Audio and Speech Processing · Cross-listed from cs.SD
AudioGAN: A Compact and Efficient Framework for Real-Time High-Fidelity Text-to-Audio Generation
HaeChun Chung
Text-to-audio (TTA) generation can significantly benefit the media industry by reducing production costs and enhancing work efficiency. However, most current TTA models (primarily
diffusion-based) suffer from slow inference speeds and high computational costs. In this paper, we introduce AudioGAN, the first successful Generative
Adversarial Networks (GANs)-based TTA framework that generates audio in a single pass, thereby reducing model complexity and inference time. To overcome the inherent difficulties in training GANs, we integrate multiple ,contrastive losses and propose innovative components Single-Double-Triple (SDT)
Attention and Time-Frequency Cross-
Attention (TF-CA). Extensive experiments on the AudioCaps
dataset demonstrate that AudioGAN achieves state-of-the-art performance while using 90% fewer parameters and running 20 times faster, synthesizing audio in under one second. These results establish AudioGAN as a practical and powerful solution for real-time TTA.
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Signal Processing
Concurrence: A dependence criterion for time series, applied to biological data
Evangelos Sariyanidi, John D. Herrington, Lisa Yankowitz et al. (+8 more)
Measuring the statistical dependence between observed signals is a primary tool for scientific discovery. However, biological systems often exhibit complex non-linear interactions that currently cannot be captured without a priori knowledge or large
datasets. We introduce a criterion for dependence, whereby two time series are deemed dependent if one can construct a classifier that distinguishes between temporally aligned vs. misaligned segments extracted from them. We show that this criterion, concurrence, is theoretically linked with dependence, and can become a standard approach for scientific analyses across disciplines, as it can expose relationships across a wide spectrum of signals (fMRI, physiological and behavioral data) without ad-hoc parameter tuning or large amounts of data.
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All authors Evangelos Sariyanidi, John D. Herrington, Lisa Yankowitz, Pratik Chaudhari, Theodore D. Satterthwaite, Casey J. Zampella, Jeffrey S. Morris, Edward Gunning, Robert T. Schultz, Russell T. Shinohara, Birkan Tunc
Systems and Control
Lyapunov-based Adaptive Transformer (LyAT) for Control of Stochastic Nonlinear Systems
Saiedeh Akbari, Xuehui Shen, Wenqian Xue et al. (+2 more)
This paper presents a novel Lyapunov-based Adaptive
Transformer (LyAT) controller for
stochastic nonlinear systems. While
transformers have shown promise in various control applications due to sequential modeling through self-
attention mechanisms, they have not been used within adaptive control architectures that provide stability guarantees. Existing
transformer-based approaches for control rely on offline training with fixed weights, resulting in open-loop implementations that lack real-time adaptation capabilities and stability assurances. To address these limitations, a continuous LyAT controller is developed that adaptively estimates drift and
diffusion uncertainties in
stochastic dynamical systems without requiring offline pre-training. A key innovation is the analytically derived adaptation law constructed from a Lyapunov-based stability analysis, which enables real-time weight updates while guaranteeing probabilistic uniform ultimate boundedness of tracking and parameter estimation errors. Experimental validation on a quadrotor demonstrates the performance of the developed controller.
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All authors Saiedeh Akbari, Xuehui Shen, Wenqian Xue, Jordan C. Insinger, Warren E. Dixon
Systems and Control · Cross-listed from cs.RO
SORS: A Modular, High-Fidelity Simulator for Soft Robots
Manuel Mekkattu, Mike Y. Michelis and Robert K. Katzschmann
The deployment of complex soft
robots in multiphysics environments requires advanced simulation frameworks that not only capture interactions between different types of material, but also translate accurately to real-world performance. Soft
robots pose unique modeling challenges due to their large nonlinear deformations, material incompressibility, and contact interactions, which complicate both numerical stability and physical accuracy. Despite recent progress, robotic simulators often struggle with modeling such phenomena in a scalable and application-relevant manner. We present SORS (Soft Over Rigid Simulator), a versatile, high-fidelity simulator designed to handle these complexities for soft
robot applications. Our energy-based framework, built on the finite element method, allows modular extensions, enabling the inclusion of custom-designed material and actuation models. To ensure physically consistent contact handling, we integrate a constrained nonlinear optimization based on sequential quadratic programming, allowing for stable and accurate modeling of contact phenomena. We validate our simulator through a diverse set of real-world experiments, which include cantilever deflection, pressure-actuation of a soft robotic arm, and contact interactions from the PokeFlex
dataset. In addition, we showcase the potential of our framework for control optimization of a soft robotic leg. These tests confirm that our simulator can capture both fundamental material behavior and complex actuation dynamics with high physical fidelity. By bridging the sim-to-real gap in these challenging domains, our approach provides a validated tool for prototyping next-generation soft
robots, filling the gap of extensibility, fidelity, and usability in the soft robotic ecosystem.
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Image and Video Processing
MCR-VQGAN: A Scalable and Cost-Effective Tau PET Synthesis Approach for Alzheimer's Disease Imaging
Jin Young Kim, Jeremy Hudson, Jeongchul Kim et al. (+2 more)
Tau positron emission
tomography (PET) is a critical diagnostic modality for Alzheimer's disease (AD), but its widespread clinical adoption is hindered by radiation exposure, limited availability, high clinical workload, and substantial financial costs. To address these limitations, we propose the Multi-scale CBAM Residual Vector Quantized Generative
Adversarial Network (MCR-VQGAN) to synthesize high-fidelity tau PET images from structural T1-weighted MRI. MCR-VQGAN advances the standard VQGAN architecture through three enhancements: multi-scale convolutions, ResNet blocks, and Convolutional Block
Attention Modules (CBAM), which collectively improve the capture of local and global features. Using 222 paired T1-weighted MRI and tau PET scans from the ADNI database, we trained and compared MCR-VQGAN against cGAN, WGAN-GP, CycleGAN, and baseline VQGAN. MCR-VQGAN achieved superior image synthesis performance across all metrics (MSE = 0.0056 +/- 0.0061, PSNR = 30.65 +/- 4.47 dB, SSIM = 0.9263 +/- 0.0469). A CNN-based AD classifier trained on real tau PET achieved comparable accuracy on real (63.64%) and synthetic (65.91%) images, indicating that diagnostically relevant features are preserved. Regional SUVR-equivalent analysis across Braak-defined ROIs further indicated strong agreement between real and synthetic tau PET (Pearson r = 0.78-0.88; ICC = 0.71-0.84), with the strongest agreement in Braak V/VI (ICC = 0.838). Together, these results suggest that MCR-VQGAN offers a promising and scalable surrogate for conventional tau PET imaging, potentially improving the accessibility of tau biomarkers for AD research and clinical workflows.
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All authors Jin Young Kim, Jeremy Hudson, Jeongchul Kim, Qing Lyu, Christopher T. Whitlow
Image and Video Processing
In search of truth: Evaluating concordance of AI-based anatomy segmentation models
Lena Giebeler, Deepa Krishnaswamy, David Clunie et al. (+9 more)
Purpose AI-based methods for anatomy
segmentation can help automate characterization of large imaging
datasets. The growing number of similar in functionality models raises the challenge of evaluating them on
datasets that do not contain ground truth annotations. We introduce a practical framework to assist in this task. Approach We harmonize the
segmentation results into a standard, interoperable representation, which enables consistent, terminology-based labeling of the structures. We extend 3D Slicer to streamline loading and comparison of these harmonized
segmentations, and demonstrate how standard representation simplifies review of the results using interactive summary plots and browser-based visualization using OHIF Viewer. To demonstrate the utility of the approach we apply it to evaluating
segmentation of 31 anatomical structures (lungs, vertebrae, ribs, and heart) by six open-source models - TotalSegmentator 1.5 and 2.6, Auto3DSeg, MOOSE, MultiTalent, and CADS - for a sample of Computed
Tomography (CT) scans from the publicly available National Lung Screening Trial (NLST)
dataset. Results We demonstrate the utility of the framework in enabling automating loading, structure-wise inspection and comparison across models. Preliminary results ascertain practical utility of the approach in allowing quick detection and review of problematic results. The comparison shows excellent agreement segmenting some (e.g., lung) but not all structures (e.g., some models produce invalid vertebrae or rib
segmentations). Conclusions The resources developed are linked from https://imagingdatacommons.github.io/
segmentation-comparison/ including
segmentation harmonization scripts, summary plots, and visualization tools. This work assists in model evaluation in absence of ground truth, ultimately enabling informed model selection.
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All authors Lena Giebeler, Deepa Krishnaswamy, David Clunie, Jakob Wasserthal, Lalith Kumar Shiyam Sundar, Andres Diaz-Pinto, Klaus H. Maier-Hein, Murong Xu, Bjoern Menze, Steve Pieper, Ron Kikinis, Andrey Fedorov
Systems and Control
A Comprehensive Benchmark Platform for Process Control Research of Outdoor Microalgae Raceway Reactors
Enrique Rodríguez-Miranda, Pablo Otálora, José González-Hernández et al. (+2 more)
This paper presents a benchmarking framework to evaluate process control strategies in outdoor microalgae raceway reactors, integrating four key control regulation tasks: pH, dissolved oxygen (DO), culture volume through coordinated harvest-dilution actions, and temperature via a sump-mounted spiral heat exchanger. The
benchmark is built upon a high-fidelity, experimentally calibrated dynamic model that captures the strongly coupled thermal, physicochemical, and biological processes governing industrial-scale open raceway ponds. A closed-loop simulation environment is provided, featuring realistic actuator constraints, gas transport delays, stiff integration, and a fully specified scenario based on multi-day outdoor disturbances (irradiance, temperature, wind, and humidity). Four user-replaceable controllers define the manipulation of CO2 injection, air bubbling, harvest/dilution sequencing, and heat-exchanger operation. The platform computes a unified global performance index, in addition to individual metrics for each control problem, combining tracking error, gas and energy usage, and biomass productivity, enabling consistent and quantitative comparison of alternative control strategies. Baseline regulatory architectures (On/Off, PI/PID, and Economic Model Predictive Control (EMPC)) are included to illustrate the
benchmark use for classical and advanced control methods. By providing an openly specified, reproducible, and computationally tractable
benchmark with well-defined function interfaces, this work aims to bridge control methodology and outdoor algal bioprocess engineering, and to support the development of multivariable control strategies for disturbance-rich environmental systems.
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All authors Enrique Rodríguez-Miranda, Pablo Otálora, José González-Hernández, José Luis Guzmán, Manuel Berenguel
Image and Video Processing
SNIC: Synthesized Noisy Images using Calibration
Nik Bhatt
Training advanced denoising models requires large
datasets of high-fidelity, physically accurate images. While heteroscedastic noise models can simulate realistic noise, methodologies for their calibration remain under-explored, and large-scale calibrated
datasets are scarce. We present a rigorous calibration and tuning pipeline for building high-quality heteroscedastic noise models across a range of sensors, incorporating dark frames to capture signal-independent noise. When evaluated with a state-of-the-art denoiser, our synthesized noisy RAW images reduce the Peak Signal to Noise Ratio (PSNR) gap to real-world noise by 54-64% compared to synthesized RAW images created using manufacturer-provided noise profiles, which fail to account for smart-phone ISP processing that suppresses noise in RAW files during calibration. Leveraging our pipeline, we introduce the Synthesized Noisy Images using Calibration (SNIC)
dataset: over 6600 images across 30 scenes and four sensors (DSLR, point-and-shoot, and smartphone), with open-source calibration code and noise models. To our knowledge, SNIC is the only publicly available
dataset with calibrated synthesized noise providing paired RAW and TIFF data, offering a new resource for researchers developing noise reduction models.
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Image and Video Processing · Cross-listed from cs.CR
Secure AI-Driven Super-Resolution for Real-Time Mixed Reality Applications
Mohammad Waquas Usmani, Sankalpa Timilsina, Michael Zink et al. (+1 more)
Immersive formats such as 360{\deg} and 6DoF point cloud
videos require high bandwidth and low latency, posing challenges for real-time AR/VR streaming. This work focuses on reducing bandwidth consumption and encryption/decryption delay, two key contributors to overall latency. We design a system that downsamples point cloud content at the origin server and applies partial encryption. At the client, the content is decrypted and upscaled using an ML-based super-resolution model. Our evaluation demonstrates a nearly linear reduction in bandwidth/latency, and encryption/decryption overhead with lower downsampling resolutions, while the super-resolution model effectively reconstructs the original full-resolution point clouds with minimal error and modest inference time.
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All authors Mohammad Waquas Usmani, Sankalpa Timilsina, Michael Zink, Susmit Shannigrahi
Image and Video Processing
BioimageAIpub: a toolbox for AI-ready bioimaging data publishing
Stefan Dvoretskii, Anwai Archit, Constantin Pape et al. (+2 more)
Modern bioimage analysis approaches are data hungry, making it necessary for researchers to scavenge data beyond those collected within their (bio)imaging facilities. In addition to scale, bioimaging
datasets must be accompanied with suitable, high-quality annotations and metadata. Although established data repositories such as the Image Data Resource (IDR) and BioImage Archive offer rich metadata, their contents typically cannot be directly consumed by image analysis tools without substantial data wrangling. Such a tedious assembly and conversion of (meta)data can account for a dedicated amount of time investment for researchers, hindering the development of more powerful analysis tools. Here, we introduce BioimageAIpub, a workflow that streamlines bioimaging data conversion, enabling a seamless upload to HuggingFace, a widely used platform for sharing machine learning
datasets and models.
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All authors Stefan Dvoretskii, Anwai Archit, Constantin Pape, Josh Moore, Marco Nolden
Image and Video Processing
Feifei Zhang, Zhenhong Jia, Sensen Song et al. (+3 more)
Medical image
segmentation is constrained by sparse pathological annotations. Existing augmentation strategies, from conventional transforms to random masking for self-supervision, are feature-agnostic: they often corrupt critical diagnostic semantics or fail to prioritize essential features. We introduce "Keep the Core," a novel data-centric paradigm that uses
adversarial priors to guide both augmentation and masking in a significance-preserving manner. Our approach uses SAGE (Sparse
Adversarial Gated
Estimator), an offline module identifying minimal
tokens whose micro-perturbation flips
segmentation boundaries. SAGE forges the
Token Importance Map $W$ by solving an
adversarial optimization problem to maximally degrade performance, while an $\ell_1$ sparsity penalty encourages a compact set of sensitive
tokens. The online KEEP (Key-region Enhancement & Preservation) module uses $W$ for a two-pronged augmentation strategy: (1) Semantic-Preserving Augmentation: High-importance
tokens are augmented, but their original pixel values are strictly restored. (2) Guided-Masking Augmentation: Low-importance
tokens are selectively masked for an $\text{MAE}$-style reconstruction, forcing the model to learn robust representations from preserved critical features. "Keep the Core" is backbone-agnostic with no inference overhead. Extensive experiments show SAGE's structured priors and KEEP's region-selective mechanism are highly complementary, achieving state-of-the-art
segmentation robustness and generalization on 2D medical
datasets.
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All authors Feifei Zhang, Zhenhong Jia, Sensen Song, Fei Shi, Aoxue Chen, Dayong Ren
Signal Processing
HiLTS: Human in the Loop Therapeutic System: A Wireless-enabled Precision Medicine Platform for Brainwave Entrainment
Arfan Ghani
Epileptic seizures arise from abnormally synchronised neural activity and remain a major global health challenge, affecting more than 50 million people worldwide. Despite advances in pharmacological interventions, a significant proportion of patients continue to experience uncontrolled seizures, underscoring the need for alternative neuromodulation strategies. Rhythmic neural entrainment has recently emerged as a promising mechanism for disrupting pathological synchrony, but most existing systems rely on complex analogue electronics or high-power stimulation hardware. This study investigates a minimal digital custom-designed chip that generates a stable 6 Hz oscillation capable of entraining epileptic seizure activity. Using a publicly available
EEG seizure
dataset, we extracted and averaged analogue seizure waveforms, digitised them to emulate neural front-ends, and directly interfaced the digitised signals with digital output recordings acquired from the chip using a Saleae Logic analyser. The chip pulse train was resampled and low-pass-reconstructed to produce an analogue 6 Hz waveform, allowing direct comparison between seizure morphology, its digitised representation, and the entrained output. Frequency-domain and time-domain analyses demonstrate that the chip imposes a narrow-band 6 Hz rhythm that overrides the broadband spectral profile of seizure activity. These results provide a proof-of-concept for low-power digital custom-designed entrainment as a potential pathway toward simplified, wearable seizure-interruption devices for precision medicine and future healthcare devices.
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Systems and Control · Cross-listed from cs.LG
Multi-Modal Semantic Communication
Matin Mortaheb, Erciyes Karakaya and Sennur Ulukus
Semantic communication aims to transmit information most relevant to a task rather than raw data, offering significant gains in communication efficiency for applications such as telepresence, augmented reality, and remote sensing. Recent
transformer-based approaches have used self-
attention maps to identify informative regions within images, but they often struggle in complex scenes with multiple objects, where self-
attention lacks explicit task guidance. To address this, we propose a novel Multi-Modal Semantic Communication framework that integrates text-based user queries to guide the information extraction process. Our proposed system employs a cross-modal
attention mechanism that fuses visual features with
language embeddings to produce soft relevance scores over the visual data. Based on these scores and the instantaneous channel bandwidth, we use an
algorithm to transmit image patches at adaptive resolutions using independently trained encoder-decoder pairs, with total bitrate matching the channel capacity. At the receiver, the patches are reconstructed and combined to preserve task-critical information. This flexible and goal-driven design enables efficient semantic communication in complex and bandwidth-constrained environments.
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Image and Video Processing
Radiomics and Clinical Features in Predictive Modelling of Brain Metastases Recurrence
Ines Faria, Matheus Silva, Crystian Saraiva et al. (+2 more)
Brain metastases affect approximately between 20% and 40% of cancer patients and are commonly treated with radiotherapy or radiosurgery. Early prediction of recurrence following treatment could enable timely clinical intervention and improve patient outcomes. This study proposes an artificial intelligence based approach for predicting
brain metastasis recurrence using
multimodal imaging and clinical data. A retrospective cohort of 97 patients was collected, including Computed
Tomography (CT) and
Magnetic Resonance Imaging (MRI) acquired before treatment and at first follow-up, together with relevant clinical variables. Image preprocessing included CT windowing and artifact reduction, MRI enhancement, and
multimodal CT MRI registration. After applying inclusion criteria, 53 patients were retained for analysis. Radiomics features were extracted from the imaging data, and delta radiomics was employed to characterize temporal changes between pre-treatment and follow-up scans. Multiple machine learning classifiers were trained and evaluated, including an analysis of discrepancies between treatment planning target volumes and delivered isodose volumes. Despite limitations related to sample size and class imbalance, the results demonstrate the feasibility of radiomics based models, namely ensemble models, for recurrence prediction and suggest a potential association between radiation dose discrepancies and recurrence risk. This work supports further investigation of AI-driven tools to assist clinical decision-making in
brain metastasis management.
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All authors Ines Faria, Matheus Silva, Crystian Saraiva, Jose Soares, Victor Alves
Systems and Control
Service-Oriented Fast Frequency Response from Flexible Loads and Energy Storage in Low-Inertia Power Systems
Xiaojie Tao and Rajit Gadh
The increasing penetration of inverter-based renewable generation has significantly reduced system inertia, making modern power grids more vulnerable to rapid frequency deviations following disturbances. While a wide range of flexible resources-including electric vehicles (EVs), data centers, and battery energy storage systems (BESS)-have demonstrated the physical capability to provide fast frequency response (FFR), existing studies primarily focus on individual resource performance or controller-level designs. A systematic framework that translates heterogeneous FFR capabilities into deployable, system-level frequency services remains largely unexplored. This paper proposes a service-oriented coordination framework for fast frequency response from flexible loads and energy storage, bridging the gap between physical capability assessment and grid-operational utilization. The framework decomposes frequency support into multiple time-critical service layers based on response speed, power capacity, and energy sustainability, and dynamically allocates FFR responsibilities among heterogeneous resources accordingly. By explicitly accounting for response latency, saturation limits, and energy constraints, the proposed approach enables coordinated dispatch that prioritizes ultra-fast resources for initial frequency arrest while leveraging slower but energy-rich resources to sustain recovery.
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Systems and Control
Enhancing industrial microalgae production through Economic Model Predictive Control
Pablo Otálora, Sigurd Skogestad, José Luis Guzmán et al. (+1 more)
The industrial production of microalgae is an important and sustainable process, but its actual competitiveness is closely related to its optimization. The biological nature of the process hinders this task, mainly due to the high nonlinearity of the process along with its changing nature, features that make its modeling, control and optimization remarkably challenging. This paper presents an economic optimization framework aiming to enhance the operation of such systems. An Economic Model Predictive Controller is proposed, centralizing the decision making and achieving the theoretical optimal operation. Different scenarios with changing
climate conditions are presented, and a comparison with the typical, non-optimized industrial process operation is established. The obtained results achieve economic optimization and dynamic stability of the process, while providing some insight into the priorities during process operation at industrial level, and justifying the use of optimal controllers over traditional operation.
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All authors Pablo Otálora, Sigurd Skogestad, José Luis Guzmán, Manuel Berenguel
Systems and Control
Operator-Theoretic Joint Estimation of Aging-Aware State of Charge and Control-Informed State of Health
Rahmat K. Adesunkanmi, Adel Alaeddini and Mahesh Krishnamurthy
Accurate estimation of a battery's state of charge and state of health is essential for safe and reliable battery management. Existing approaches often decouple these two states, lack stability guarantees, and exhibit limited generalization across operating conditions. This study introduces a unified operator-theoretic framework for aging-aware state of charge and control-informed state of health estimation. The architecture couples a Koopman-based latent dynamics model, which enables linear
forecasting of nonlinear discharge-capacity
evolution under varying operational conditions, with a neural operator that maps measurable intra-cycle signals to state of charge. The predicted discharge capacity is incorporated as a static correction within the neural operator pathway, yielding an age-aware state of charge estimate. Stability is ensured through spectral-radius clipping of the Koopman operator. The overall framework is trained end-to-end and evaluated on real-world lithium-ion battery
datasets, demonstrating real-time capability while maintaining stable dynamics. To handle condition shifts and unseen regimes, the method integrates both zero-shot and few-shot out-of-distribution adaptation using only a limited number of cycles. Results show accurate and stable capacity forecasts, competitive state of charge trajectories on held-out cycles, and a direct, model-consistent mechanism for tracking capacity fade as a surrogate for state of health across diverse operating conditions.
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Signal Processing · Cross-listed from cs.CV
Persistent feature reconstruction of resident space objects (RSOs) within inverse synthetic aperture radar (ISAR) images
Morgan Coe, Gruffudd Jones, Leah-Nani Alconcel et al. (+1 more)
With the rapidly growing population of resident space objects (RSOs) in the near-Earth space environment, detailed information about their condition and capabilities is needed to provide Space Domain Awareness (SDA). Space-based sensing will enable inspection of RSOs at shorter ranges, independent of atmospheric effects, and from all aspects. The use of a sub-THz inverse synthetic aperture radar (ISAR) imaging and sensing system for SDA has been proposed in previous work, demonstrating the achievement of sub-cm image resolution at ranges of up to 100 km. This work focuses on recognition of external structures by use of sequential feature detection and tracking throughout the aligned ISAR images of the satellites. The Hough transform is employed to detect linear features, which are tracked throughout the sequence. ISAR imagery is generated via a metaheuristic simulator capable of modelling encounters for a variety of deployment scenarios. Initial frame-to-frame
alignment is achieved through a series of affine transformations to facilitate later association between image features. A gradient-by-ratio method is used for edge detection within individual ISAR images, and edge magnitude and direction are subsequently used to inform a double-weighted Hough transform to detect features with high accuracy. Feature
evolution during sequences of frames is analysed. It is shown that the use of feature tracking within sequences with the proposed approach will increase confidence in feature detection and classification, and an example use-case of robust detection of shadowing as a feature is presented.
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All authors Morgan Coe, Gruffudd Jones, Leah-Nani Alconcel, Marina Gashinova
Systems and Control
Scheduling the Charge of Temporally Flexible Electric Vehicles: a Market-based Approach
Sabri El Amrani, Thibaut Horel, Saurabh Vaishampayan et al. (+2 more)
The increasing electrification of human activities and the rapid integration of variable renewable energy sources strain the power grid. A solution to address the need for more grid storage is to use the battery of electric vehicles as a back-up capacity. However, drivers tend to disconnect their electric vehicle when its battery is needed the most. We propose a charge scheduler that incentivizes drivers to delay their disconnection to improve vehicle-to-grid services. We also leverage drivers' temporal flexibility to alleviate congestion in oversubscribed charging stations. We formulate the computation of an optimal flexible schedule as a mixed-integer quadratic problem. We tractably approximate its solution using the Alternating Direction Method of Multipliers. Considering the possibility that strategic drivers misreport their charging preferences to the station coordinator, we then propose a Vickrey-Clarke-Groves mechanism that incentivizes truthful reporting. We conclude with a simulated case study using real-world data to quantitatively assess the added value of drivers' temporal flexibility for enhancing vehicle-to-grid services and reducing station congestion.
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All authors Sabri El Amrani, Thibaut Horel, Saurabh Vaishampayan, Maryam Kamgarpour, Munther A. Dahleh
Systems and Control · Cross-listed from math.OC
Exact Learning of Linear Model Predictive Control Laws using Oblique Decision Trees with Linear Predictions
Jiayang Ren, Qiangqiang Mao, Tianwei Zhao et al. (+1 more)
Model Predictive Control (MPC) is a powerful strategy for constrained multivariable systems but faces computational challenges in real-time deployment due to its online optimization requirements. While explicit MPC and neural network approximations mitigate this burden, they suffer from scalability issues or lack interpretability, limiting their applicability in safety-critical systems. This work introduces a data-driven framework that directly learns the Linear MPC control law from sampled state-action pairs using Oblique Decision Trees with Linear Predictions (ODT-LP), achieving both computational efficiency and interpretability. By leveraging the piecewise affine structure of Linear MPC, we prove that the Linear MPC control law can be replicated by finite-depth ODT-LP models. We develop a gradient-based training
algorithm using smooth approximations of tree routing functions to learn this structure from grid-sampled Linear MPC solutions, enabling end-to-end optimization. Input-to-state stability is established under bounded approximation errors, with explicit error decomposition into learning inaccuracies and sampling errors to inform model design. Numerical experiments demonstrate that ODT-LP controllers match MPC's closed-loop performance while reducing online evaluation time by orders of magnitude compared to MPC, explicit MPC, neural network, and random forest counterparts. The transparent tree structure enables formal verification of control logic, bridging the gap between computational efficiency and certifiable reliability for safety-critical systems.
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All authors Jiayang Ren, Qiangqiang Mao, Tianwei Zhao, Yankai Cao
Signal Processing · Cross-listed from cs.IT
Reducing Pilots in Channel Estimation with Predictive Foundation Models
Xingyu Zhou, Le Liang, Hao Ye et al. (+3 more)
Accurate channel state information (CSI) acquisition is essential for modern wireless systems, which becomes increasingly difficult under large
antenna arrays, strict pilot overhead constraints, and diverse deployment environments. Existing artificial intelligence-based solutions often lack robustness and fail to generalize across scenarios. To address this limitation, this paper introduces a predictive-foundation-model-based channel estimation framework that enables accurate, low-overhead, and generalizable CSI acquisition. The proposed framework employs a predictive foundation model trained on large-scale cross-domain CSI data to extract universal channel representations and provide predictive priors with strong cross-scenario transferability. A pilot processing network based on a
vision transformer architecture is further designed to capture spatial, temporal, and frequency correlations from pilot observations. An efficient fusion mechanism integrates predictive priors with real-time measurements, enabling reliable CSI reconstruction even under sparse or noisy conditions. Extensive evaluations across diverse configurations demonstrate that the proposed
estimator significantly outperforms both classical and data-driven baselines in accuracy, robustness, and generalization capability.
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All authors Xingyu Zhou, Le Liang, Hao Ye, Jing Zhang, Chao-Kai Wen, Shi Jin
Signal Processing
Deep Reinforcement Learning for EH-Enabled Cognitive-IoT Under Jamming Attacks
Nadia Abdolkhani, Nada Abdel Khalek and Walaa Hamouda
In the evolving landscape of the Internet of Things (IoT), integrating cognitive
radio (CR) has become a practical solution to address the challenge of spectrum scarcity, leading to the development of cognitive IoT (CIoT). However, the vulnerability of
radio communications makes
radio jamming attacks a key concern in CIoT networks. In this paper, we introduce a novel deep reinforcement learning (DRL) approach designed to optimize throughput and extend network lifetime of an energy-constrained CIoT system under jamming attacks. This DRL framework equips a CIoT device with the autonomy to manage energy harvesting (EH) and data transmission, while also regulating its transmit power to respect spectrum-sharing constraints. We formulate the optimization problem under various constraints, and we model the CIoT device's interactions within the channel as a model-free Markov decision process (MDP). The MDP serves as a foundation to develop a double deep Q-network (DDQN), designed to help the CIoT
agent learn the optimal communication policy to navigate challenges such as dynamic channel occupancy, jamming attacks, and channel fading while achieving its goal. Additionally, we introduce a variant of the upper confidence bound (UCB)
algorithm, named UCB-IA, which enhances the CIoT network's ability to efficiently navigate jamming attacks within the channel. The proposed DRL
algorithm does not rely on prior knowledge and uses locally observable information such as channel occupancy, jamming activity, channel gain, and energy arrival to make decisions. Extensive simulations prove that our proposed DRL
algorithm that utilizes the UCB-IA strategy surpasses existing
benchmarks, allowing for a more adaptive, energy-efficient, and secure spectrum sharing in CIoT networks.
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Image and Video Processing
An Open-Source Framework for Quality-Assured Smartphone-Based Visible Light Iris Recognition
Naveenkumar G. Venkataswamy, Yu Liu, Soumyabrata Dey et al. (+2 more)
Smartphone-based iris recognition in the visible spectrum (VIS) offers a low-cost and accessible biometric alternative but remains a challenge due to lighting variability, pigmentation effects, and the limited adoption of standardized capture protocols. In this work, we present CUVIRIS, a
dataset of 752 ISO/IEC 29794-6 compliant iris images from 47 subjects, collected with a custom Android application that enforces real-time framing, sharpness assessment, and quality feedback. We further introduce LightIrisNet, a MobileNetV3-based multi-task
segmentation model optimized for on-device deployment. In addition, we adapt IrisFormer, a
transformer-based matcher, to the VIS domain. We evaluate OSIRIS and IrisFormer under a standardized protocol and
benchmark against published CNN baselines reported in prior work. On CUVIRIS, the open-source OSIRIS system achieves a TAR of 97.9% at FAR = 0.01 (EER = 0.76%), while IrisFormer, trained only on the UBIRIS.v2
dataset, achieves an EER of 0.057%. To support reproducibility, we release the Android application, LightIrisNet, trained IrisFormer weights, and a subset of the CUVIRIS
dataset. These results show that, with standardized acquisition and VIS-adapted lightweight models, accurate iris recognition on commodity smartphones is feasible under controlled conditions, bringing this modality closer to practical deployment.
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All authors Naveenkumar G. Venkataswamy, Yu Liu, Soumyabrata Dey, Stephanie Schuckers, Masudul H. Imtiaz
Signal Processing
Optimum Discrete Beamforming via Minkowski Sum of Polygons
Heedong Do and Angel Lozano
This letter casts the problem of optimum discrete beamforming as the computation of the Minkowski sum of convex polygons, which is itself a convex polygon. The number of vertices of the latter is at most the sum of the number of vertices of the original polygons, enabling its efficient computation. This original and intuitive formulation confirms that the optimum beamforming solution can be found efficiently.
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Image and Video Processing
Nine Years of Pediatric Iris Recognition: Evidence for Biometric Permanence
Naveenkumar G Venkataswamy, Masudul H Imtiaz and Stephanie Schuckers
Biometric permanence in pediatric populations remains poorly understood despite widespread deployment of iris recognition for children in national identity programs such as India's Aadhaar and trusted traveler programs like Canada's NEXUS. This study presents a comprehensive longitudinal evaluation of pediatric iris recognition, analyzing 276 subjects enrolled between ages 4-12 and followed up to nine years through adolescence. Using 18,318 near-infrared iris images acquired semi-annually, we evaluated commercial (VeriEye) and open-source (OpenIris) systems through linear mixed-effects models that disentangle enrollment age, developmental maturation, and elapsed time while controlling for image quality and physiological factors. False non-match rates remained below 0.5% across the nine-year period for both matchers using pediatric-calibrated thresholds, approaching adult-level performance. However, we reveal significant
algorithm-dependent temporal behaviors: VeriEye's apparent decline reflects developmental confounding across enrollment cohorts rather than genuine template aging, while OpenIris exhibits modest but genuine temporal aging (0.5 standard deviations over eight years). Image quality and pupil dilation constancy dominated longitudinal performance, with dilation effects reaching 3.0-3.5 standard deviations, substantially exceeding temporal factors. Failures concentrated in 9.4% of subjects with persistent acquisition challenges rather than accumulating with elapsed time, confirming acquisition conditions as the primary limitation. These findings justify extending conservative re-enrollment policies, potentially to 10-12 year validity periods for high-quality enrollments at ages 7+, and demonstrate iris recognition remains viable throughout childhood and adolescence with proper imaging control.
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Systems and Control
Ising Machines for Model Predictive Path Integral-Based Optimal Control
Lorin Werthen-Brabants and Pieter Simoens
We present a sampling-based Model Predictive Control (MPC) method that implements Model Predictive Path Integral (MPPI) as an Ising machine, suitable for novel forms of probabilistic computing. By expressing the control problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem, we map MPC onto an energy landscape suitable for Gibbs sampling from an Ising model. This formulation enables efficient exploration of (near-)optimal control trajectories. We demonstrate that the approach achieves accurate trajectory tracking compared to a reference MPPI implementation, highlighting the potential of Ising-based MPPI for real-time control in robotics and autonomous systems.
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Audio and Speech Processing · Cross-listed from cs.SD
A Conditioned UNet for Music Source Separation
Ken O'Hanlon, Basil Woods, Lin Wang et al. (+1 more)
In this paper we propose a conditioned UNet for Music Source Separation (MSS). MSS is generally performed by multi-output neural networks, typically UNets, with each output representing a particular stem from a predefined instrument vocabulary. In contrast, conditioned MSS networks accept an audio query related to a stem of interest alongside the signal from which that stem is to be extracted. Thus, a strict vocabulary is not required and this enables more realistic tasks in MSS. The potential of conditioned approaches for such tasks has been somewhat hidden due to a lack of suitable data, an issue recently addressed with the MoisesDb
dataset. A recent method, Banquet, employs this
dataset with promising results seen on larger vocabularies. Banquet uses Bandsplit RNN rather than a UNet and the authors state that UNets should not be suitable for conditioned MSS. We counter this argument and propose QSCNet, a novel conditioned UNet for MSS that integrates network conditioning elements in the Sparse Compressed Network for MSS. We find QSCNet to outperform Banquet by over 1dB SNR on a couple of MSS tasks, while using less than half the number of parameters.
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All authors Ken O'Hanlon, Basil Woods, Lin Wang, Mark Sandler
Image and Video Processing · Cross-listed from cs.CV
The LUMirage: An independent evaluation of zero-shot performance in the LUMIR challenge
Rohit Jena, Pratik Chaudhari and James C. Gee
The LUMIR challenge represents an important
benchmark for evaluating deformable image registration methods on large-scale neuroimaging data. While the challenge demonstrates that modern deep learning methods achieve competitive accuracy on T1-weighted MRI, it also claims exceptional zero-shot generalization to unseen contrasts and resolutions, assertions that contradict established understanding of domain shift in deep learning. In this paper, we perform an independent re-evaluation of these zero-shot claims using rigorous evaluation protocols while addressing potential sources of instrumentation bias. Our findings reveal a more nuanced picture: (1) deep learning methods perform comparably to iterative optimization on in-distribution T1w images and even on human-adjacent species (macaque), demonstrating improved task understanding; (2) however, performance degrades significantly on out-of-distribution contrasts (T2, T2*, FLAIR), with Cohen's d scores ranging from 0.7-1.5, indicating substantial practical impact on downstream clinical workflows; (3) deep learning methods face scalability limitations on high-resolution data, failing to run on 0.6 mm isotropic images, while iterative methods benefit from increased resolution; and (4) deep methods exhibit high sensitivity to preprocessing choices. These results align with the well-established literature on domain shift and suggest that claims of universal zero-shot superiority require careful scrutiny. We advocate for evaluation protocols that reflect practical clinical and research workflows rather than conditions that may inadvertently favor particular method classes.
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Systems and Control · Cross-listed from quant-ph
QuantGraph: A Receding-Horizon Quantum Graph Solver
Pranav Vaidhyanathan, Aristotelis Papatheodorou, David R. M. Arvidsson-Shukur et al. (+3 more)
Dynamic programming is a cornerstone of graph-based optimization. While effective, it scales unfavorably with problem size. In this work, we present QuantGraph, a two-stage
quantum-enhanced framework that casts local and global graph-optimization problems as
quantum searches over discrete trajectory spaces. The solver is designed to operate efficiently by first finding a sequence of locally optimal transitions in the graph (local stage), without considering full trajectories. The accumulated cost of these transitions acts as a threshold that prunes the search space (up to 60% reduction for certain examples). The subsequent global stage, based on this threshold, refines the solution. Both stages utilize variants of the Grover-adaptive-search
algorithm. To achieve scalability and robustness, we draw on principles from control theory and embed QuantGraph's global stage within a receding-horizon model-predictive-control scheme. This classical layer stabilizes and guides the
quantum search, improving precision and reducing computational burden. In practice, the resulting closed-loop system exhibits robust behavior and lower overall complexity. Notably, for a fixed query budget, QuantGraph attains a 2x increase in control-discretization precision while still benefiting from Grover-search's inherent quadratic speedup compared to classical methods.
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All authors Pranav Vaidhyanathan, Aristotelis Papatheodorou, David R. M. Arvidsson-Shukur, Mark T. Mitchison, Natalia Ares, Ioannis Havoutis
Systems and Control · Cross-listed from cs.SE
A Container-based Approach For Proactive Asset Administration Shell Digital Twins
Carsten Ellwein, Jingxi Zhang, Andreas Wortmann et al. (+1 more)
In manufacturing, digital twins, realized as Asset Administration Shells (AAS), have emerged as a prevalent practice. These digital replicas, often utilized as structured repositories of asset-related data, facilitate interoperability across diverse systems. However, extant approaches treat the AAS as a static information model, lacking support for dynamic service integration and system adaptation. The existing body of literature has not yet thoroughly explored the potential for integrating executable behavior, particularly in the form of containerized services, into or from the AAS. This integration could serve to enable proactive functionality. In this paper, we propose a submodel-based architecture that introduces a structured service notion to the AAS, enabling services to dynamically interact with and adapt AAS instances at runtime. This concept is implemented through the extension of a submodel with behavioral definitions, resulting in a modular event-driven architecture capable of deploying containerized services based on embedded trigger conditions. The approach is illustrated through a case study on a 3-axis milling machine. Our contribution enables the AAS to serve not only as a passive digital representation but also as an active interface for executing added-value services.%, thereby laying the foundation for future AI-driven adaptation and system-level intelligence in digital twin environments.
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All authors Carsten Ellwein, Jingxi Zhang, Andreas Wortmann, Antony Ayman Alfy Meckhael