Papers

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Electrical Engineering and Systems Science

eess Watch Electrical Engineering and Systems Science

Sunday 9 November

Signal Processing

Sum Rate and Worst Case SINR Optimization in Multi HAPS Ground Integrated Networks

Shasha Liu, Hayssam Dahrouj, Abla Kammoun et al. (+1 more)

Balancing throughput and fairness promises to be a key enabler for achieving large-scale digital inclusion in future vertical heterogeneous networks (VHetNets). In an attempt to address the global digital divide problem, this paper explores a multi-high-altitude platform system (HAPS)-ground integrated network, in which multiple HAPSs collaborate with ground base stations (BSs) to enhance the users' quality of service on the ground to achieve the highly sought-after digital equity. To this end, this paper considers maximizing both the network-wide weighted sum rate function and the worst-case signal-to-interference-plus-noise ratio (SINR) function subject to the same system level constraints. More specifically, the paper tackles the two different optimization problems so as to balance throughput and fairness, by accounting for the individual HAPS payload connectivity constraints, HAPS and BS distinct power limitations, and per-user rate requirements. This paper solves the considered problems using techniques from optimization theory by adopting a generalized assignment problem (GAP)-based methodology to determine the user association variables, jointly with successive convex approximation (SCA)-based iterative algorithms for optimizing the corresponding beamforming vectors. One of the main advantages of the proposed algorithms is their amenability for distributed implementation across the multiple HAPSs and BSs. The simulation results particularly validate the performance of the presented algorithms, demonstrating the capability of multi-HAPS networks to boost-up the overall network digital inclusion toward democratizing future digital services.

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All authors Shasha Liu, Hayssam Dahrouj, Abla Kammoun, Mohamed-Slim Alouini

Systems and Control

Partial-Power Flow Controller, Voltage Regulator, and Energy Router for Hybrid AC-DC Grids

Ehsan Asadi, Davood Keshavarzi, Alexander Koehler et al. (+2 more)

The share of electronically converted power from renewable sources, loads, and storage is continuously growing in the low- and medium-voltage grids. These sources and loads typically rectify the grid AC to DC, e.g., for a DC link, so that a DC grid could eliminate hardware and losses of these conversion stages. However, extended DC grids lack the stabilizing nature of AC impedances so that the voltage is more fragile and power flows may need active control, particularly if redundancy as known from AC, such as rings and meshing, is desired. Furthermore, a DC infrastructure will not replace but will need to interface with the existing AC grid. This paper presents a partial-power energy router architecture that can interface multiple AC and DC lines to enable precise control of voltages and both active as well as reactive power flows. The proposed system uses modular low-voltage high-current series modules supplied through dual active bridges. These modules only need to process a small share of the voltage to control large power flows. The topology reduces component size, cost, energy losses, and reliability more than three times compared to conventional technology. The optional integration of battery energy storage can furthermore eliminate the need for the sum of the power flows of all inputs to be zero at all times. Through dynamic voltage injection relative to the line voltage, the modules effectively balance feeder currents, regulate reactive power, and improve the power factor in AC grids. Real-time hardware-in-the-loop and prototype measurements validate the proposed energy router's performance under diverse operating conditions. Experimental results confirm the series module's functionality in both AC and DC grids as an effective solution for controlling extended grids, including power sharing, voltage, and power quality.

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All authors Ehsan Asadi, Davood Keshavarzi, Alexander Koehler, Nima Tashakor, Stefan Goetz

Signal Processing · Cross-listed from cs.AR

Precision-Scalable Microscaling Datapaths with Optimized Reduction Tree for Efficient NPU Integration

Stef Cuyckens, Xiaoling Yi, Robin Geens et al. (+4 more)

Emerging continual learning applications necessitate next-generation neural processing unit (NPU) platforms to support both training and inference operations. The promising Microscaling (MX) standard enables narrow bit-widths for inference and large dynamic ranges for training. However, existing MX multiply-accumulate (MAC) designs face a critical trade-off: integer accumulation requires expensive conversions from narrow floating-point products, while FP32 accumulation suffers from quantization losses and costly normalization. To address these limitations, we propose a hybrid precision-scalable reduction tree for MX MACs that combines the benefits of both approaches, enabling efficient mixed-precision accumulation with controlled accuracy relaxation. Moreover, we integrate an 8x8 array of these MACs into the state-of-the-art (SotA) NPU integration platform, SNAX, to provide efficient control and data transfer to our optimized precision-scalable MX datapath. We evaluate our design both on MAC and system level and compare it to the SotA. Our integrated system achieves an energy efficiency of 657, 1438-1675, and 4065 GOPS/W, respectively, for MXINT8, MXFP8/6, and MXFP4, with a throughput of 64, 256, and 512 GOPS.

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All authors Stef Cuyckens, Xiaoling Yi, Robin Geens, Joren Dumoulin, Martin Wiesner, Chao Fang, Marian Verhelst

Systems and Control

Coherency Analysis in Nonlinear Heterogeneous Power Networks: A Blended Dynamics Approach

Yixuan Liu, Yingzhu Liu and Pengcheng You

Power system coherency refers to the phenomenon that machines in a power network exhibit similar frequency responses after disturbances, and is foundational for model reduction and control design. Despite abundant empirical observations, the understanding of coherence in complex power networks remains incomplete where the dynamics could be highly heterogeneous, nonlinear, and increasingly affected by persistent disturbances such as renewable energy fluctuations. To bridge this gap, this paper extends the blended dynamics approach, originally rooted in consensus analysis of multi-agent systems, to develop a novel coherency analysis in power networks. We show that the frequency responses of coherent machines coupled by nonlinear power flow can be approximately represented by the blended dynamics, which is a weighted average of nonlinear heterogeneous nodal dynamics, even under time-varying disturbances. Specifically, by developing novel bounds on the difference between the trajectories of nodal dynamics and the blended dynamics, we identify two key factors – either high network connectivity or small time-variation rate of disturbances – that contribute to coherence. They enable the nodal frequencies to rapidly approach the blended-dynamics trajectory from arbitrary initial state. Furthermore, they ensure the frequencies closely follow this trajectory in the long term, even when the system does not settle to an equilibrium. These insights contribute to the understanding of power system coherency and are further supported by simulation results.

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

ELEGANCE: Efficient LLM Guidance for Audio-Visual Target Speech Extraction

Wenxuan Wu, Shuai Wang, Xixin Wu et al. (+2 more)

Audio-visual target speaker extraction (AV-TSE) models primarily rely on visual cues from the target speaker. However, humans also leverage linguistic knowledge, such as syntactic constraints, next word prediction, and prior knowledge of conversation, to extract target speech. Inspired by this observation, we propose ELEGANCE, a novel framework that incorporates linguistic knowledge from large language models (LLMs) into AV-TSE models through three distinct guidance strategies: output linguistic constraints, intermediate linguistic prediction, and input linguistic prior. Comprehensive experiments with RoBERTa, Qwen3-0.6B, and Qwen3-4B on two AV-TSE backbones demonstrate the effectiveness of our approach. Significant improvements are observed in challenging scenarios, including visual cue impaired, unseen languages, target speaker switches, increased interfering speakers, and out-of-domain test set. Demo page: https://alexwxwu.github.io/ELEGANCE/.

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All authors Wenxuan Wu, Shuai Wang, Xixin Wu, Helen Meng, Haizhou Li

Signal Processing

Blocker-Aware Beamforming and Dynamic Power Allocation for Multicarrier ISAC-NOMA Systems

Abdulahi Abiodun Badrudeen, Nakyung Lee, Adam Dubs et al. (+1 more)

This paper proposes a blocker-aware multicarrier integrated sensing and communication (ISAC)-non orthogonal multiple access (NOMA) system, leveraging hybrid beamforming and dynamic power allocation to enhance spectrum efficiency in 6G networks. Recognizing the performance degradation caused by environmental blockers, the system introduces a joint waveform design that ensures robust operation under varying channel conditions. A channel switching mechanism is deployed to reroute communication through alternative non-line-of-sight paths when the primary line-of-sight links are obstructed. Moreover, a dynamic power allocation strategy enforces a minimum rate constraint for the weak NOMA user, ensuring consistent quality of service. Extensive simulations over multiple blockage scenarios and signal to noise (SNR) conditions validate the effectiveness of the proposed solution. Notably, under severe blockage, the system achieves up to a 400% sensing rate enhancement at 15 dB SNR, with only a 20% reduction in communication rate. These results corroborate the system's ability to adapt and optimize joint sensing-communication performance in practical deployment environments.

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All authors Abdulahi Abiodun Badrudeen, Nakyung Lee, Adam Dubs, Sunwoo Kim

Systems and Control · Cross-listed from cs.GT

LLM-Guided Reinforcement Learning with Representative Agents for Traffic Modeling

Hanlin Sun and Jiayang Li

Large language models (LLMs) are increasingly used as behavioral proxies for self-interested travelers in agent-based traffic models. Although more flexible and generalizable than conventional models, the practical use of these approaches remains limited by scalability due to the cost of calling one LLM for every traveler. Moreover, it has been found that LLM agents often make opaque choices and produce unstable day-to-day dynamics. To address these challenges, we propose to model each homogeneous traveler group facing the same decision context with a single representative LLM agent who behaves like the population's average, maintaining and updating a mixed strategy over routes that coincides with the group's aggregate flow proportions. Each day, the LLM reviews the travel experience and flags routes with positive reinforcement that they hope to use more often, and an interpretable update rule then converts this judgment into strategy adjustments using a tunable (progressively decaying) step size. The representative-agent design improves scalability, while the separation of reasoning from updating clarifies the decision logic while stabilizing learning. In classic traffic assignment settings, we find that the proposed approach converges rapidly to the user equilibrium. In richer settings with income heterogeneity, multi-criteria costs, and multi-modal choices, the generated dynamics remain stable and interpretable, reproducing plausible behavioral patterns well-documented in psychology and economics, for example, the decoy effect in toll versus non-toll road selection, and higher willingness-to-pay for convenience among higher-income travelers when choosing between driving, transit, and park-and-ride options.

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Signal Processing

Fast Reconstruction of Motion-Corrupted Data with Mobile-GRAPPA: Motion and dB0 Inhomogeneity Correction Leveraging Efficient GRAPPA

Yimeng Lin, Nan Wang, Daniel Abraham et al. (+5 more)

Advanced motion navigations now enable rapid tracking of subject motion and dB0-induced phase, but accurately incorporating this high-temporal-resolution information into SENSE (Aligned-SENSE) is often computationally prohibitive. We propose "Mobile-GRAPPA", a k-space "cleaning" approach that uses local GRAPPA operators to remove motion and dB0 related corruption so that the resulting data can be reconstructed with standard SENSE. We efficiently train a family of k-space-position-specific Mobile-GRAPPA kernels via a lightweight multilayer perceptron (MLP) and apply them across k-space to generate clean data. In experiments on highly motion-corrupted 1-mm whole-brain GRE (Tacq = 10 min; 1,620 motion/dB0 trackings) and EPTI (Tacq = 2 min; 544 trackings), Mobile-GRAPPA enabled accurate reconstruction with negligible time penalty, whereas full Aligned-SENSE was impractical (reconstruction times > 10 h for GRE and > 10 days for EPTI). These results show that Mobile-GRAPPA incorporates detailed motion and dB0 tracking into SENSE with minimal computational overhead, enabling fast, high-quality reconstructions of challenging data.

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All authors Yimeng Lin, Nan Wang, Daniel Abraham, Daniel Polak, Xiaozhi Cao, Aizada Nurdinova, Stephen Cauley, Kawin Setsompop

Audio and Speech Processing

IDMap: A Pseudo-Speaker Generator Framework Based on Speaker Identity Index to Vector Mapping

Zeyan Liu, Liping Chen, Kong Aik Lee et al. (+1 more)

Facilitated by the speech generation framework that disentangles speech into content, speaker, and prosody, voice anonymization is accomplished by substituting the original speaker embedding vector with that of a pseudo-speaker. In this framework, the pseudo-speaker generation forms a fundamental challenge. Current pseudo-speaker generation methods demonstrate limitations in the uniqueness of pseudo-speakers, consequently restricting their effectiveness in voice privacy protection. Besides, existing model-based methods suffer from heavy computation costs. Especially, in the large-scale scenario where a huge number of pseudo-speakers are generated, the limitations of uniqueness and computational inefficiency become more significant. To this end, this paper proposes a framework for pseudo-speaker generation, which establishes a mapping from speaker identity index to speaker vector in the feedforward architecture, termed IDMap. Specifically, the framework is specified into two models: IDMap-MLP and IDMap-Diff. Experiments were conducted on both small- and large-scale evaluation datasets. Small-scale evaluations on the LibriSpeech dataset validated the effectiveness of the proposed IDMap framework in enhancing the uniqueness of pseudo-speakers, thereby improving voice privacy protection, while at a reduced computational cost. Large-scale evaluations on the MLS and Common Voice datasets further justified the superiority of the IDMap framework regarding the stability of the voice privacy protection capability as the number of pseudo-speakers increased. Audio samples and open-source code can be found in https://github.com/VoicePrivacy/IDMap.

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All authors Zeyan Liu, Liping Chen, Kong Aik Lee, Zhenhua Ling

Systems and Control

Learning-Based Robust Bayesian Persuasion with Conformal Prediction Guarantees

Heeseung Bang and Andreas A. Malikopoulos

Classical Bayesian persuasion assumes that senders fully understand how receivers form beliefs and make decisions–an assumption that rarely holds when receivers possess private information or exhibit non-Bayesian behavior. In this paper, we develop a learning-based framework that integrates neural networks with conformal prediction to achieve robust persuasion under uncertainty about receiver belief formation. The proposed neural architecture learns end-to-end mappings from receiver observations and sender signals to action predictions, eliminating the need to identify belief mechanisms explicitly. Conformal prediction constructs finite-sample valid prediction sets with provable marginal coverage, enabling principled, distribution-free robust optimization. We establish exact coverage guarantees for the data-generating policy and derive bounds on coverage degradation under policy shifts. Furthermore, we provide neural network approximation and estimation error bounds, with sample complexity $O(d \log(|\mathcal{U}||\mathcal{Y}||\mathcal{S}|)/\varepsilon^2)$, where $d$ denotes the effective network dimension, and finite-sample lower bounds on the sender's expected utility. Numerical experiments on smart-grid energy management illustrate the framework's robustness.

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Image and Video Processing

SPASHT: An image-enhancement method for sparse-view MPI SPECT

Zezhang Yang, Zitong Yu, Nuri Choi et al. (+4 more)

Single-photon emission computed tomography for myocardial perfusion imaging (MPI SPECT) is a widely used diagnostic tool for coronary artery disease. However, the procedure requires considerable scanning time, leading to patient discomfort and the potential for motion-induced artifacts. Reducing the number of projection views while keeping the time per view unchanged provides a mechanism to shorten the scanning time. However, this approach leads to increased sampling artifacts, higher noise, and hence limited image quality. To address these issues, we propose sparseview SPECT image enhancement (SPASHT), inherently training the algorithm to improve performance on defect-detection tasks. We objectively evaluated SPASHT on the clinical task of detecting perfusion defects in a retrospective clinical study using data from patients who underwent MPI SPECT, where the defects were clinically realistic and synthetically inserted. The study was conducted for different numbers of fewer projection views, including 1/6, 1/3, and 1/2 of the typical projection views for MPI SPECT. Performance on the detection task was quantified using area under the receiver operating characteristic curve (AUC). Images obtained with SPASHT yielded significantly improved AUC compared to those obtained with the sparse-view protocol for all the considered numbers of fewer projection views. To further assess performance, a human observer study on the task of detecting perfusion defects was conducted. Results from the human observer study showed improved detection performance with images reconstructed using SPASHT compared to those from the sparse-view protocol. The results provide evidence of the efficacy of SPASHT in improving the quality of sparse-view MPI SPECT images and motivate further clinical validation.

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All authors Zezhang Yang, Zitong Yu, Nuri Choi, Janice Tania, Wenxuan Xue, Barry A. Siegel, Abhinav K. Jha

Systems and Control

A Passive Software-Defined Radio-based mmWave Sensing System for Blind Integrated Communication and Sensing

Shiqi Liu, Hang Song, Bo Wei et al. (+2 more)

Integrated Sensing and Communication (ISAC) is considered as a key component of future 6G technologies, especially in the millimeter-wave (mmWave) bands. Recently, the performances of ISAC were experimentally evaluated and demonstrated in various scenarios by developing ISAC systems. These systems generally consist of coherent transmitting (Tx) and receiving (Rx) modules. However, actively transmitting radio waves for experiments is not easy due to regulatory restrictions of radio. Meanwhile, the Tx/Rx should be synchronized and Rx need the information of Tx. In this paper, a fully passive mmWave sensing system is developed with software-defined radio for blind ISAC. It only consists of a passive Rx module which does not depend on the Tx. Since the proposed system is not synchronized with Tx and has no knowledge of the transmitted signals, a differential structure with two oppositely-oriented receivers is introduced to realize the sensing function. This structure can mitigate the influences of unknown source signals and other distortions. With the proposed sensing system, the ambient mmWave communication signals are leveraged for sensing without interrupting the existing systems. It can be deployed for field applications such as signal detection and dynamic human activity recognition since it does not emit signals. The efficacy of the developed system is first verified with a metallic plate with known motion pattern. The measured Doppler spectrogram shows good agreement with the simulation results, demonstrating the correctness of the sensing results. Further, the system is evaluated in complex scenarios, including handwaving, single- and multi-person motion detection. The sensing results successfully reflect the corresponding motions, demonstrating that the proposed sensing system can be utilized for blind ISAC in various applications.

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All authors Shiqi Liu, Hang Song, Bo Wei, Nopphon Keerativoranan, Jun-ichi Takada

Signal Processing

Meta-Learning-Driven GFlowNets for 3D Directional Modulation in Mobile Wireless Systems

Zhihao Tao and Athina P. Petropulu

In our prior work we have proposed the use of GFlowNets, a generative AI (GenAI) framework, for designing a secure communication system comprising a time-modulated intelligent reflecting surface (TM-IRS). However, GFlowNet-based approaches assume static environments, limiting their applicability in mobile wireless networks. In this paper, we proposes a novel Meta-GFlowNet framework that achieves rapid adaptation to dynamic conditions using model-agnostic meta-learning. As the communication user is moving, the framework learns a direction-general prior across user directions via inner trajectory-balance updates and outer meta-updates, enabling quick convergence to new user directions. The approach requires no labeled data, employing a pseudo-supervised consistency objective derived from the learned reward by GFlowNet and the actual sum-rate reward of the TM-IRS system. Simulation results show that the proposed method attains faster adaptation and higher secrecy performance than retrained GFlowNets, offering an efficient GenAI framework for dynamic wireless environments. Although the scenario considered here focuses on directional modulation-based physical-layer security, the proposed framework can also be applied to other mobile wireless systems, such as joint sensing-communication networks, that utilize GFlowNets.

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Signal Processing

Hierarchically Block-Sparse Recovery With Prior Support Information

Liyang Lu, Haochen Wu, Wenbo Xu et al. (+2 more)

We provide new recovery bounds for hierarchical compressed sensing (HCS) based on prior support information (PSI). A detailed PSI-enabled reconstruction model is formulated using various forms of PSI. The hierarchical block orthogonal matching pursuit with PSI (HiBOMP-P) algorithm is designed in a recursive form to reliably recover hierarchically block-sparse signals. We derive exact recovery conditions (ERCs) measured by the mutual incoherence property (MIP), wherein hierarchical MIP concepts are proposed, and further develop reconstructible sparsity levels to reveal sufficient conditions for ERCs. Leveraging these MIP analyses, we present several extended insights, including reliable recovery conditions in noisy scenarios and the optimal hierarchical structure for cases where sparsity is not equal to zero. Our results further confirm that HCS offers improved recovery performance even when the prior information does not overlap with the true support set, whereas existing methods heavily rely on this overlap, thereby compromising performance if it is absent.

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All authors Liyang Lu, Haochen Wu, Wenbo Xu, Zhaocheng Wang, H. Vincent Poor

Saturday 8 November

Systems and Control · Cross-listed from cs.MA

MTTR-A: Measuring Cognitive Recovery Latency in Multi-Agent Systems

Barak Or

Reliability in multi-agent systems (MAS) built on large language models is increasingly limited by cognitive failures rather than infrastructure faults. Existing observability tools describe failures but do not quantify how quickly distributed reasoning recovers once coherence is lost. We introduce MTTR-A (Mean Time-to-Recovery for Agentic Systems), a runtime reliability metric that measures cognitive recovery latency in MAS. MTTR-A adapts classical dependability theory to agentic orchestration, capturing the time required to detect reasoning drift and restore coherent operation. We further define complementary metrics, including MTBF and a normalized recovery ratio (NRR), and establish theoretical bounds linking recovery latency to long-run cognitive uptime. Using a LangGraph-based benchmark with simulated drift and reflex recovery, we empirically demonstrate measurable recovery behavior across multiple reflex strategies. This work establishes a quantitative foundation for runtime cognitive dependability in distributed agentic systems.

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Systems and Control

Dynamic Modeling and Control of Phosphate-Pebble Drying Systems – A Comprehensive Approach

Jose M. Campos-Salazar, Felipe Santander and Eduardo Keim

Dryers play a central role in the processing of phosphate rock, where moisture removal is essential for downstream handling and energy efficiency. Due to the inherently nonlinear and multivariable nature of these systems, accurate modeling and control remain industrial challenges. This article presents a comprehensive nonlinear dynamic model of a phosphate-pebble rotary drying process, built from first principles to capture coupled heat and mass transfer, evaporation kinetics, and subsystem interactions.

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Image and Video Processing

Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation

Jyun-Ping Kao, Shinyeong Rho, Shahar Lazarev et al. (+5 more)

Early diagnosis of attention-deficit/hyperactivity disorder (ADHD) in children plays a crucial role in improving outcomes in education and mental health. Diagnosing ADHD using neuroimaging data, however, remains challenging due to heterogeneous presentations and overlapping symptoms with other conditions. To address this, we propose a novel parameter-efficient transfer learning approach that adapts a large-scale 3D convolutional foundation model, pre-trained on CT images, to an MRI-based ADHD classification task. Our method introduces Low-Rank Adaptation (LoRA) in 3D by factorizing 3D convolutional kernels into 2D low-rank updates, dramatically reducing trainable parameters while achieving superior performance. In a five-fold cross-validated evaluation on a public diffusion MRI database, our 3D LoRA fine-tuning strategy achieved state-of-the-art results, with one model variant reaching 71.9% accuracy and another attaining an AUC of 0.716. Both variants use only 1.64 million trainable parameters (over 113x fewer than a fully fine-tuned foundation model). Our results represent one of the first successful cross-modal (CT-to-MRI) adaptations of a foundation model in neuroimaging, establishing a new benchmark for ADHD classification while greatly improving efficiency.

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All authors Jyun-Ping Kao, Shinyeong Rho, Shahar Lazarev, Hyun-Hae Cho, Fangxu Xing, Taehoon Shin, C.-C. Jay Kuo, Jonghye Woo

Audio and Speech Processing

BSCodec: A Band-Split Neural Codec for High-Quality Universal Audio Reconstruction

Haoran Wang, Jiatong Shi, Jinchuan Tian et al. (+3 more)

Neural audio codecs have recently enabled high-fidelity reconstruction at high compression rates, especially for speech. However, speech and non-speech audio exhibit fundamentally different spectral characteristics: speech energy concentrates in narrow bands around pitch harmonics (80-400 Hz), while non-speech audio requires faithful reproduction across the full spectrum, particularly preserving higher frequencies that define timbre and texture. This poses a challenge: speech-optimized neural codecs suffer degradation on music or sound. Treating the full spectrum holistically is suboptimal: frequency bands have vastly different information density and perceptual importance by content type, yet full-band approaches apply uniform capacity across frequencies without accounting for these acoustic structures. To address this gap, we propose BSCodec (Band-Split Codec), a novel neural audio codec architecture that splits the spectral dimension into separate bands and compresses each band independently. Experimental results demonstrate that BSCodec achieves superior reconstruction over baselines across sound and music, while maintaining competitive quality in the speech domain, when trained on the same combined dataset of speech, music and sound. Downstream benchmark tasks further confirm that BSCodec shows strong potential for use in downstream applications.

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All authors Haoran Wang, Jiatong Shi, Jinchuan Tian, Bohan Li, Kai Yu, Shinji Watanabe

Systems and Control · Cross-listed from cs.FL

Secret Protection in Labeled Petri Nets

Stefan Haar, Tomáš Masopust and Jakub Večeřa

We study the secret protection problem (SPP), where the objective is to find a policy of minimal cost ensuring that every execution path from an initial state to a secret state contains a sufficient number of protected events. The problem was originally introduced and studied in the setting of finite automata. In this paper, we extend the framework to labeled Petri nets. We consider two variants of the problem: the Parikh variant, where all occurrences of protected events along an execution path contribute to the security requirement, and the indicator variant, where each protected event is counted only once per execution path. We show that both variants can be solved in exponential space for labeled Petri nets, and that their decision versions are ExpSpace-complete. As a consequence, there is no polynomial-time or polynomial-space algorithm for these problems.

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Systems and Control

A Multi-Criterion Approach to Smart EV Charging with CO2 Emissions and Cost Minimization

Giuseppe C. Calafiore, Luca Ambrosino, Khai Manh Nguyen et al. (+3 more)

We study carbon-aware smart charging in a fossil-dominated grid by coupling a simplified hydro-thermal-renewable dispatch model with a tractable linear charging scheduler. The case study is informed by Vietnam's regional data. Thermal units remain dominant, renewables are time-varying, and hydropower is modeled through a single reservoir budget. From the day-ahead dispatch we derive hourly carbon intensity and a corresponding carbon-cost signal; these are combined with a local time-of-use tariff in the EV charging problem. The resulting weighted-sum linear program is multi-objective: by sweeping the trade-off coefficient, we recover the supported Pareto frontier between electricity cost and charging-associated emissions. In a 300-EV public-charging scenario with a 0.8 MW feeder cap, the proposed carbon-aware scheduler preserves the 19.8% bill reduction of a cost-only optimizer while lowering charging-associated emissions by 7.3%; a more carbon-focused tuning still remains 12.6% cheaper and 9.3% cleaner than a FIFO baseline. A hydro-sensitivity study shows that changing the reservoir budget by +/- 20% moves the mean grid carbon intensity from 360 to 466 g/kWh, yet the carbon-aware scheduler remains consistently cheaper and cleaner than FIFO. The dispatch and charging LPs solve in few milliseconds on a standard desktop computer, showing that the framework is lightweight enough for repeated day-ahead studies.

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All authors Giuseppe C. Calafiore, Luca Ambrosino, Khai Manh Nguyen, Minh Binh Vu, Riadh Zorgati, Laurent El Ghaoui

Image and Video Processing · Cross-listed from physics.optics

Video-rate gigapixel ptychography via space-time neural field representations

Ruihai Wang, Qianhao Zhao, Zhixuan Hong et al. (+14 more)

Achieving gigapixel space-bandwidth products (SBP) at video rates represents a fundamental challenge in imaging science. Here we demonstrate video-rate ptychography that overcomes this barrier by exploiting spatiotemporal correlations through neural field representations. Our approach factorizes the space-time volume into low-rank spatial and temporal features, transforming SBP scaling from sequential measurements to efficient correlation extraction. The architecture employs dual networks for decoding real and imaginary field components, avoiding phase-wrapping discontinuities plagued in amplitude-phase representations. A gradient-domain loss on spatial derivatives ensures robust convergence. We demonstrate video-rate gigapixel imaging with centimeter-scale coverage while resolving 308-nm linewidths. Validations span from monitoring sample dynamics of crystals, bacteria, stem cells, microneedle to characterizing time-varying probes in extreme ultraviolet experiments, demonstrating versatility across wavelengths. By transforming temporal variations from a constraint into exploitable correlations, we establish that gigapixel video is tractable with single-sensor measurements, making ptychography a high-throughput sensing tool for monitoring mesoscale dynamics without lenses.

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All authors Ruihai Wang, Qianhao Zhao, Zhixuan Hong, Qiong Ma, Tianbo Wang, Lingzhi Jiang, Liming Yang, Shaowei Jiang, Feifei Huang, Thanh D. Nguyen, Leslie Shor, Daniel Gage, Mary Lipton, Christopher Anderton, Arunima Bhattacharjee, David Brady, Guoan Zheng

Image and Video Processing · Cross-listed from physics.optics

Deep-ultraviolet ptychographic pocket-scope (DART): mesoscale lensless molecular imaging with label-free spectroscopic contrast

Ruihai Wang, Qianhao Zhao, Julia Quinn et al. (+11 more)

The mesoscale characterization of biological specimens has traditionally required compromises between resolution, field-of-view, depth-of-field, and molecular specificity, with most approaches relying on external labels. Here we present the Deep-ultrAviolet ptychogRaphic pockeT-scope (DART), a handheld platform that transforms label-free molecular imaging through intrinsic deep-ultraviolet spectroscopic contrast. By leveraging biomolecules' natural absorption fingerprints and combining them with lensless ptychographic microscopy, DART resolves down to 308-nm linewidths across centimeter-scale areas while maintaining millimeter-scale depth-of-field. The system's virtual error-bin methodology effectively eliminates artifacts from limited temporal coherence and other optical imperfections, enabling high-fidelity molecular imaging without lenses. Through differential spectroscopic imaging at deep-ultraviolet wavelengths, DART quantitatively maps nucleic acid and protein distributions with femtogram sensitivity, providing an intrinsic basis for explainable virtual staining. We demonstrate DART's capabilities through molecular imaging of tissue sections, cytopathology specimens, blood cells, and neural populations, revealing detailed molecular contrast without external labels. The combination of high-resolution molecular mapping and broad mesoscale imaging in a portable platform opens new possibilities from rapid clinical diagnostics, tissue analysis, to biological characterization in space exploration.

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All authors Ruihai Wang, Qianhao Zhao, Julia Quinn, Liming Yang, Yuhui Zhu, Feifei Huang, Chengfei Guo, Tianbo Wang, Pengming Song, Michael Murphy, Thanh D. Nguyen, Andrew Maiden, Francisco E. Robles, Guoan Zheng

Systems and Control

Model-free Adaptive Output Feedback Vibration Suppression in a Cantilever Beam

Juan Augusto Paredes Salazar and Ankit Goel

This paper presents a model-free adaptive control approach to suppress vibrations in a cantilevered beam excited by an unknown disturbance. The cantilevered beam under harmonic excitation is modeled using a lumped parameter approach. Based on retrospective cost optimization, a sampled-data adaptive controller is developed to suppress vibrations caused by external disturbances. Both displacement and acceleration measurements are considered for feedback. Since acceleration measurements are more sensitive to spillover, which excites higher frequency modes, a filter is developed to extract key displacement information from the acceleration data and enhance suppression performance. The vibration suppression performance is compared using both displacement and acceleration measurements.

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Image and Video Processing · Cross-listed from physics.optics

Multiscale aperture synthesis imager

Ruihai Wang, Qianhao Zhao, Tianbo Wang et al. (+7 more)

Synthetic aperture imaging has enabled breakthrough observations from radar to astronomy. However, optical implementation remains challenging due to stringent wavefield synchronization requirements among multiple receivers. Here we present the multiscale aperture synthesis imager (MASI), which utilizes parallelism to break complex optical challenges into tractable sub-problems. MASI employs a distributed array of coded sensors that operate independently yet coherently to surpass the diffraction limit of single receiver. It combines the propagated wavefields from individual sensors through a computational phase synchronization scheme, eliminating the need for overlapping measurement regions to establish phase coherence. Light diffraction in MASI naturally expands the imaging field, generating phase-contrast visualizations that are substantially larger than sensor dimensions. Without using lenses, MASI resolves sub-micron features at ultralong working distances and reconstructs 3D shapes over centimeter-scale fields. MASI transforms the intractable optical synchronization problem into a computational one, enabling practical deployment of scalable synthetic aperture systems at optical wavelengths.

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All authors Ruihai Wang, Qianhao Zhao, Tianbo Wang, Mitchell Modarelli, Peter Vouras, Zikun Ma, Zhixuan Hong, Kazunori Hoshino, David Brady, Guoan Zheng

Systems and Control

Koopman Operator for Stability Analysis: Theory with a Linear–Radial Product Reproducing Kernel

Wentao Tang and Xiuzhen Ye

Koopman operator, as a fully linear representation of nonlinear dynamical systems, if well-defined on a reproducing kernel Hilbert space (RKHS), can be efficiently learned from data. For stability analysis and control-related problems, it is desired that the defining RKHS of the Koopman operator should account for both the stability of an equilibrium point (as a local property) and the regularity of the dynamics on the state space (as a global property). To this end, we show that by using the product kernel formed by the linear kernel and a Wendland radial kernel, the resulting RKHS is invariant under the action of Koopman operator (under certain smoothness conditions). Furthermore, when the equilibrium is asymptotically stable, the spectrum of Koopman operator is provably confined inside the unit circle, and escapes therefrom upon bifurcation. Thus, the learned Koopman operator with provable probabilistic error bound provides a stability certificate. In addition to numerical verification, we further discuss how such a fundamental spectrum–stability relation would be useful for Koopman-based control.

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Signal Processing

Orbital Errors in LEO Satellite Positioning: Modeling, Analysis, and Bayesian Calibration

Jie Ma, Pinjun Zheng, Xing Liu et al. (+3 more)

Low-Earth orbit (LEO) satellites offer a promising alternative to global navigation satellite systems for precise positioning. However, their relatively low altitudes make them more susceptible to orbital perturbations, which in turn degrade positioning accuracy. In this work, we study (i) the mechanisms through which orbital errors affect positioning performance and (ii) the fundamental calibration strategies for mitigating low-Earth orbital errors. We first model the satellite orbit and analyze historical two-line element data to characterize the statistical behavior of low-Earth orbital errors. Based on these real-data statistics, we derive the misspecified Cramér-Rao bound to quantify the impact of orbital errors on positioning performance. Subsequently, we develop a Bayesian orbit calibration framework that leverages the learned orbital error statistics as prior information to enhance calibration accuracy. This work sheds light on orbital error modeling, real-data-based evaluation, theoretical impact analysis, and calibration methodologies. Extensive simulations show that stale orbital information can severely degrade positioning performance, whereas learning and incorporating orbital error statistics can effectively enhance orbit calibration and ultimately improve positioning accuracy.

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All authors Jie Ma, Pinjun Zheng, Xing Liu, Yuchen Zhang, Ali A. Nasir, Tareq Y. Al-Naffouri

Signal Processing

Online Learning of Modular Bayesian Deep Receivers: Single-Step Adaptation with Streaming Data

Yakov Gusakov, Osvaldo Simeone, Tirza Routtenberg et al. (+1 more)

Deep neural network (DNN)-based receivers offer a powerful alternative to classical model-based designs for wireless communication, especially in complex and nonlinear propagation environments. However, their adoption is challenged by the rapid variability of wireless channels, which makes pre-trained static DNN-based receivers ineffective, and by the latency and computational burden of online stochastic gradient descent (SGD)-based learning. In this work, we propose an online learning framework that enables rapid low-complexity adaptation of DNN-based receivers. Our approach is based on two main tenets. First, we cast online learning as Bayesian tracking in parameter space, enabling a single-step adaptation, which deviates from multi-epoch SGD . Second, we focus on modular DNN architectures that enable parallel, online, and localized variational Bayesian updates. Simulations with practical communication channels demonstrate that our proposed online learning framework can maintain a low error rate with markedly reduced update latency and increased robustness to channel dynamics as compared to traditional gradient descent based method.

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All authors Yakov Gusakov, Osvaldo Simeone, Tirza Routtenberg, Nir Shlezinger

Signal Processing

Towards Human-AI-Robot Collaboration and AI-Agent based Digital Twins for Parkinson's Disease Management: Review and Outlook

Hassan Hizeh, Rim Chighri, Muhammad Mahboob Ur Rahman et al. (+3 more)

The current body of research on Parkinson's disease (PD) screening, monitoring, and management has evolved along two largely independent trajectories. The first research community focuses on multimodal sensing of PD-related biomarkers using noninvasive technologies such as inertial measurement units (IMUs), force/pressure insoles, electromyography (EMG), electroencephalography (EEG), speech and acoustic analysis, and RGB/RGB-D motion capture systems. These studies emphasize data acquisition, feature extraction, and machine learning-based classification for PD screening, diagnosis, and disease progression modeling. In parallel, a second research community has concentrated on robotic intervention and rehabilitation, employing socially assistive robots (SARs), robot-assisted rehabilitation (RAR) systems, and virtual reality (VR)-integrated robotic platforms for improving motor and cognitive function, enhancing social engagement, and supporting caregivers. Despite the complementary goals of these two domains, their methodological and technological integration remains limited, with minimal data-level or decision-level coupling between the two. With the advent of advanced artificial intelligence (AI), including large language models (LLMs), agentic AI systems, a unique opportunity now exists to unify these research streams. We envision a closed-loop sensor-AI-robot framework in which multimodal sensing continuously guides the interaction between the patient, caregiver, humanoid robot (and physician) through AI agents that are powered by a multitude of AI models such as robotic and wearables foundation models, LLM-based reasoning, reinforcement learning, and continual learning. Such closed-loop system enables personalized, explainable, and context-aware intervention, forming the basis for digital twin of the PD patient that can adapt over time to deliver intelligent, patient-centered PD care.

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All authors Hassan Hizeh, Rim Chighri, Muhammad Mahboob Ur Rahman, Mohamed A. Bahloul, Ali Muqaibel, Tareq Y. Al-Naffouri

Signal Processing

Invariants in Eddy Current Testing via Dimensional Analysis

Vincenzo Mottola, Alessandro Sardellitti, Filippo Milano et al. (+3 more)

The Buckingham's $\pi$, theorem has been recently introduced in the context of Non destructive Testing & Evaluation (NdT&E) , giving a theoretical basis for developing simple but effective methods for multi-parameter estimation via dimensional analysis. Dimensional groups, or $\pi-$groups, allow for the reduction of the number of parameters affecting the dimensionless measured quantities. In many real-world applications, the main interest is in estimating only a subset of the variables affecting the measurements. An example is estimating the thickness and electrical conductivity of a plate from Eddy Current Testing data, regardless of the lift-off of the probe, which may be either uncertain and/or variable. Alternatively, one may seek to estimate thickness and lift-off while neglecting the influence of the electrical conductivity, or to estimate the electrical conductivity and the lift-off, neglecting the thickness. This is where the concept of invariants becomes crucial. An invariant transformation is a mathematical mapping that makes the measured signal independent of one or more of these uncertain parameters. Invariant transformations provide a way to isolate useful signals from uncertain ones, improving the accuracy and reliability of the NdT results. The main contribution of this paper is a systematic method to derive invariant transformations for frequency domain Eddy Current Testing data, via dimensional analysis. The proposed method is compatible with real-time and in-line operations. After its theoretical foundation is introduced, the method is validated by means of experimental data, with reference to configurations consisting of plates with different thicknesses, electrical conductivity, and lift-off. The experimental validation proves the effectiveness of the method in achieving excellent accuracy on a wide range of parameters of interest.

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All authors Vincenzo Mottola, Alessandro Sardellitti, Filippo Milano, Luigi Ferrigno, Marco Laracca, Antonello Tamburrino

Systems and Control

Probe-and-Release Coordination of Platoons at Highway Bottlenecks with Unknown Parameters

Yi Gao, Xi Xiong, Karl H. Johansson et al. (+1 more)

This paper considers coordination of platoons of connected and autonomous vehicles (CAVs) at mixed-autonomy bottlenecks in the face of three practically important factors, viz. time-varying traffic demand, random CAV platoon sizes, and capacity breakdowns. Platoon coordination is essential to smoothen the interaction between CAV platoons and non-CAV traffic. Based on a fluid queuing model, we develop a "probe-and-release" algorithm that simultaneously estimates environmental parameters and coordinates CAV platoons for traffic stabilization. We show that this algorithm ensures bounded estimation errors and bounded traffic queues. The proof builds on a Lyapunov function that jointly penalizes estimation errors and traffic queues and a drift argument for an embedded Markov process. We validate the proposed algorithm in a standard micro-simulation environment and compare against a representative deep reinforcement learning method in terms of control performance and computational efficiency.

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All authors Yi Gao, Xi Xiong, Karl H. Johansson, Li Jin