Plug-in Losses for Evidential Deep Learning: A Simplified Framework for Uncertainty Estimation that Includes the Softmax Classifier
Berk Hayta, Hannah Laus, Simon Mittermaier et al. (+1 more)
Real-world sensor-based learning systems require uncertainty estimation that is both reliable and computationally efficient. Evidential Deep Learning (EDL) provides single-pass uncertainty estimation by modeling the class probabilities via Dirichlet distributions, where the Dirichlet parameters are predicted by a learned neural network mapping. However, this approach can lead to computational challenges, as Dirichlet expected objectives are more complex than standard supervised learning losses, complicating their analysis and implementation. We address this issue by approximating the objective of the first-order empirical risk minimization problem induced by EDL with a plug-in loss evaluated at the Dirichlet mean and show that, under mild assumptions, the approximation error decays with growing evidence for a broad class of loss functions, including mean-squared error and cross-
entropy loss. As a special case, our analysis provides justification for the use of softmax in the context of uncertainty estimation, since under a particular evidence-to-Dirichlet mapping, our framework includes the standard softmax classifier. We validate the proposed simplified objectives on the Google
Speech Commands
dataset and show that they achieve predictive accuracy and selective prediction performance comparable to classical EDL, while being simpler to implement using standard deep learning losses and training pipelines. To the best of our knowledge, this empirical analysis is the first to obtain coverage-accuracy trade-offs for
speech recognition tasks through EDL.
Read the full abstract ▾
All authors Berk Hayta, Hannah Laus, Simon Mittermaier, Felix Krahmer
Audio and Speech Processing · Cross-listed from cs.AI
Beyond Acoustic Emotion Recognition: Multimodal Pathos Analysis in Political Speech Using LLM-Based and Acoustic Emotion Models
Juergen Dietrich
We investigate whether acoustic
emotion recognition models can serve as proxies for the Pathos dimension in political
speech analysis, as operationalised by the TRUST multi-
agent large
language model (LLM) pipeline. Using a Bundestag plenary
speech by Felix Banaszak (51 segments, 245 s) as a case study, we compare three analysis modalities: (1)
emotion2vec_plus_large, an acoustic
speech emotion recognition (SER) model whose continuous Arousal and Valence values are derived via post-hoc Russell Circumplex projection; (2) Gemini 2.5 Flash, an LLM analysing the full
speech audio together with its transcript in an open-ended, context-aware fashion; and (3) TRUST-Pathos scores from a three-advocate LLM supervisor ensemble. Spearman rank correlations reveal that Gemini Valence correlates strongly with TRUST-Pathos (rho = +0.664, p < 0.001), whereas
emotion2vec Valence does not (rho = +0.097, p = 0.499). We further demonstrate, via a systematic quality evaluation of the Berlin Database of Emotional
Speech (EMO-DB) using Gemini in an open-ended annotation paradigm, that standard SER
benchmark corpora suffer from acted
speech, cultural bias, and category incompatibility. Our results suggest that LLM-based
multimodal analysis captures semantically defined political
emotion substantially better than acoustic models alone, while acoustic features remain informative for low-level Arousal estimation. Future work will extend this approach to
video-based analysis incorporating facial expression and gaze.
Read the full abstract ▾
Systems and Control
Dynamic Lane Allocation in UAM Corridors for Efficient Multimodal Door-to-Door Mobility
Jung Ho Park, Jordan Kam, Vishwanath Bulusu et al. (+2 more)
This article presents dynamic directional lane allocation in urban air mobility (UAM) corridors as a discrete-time mixed-integer linear program (MILP). This formulation activates, deactivates, and reverses lane direction as bi-directional airspace demand evolves. We model demand from disaggregate ground travel data by decomposing each trip into a multi-modal sequence with first-, middle-, and last-mile legs and routing the UAM-served middle-mile segment through a vertiport-side dispatch model. We use the San Francisco Bay Area as a case study by placing a multi-region spanning corridor between Contra Costa county and Silicon Valley. We find that the dynamic policy cuts unused airspace capacity by 5x, increases mean lane utilization from 36-48% to 67% at the same service level relative to baselines, and reduces commuting-population mean travel time by up to 21.6%. These results show that dynamic configuration of airspace capacity alleviates a significant percentage of the under-utilization issue of lane-based UAM airspace design and UAM concept of operations. This dynamic allocation also provides a safe, structural way to increase throughput, making UAM a more viable complement to
multimodal door-to-door mobility systems.
Read the full abstract ▾
All authors Jung Ho Park, Jordan Kam, Vishwanath Bulusu, Alexandre Bayen, Raja Sengupta
Systems and Control · Cross-listed from cs.RO
N3P: Accelerated Automated Parking via a Learning-Based Naturalistic Three-Stage Scheme
Yifan Xue, Toktam Mohammadnejad, Faizan M Tariq et al. (+5 more)
Autonomous parking requires efficient path planning that ensures kinematic feasibility and collision avoidance in constrained environments. Hybrid A* is widely used but computationally expensive, while reinforcement learning (RL) methods lack reliability and often struggle with long-horizon
geometric constraints, leading to suboptimal trajectories. We present N3P, a fast learning-based three-stage framework for automated parking. By introducing an intermediate preparatory pose and using a learning module to predict it, N3P decomposes the maneuver into simpler subproblems, thereby reducing computational complexity and accelerating path generation. We validate the framework by integrating it with Hybrid A*
algorithms. Experiments in perpendicular and parallel parking scenarios show that N3P-enhanced Hybrid A* speeds up planning by more than 80%. It also outperforms RL baselines in success rate and trajectory quality, producing shorter trajectories with fewer gear changes, while achieving comparable or lower planning time in most cases.
Read the full abstract ▾
All authors Yifan Xue, Toktam Mohammadnejad, Faizan M Tariq, Sangjae Bae, David Isele, Yosuke Sakamoto, Nadia Figueroa, Jovin D'sa
Systems and Control · Cross-listed from cs.CR
TriSweep: A Four-Drone Swarm Framework for Electromagnetic Side-Channel Analysis
Eric Yocam and Varghese Vaidyan
Electromagnetic (EM) side-channel analysis traditionally assumes a stationary, close-proximity probe - a threat model that underestimates aerial adversaries. TriSweep is a simulation framework that designs and evaluates a four-drone swarm architecture for autonomous standoff EM-SCA of embedded microcontrollers at 0.25-1.5 m. Three spatially specialized collector drones - Anchor (full-spectrum), Mask Probe (mask-register loading leakage), and Cipher Probe (masked SubBytes output leakage) - feed a stationary Accumulator drone that performs coherent combining (+4.8 dB SNR gain) and second-order mask cancellation via a centered product of the two spatially separated leakage streams. Evaluated against three real ANSSI ASCAD
datasets (ATmega8515 masked AES-128 and 50/100-sample desynchronized variants), the framework achieves a simulated key rank of 18 +/- 1.7 (five-seed) at 0.25 m on the primary masked
dataset. Profiling-trace cross-correlation
alignment reduces single-drone rank from 89 to 21 on the 100-sample-jitter variant, demonstrating compensation for drone hover vibration. A two-channel CNN in the Accumulator converges to a loss of 0.454 (vs. random baseline 5.545) and improves rank on desynchronized
datasets. No physical hardware has been fabricated; prototype construction is the planned next step.
Read the full abstract ▾
Systems and Control · Cross-listed from cs.MA
A Generalized Nash Equilibrium-Seeking Scheme for Trauma Resuscitation
Promise Ekpo, Angelique Taylor and Lekan Molu
Trauma resuscitation is a clinical process for treating life-threatening physiological disorders in safety-critical environments, driven by the experience of healthcare workers (HCWs). Designing and optimizing quantifiable metrics that accurately capture HCW decisions may augment current resuscitation procedures with the potential to improve patient outcomes. This motivates our socio-technical formulation of trauma resuscitation as a distributed generalized Nash
equilibrium (GNE)-seeking game with coupled inequality constraints. This method is optimized over a time-varying communication graph. We introduce novel insights from clinical experience to model HCWs behavior. This work facilitates the best possible resuscitation outcome given HCWs workloads, schedules, competencies, and limited resources.
Read the full abstract ▾
Signal Processing
A Metalens-based Bicycle Safety Reflector for Autonomous Vehicle Radars
Sepideh Ghasemi, Jimmy Hester and Aline Eid
With the rising number of interactions between autonomous or sensor-assisted vehicles – especially in poor weather conditions – come the need and opportunity for a new class of bicycle safety reflectors designed to enhance cyclist visibility to radars. To this effect, the first retrodirective planar metalens-based tag operating in the millimeter-wave automotive frequency range is proposed. The compact, lightweight ($0.61~\mathrm{g}$) design consists of two layers: a metalens layer and a patch
antenna pixel layer. The metalens focuses incoming plane waves from different incidence angles onto corresponding patch
antenna pixels on the second layer, which re-radiate the signal back through the metalens, enabling retrodirective operation. The proposed tag was thoroughly evaluated, demonstrating reliable detection beyond 70 m and a peak monostatic radar cross section (RCS) of $3.54~\mathrm{dBsm}$ with stable retrodirectivity over $\pm 40^\circ$, providing an average gain improvement of $7.58~\mathrm{dB}$ and an RCS enhancement of $15.16~\mathrm{dB}$ relative to a lens-less reference. A realistic deployment scenario on a metallic bicycle demonstrated up to a 110x improvement in its detectability at broadside. These results highlight the potential of the proposed passive tag to operate as a low-cost, lightweight, and easily integrable bicycle safety reflector for next-generation autonomous vehicle radar systems.
Read the full abstract ▾
Image and Video Processing · Cross-listed from cs.CV
SegCompass: Exploring Interpretable Alignment with Sparse Autoencoders for Enhanced Reasoning Segmentation
Zhenyu Lu, Liupeng Li, Jinpeng Wang et al. (+4 more)
While large
language models provide strong compositional
reasoning, existing
reasoning segmentation pipelines fail to transparently connect this
reasoning to visual perception. Current methods, such as latent query
alignment, are end-to-end yet opaque "black boxes". Conversely, textual localization readout is merely readable, not truly interpretable, often functioning as an unconstrained post-hoc step. To bridge this interpretability gap, we propose SegCompass, an end-to-end model that leverages a Sparse Autoencoder (SAE) to forge an explicit, interpretable, and differentiable
alignment pathway. Given an image-instruction pair, SegCompass first generates a chain-of-thought (CoT) trace. The core of our method is an SAE that maps both the CoT and visual
tokens into a shared, high-dimensional sparse concept space. A query codebook selects salient concepts from this space, which are then spatially grounded by a slot mapper into a multi-slot heatmap that guides the final mask decoder. The entire model is trained jointly, unifying reinforcement learning for the
reasoning path with standard
segmentation supervision. This SAE-driven interface provides a "white-box" connection that is significantly more traceable than latent queries and more coherent than textual readouts. Extensive experiments on five challenging
benchmarks demonstrate that SegCompass matches or surpasses state-of-the-art performance. Crucially, our visual and quantitative analyses show a strong correlation between the quality of the learned sparse concepts and final mask accuracy, confirming that SegCompass achieves superior results through its enhanced and inspectable
alignment. Code is available at https://github.com/ZhenyuLU-Heliodore/SegCompass.
Read the full abstract ▾
All authors Zhenyu Lu, Liupeng Li, Jinpeng Wang, Haoqian Kang, Yan Feng, Ke Chen, Yaowei Wang
Systems and Control · Cross-listed from math.OC
Output regulation via input-output data
Andrea Bisoffi, Wenjie Liu, Zhongjie Hu et al. (+1 more)
From a multi-input-multi-output (
MIMO) discrete-time linear system, we collect input-output data affected by noise in the form of an unknown exosignal and, from these data points (without knowledge of the system model), we design a feedback controller that asymptotically annihilates the effect of that exosignal on the output. This amounts to solving an output regulation problem purely from input-output data, for
MIMO linear systems. The design of the controller corresponds to a semidefinite program and is pursued on a suitable auxiliary system. Such design carries over from the auxiliary system to the original one by a rigorous examination of the relation between the solutions of the two systems.
Read the full abstract ▾
All authors Andrea Bisoffi, Wenjie Liu, Zhongjie Hu, Claudio De Persis
Signal Processing
UAV-based Energy-Efficient Data Collection in Smart Grids with ISAC QoS Guarantees
Yibin Xie, Jin Zhao, Indrakshi Dey et al. (+1 more)
Dynamic line rating (DLR) is a methodology that requires timely monitoring data to determine the real-time ampacity of power lines. However, DLR monitoring devices (MD) are vulnerable to connectivity disruptions, leading to missing or delayed data. Although unmanned aerial vehicles (
UAV) can enable resilient data collection from MD, their limited onboard energy challenges timely monitoring over extended transmission corridors with flight hazards. This paper proposes a cooperative
UAV-based data collection framework with integrated sensing and communication (
ISAC) to support timely DLR updates. In this framework,
ISAC is employed to maintain the sensing and communication quality required for safe and cooperative
UAV data collection. Accordingly, a joint energy minimization problem is formulated over
UAV trajectories and collection scheduling under
ISAC constraints. To solve it, a hybrid
algorithm combining deep reinforcement learning (DRL) and semidefinite relaxation (SDR) is proposed, where DRL optimizes the trajectory and collection scheduling, while SDR is used to handle the non-convex
ISAC constraints. Simulation results show that the proposed scheme reduces energy consumption by up to 34.6% compared with offline
benchmarks and by about 2.2% compared with the separated sensing-and-communication baseline, while satisfying the minute-level timescale requirement of DLR.
Read the full abstract ▾
All authors Yibin Xie, Jin Zhao, Indrakshi Dey, Nicola Marchetti
Systems and Control · Cross-listed from math.OC
Global Convergence of Control-Based Lagrangian Flows for Non-Convex Optimization
Simone Pirrera, Francesco Ripa, Daniele Astolfi et al. (+3 more)
This paper studies the continuous-time dynamics generated by control-theoretic Lagrangian methods for equality-constrained optimization. In particular, we consider dynamics induced by proportional-integral and feedback linearization controllers, which have recently been proposed as alternatives to primal-dual gradient methods. Unlike global convergence results for these dynamics, which rely on strong convexity of the objective function or boundedness assumptions, we exploit the
geometric structure induced by the constraints. Specifically, we show global exponential convergence for non-convex problems that satisfy a suitable convexity property when restricted to the constraint
manifold.
Read the full abstract ▾
All authors Simone Pirrera, Francesco Ripa, Daniele Astolfi, Vito Cerone, Sophie M. Fosson, Diego Regruto
Systems and Control · Cross-listed from cs.AI
KAPPS: A knowledge-based CPPS Architecture for the Circular Factory
Etienne Hoffmann, Jan-Felix Klein, Sören Weindel et al. (+7 more)
While linear manufacturing relies on homogeneous materials and predefined process sequences, circular manufacturing reintroduces used products with heterogeneous and uncertain conditions. This shift demands manufacturing systems capable of handling variable product states, dynamically reconfigurable processes, and the integration of human and machine knowledge. Conventional manufacturing IT architectures, designed for stable structures and deterministic execution, are unable to meet these requirements, as they cannot adequately represent and manage the uniqueness of individual components at runtime. Following a design science methodology for developing a Cyber Physical Production System for circular manufacturing, we derive 14 requirements from five complementary perspectives. Based on these requirements, we design KAPPS, a knowledge-based architecture that uses an ontology-grounded knowledge graph as a unifying data backbone, combined with a semantic interface layer to enable consistent data and information integration,
reasoning, and communication across heterogeneous systems and services, turning the knowledge graph from an integration layer into the factories authoritative write-time state. KAPPS incorporates modules for constraint enforcement and event-driven planning, enabling incremental adaptation of execution plans under uncertainty and human-machine knowledge exchange. The applicability of KAPPS is demonstrated through two implemented use cases: (i) Anomaly detection and learning through knowledge graph mediated services and (ii) runtime constraint enforcement in a modular conveyor system. Subsequently, the architecture is evaluated against the 14 requirements (ed. abstract shortened)
Read the full abstract ▾
All authors Etienne Hoffmann, Jan-Felix Klein, Sören Weindel, Max Goebels, Sebastian Behrendt, Daniel Hernández, Ratan Bahadur Thapa, Jürgen Fleischer, Kai Furmans, Steffen Staab
Systems and Control · Cross-listed from quant-ph
QuCtrl-BELL: A Compiler-Driven Sub-Microsecond Feedback Control Stack for Scalable Trapped-Ion Quantum Experiments
Junpeng She, Ruoyu Yan, Zhizhen Qin et al. (+5 more)
As trapped-ion
quantum computing scales to larger
qubit registers and more complex control protocols, classical control systems face a fundamental tradeoff: sub-microsecond board-level feedback requires tight hardware coupling, whereas maintainability and extensibility require clean, modular software abstractions. This paper presents QuCtrl-BELL (Bell), a compiler-driven software stack for trapped-ion
quantum control. The design resolves this tradeoff by decoupling control flow – including loops, branches, and synchronization – from hardware state data. A Python-embedded domain-specific
language (DSL) is lowered through a six-stage transpilation pipeline covering control flow graph (CFG) construction, static single-assignment (SSA) conversion, liveness analysis, and graph-coloring register allocation. The compiler generates deterministic distributed board-level programs and compact step-table data. A cross-board synchronization protocol supports feedback loops with latency below 700~ns without host intervention. Bell is deployed and evaluated on the QuCtrl-BELL platform (RISC-V + PXIe), demonstrating that a compiler-based infrastructure can provide programmability, deterministic timing, and modularity for scalable trapped-ion
quantum control.
Read the full abstract ▾
All authors Junpeng She, Ruoyu Yan, Zhizhen Qin, Zhanyu Li, Zhongtao Shen, Zichao Zhou, Binxiang Qi, Luming Duan
Image and Video Processing
Time-varying rPPG signal separation via block-sparse signal model
Kosuke Kurihara, Yoshihiro Maeda, Daisuke Sugimura et al. (+1 more)
Remote photoplethysmography (rPPG) enables non-contact measurement of cardiac pulse signals by analyzing subtle color changes in facial
videos. Nevertheless, extracting rPPG signals remains challenging because of their extremely weak signal strength and susceptibility to illumination noise. In this paper, we propose an rPPG signal extraction method that exploits the quasi-periodic characteristics of rPPG signals. Our approach models quasi-periodicity of the rPPG signal, which arises from the stable cardiac cycle, as a block-sparse structure in the time-frequency domain. To incorporate a block-sparse model and enable adaptive signal separation under illumination fluctuations, we construct a time-varying signal separation framework. Experiments using a public
dataset demonstrate the effectiveness of our method.
Read the full abstract ▾
All authors Kosuke Kurihara, Yoshihiro Maeda, Daisuke Sugimura, Takayuki Hamamoto
Signal Processing
Experimental Comparison of Local and Over-the-Air Phase Calibration for MIMO Arrays
Carl Collmann, Ahmad Nimr and Gerhard Fettweis
Communication performance and channel estimation accuracy in
MIMO systems are known to be limited by hardware impairments. Specifically, the presence of phase impairments, such as phase noise, makes real-time coherent transmission a challenging task. While phase impairment compensation is typically performed at the receiver, practical methods for enabling coherent transmission at the transmitter side remain underexplored. Established methods for OTA calibration of
MIMO systems face several limitations such as assumptions of phase stationarity and accurate channel knowledge. In this work, a real-time local phase calibration method is experimentally compared with OTA calibration on a fully digital array of USRP X310 software-defined
radios. Using RMS cycle-to-cycle jitter as a metric, it is shown that for low and high synchronization signal bandwidths, both approaches effectively eliminate phase drift and whiten the phase noise. Local calibration achieves higher phase stability and is channel-independent, whereas OTA calibration requires no additional hardware but is sensitive to multipath effects and channel-induced impairments. Practical deployment trade-offs are discussed based on the measurement results.
Read the full abstract ▾
Signal Processing
Propagation-Consistent Wireless Environment Digital Twin Construction Under Sparse Measurements
Junjie Ai, Shurui Xu, Yanqing Ren et al. (+6 more)
Digital twins (DTs) are promising for wireless deployment, optimization, and data generation, but building a propagation-faithful twin from sparse real measurements remains difficult. This paper proposes a wireless environment digital twin (WEDT) construction paradigm that evolves a reconstructed
geometric DT into a propagation-consistent wireless environment representation through calibration of a scene-level electromagnetic (EM) property field. Instead of directly fitting link-specific channel responses, the proposed paradigm first constructs a geometry-prior
Bayesian channel map (BCM) to convert sparse position-labeled channel state information (CSI) into dense probabilistic supervision with uncertainty estimates. It then embeds the learnable EM property field into differentiable ray tracing (RT) based channel computation, thereby enabling calibration through an explicit propagation chain. Experiments in both public and real-world scenes show that WEDT achieves accurate channel prediction, generalizes to unseen transceiver
topologies, and remains effective across different sampling conditions. WEDT also offers utility for material-related environment sensing, more reliable physical-layer planning, and higher-quality synthetic data generation for wireless AI. These results demonstrate the value of the proposed paradigm for propagation-consistent WEDT construction and related wireless applications.
Read the full abstract ▾
All authors Junjie Ai, Shurui Xu, Yanqing Ren, Zhuoyu Liu, Weicong Chen, Wankai Tang, Xiao Li, Chao-Kai Wen, Shi Jin
Signal Processing
Low-Complexity Tensor Beamforming for RIS-Aided Multiuser Multistream MIMO Systems
Bruno Sokal, André L. F. de Almeida and Martin Haardt
We address joint active and passive beamforming for uplink RIS-assisted multi-user multi-stream
MIMO systems with joint detection. The coupled design of the receive combiner, block-diagonal user precoders, and RIS phase vector is formulated through a third-order composite channel tensor. Exploiting this multilinear structure, we propose a multi-stream tensor alternating optimization method that updates the combiner, user precoders, and RIS coefficients via low-dimensional tensor projections. Simulations show that the proposed method approaches a multi-start alternating-optimization
benchmark while reducing computational complexity and improving large-RIS scaling.
Read the full abstract ▾
Signal Processing · Cross-listed from stat.ME
From Volterra Series to Kunchenko Stochastic Polynomials: Half a Century of Non-Gaussian Estimation Methodology
Serhii Zabolotnii
This paper reconstructs the half-century
evolution of the scientific school founded by Yuriy P. Kunchenko (1939–2006) as the development of a semiparametric methodology for non-Gaussian estimation. Starting with Kunchenko's 1972/1973 dissertation applying Volterra series to estimate parameters of random processes, the trajectory is followed through 2006–2026. Kunchenko
stochastic polynomials are presented as a coherent family of moment-cumulant procedures: the
polynomial maximization method (PMM) for parameter estimation,
polynomial criteria for hypothesis testing, and decomposition in spaces with a generating element. The paper details the school's structure: a verified genealogy of 15 defended dissertations, collaborations in Poland, Slovakia, and Germany, and the R package EstemPMM. A recent 2026 paper on Volterra-based signal processing is analyzed, showing how Kunchenko's nonlinear formulation reappears in applied
radio engineering. We build a formal bridge between finite Volterra models and generalized Kunchenko
polynomials, while separating the MMSE/L2 criterion from PMM: the former is a covariance projection for kernel adaptation, whereas PMM is a parameter-dependent moment procedure. PMM efficiency claims are stated conditionally: gains require that moments exist, the centered correlant
matrix is nondegenerate, and the variance reduction coefficient is below one. The concluding research program operationalizes the historical reconstruction into testable statistical and signal-processing tasks.
Read the full abstract ▾