Image and Video Processing
Projection Guided Personalized Federated Learning for Low Dose CT Denoising
Anas Zafar, Muhammad Waqas, Amgad Muneer et al. (+2 more)
Low-dose CT (LDCT) reduces radiation exposure but introduces protocol-dependent noise and artifacts that vary across institutions. While
federated learning enables collaborative training without centralizing patient data, existing methods personalize in image space, making it difficult to separate scanner noise from patient anatomy. We propose ProFed (Projection Guided Personalized
Federated Learning), a framework that complements the image space approach by performing dual-level personalization in the projection space, where noise originates during CT measurements before reconstruction combines protocol and anatomy effects. ProFed introduces: (i) anatomy-aware and protocol-aware networks that personalize CT reconstruction to patient and scanner-specific features, (ii) multi-constraint projection losses that enforce consistency with CT measurements, and (iii) uncertainty-guided selective aggregation that weights clients by prediction confidence. Extensive experiments on the Mayo Clinic 2016
dataset demonstrate that ProFed achieves 42.56 dB PSNR with CNN backbones and 44.83 dB with
Transformers, outperforming 11
federated learning baselines, including the physics-informed SCAN-PhysFed by +1.42 dB.
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All authors Anas Zafar, Muhammad Waqas, Amgad Muneer, Rukhmini Bandyopadhyay, Jia Wu
Image and Video Processing · Cross-listed from cs.CV
Anchor Forcing: Anchor Memory and Tri-Region RoPE for Interactive Streaming Video Diffusion
Yang Yang, Tianyi Zhang, Wei Huang et al. (+6 more)
Interactive long
video generation requires
prompt switching to introduce new subjects or events, while maintaining perceptual fidelity and coherent motion over extended horizons. Recent distilled streaming
video diffusion models reuse a rolling KV cache for long-range generation, enabling
prompt-switch interaction through re-cache at each switch. However, existing streaming methods still exhibit progressive quality degradation and weakened motion dynamics. We identify two failure modes specific to interactive streaming generation: (i) at each
prompt switch, current cache maintenance cannot simultaneously retain KV-based semantic context and recent latent cues, resulting in weak boundary conditioning and reduced perceptual quality; and (ii) during
distillation, unbounded time indexing induces a positional distribution shift from the pretrained backbone's bounded RoPE regime, weakening pretrained motion priors and long-horizon motion retention. To address these issues, we propose Anchor Forcing, a cache-centric framework with two designs. First, an anchor-guided re-cache mechanism stores KV states in anchor caches and warm-starts re-cache from these anchors at each
prompt switch, reducing post-switch evidence loss and stabilizing perceptual quality. Second, a tri-region RoPE with region-specific reference origins, together with RoPE re-
alignment distillation, reconciles unbounded streaming indices with the pretrained RoPE regime to better retain motion priors. Experiments on long
videos show that our method improves perceptual quality and motion metrics over prior streaming baselines in interactive settings. Project page: https://github.com/vivoCameraResearch/Anchor-Forcing
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All authors Yang Yang, Tianyi Zhang, Wei Huang, Jinwei Chen, Boxi Wu, Xiaofei He, Deng Cai, Bo Li, Peng-Tao Jiang
Systems and Control · Cross-listed from physics.optics
Physics-Guided Inverse Design of Optical Waveforms for Nonlinear Electromagnetic Dynamics
Hao Zhang, Jack Hirschman, Randy Lemons et al. (+7 more)
Structured optical waveforms are emerging as powerful control fields for the next generation of complex photonic and electromagnetic systems, where the temporal structure of light can determine the ultimate performance of scientific instruments. However, identifying optimal optical drive fields in strongly nonlinear regimes remains challenging because the mapping between optical inputs and system response is high-dimensional and typically accessible only through computationally expensive simulations. Here, we present a physics-guided deep learning framework for the inverse design of optical temporal waveforms. By training a light-weighted surrogate model on simulations, the method enables gradient-based synthesis of optical profiles that compensate nonlinear field distortions in driven particle-field systems. As a representative application, we apply the approach to the generation of electron beams used in advanced
photon and particle sources. The learned optical waveform actively suppresses extrinsic emittance growth by more than 52% compared with conventional Gaussian operation and by approximately 9% relative to the theoretical flattop limit in simulation. We further demonstrate experimental feasibility by synthesizing the predicted waveform using a programmable pulse-shaping platform; incorporating the measured optical profile into beamline simulations yields a 31% reduction in the extrinsic emittance contribution. Beyond accelerator applications, this work establishes a general way for physics-guided inverse design of optical control fields, enabling structured light to approach fundamental performance limits in nonlinear photonic and high-frequency electromagnetic systems.
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All authors Hao Zhang, Jack Hirschman, Randy Lemons, Nicole R. Neveu, Joseph Robinson, Auralee L. Edelen, Tor O. Raubenheimer, Dan Wang, Ji Qiang, Sergio Carbajo
Signal Processing
Distribution-Aware GMD Transceiver Design for Probabilistic Shaping in MIMO
Tzu-Hsuan Chou, Chih-Hao Liu and Jing Jiang
Multiple-input multiple-output (
MIMO) transceiver and probabilistic shaping (PS) are key enablers for high spectral efficiency in 6G wireless networks. This work proposes a distribution-aware
MIMO transceiver optimized for PS constellation symbols, including a
Bayesian geometric mean decomposition (BGMD) precoder and a maximum a posteriori VBLAST (MAP-VBLAST) detector. The BGMD precoder uses PS priors in the derivation and equalizes layer gains to facilitate a single modulation and coding scheme for low-complexity transmissions while preserving channel capacity. MAP-VBLAST leverages these PS priors for optimal MAP detection within a successive interference cancellation (SIC) framework. Furthermore, a new codeword-to-layer mapping scheme, termed layer-contained
MIMO (LC-
MIMO), is proposed. By containing each codeblock (CB) within a single layer, LC-
MIMO enables SIC at the CB level, allowing the receiver to exploit the error-correction capability of channel coding to mitigate error propagation. Numerical results show that the BGMD transceiver with LC-
MIMO achieves notable performance gains over state-of-the-art methods.
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Image and Video Processing
Unmasking Biases and Reliability Concerns in Convolutional Neural Networks Analysis of Cancer Pathology Images
Michael Okonoda, Eder Martinez, Abhilekha Dalal et al. (+1 more)
Convolutional Neural Networks have shown promising effectiveness in identifying different types of cancer from radiographs. However, the opaque nature of CNNs makes it difficult to fully understand the way they operate, limiting their assessment to empirical evaluation. Here we study the soundness of the standard practices by which CNNs are evaluated for the purpose of cancer pathology. Thirteen highly used cancer
benchmark datasets were analyzed, using four common CNN architectures and different types of cancer, such as melanoma, carcinoma, colorectal cancer, and lung cancer. We compared the accuracy of each model with that of
datasets made of cropped segments from the background of the original images that do not contain clinically relevant content. Because the rendered
datasets contain no clinical information, the null hypothesis is that the CNNs should provide mere chance-based accuracy when classifying these
datasets. The results show that the CNN models provided high accuracy when using the cropped segments, sometimes as high as 93%, even though they lacked biomedical information. These results show that some CNN architectures are more sensitive to bias than others. The analysis shows that the common practices of machine learning evaluation might lead to unreliable results when applied to cancer pathology. These biases are very difficult to identify, and might mislead researchers as they use available
benchmark datasets to test the efficacy of CNN methods.
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All authors Michael Okonoda, Eder Martinez, Abhilekha Dalal, Lior Shamir
Audio and Speech Processing
Room Impulse Response Completion Using Signal-Prediction Diffusion Models Conditioned on Simulated Early Reflections
Zeyu Xu, Andreas Brendel, Albert G. Prinn et al. (+1 more)
Room impulse responses (RIRs) are fundamental to audio data augmentation, acoustic signal processing, and immersive audio rendering. While
geometric simulators such as the image source method (ISM) can efficiently generate early reflections, they lack the realism of measured RIRs due to missing acoustic wave effects. We propose a
diffusion-based RIR completion method using signal-prediction conditioned on ISM-simulated direct-path and early reflections. Unlike state-of-the-art methods, our approach imposes no fixed duration constraint on the input early reflections. We further incorporate classifier-free guidance to steer generation toward a target distribution learned from physically realistic RIRs simulated with the Treble SDK. Objective evaluation demonstrates that the proposed method outperforms a state-of-the-art baseline in early RIR completion and energy decay curve reconstruction.
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All authors Zeyu Xu, Andreas Brendel, Albert G. Prinn, Emanuël A. P. Habets
Systems and Control
Compensation of Input/Output Delays for Retarded Systems by Sequential Predictors: A Lyapunov-Halanay Method
Xin Yu and Wei Lin
This paper presents a Lyapunov-Halanay method to study global asymptotic stabilization (GAS) of nonlinear retarded systems subject to large constant delays in input/output - a challenging problem due to their inherent destabilizing effects. Under the conditions of global Lipschitz continuity (GLC) and global exponential stabilizability (GES) of the retarded system without input delay, a state feedback controller is designed based on sequential predictors to make the closed-loop retarded system GAS. Moreover, if the retarded system with no output delay permits a global exponential observer, a dynamic output compensator is also constructed based on sequential predictors, achieving GAS of the corresponding closed-loop retarded system with input/output delays. The predictor based state and output feedback stabilization results are then extended to a broader class of nonlinear retarded systems with input/output delays, which may not be GES but satisfy global asymptotic stabilizability/observability and suitable ISS conditions. As an application, a pendulum system with delays in the state, input and output is used to illustrate the effectiveness of the proposed state and output feedback control strategies based on sequential predictors.
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Systems and Control
Ising-ReRAM: A Low Power Ising Machine ReRAM Crossbar for NP Problems
Everest Bloomer, Irem Didin, Ching-Yi Lin et al. (+1 more)
Computational workloads are growing exponentially, driving power consumption to unsustainable levels. Efficiently distributing large-scale networks is an NP-Complete problem equivalent to Boolean satisfiability (SAT), making it one of the core challenges in modern computation. To address this, physics and device inspired methods such as Ising systems have been explored for solving SAT more efficiently. In this work, we implement an Ising model equivalence of the 3-SAT problem using a ReRAM crossbar fabricated in the Skywater 130 nm CMOS process. Our ReRAM-based
algorithm achieves $91.0\%$ accuracy in
matrix representation across iterative reprogramming cycles. Additionally, we establish a foundational energy profile by measuring the energy costs of small sub-
matrix structures within the problem space, demonstrating under linear growth trajectory for combining sub-
matrices into larger problems. These results demonstrate a promising platform for developing scalable architectures to accelerate NP-Complete problem solving.
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All authors Everest Bloomer, Irem Didin, Ching-Yi Lin, Sahil Shah
Systems and Control · Cross-listed from cs.RO
Push, Press, Slide: Mode-Aware Planar Contact Manipulation via Reduced-Order Models
Melih Özcan, Umut Orguner and Ozgur S. Oguz
Non-prehensile planar manipulation, including pushing and press-and-slide, is critical for diverse robotic tasks, but notoriously challenging due to hybrid contact mechanics, under-actuation, and asymmetric friction limits that traditionally necessitate computationally expensive iterative control. In this paper, we propose a mode-aware framework for planar manipulation with one or two robotic arms based on contact
topology selection and reduced-order kinematic modeling. Our core insight is that complex wrench-twist limit surface mechanics can be abstracted into a discrete library of physically intuitive models. We systematically map various single-arm and bimanual contact
topologies to simple non-holonomic formulations, e.g. unicycle for simplified press-and-slide motion. By anchoring trajectory generation to these reduced-order models, our framework computes the required object wrench and distributes feasible, friction-bounded contact forces via a direct algebraic allocator. We incorporate manipulator kinematics to ensure long-horizon feasibility and demonstrate our fast, optimization-free approach in simulation across diverse single-arm and bimanual manipulation tasks. Supplementary
videos and additional information are available at: https://sites.google.com/view/pushpressslide
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Audio and Speech Processing
MamTra: A Hybrid Mamba-Transformer Backbone for Speech Synthesis
Tan Dat Nguyen, Sangmin Bae, Joon Son Chung et al. (+1 more)
Despite the remarkable quality of LLM-based text-to-
speech systems, their reliance on autoregressive
Transformers leads to quadratic computational complexity, which severely limits practical applications. Linear-time alternatives, notably Mamba, offer a potential remedy; however, they often sacrifice the global context essential for expressive synthesis. In this paper, we propose MamTra, an interleaved Mamba-
Transformer framework designed to leverage the advantages of Mamba's efficiency and
Transformers' modeling capability. We also introduce novel knowledge transfer strategies to distill insights from a pretrained
Transformer into our hybrid architecture, thereby bypassing the prohibitive costs of training from scratch. Systematic experiments identify the optimal hybrid configuration, and demonstrate that MamTra reduces inference VRAM usage by up to 34% without compromising
speech fidelity - even trained on only 2% of the original training
dataset. Audio samples are available at https://mamtratts.github.io.
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All authors Tan Dat Nguyen, Sangmin Bae, Joon Son Chung, Ji-Hoon Kim
Systems and Control
Optimizing Task Completion Time Updates Using POMDPs
Duncan Eddy, Esen Yel, Emma Passmore et al. (+4 more)
Managing announced task completion times is a fundamental control problem in project management. While extensive research exists on estimating task durations and task scheduling, the problem of when and how to update completion times communicated to stakeholders remains understudied. Organizations must balance announcement accuracy against the costs of frequent timeline updates, which can erode stakeholder trust and trigger costly replanning. Despite the prevalence of this problem, current approaches rely on static predictions or ad-hoc policies that fail to account for the sequential nature of announcement management. In this paper, we formulate the task announcement problem as a Partially Observable Markov Decision Process (POMDP) where the control policy must decide when to update announced completion times based on noisy observations of true task completion. Since most state variables (current time and previous announcements) are fully observable, we leverage the Mixed Observability MDP (MOMDP) framework to enable more efficient policy optimization. Our reward structure captures the dual costs of announcement errors and update frequency, enabling synthesis of optimal announcement control policies. Using off-the-shelf solvers, we generate policies that act as feedback controllers, adaptively managing announcements based on belief state
evolution. Simulation results demonstrate significant improvements in both accuracy and announcement stability compared to baseline strategies, achieving up to 75% reduction in unnecessary updates while maintaining or improving prediction accuracy.
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All authors Duncan Eddy, Esen Yel, Emma Passmore, Niles Egan, Grayson Armour, Dylan M. Asmar, Mykel J. Kochenderfer
Image and Video Processing · Cross-listed from q-bio.QM
SHREC: A Spectral Embedding-Based Approach for Ab-Initio Reconstruction of Helical Molecules
Guy Shapira and Yoel Shkolnisky
Cryo-electron microscopy (cryo-EM) has emerged as a powerful technique for determining the three-dimensional structures of biological molecules at near-atomic resolution. However, reconstructing helical assemblies presents unique challenges due to their inherent
symmetry and the need to determine unknown helical
symmetry parameters. Traditional approaches require an accurate initial estimation of these parameters, which is often obtained through trial and error or prior knowledge. These requirements can lead to incorrect reconstructions, limiting the reliability of ab initio helical reconstruction. In this work, we present SHREC (Spectral Helical REConstruction), an
algorithm that directly recovers the projection angles of helical segments from their two-dimensional cryo-EM images, without requiring prior knowledge of helical
symmetry parameters. Our approach leverages the insight that projections of helical segments form a one-dimensional
manifold, which can be recovered using spectral
embedding techniques. Experimental validation on publicly available
datasets demonstrates that SHREC achieves high resolution reconstructions while accurately recovering helical parameters, requiring only knowledge of the specimen's axial
symmetry group. By eliminating the need for initial
symmetry estimates, SHREC offers a more robust and automated pathway for determining helical structures in cryo-EM.
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Systems and Control
Conformalized Data-Driven Reachability Analysis with PAC Guarantees
Yanliang Huang, Zhen Zhang, Peng Xie et al. (+2 more)
Data-driven reachability analysis computes over-approximations of reachable sets directly from noisy data. Existing deterministic methods require either known noise bounds or system-specific structural parameters such as Lipschitz constants. We propose Conformalized Data-Driven Reachability (CDDR), a framework that provides Probably Approximately Correct (PAC) coverage guarantees through the Learn Then Test (LTT) calibration procedure, requiring only that calibration and test trajectories be independently and identically distributed. CDDR is developed for three settings: linear time-invariant (LTI) systems with unknown process noise distributions, LTI systems with bounded measurement noise, and general nonlinear systems including non-Lipschitz dynamics. Experiments on a 5-dimensional LTI system under Gaussian and heavy-tailed Student-t noise and on a 2-dimensional non-Lipschitz system with fractional damping demonstrate that CDDR achieves valid coverage where deterministic methods do not provide formal guarantees. Under anisotropic noise, a normalized score function reduces the reachable set volume while preserving the PAC guarantee.
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All authors Yanliang Huang, Zhen Zhang, Peng Xie, Zhuoqi Zeng, Amr Alanwar
Systems and Control
Technology configurations for decarbonizing residential heat supply through district heating and implications for the electricity network
Christian Doh Dinga, Francesco Lombardi, Roald Arkesteijn et al. (+4 more)
District heating networks (DHNs) have significant potential to decarbonize residential heating and accelerate the energy transition. However, designing carbon-neutral DHNs requires balancing several objectives, including economic costs, social acceptance, long-term uncertainties, and grid-integration challenges arising from electrification. By combining modeling-to-generate-alternatives with power flow simulation techniques, we develop a decision-support method for designing carbon-neutral DHNs that are cost-effective, socially acceptable, and impose minimal impacts on the electricity grid. Applying our method to a Dutch case, we find substantial diversity in how carbon-neutral DHNs can be designed. The flexibility in technology choice, sizing, and location enables accommodating different real-world needs and achieving high electrification levels without increasing grid loading. For instance, intelligently located heat pumps and thermal storage can limit grid stress even when renewable baseload heat sources and green-fuel boilers are scarce. Using our method, planners can explore diverse carbon-neutral DHN designs and identify the design that best balances stakeholders' preferences.
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All authors Christian Doh Dinga, Francesco Lombardi, Roald Arkesteijn, Arjan van Voorden, Sander van Rijn, Laurens James de Vries, Milos Cvetkovic
Systems and Control
Integrated Online Monitoring and Adaptation of Process Model Predictive Controllers
Samuel Mallick, Laura Boca de de Giuli, Alessio La Bella et al. (+3 more)
This paper addresses the design of an event-triggered, data-based, and performance-oriented adaption method for model predictive control (MPC). The performance of such a strategy strongly depends on the accuracy of the prediction model, which may require online adaption to prevent performance degradation under changing operating conditions. Unlike existing methods that continuously update model and control parameters from data, potentially leading to catastrophic forgetting and unnecessary control modifications, we propose a novel approach based on statistical monitoring of closed-loop performance indicators. This framework enables the detection of performance degradation, and, when required, controller adaption is performed via reinforcement learning and identification techniques. The proposed strategy is validated on a high-fidelity simulation of a district heating system
benchmark.
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All authors Samuel Mallick, Laura Boca de de Giuli, Alessio La Bella, Azita Dabiri, Bart De Schutter, Riccardo Scattolini
Signal Processing
Simultaneous Multi-Modal Covert Communications: Analysis and Optimization
Justin H. Kong, Terrence J. Moore and Fikadu T. Dagefu
This paper investigates the problem of covert communications in a heterogeneous wireless network where multiple communication modalities are used simultaneously. In this setup, a legitimate transmitter sends confidential data to its receiver by selecting multiple modalities with the goal of maximizing communication covertness against a passive adversary (Willie) while satisfying a transmission rate requirement. We analyze two distinct scenarios for a given observation time by Willie. The two scenarios are: (i) Willie knows the modalities selected by the friendly transmitter, and (ii) Willie is unaware of the selected modalities. We first derive the optimal detector for Willie that minimizes the detection error probability (DEP) in both cases. For the first scenario, we derive an exact expression for the DEP and provide a computationally efficient approximation. For the second scenario, we introduce the DEP expressions in the low-signal-to-noise ratio (SNR) regime at Willie. Building on this analysis, we propose a novel low-complexity modality set selection technique designed to maximize the DEP subject to a rate constraint. Numerical simulations validate the derived analytical expressions and demonstrate that the proposed modality set selection technique achieves near-optimal performance, outperforming
benchmark schemes.
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Image and Video Processing · Cross-listed from cs.CV
O3N: Omnidirectional Open-Vocabulary Occupancy Prediction for Embodied Intelligent Robotics
Mengfei Duan, Hao Shi, Fei Teng et al. (+4 more)
The rapid
evolution of consumer electronics toward
embodied intelligence has accelerated the emergence of Consumer
Embodied Intelligent Robotics (CEIRs), where intelligent devices are expected to perceive, understand, and interact with complex real-world environments. Understanding and reconstructing the 3D world through omnidirectional perception is therefore becoming increasingly important for CEIRs operating in complex and dynamic environments. However, existing
vision-based 3D occupancy prediction methods are constrained by limited perspective inputs and a predefined training distribution, making them difficult to support
embodied intelligent systems that require comprehensive and safe perception of scenes in open-world exploration. To address this, we present O3N, the first framework for open-vocabulary occupancy prediction from a single omnidirectional RGB image. O3N embeds omnidirectional voxels in a polar-spiral
topology via the Polar-spiral Mamba (PsM) module, enabling continuous spatial representation and long-range context modeling across 360{\deg}. The Occupancy Cost Aggregation (OCA) module introduces a principled mechanism for unifying
geometric and semantic supervision within the voxel space, ensuring consistency between reconstructed geometry and underlying semantic structure. Moreover, Natural Modality
Alignment (NMA) establishes a gradient-free
alignment pathway that harmonizes visual features, voxel
embeddings, and text semantics, forming a consistent pixel-voxel-text representation triad for open-world perception. Extensive experiments on multiple models demonstrate that our method not only achieves state-of-the-art performance on QuadOcc and Human360Occ
benchmarks but also exhibits remarkable cross-scene generalization and semantic scalability. The source code will be made publicly available at https://github.com/MengfeiD/O3N.
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All authors Mengfei Duan, Hao Shi, Fei Teng, Guoqiang Zhao, Yuheng Zhang, Zhiyong Li, Kailun Yang
Systems and Control
Maximum-Entropy Random Walks on Hypergraphs
Anqi Dong, Anzhi Sheng, Xin Mao et al. (+1 more)
Random walks are fundamental tools for analyzing complex networked systems, including social networks, biological systems, and communication infrastructures. While classical random walks focus on pairwise interactions, many real-world systems exhibit higher-order interactions naturally modeled by hypergraphs. Existing random walk models on hypergraphs often focus on undirected structures or do not incorporate
entropy-based inference, limiting their ability to capture directional flows, uncertainty, or information
diffusion in complex systems. In this article, we develop a maximum-
entropy random walk framework on directed hypergraphs with two interaction mechanisms: broadcasting where a pivot node activates multiple receiver nodes and merging where multiple pivot nodes jointly influence a receiver node. We infer a transition kernel via a Kullback–Leibler divergence projection onto constraints enforcing stochasticity and stationarity. The resulting optimality conditions yield a multiplicative scaling form, implemented using Sinkhorn–Schrödinger-type iterations with tensor contractions. We further analyze ergodicity, including projected linear kernels for broadcasting and tensor spectral criteria for
polynomial dynamics in merging. The effectiveness of our framework is demonstrated with both synthetic and real-world examples.
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All authors Anqi Dong, Anzhi Sheng, Xin Mao, Can Chen
Image and Video Processing · Cross-listed from cs.CV
Towards Universal Computational Aberration Correction in Photographic Cameras: A Comprehensive Benchmark Analysis
Xiaolong Qian, Qi Jiang, Yao Gao et al. (+5 more)
Prevalent Computational Aberration Correction (CAC) methods are typically tailored to specific optical systems, leading to poor generalization and labor-intensive re-training for new lenses. Developing CAC paradigms capable of generalizing across diverse photographic lenses offers a promising solution to these challenges. However, efforts to achieve such cross-lens universality within consumer photography are still in their early stages due to the lack of a comprehensive
benchmark that encompasses a sufficiently wide range of optical aberrations. Furthermore, it remains unclear which specific factors influence existing CAC methods and how these factors affect their performance. In this paper, we present comprehensive experiments and evaluations involving 24 image restoration and CAC
algorithms, utilizing our newly proposed UniCAC, a large-scale
benchmark for photographic cameras constructed via automatic optical design. The Optical Degradation Evaluator (ODE) is introduced as a novel framework to objectively assess the difficulty of CAC tasks, offering credible quantification of optical aberrations and enabling reliable evaluation. Drawing on our comparative analysis, we identify three key factors – prior utilization, network architecture, and training strategy – that most significantly influence CAC performance, and further investigate their respective effects. We believe that our
benchmark,
dataset, and observations contribute foundational insights to related areas and lay the groundwork for future investigations.
Benchmarks, codes, and Zemax files will be available at https://github.com/XiaolongQian/UniCAC.
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All authors Xiaolong Qian, Qi Jiang, Yao Gao, Lei Sun, Zhonghua Yi, Kailun Yang, Luc Van Gool, Kaiwei Wang
Systems and Control · Cross-listed from cs.RO
Decentralized Cooperative Localization for Multi-Robot Systems with Asynchronous Sensor Fusion
Nivand Khosravi, Niusha Khosravi, Mohammad Bozorg et al. (+1 more)
Decentralized cooperative localization (DCL) is a promising approach for nonholonomic mobile
robots operating in GPS-denied environments with limited communication infrastructure. This paper presents a DCL framework in which each
robot performs localization locally using an Extended Kalman Filter, while sharing measurement information during update stages only when communication links are available and companion
robots are successfully detected by
LiDAR. The framework preserves cross-correlation consistency among
robot state estimates while handling asynchronous sensor data with heterogeneous sampling rates and accommodating accelerations during dynamic maneuvers. Unlike methods that require pre-aligned coordinate systems, the proposed approach allows
robots to initialize with arbitrary reference-frame orientations and achieves automatic
alignment through transformation
matrices in both the prediction and update stages. To improve robustness in feature-sparse environments, we introduce a dual-landmark evaluation framework that exploits both static environmental features and mobile
robots as dynamic landmarks. The proposed framework enables reliable detection and feature extraction during sharp turns, while prediction accuracy is improved through information sharing from mutual observations. Experimental results in both Gazebo simulation and real-world basement environments show that DCL outperforms centralized cooperative localization (CCL), achieving a 34% reduction in RMSE, while the dual-landmark variant yields an improvement of 56%. These results demonstrate the applicability of DCL to challenging domains such as enclosed spaces,
underwater environments, and feature-sparse terrains where conventional localization methods are ineffective.
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All authors Nivand Khosravi, Niusha Khosravi, Mohammad Bozorg, Masoud S. Bahraini
Systems and Control · Cross-listed from cs.DB
Numerical benchmark for damage identification in Structural Health Monitoring
Francesca Marafini, Giacomo Zini, Alberto Barontini et al. (+4 more)
The availability of a
dataset for validation and verification purposes of novel data-driven strategies and/or hybrid physics-data approaches is currently one of the most pressing challenges in the engineering field. Data ownership, security, access and metadata handiness are currently hindering advances across many fields, particularly in Structural Health Monitoring (SHM) applications. This paper presents a simulated SHM
dataset, comprised of dynamic and static measurements (i.e., acceleration and displacement), and includes the conceptual framework designed to generate it. The simulated measurements were generated to incorporate the effects of Environmental and Operational Variations (EOVs), different types of damage, measurement noise and sensor faults and malfunctions, in order to account for scenarios that may occur during real acquisitions. A fixed-fixed steel beam structure was chosen as reference for the numerical
benchmark. The simulated monitoring was operated under the assumptions of a Single Degree of Freedom (SDOF) for generating acceleration records and of the Euler-Bernoulli beam for the simulated displacement measurements. The generation process involved the use of parallel computation, which is detailed within the provided open-source code. The generated data is also available open-source, thus ensuring reproducibility, repeatability and accessibility for further research. The comprehensive description of data types, formats, and collection methodologies makes this
dataset a valuable resource for researchers aiming to develop or refine SHM techniques, fostering advancements in the field through accessible, high-quality synthetic data.
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All authors Francesca Marafini, Giacomo Zini, Alberto Barontini, Nuno Mendes, Alice Cicirello, Michele Betti, Gianni Bartoli
Systems and Control · Cross-listed from cs.RO
Flight through Narrow Gaps with Morphing-Wing Drones
Julius Wanner, Hoang-Vu Phan, Charbel Toumieh et al. (+1 more)
The size of a narrow gap traversable by a fixed-wing drone is limited by its wingspan. Inspired by birds, here, we enable the traversal of a gap of sub-wingspan width and height using a morphing-wing drone capable of temporarily sweeping in its wings mid-flight. This maneuver poses control challenges due to sudden lift loss during gap-passage at low flight speeds and the need for precisely timed wing-sweep actuation ahead of the gap. To address these challenges, we first develop an aerodynamic model for general wing-sweep morphing drone flight including low flight speeds and post-stall angles of attack. We integrate longitudinal drone dynamics into an optimal reference trajectory generation and Nonlinear Model Predictive Control framework with runtime adaptive costs and constraints. Validated on a 130 g wing-sweep-morphing drone, our method achieves an average altitude error of 5 cm during narrow-gap passage at forward speeds between 5 and 7 m/s, whilst enforcing fully swept wings near the gap across variable threshold distances. Trajectory analysis shows that the drone can compensate for lift loss during gap-passage by accelerating and pitching upwards ahead of the gap to an extent that differs between reference trajectory optimization objectives. We show that our strategy also allows for accurate gap passage on hardware whilst maintaining a constant forward flight speed reference and near-constant altitude.
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All authors Julius Wanner, Hoang-Vu Phan, Charbel Toumieh, Dario Floreano
Audio and Speech Processing
Dr. SHAP-AV: Decoding Relative Modality Contributions via Shapley Attribution in Audio-Visual Speech Recognition
Umberto Cappellazzo, Stavros Petridis and Maja Pantic
Audio-Visual
Speech Recognition (AVSR) leverages both acoustic and visual information for robust recognition under noise. However, how models balance these modalities remains unclear. We present Dr. SHAP-AV, a framework using Shapley values to analyze modality contributions in AVSR. Through experiments on six models across two
benchmarks and varying SNR levels, we introduce three analyses: Global SHAP for overall modality balance, Generative SHAP for contribution dynamics during decoding, and Temporal
Alignment SHAP for input-output correspondence. Our findings reveal that models shift toward visual reliance under noise yet maintain high audio contributions even under severe degradation. Modality balance evolves during generation, temporal
alignment holds under noise, and SNR is the dominant factor driving modality weighting. These findings expose a persistent audio bias, motivating ad-hoc modality-weighting mechanisms and Shapley-based attribution as a standard AVSR diagnostic.
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Signal Processing
A Joint JSCC-Resource Allocation Framework for QoS-Aware Semantic Communication in LEO Satellite-based EO Missions
Hung Nguyen-Kha, Ti Ti Nguyen, Vu Nguyen Ha et al. (+3 more)
In Earth observation (EO) missions with Low Earth orbit (LEO) satellites, high-resolution image acquisition generates a massive data volume that poses a significant challenge for transmission under the limited satellite power budget, while LEO movement introduces dynamic systems. To enable efficient image transmission, this paper employs semantic communication (SemCom) with joint source-channel coding (JSCC), which focuses on transmitting meaningful information to reduce power consumption. Under a quality-of-service (QoS) requirement defined by image reconstruction quality, this work aims to minimize the total transmit power by jointly optimizing the JSCC encoder-decoder parameters and resource allocation. However, the implicit relationship among JSCC parameters, link quality, and image quality, coupled with the presence of mixed integer-continuous variables, makes the problem difficult to solve directly. To address this, a curve-fitting model is proposed to approximate the JSCC compression-SNR-quality relationship. Then, the joint compression ratio-resource allocation (JCRRA)
algorithm is proposed to address the underlying problem. Numerical results demonstrate that the proposed method achieves substantial power savings compared to both greedy
algorithms and conventional transmission paradigms.
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All authors Hung Nguyen-Kha, Ti Ti Nguyen, Vu Nguyen Ha, Eva Lagunas, Symeon Chatzinotas, Bjorn Ottersten
Signal Processing
Array Geometry-Centric Axial Sidelobe Interference Analysis for Near-Field Multi-User MIMO
Ahmed Hussain, Asmaa Abdallah, Abdulkadir Celik et al. (+1 more)
With the deployment of large
antenna arrays at high-frequency bands, future wireless communication systems are likely to operate in the radiative near-field (NF). Unlike far-field beam steering, NF beams can be focused on a spatial region with finite depth, enabling user multiplexing in both range and angle. In NF multiuser multiple-input multiple-output (MU-
MIMO) systems, achievable rates are limited by interference arising from sidelobes in both the axial (range) and lateral (angle) dimensions. This work investigates how axial sidelobes (ASLs) vary with array geometry. Closed-form array gain expressions are derived to characterize ASLs for uniform planar arrays. Analytical results show that the uniform square array (USA) yields the lowest ASLs, followed by the uniform concentric circular array (UCCA), uniform linear array (ULA), and uniform circular array (UCA). Specifically, the USA achieves a peak sidelobe level (PSLL) of -17.6 dB versus -7.9 dB for the UCA. Numerical simulations confirm that the USA provides superior sidelobe suppression and highest sumrate performance.
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All authors Ahmed Hussain, Asmaa Abdallah, Abdulkadir Celik, Ahmed M. Eltawil
Systems and Control · Cross-listed from quant-ph
Approximate Reduced Lindblad Dynamics via Algebraic and Adiabatic Methods
Tommaso Grigoletto, Alain Sarlette, Francesco Ticozzi et al. (+1 more)
We present an algebraic framework for approximate model reduction of Markovian open
quantum dynamics that guarantees complete positivity and trace preservation by construction. First, we show that projecting a Lindblad generator on its center
manifold – the space spanned by eigenoperators with purely imaginary
eigenvalue – yields an asymptotically exact reduced
quantum dynamical
semigroup whose dynamics is
unitary, with exponentially decaying transient error controlled by the generator's spectral gap. Second, for analytic perturbations of a Lindblad generator with a tractable center
manifold, we propose a perturbative reduction that keeps the reduced space fixed at the unperturbed center
manifold. The resulting generator is shown to remain a valid Lindbladian; explicit finite-time error bounds that quantify leakage from the unperturbed center sector are provided. We further clarify the connection to adiabatic elimination methods, by both showing how the algebraic reduction can be directly related to a first-order adiabatic-elimination and by providing sufficient conditions under which the latter method can be applied while preserving complete positivity. We showcase the usefulness of our techniques in dissipative many-body
quantum systems exhibiting non-stationary long-time dynamics.
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All authors Tommaso Grigoletto, Alain Sarlette, Francesco Ticozzi, Lorenza Viola
Systems and Control
Robust Parametric Microgrid Dispatch Under Endogenous Uncertainty of Operation- and Temperature-Dependent Battery Degradation
Rui Xie, Jun Wang, Jiaxu Duan et al. (+3 more)
Batteries play a critical role in microgrid energy management by ensuring power balance, enhancing renewable utilization, and reducing operational costs. However, battery degradation poses a significant challenge, particularly under extreme temperatures. This paper investigates the optimal trade-off between battery degradation and operational costs in microgrid dispatch to find a robust cost-effective strategy from a full life-cycle perspective. A key challenge arises from the endogenous uncertainty (or decision-dependent uncertainty, DDU) of battery degradation: Dispatch decisions influence the probability distribution of battery degradation, while in turn degradation changes battery operation model and thus affects dispatch. In this paper, we first develop an XGBoost-based probabilistic degradation model trained on experimental data across varying temperature conditions. We then formulate a parametric model predictive control (MPC) framework for microgrid dispatch, where the weight parameters of the battery degradation penalty terms are tuned through long-term simulation of degradation and dispatch interactions. Case studies validate the effectiveness of the proposed approach.
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All authors Rui Xie, Jun Wang, Jiaxu Duan, Chao Ma, Yunhui Liu, Yue Chen