Systems and Control · Cross-listed from cs.LG
Two-Layer Linear Auto-Regressive Models Estimate Latent States
Yahya Sattar, Sunmook Choi, Leo Maynard-Zhang et al. (+3 more)
Auto-regressive models have emerged as powerful tools for sequential data, from
language to
video. Understanding how and why these models learn latent representations remains an open theoretical question. In this work, we demonstrate that when trained by empirical risk minimization on data from partially observed linear dynamical systems, two-layer linear auto-regressive models naturally learn to approximate Kalman filtering. In particular, we show that the learned hidden representation coincides, up to a similarity transformation, with the state estimates produced by the optimal (Kalman) filter, even though the model has no explicit knowledge of the underlying dynamics or state. The result follows from three main insights. First, we establish that the Kalman filter is well approximated by an auto-regressive model with bounded truncation error. Second, we show that despite non-convexity, the two-layer optimization landscape is benign, i.e., all stationary points are either strict saddles or global minima. Finally, as our main contributions, we provide finite-sample guarantees on prediction error, parameter estimation error, and latent state recovery. Numerical simulations support the theoretical results and demonstrate that the latent representations of auto-regressive models recover state estimates.
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All authors Yahya Sattar, Sunmook Choi, Leo Maynard-Zhang, Yassir Jedra, Maryam Fazel, Sarah Dean
Image and Video Processing · Cross-listed from cs.LG
Fed-FBD: Federated Functional Block Diversification for Isolation, Privacy, and Surgical Unlearning
Weijie Chen and Alan B. McMillan
Federated learning (FL) enables collaborative model training without sharing raw patient data, but standard approaches such as FedAvg treat each client as a black box and provide no mechanism for isolating an
adversarial contributor,
auditing per-client influence, or honoring a departed participant's right to be forgotten. We present Fed-FBD (
Federated Functional Block Diversification), a modular
federated architecture that decomposes a ResNet backbone into six functional blocks (the stem, four residual groups, and the classification head) and maintains a warehouse of N color variants, each assembled from independently tracked and contributor-stamped blocks. Fed-FBD provides three capabilities absent in FedAvg: (i) architecturally guaranteed block-level isolation, so that an
adversarial or mislabelled client cannot contaminate the clean colous; (ii)
privacy-by-design, where membership inference advantage is already indistinguishable from chance before any
privacy mechanism is applied; and (iii)
surgical machine unlearning of a departed participant's contribution at sub-second cost and without retraining. Experiments on six MedMNIST-2D
datasets, PathMNIST at 224x224, and CIFAR-10 show that Fed-FBD trades a modest 0.3%-3.1% IID accuracy gap on the adequately sized
datasets for these guarantees, remains within 0.8%-4.0% of FedAvg at Dirichlet alpha=1.0 on three of four
datasets, and confines all six
adversarial attacks we study to the poisoned client's own blocks with at most +/-0.01 AUC drift on the clean colors.
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Systems and Control · Cross-listed from cs.NI
Free-Placement Optimization of Ground Station Locations for Low-Earth Orbit Satellites
Grace Ra Kim, Duncan Eddy, Vedant Srinivas et al. (+1 more)
Rapidly expanding low Earth orbit satellite constellations are placing increasing demands on terrestrial ground networks, motivating the development of more efficient ground station network designs. Current approaches select sites from predefined locations, limiting optimization to existing infrastructure and constraining performance. In contrast, free-placement optimization operates over a continuous spatial domain on Earth, broadening the search space and allowing higher-throughput configurations at the cost of potentially requiring new infrastructure deployment. In this work, we introduce SCORE (Sequential Cyclic Optimization via Refinement & Evaluation), a two-stage free-placement method for ground station design. SCORE combines sequential coordinate selection with cyclic refinement to manage high-dimensionality, non-convexity, and local minima that challenge global optimizers. We
benchmark SCORE against one-shot methods such as differential
evolution (DE) and integer programming approaches using locations from Kongsberg Satellite Services and the World Teleport Association. Tests across two commercial Earth observation constellations (Capella Space and ICEYE) and one synthetic Walker-
Star constellation show that SCORE requires up to 5x fewer function evaluations to converge relative to DE while improving downlink throughput by up to 13%. Compared to fixed-site methods, unconstrained SCORE achieves up to 15% greater total downlink, establishing a strong empirical performance
benchmark for flexible placement; infrastructure-constrained SCORE retains over 92% of this gain while restricting placement to within proximity of existing fiber and power infrastructure. We also explore trade-offs between expanding existing stations and deploying new sites, informing future ground network design for operational constellations.
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All authors Grace Ra Kim, Duncan Eddy, Vedant Srinivas, Mykel J. Kochenderfer
Systems and Control
Modeling and Estimation of Solid Electrolyte Interphase during Formation in Battery Manufacturing
Zhiwen Wan, Hamidreza Movahedi, Wenxue Liu et al. (+4 more)
The solid electrolyte interphase (SEI) - a critical passivation layer that governs the longevity, safety, and efficiency of lithium-ion batteries - is created during the last step in
cell manufacturing called
cell formation. Conventional
cell formation protocols are largely empirical, resulting in long processing times and limited control over the SEI growth rate that influences SEI quality and lifetime performance. This paper develops a control-oriented, semi-empirical model to estimate SEI thickness growth from terminal voltage and
cell expansion measurements acquired in-operando during manufacturing using low-cost micrometer-precision integrated-sensing fixture. Model parameters are calibrated against
cell formation data, and an unscented Kalman filter is employed to estimate the SEI film growth. The results lay the foundation for future closed-loop control of SEI growth, enabling high-quality and more efficient formation processes.
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All authors Zhiwen Wan, Hamidreza Movahedi, Wenxue Liu, Jingchen Ma, Jason B. Siegel, Andrew Weng, Anna Stefanopoulou
Systems and Control · Cross-listed from cs.LG
Individual Control Barrier Functions-Guided Diffusion Model for Safe Offline Multi-Agent Reinforcement Learning
Qingyun Guo, Junyi Shi, Jianuo Huang et al. (+1 more)
Offline reinforcement learning allows control policies to be learned directly from data without online interaction, making it suitable for safety-critical tasks. Recent studies have applied
diffusion models to offline reinforcement learning to leverage their strong capacity for modeling complex data distributions. However, existing approaches primarily focus on single-
agent settings, leaving the safety challenges in multi-
agent environments largely unexplored. In this work, we propose a safe offline multi-
agent reinforcement learning
algorithm that embeds neural individual control barrier functions into the
diffusion model to enhance safety during trajectory generation, with control policies recovered through inverse dynamics. We evaluate our
algorithm across diverse
benchmarks, demonstrating substantial safety improvements while maintaining competitive rewards.
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All authors Qingyun Guo, Junyi Shi, Jianuo Huang, Tianyu Shi
Systems and Control · Cross-listed from cs.RO
FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning
Steven Oh, Jason Jingzhou Liu, Tony Tao et al. (+5 more)
Contact-rich manipulation requires force sensitivity, but many
robot arms lack dedicated force sensors due to their high cost. We present Neural External Torque Estimation (NEXT), a data-driven method that estimates external joint torques without needing any dedicated force sensors. NEXT trains in 1 minute from only 10 minutes of free-motion data, yet achieves estimates comparable to dedicated joint-torque sensors. NEXT enables force-feedback teleoperation on low-cost arms and improves policy learning through Force-Informed Re-Sampling Training (FIRST), which up-samples pre-contact and contact segments during behavior cloning. Across five long-horizon tasks, FIRST outperforms prior force-aware policies by over 17% in task progress. Together, NEXT and FIRST bring force-aware teleoperation and policy learning to off-the-shelf
robots without additional sensing hardware.
Video results and code are available at https://jasonjzliu.com/factr2
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All authors Steven Oh, Jason Jingzhou Liu, Tony Tao, Philip Han, Kenneth Shaw, Satoshi Funabashi, Ruslan Salakhutdinov, Deepak Pathak
Systems and Control · Cross-listed from cs.RO
Traceable Virtual Sea Trials in the Marine Robotics Unity Simulator for Manoeuvring Assessment of Unmanned Surface Vehicles
Paria Rezayan
Accurate identification of hydrodynamic derivatives is essential for precise control and autonomous
navigation of Unmanned Surface Vehicles (USVs). However, acquiring high-fidelity manoeuvring data from physical sea trials is often constrained by cost, safety, and environmental disturbances. Standard manoeuvring trials, particularly Turning Circle (TC) and Zig-Zag (ZZ), remain fundamental to IMO and ITTC assessment procedures because they provide comparable performance metrics reflective of underlying hydrodynamic behaviour. This paper extends the open-source Marine Robotics Unity Simulator (MARUS) by introducing a standardised Virtual Sea Trial framework for automated execution and data generation of TC/ZZ manoeuvres. The framework provides traceable command-actuation logging, system-identification (SI)-focused data conditioning, and automated extraction of IMO/ITTC-aligned manoeuvring metrics. A key contribution is a dedicated TC/ZZ data acquisition and post-processing pipeline, improving the repeatability and auditability of simulator-based manoeuvres while producing SI-ready
datasets for hydrodynamic-derivative identification and digital-twin workflows. The framework also provides explicit command-execution separation for differential-thrust steering, where manoeuvre inputs are recorded as ordered rudder-equivalent commands and realised actuation is logged as an execution-level proxy derived from applied thrust. Case study results demonstrate repeatable and IMO-compliant manoeuvre behaviour. For TC tests, the normalised advance differs by approximately 3.9% between port and starboard turns, while the tactical diameter differs by 4.6-4.7%. For ZZ tests, first and second overshoot excesses remain below 1 degree for both +/-10-degree and +/-20-degree manoeuvres, satisfying IMO criteria, while peak yaw rates range from approximately 4.1 to 5.8 degrees/second.
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Systems and Control
Analysis of a Distributed Optimization-Based Control Architecture for Inverter-Interfaced Virtual Power Plants
Vivek Khatana, Soham Chakraborty and Murti V. Salapaka
We develop a large-signal stability analysis for a sampled-data, optimization-based secondary controller for inverter-interfaced distributed energy resources in virtual power plants.
Audio and Speech Processing
HALO: Half-Frame-Rate Adaptive Learnable Operator for Lightweight STFT-Based Speech Enhancement
Jiadong Zhao, Dahan Wang, Yu Sun et al. (+5 more)
STFT-based
speech enhancement typically adopts overlapping analysis frames. While overlap is essential for stable STFT processing, it makes adjacent frames highly correlated, causing redundant computation in lightweight models. We propose Half-frame-rate Adaptive Learnable Operator (
HALO), a causal plug-in module that halves the internal frame rate without altering the STFT procedure. Broadly applicable to many lightweight models,
HALO applies adaptive rate reduction before the backbone and restoration afterward, reconstructing the full-rate spectrum on the original STFT grid. Both reduction and restoration are implemented with lightweight dynamic convolutions. By halving the processed frame rate,
HALO reduces backbone compute cost with no added algorithmic latency, freeing budget for channel widening. Experiments on the DNS3
dataset show consistent gains across diverse lightweight models under matched complexity, demonstrating the effectiveness of reducing overlap-induced redundancy.
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All authors Jiadong Zhao, Dahan Wang, Yu Sun, Leyan Yang, Xiaobin Rong, Shiruo Sun, Yuxiang Hu, Jing Lu
Systems and Control
Least-Squares State Estimation, LQR and LQ-Tracking
Bassam Bamieh
This note is a tutorial on the Least-Squares State
Estimator (LSSE) (the deterministic version of the Kalman-Bucy filter) and related topics. The LSSE is formulated as finding the state trajectory consistent with the system's equations with the minimal amount of L2 process and measurement uncertainty. As stated, this is an input-signal design problem with linear dynamics and affine-quadratic objective in the state and inputs, and therefore a deterministic optimal control problem. We explore its relations to other problems such as the Linear Quadratic Regulator (LQR) with initial or final conditions, as well as the Linear Quadratic (LQ)-tracking problem. Several related topics such as the use of homogeneous coordinates and time reversal in optimal control are explored. The emergence of dynamical controllers/
estimators in both LQ-tracking and LSSE as opposed to memoryless ones (as in LQR) is highlighted. It is seen to be a consequence of the affine-quadratic, rather than a purely quadratic form of the cost objective. The relations with the
stochastic version of the Kalman-Bucy filter are explicitly highlighted, as well as characterizations in terms of certainty (information)
matrices, versus covariance
matrices.
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Signal Processing
Bending the Rules of Propagation: Caustic Beamforming for Next-Generation Wireless Systems
Shicong Liu, Xianghao Yu and Robert Schober
Conventional beamforming techniques primarily steer energy along desired directions or focus it at specific locations. These techniques become fragile when facing frequent blockage and highly dynamic propagation environments. In this article, we present caustic beamforming as a new paradigm for wireless beam control. First, we classify representative caustic beams according to their underlying mathematical origins and present three unique properties, namely self-bending, self-healing, and near-field non-diffracting. Building on these propagation properties, we then propose several application scenarios in sixth-generation (6G) networks. We undertake two case studies focused on physical layer security and service stability that highlight the capability of caustic beams to bypass potential eavesdroppers, deliver more uniform coverage, and sustain blockage-resilient links. We further discuss the enabling hardware architectures that facilitate practical deployments, and finally outline key open challenges regarding caustic beams that require further research.
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Signal Processing
Near Field Multi-Band Localization: CRB, Efficient Estimator, and Threshold SNR
Roberto Bomfin, Marco Mezzavilla, Sundeep Rangan et al. (+1 more)
This paper presents a theoretical framework for multi-band localization for a single-path single-input multiple-output (SIMO) system. We derive closed-form Cramer-Rao bounds (CRBs) for angle-of-arrival (AoA) and distance for uniform linear arrays (ULAs), and an intermediate
matrix-form formulation for arbitrary array shapes. We also develop
benchmark single- and multi-band maximum-likelihood (ML)
estimators for AoA-Distance, leveraging a structured Levenberg-Marquardt (LM) refinement procedure. A key contribution is an analytical characterization of the threshold SNR (TSNR) for the proposed
estimators. This is the SNR threshold at which the
estimator transitions from "off the chart" to CRB-approaching performance, for both TDoA and distance estimation. Numerical simulations confirm that the proposed single- and multi-band
estimators achieve the CRB at SNRs above the predicted TSNR, and that multi-band processing simultaneously improves estimation accuracy and reduces SNR requirements. The resulting framework provides a rigorous foundation for next-generation multi-band localization and can be readily extended to elevation estimation, distributed arrays, and multi-path environments.
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All authors Roberto Bomfin, Marco Mezzavilla, Sundeep Rangan, Marwa Chafii
Image and Video Processing · Cross-listed from cs.CV
Bridging the Modality Gap in Forensic Image Retrieval
Ricardo González-Gazapo, Annette Morales-González, Yoanna Martínez-Díaz et al. (+2 more)
Automated image
retrieval plays an increasingly critical role in modern forensic analysis, supporting investigative workflows that rely on efficient comparison of visual evidence. While prior work has focused primarily on developing and optimizing
multimodal retrieval systems, limited
attention has been paid to evaluating the forensic applicability of these technologies across diverse real-world scenarios. In this study, we present a unified
retrieval framework adapted to four key forensic tasks: (1) tattoo image
retrieval given a tattoo query image; (2) tattoo
retrieval guided by human-expert textual descriptions, modelling the common situation where a witness verbally describes a tattoo; (3) tattoo
retrieval from hand-drawn sketches; and (4) face
retrieval from forensic face sketches. Our system leverages a
multimodal large
language model (MLLM) to automatically generate structured textual descriptions for all queries and gallery images, followed by sentence-
transformer embedding for text-based comparison. We evaluate
retrieval using visual-only
embeddings, text-only
embeddings and a
multimodal fusion strategy that combines text- and image-based similarity scores derived from state-of-the-art visual feature extractors relevant to each task. The fusion of modalities consistently improves
retrieval precision and robustness, especially in scenarios where visual information is limited or noisy (e.g., sketches, partial tattoos, or fragmented witness statements). This work highlights the forensic value of a unified
multimodal retrieval pipeline and demonstrates how modern MLLMs can operationalize challenging forensic tasks that traditionally rely on manual expert analysis. Our results position
multimodal retrieval as a promising tool for supporting investigative workflows involving tattoos, facial composites, and witness descriptions.
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All authors Ricardo González-Gazapo, Annette Morales-González, Yoanna Martínez-Díaz, Heydi Méndez-Vázquez, Milton García-Borroto
Signal Processing
LLM-Based Digital Twin Intelligence for Application-Aware Network Selection in 6G Heterogeneous Wireless Networks
Brahim Mefgouda, Anis Bara, Lina Bariah et al. (+3 more)
Future 6G heterogeneous wireless networks (HWNs) are expected to support multiple
radio access technologies (RATs), dynamic wireless environments, and applications with diverse quality-of-service (QoS) requirements. In such environments, network selection (NS) cannot rely only on instantaneous
radio measurements or static ranking rules. Instead, access decisions must account for the evolving wireless state, service intent, packet-level QoS behavior, and candidate-RAT dynamics. This paper proposes a large
language model (LLM)-based digital twin (DT) framework for stable, application-aware RAT selection under candidate-set
evolution. The main idea is to shift NS from an instantaneous decision-
matrix operation to a decision process over an evolving wireless DT state. The constructed DT combines site-specific geometry, Sionna RT-based propagation descriptors, ns-3 packet-level QoS emulation, service context, candidate-RAT information, and decision
memory. Rather than acting as a general-purpose controller for 6G networks, the LLM is used for DT-grounded decision intelligence in this specific NS task. On top of this DT, a unified intent
agent translates user and service requirements into structured decision priorities for two complementary NS branches: an LLM-assisted multi-attribute decision-making branch (MADM–LLM–NS) and a direct LLM-based ranking branch (LLM–NS). To improve decision stability, the framework further introduces history-aware adaptive normalization (HAAN) and DT-
memory-driven
retrieval-augmented in-context learning (RA–ICL). Numerical results show that the proposed framework reduces rank-reversal problem and unnecessary handover events, while improving service-aware QoS satisfaction compared with representative MADM-based NS baselines.
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All authors Brahim Mefgouda, Anis Bara, Lina Bariah, Hang Zou, Yuzhi Yang, Merouane Debbah
Image and Video Processing · Cross-listed from cs.CV
An Electric Potential-Augmented Benchmark Dataset for Physics-Guided Image Reconstruction of Electrical Capacitance Tomography
Xinqi Zhang, Qiming Ma and Lihui Peng
While deep learning has significantly advanced image reconstruction of Electrical Capacitance
Tomography (ECT), most data-driven methods map directly between capacitance and permittivity distribution, treating the sensor as a black box. This overlooks the electric potential field – the fundamental physical link governing the nonlinear and ill-posed “soft-field” effect. To address this, we propose an electric potential-augmented ECT
benchmark dataset designed to explicitly integrate latent physics behind ECT into the learning process. Generated via a COMSOL-MATLAB pipeline for an eight-electrode sensor as an example, the
dataset comprises 20,000 randomized samples across four typical flow patterns. Crucially, alongside the conventional capacitance vectors and permittivity distributions depicted as images, each sample preserves eight excitation-wise full-field potential maps. Beyond data release, we provide illustrative evaluation protocols for both forward and inverse problems of ECT. Through comprehensive testing on both in-distribution (IID) and out-of-distribution (OOD) scenarios, we systematically demonstrate how the inclusion of electric potential maps enhances modeling accuracy and robustness. Fundamentally, the explicit inclusion of latent field information significantly lowers the barrier to integrating physical laws into ECT modeling, thereby establishing a standardized foundation for future physics-guided machine learning of ECT image reconstruction.
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Signal Processing
Characterization of Speech Imagery in Scalp EEG and Comparison with Motor Imagery
Bob Van Dyck, Liuyin Yang, Qiang Sun et al. (+2 more)
Speech imagery is an attractive
brain-computer interface paradigm for communication because it is endogenous and intrinsically linguistic. Yet despite growing interest, its dominant scalp-
EEG spatiotemporal characteristics remain poorly characterized. We investigated whether
speech imagery, understood here as the motor imagery of articulatory movements, exhibits the motor-related mu/alpha and beta desynchronization expected from motor imagery. In $34$ participants, we compared
speech imagery, finger motor imagery, and explicitly cued no-task trials recorded under the same trial structure, analyzing band-power dynamics across channels and time. Finger motor imagery showed the expected contralateral mu/alpha and beta desynchronization over sensorimotor areas, whereas
speech imagery showed a weaker, more distributed increase in alpha power relative to no-task. A classifier discriminating imagery from no-task reached mean balanced accuracies of $0.563 \pm 0.071$ for
speech imagery and $0.717 \pm 0.125$ for motor imagery, with band-ablation analyses showing larger, more robust alpha and beta effects for motor imagery. These results show that the dominant group-level scalp response to
speech imagery did not resemble the canonical alpha/beta desynchronization associated with motor imagery.
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All authors Bob Van Dyck, Liuyin Yang, Qiang Sun, Ang Li, Marc M. Van Hulle
Audio and Speech Processing
Which Speech Representation Better Matches Text-Native Reasoning? A Study of Speech-Text Alignment on Frame Rate and Representation
Zhen Ye, Xu Tan, Yiming Li et al. (+10 more)
Spoken dialogue models typically start from text LLM backbones, yet
reasoning often degrades when conditioning on
speech instead of text. We attribute part of this modality gap to a temporal-granularity mismatch:
speech tokens are temporally redundant and far longer than text under matched semantics, diluting per-
token semantic density and weakening text-native
reasoning dynamics. We study
speech token design as a representation selection problem and sweep frame rates under a frozen LLM backbone with a fixed information rate. To make low frame rates feasible, we introduce factorized FSQ and a lightweight non-autoregressive audio LM head, scaling capacity to nearly 300\,bits/frame without sacrificing efficient prediction. With the bottleneck removed, we sweep frame rates (50$\rightarrow$2.08\,Hz) and
alignment depth, and observe a consistent best regime for
speech QA at 4.17\,Hz with intermediate-layer representation
alignment.
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All authors Zhen Ye, Xu Tan, Yiming Li, Guangyan Zhang, Chimin Chan, Haohe Liu, Zhengxi Liu, Hongzhan Lin, Zheqi Dai, Xinshen Zhang, Peiwen Sun, Qiuqiang Kong, Wei Xue
Systems and Control
Lexicographic optimization for real-time CNC feedrate planning with coupled orientation handling
Haijia Xu and Alexander Verl
Optimization-based feedrate planning offers the potential to significantly increase machining productivity, but its industrial adoption has been limited by high computational cost and extensive tuning effort. This paper proposes a lexicographic feedrate optimization principle that adaptively balances finishing time and motion smoothness in a tuning-free manner. To further improve computational efficiency, the optimization scheme is extended by a sparsity-exploiting formulation combined with a sequential windowing strategy, enabling real-time capable execution. In addition, a unified toolpath parameterization scheme is incorporated to synchronously handle tool position and orientation within the optimization framework. For a five-axis freeform test contour, the proposed method takes 14 s on an Intel i5-3470 CPU to optimize feedrate profiles for long toolpaths with 100,000 constraint checkpoints, and 52 s on a high-performance AMD 9950X CPU to handle one million checkpoints. Compared to an industrial CNC kernel, the resulting finishing time is reduced by more than 15 %.
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Image and Video Processing
An Indoor Localization Technique Utilizing Passive Tags and 3-D Microwave Passive Radar Imaging
Quanfeng Wang, Alexander H. Paulus, Mei Song Tong et al. (+1 more)
A
privacy-compliant indoor localization approach utilizing a 3-D near-field (NF) passive radar imaging technique is presented. This technique leverages ubiquitously radiated electromagnetic fields for imaging, with passive tags introduced to enhance the strength of scattering fields, thereby enabling precise localization at the imaging level. The method also supports localization in non-ideal imaging scenarios, such as for limited bandwidth or in highly-reflective environments. Based on their geometrical properties the simple and low-cost passive tags enable intuitive differentiation between individuals or objects. Associated
privacy protection mechanisms are discussed, where the frequency-varying properties of the passive tags provide additional flexibility and potential applications under
privacy and ethical considerations. Several forms of passive tags are presented, where both simulation and experimental results validate the effectiveness of the proposed passive tag designs.
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All authors Quanfeng Wang, Alexander H. Paulus, Mei Song Tong, Thomas F. Eibert
Systems and Control
On the Dynamics and State Dependent Multiple Equilibria of a Post-Buckled Ultra-Flexible Inverted Pendulum on a Rotating Hub
Prasanna S Gandhi, Dhruvi Joshi, Vivek Natarajan et al. (+1 more)
Compliant element systems with ultra-large deformation display rich nonlinear dynamics and pose challenging control problems, which, when solved, could enable enhancements in several mechatronics applications, such as soft robotics, MEMS, and biomedical applications. This paper considers post-buckled dynamic analysis of an inverted ultra-flexible pendulum actuated by a rotary hub. We first derive a complete set of equations capturing the dynamics of the system, essential for control development, using the assumed modes method framework, considering ultra-large deformations. Constrained Lagrange formulation is used for the same. In the perfect inverted configuration with zero hub angle, the buckled beam would display two symmetric stable
equilibria and one unstable. However, as the hub angle changes on either side, the
equilibrium positions shift, and eventually two of them vanish, and we are left with only one stable
equilibrium. We use the dynamic equations to characterize this interesting phenomenon, demonstrating the continuous state dependence of multiple
equilibria. Furthermore, experimental counterparts of the
equilibrium results are meticulously obtained and discussed. Moreover, simulation results capture the nonlinear dynamics of this system. Overall, the work establishes a solid mathematical foundation with a control-amenable model for futuristic ultra-compliant mechatronic systems.
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All authors Prasanna S Gandhi, Dhruvi Joshi, Vivek Natarajan, Ravit Anand
Systems and Control
Zero Knowledge Verification of Transaction Guides for P2P Energy Trading in Distribution Networks
Hyunjoong Kim
Peer-to-peer (P2P) energy trading requires network-aware coordination because transactions are physically realized through distribution networks. However, sensitivity-based coordination causes a confidentiality-verifiability tradeoff, as network sensitivities may reveal vulnerable components while undisclosed sensitivities prevent participants from verifying utility-provided transaction guides. This paper proposes a zero-knowledge-proof-based method for verifying the computational integrity of network-constrained transaction guides with respect to committed private network data, without exposing network-sensitivity information. The guide defines admissible injection and withdrawal volumes derived from sign-decomposed sensitivity
matrices while satisfying balance, voltage, line-flow, and optimality conditions. These conditions are encoded in an arithmetic
circuit, represented as R1CS constraints and a quadratic arithmetic program, and verified using a bilinear pairing. Blockchain commitments bind the approved
circuit, public inputs, statement identifiers, proof, and verification result for tamper-evident auditability. The proposed proof certifies correct guide computation from committed network data; the authenticity of the committed network data is handled through an explicit registration and attestation assumption. Case studies on a modified IEEE 33-bus system show satisfaction of network constraints after clearing, rejection of public-input and witness-inconsistency attacks, and practical on-chain overhead, with an 806-byte proof.
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Signal Processing
Deep Reinforcement Learning for Adaptive Power Allocation in ISAC Systems with Mobile Target
Zhilin Fu, Sangmin Kim, Sangwon Hwang et al. (+4 more)
In this paper, we study the power allocation for an integrated sensing and communication (
ISAC) system which tracks a mobile target. We first model the problem as a Markov decision process, and then tackle it with a soft actor-critic (SAC) based deep reinforcement learning (DRL) approach. We also combine a Dirichlet policy, which naturally produces normalized continuous actions under random target motion. To exploit different features of sensing and communication operations, we carefully design a reward function such that the system can dynamically control power allocation to conserve resources. The simulation results demonstrate that the proposed scheme enhances tracking performance compared to other baselines while sustaining communication performance.
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All authors Zhilin Fu, Sangmin Kim, Sangwon Hwang, Jihwan Moon, Jeongwon Kim, Jaewan Kim, Inkyu Lee
Image and Video Processing · Cross-listed from cs.CV
Non-frontal face recognition using GANs and memristor-based classifiers
Semih Vazgecen, Cristian Sestito, Spyros Stathopoulos et al. (+1 more)
Face recognition systems have advanced significantly through deep learning techniques, delivering high performance and robustness in complex scenarios. However, these approaches incur substantial computational overhead, limiting their in situ applicability in resource-constrained platforms such as drones, where they can address challenges including non-frontal facial imagery. Memristor-based neuromorphic systems have emerged as a compelling approach for edge AI applications, combining biologically inspired processing with efficient and scalable computation. In this work, we propose a facial recognition framework that addresses non-frontal pose variations by integrating lightweight generative
adversarial network (GAN)-based pose frontalisation with memristor-based neuromorphic recognition. The experimental results on two
datasets demonstrate the effectiveness of combining
adversarial learning with memristive technology, achieving up to 96% identification accuracy. The proposed approach alleviates the computational bottlenecks of conventional AI and offers a scalable, efficient solution for face recognition in dynamic real-world environments.
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All authors Semih Vazgecen, Cristian Sestito, Spyros Stathopoulos, Themis Prodromakis
Signal Processing
Unlocking the Potential of Movable Antennas: General and Practical Antenna Position Optimization
Weidong Mei, Changhao Liu, Dong Wang et al. (+6 more)
Recently, movable
antenna (MA) has attracted wide
attention in wireless communications due to its potential in enhancing wireless communication performance via local movement within a confined region. However,
antenna position optimization (APO) has emerged as a major challenge for MAs, due to the lack of a tractable, analytical, and accurate channel model in terms of
antenna positions. Although existing works have developed various
algorithms for APO, most of them are based on simplified theoretical channel models, which limit their generality. To address this challenge, in this article, we present more general and effective APO
algorithms for different purposes, categorized as continuous APO and discrete APO, respectively. Continuous APO is mainly applied for flexible array signal processing to boost large-scale communication performance, while discrete APO is applied for small-scale multi-path channel reshaping. Specifically, the discrete APO discretizes the
antenna movement region into multiple sampling points and employs discrete
algorithms to determine the optimal MA positions based on the point-wise channel state information (CSI), without the need for an analytical channel model. To reduce the overhead for CSI acquisition, we also present more efficient learning-based APO
algorithms that operate without requiring full point-wise CSI. Finally, we compare the application scenarios of the proposed
algorithms and validate their effectiveness with numerical results.
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All authors Weidong Mei, Changhao Liu, Dong Wang, Xin Wei, Yiming Wu, Boyu Ning, Zhi Chen, Jun Fang, Rui Zhang
Systems and Control
Physics-guided residual Kalman learning for state-of-charge estimation of lithium iron phosphate batteries
Feng Guo, Luis D. Couto, Khiem Trad et al. (+3 more)
Accurate state of charge (SOC) estimation of lithium iron phosphate (LFP) batteries remains challenging because of their flat open-
circuit-voltage (OCV)-SOC characteristics, temperature-dependent dynamics, and sensitivity to initialization errors. Here, we propose a physics-guided residual Kalman learning (PRKL) framework for electrochemical-model-based SOC estimation. PRKL combines a control-oriented single-particle-model-based extended Kalman filter (EKF), which provides recursive physical state propagation, with a gated recurrent unit (GRU) residual learner that compensates structured EKF errors using electrochemical states and measurement features. The framework is evaluated on a public graphite/LFP
dataset covering three dynamic drive cycles, eight temperatures from -10 to 50 degrees C, and initialization offsets up to 20 percent. Using dynamic stress test (DST) and federal urban driving schedule (FUDS) cycles for training and the supplemental federal test procedure (US06) cycle for cross-profile testing within the same
cell dataset, PRKL achieves a global average root mean square error (RMSE) of 1.19 percent, corresponding to a 77 percent reduction relative to the physics-only EKF. These results show that electrochemical state information can guide residual learning and improve recursive SOC estimation for LFP batteries. The present validation supports cross-profile robustness within the studied
dataset and provides a basis for future cross-
cell, ageing-aware, and embedded-platform validation.
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All authors Feng Guo, Luis D. Couto, Khiem Trad, Ru Hong, Guangdi Hu, Mohammadhosein Safari
Systems and Control
Robust Zonotopic Control
Fouzi Tabouri, Kim Guldstrand Larsen and Christian Schilling
We propose a zonotopic framework for synthesizing a single robust state feedback controller that is certified to stabilize every plant inside a
matrix zonotope, describing linearly varying parameters or parametric uncertainty. Common robust design strategies rely on checking many vertex models or on complex gain-scheduling, leading to high offline computation and implementation complexity. Our approach finds a single gain that is provably valid across the entire parameter domain, which is simpler to implement and can reduce conservatism by exploiting the structure of the zonotope. We formulate the robust synthesis as a single convex program tailored to the zonotope representation and incorporate practical performance requirements (actuator constraints, decay rate, disturbance attenuation) into the same synthesis stage. In numerical experiments on a representative 4-state example, our controller provides larger stability coverage across the parameter domain, attains comparable transient performance and control effort to more complex designs, and significantly reduces the number and scale of offline synthesis problems required by other robust approaches, compared to common-vertex gain, $H_{\infty}$, and $\mu$-synthesis baselines.
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Systems and Control
Cooperative Switched Formation Control of Autonomous Vehicles: An Event-triggered Approach to Input Saturation and Time-delay Challenges
Ziming Wang, Guanxuan Jiang, Yihuai Zhang et al. (+2 more)
This paper presents a collaborative adaptive formation control framework for autonomous vehicles (AVs), that explicitly handles system uncertainties, input saturation, and communication delays. To overcome the inherent physical torque limits of steering and braking actuators, an input saturation compensation mechanism is introduced to render nonlinearities tractable and improve control reliability. Additionally, a delay-compensating auxiliary system is designed to mitigate the effects of communication delays and reduce tracking errors. Our framework incorporates a dynamic-threshold event-triggered control (ETC) strategy to optimize resource usage. Additionally, uncertainty observers and symmetric barrier Lyapunov functions are developed to ensure robust and safe formation maneuvers. Finally, the effectiveness of the proposed approach is validated through numerical simulations of vehicle formations, complemented by a 3D visualization
video demonstrating the dynamic fleet reconfiguration process.
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All authors Ziming Wang, Guanxuan Jiang, Yihuai Zhang, Karl H. Johansson, Apostolos I. Rikos
Signal Processing
Low-Density EEG for Seizure Detection: Evaluating CNN-RNN Architectures on a Behind-the-Ear Montage Setup
Annika Stiehl, Patrick Wingert, Nicolas Weeger et al. (+3 more)
Epilepsy affects over 50 million individuals globally, underscoring the need for automated seizure detection systems that can alleviate clinicians workload and enhance the accuracy of patient seizure diaries. In wearable
EEG applications, however, reliable detection remains challenging due to the limited spatial resolution of low-density electrode configurations, reduced signal-to-noise ratios, and the scarcity of diverse, publicly available training
datasets. This study investigates the efficacy of hybrid deep learning architectures for automated seizure detection using a simulated behind-the-ear montage derived from the Temple University Seizure Corpus (TUSZ, v2.0.3). We conduct a systematic comparison of several CNN-RNN models, including LSTM- and GRU-based variants, across multiple
EEG montages to evaluate their capacity to compensate for the loss of spatial information inherent to reduced electrode configurations. The proposed CNN-Merged model, which integrates temporal and spectral feature representations, demonstrates superior performance, achieving a ROC AUC of 85.89% and a balanced accuracy of 79.11% on the held-out test set. Furthermore, the model exhibits strong robustness across different reference montages, effectively bridging the performance gap between conventional full-scalp recordings and resource-constrained wearable systems. These findings substantiate the potential of hybrid deep learning models as a promising avenue toward robust, patient-independent seizure detection in low-density
EEG applications.
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All authors Annika Stiehl, Patrick Wingert, Nicolas Weeger, Nicole Ille, Christian Uhl, Stefan Geißelsöder
Systems and Control
Robust Tuning of Model Predictive Control for MMC-Based High-Voltage Power Systems
Victor Daniel Reyes Dreke, Rahul Rane and Aleksandra Lekić
High-voltage direct current (HDVC) transmission systems based on modular multilevel converters (MMCs) have become a key
topology in modern power systems. The dynamics of MMCs exhibit strong multivariable coupling, constraints, and uncertainties, motivating the use of model predictive control (MPC) to enhance current regulation performance. However, MPC tuning is nontrivial and does not inherently guarantee stability or robustness, particularly in the presence of model uncertainties. This paper proposes a MPC tuning method that ensures robust performance under bounded model uncertainties. This method solves a convex linear optimization problem to compute the optimal weighting
matrices Q, R, and P ensuring optimality and reproducibility. As a result, robustness is enhanced without increasing the online computation burden. The effectiveness of the method is validated through testing on a real-time digital simulator (RTDS) model of a point-to-point HVDC system. Results demonstrate improved performance compared to conventional LQR-based MPC tuning.
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Signal Processing
NARRAS: Edge-Triggered Distributed Inference for CSI-Based Localization in Vehicular IoT Networks
Rodrigo Oliver, Ricardo Vazquez Alvarez, Alejandro Lancho et al. (+1 more)
CSI-based localization with spatially distributed
antenna arrays exposes a basic resource trade-off. Each array can provide a rich view of the channel, but forwarding observations from all arrays to a fusion center is wasteful when only a few carry useful information, and the shared uplink supports only a limited number of simultaneous transmissions. We let each array decide locally whether its current observation is worth reporting, subject to a budget on the average number of active transmitters. We refer to this abstraction as Edge-Triggered Distributed Inference (ETDI). It captures a broader class of task-oriented communication problems where resource-constrained devices share an access channel for a common inference task. We instantiate ETDI for CSI-based localization, a common scenario in vehicular IoT networks. Spatially distributed remote
antenna arrays (RAAs) encode local channel state information (CSI) from user equipment (UE) transmissions into latent features, and the fusion center estimates the UE position from the subset of reported features. We propose NARRAS, a decentralized reporting policy in which each RAA combines a recurrent summary of its recent observations with a
memory of the last latent it transmitted. Training controls an explicit activity budget through differentiable activity penalties and validation-calibrated deterministic thresholds, and uses channel-chart regularization to shape the latent geometry. Experiments show that, at comparable uplink activity, NARRAS improves localization accuracy over learned and heuristic sparse-reporting strategies, while dense full-report models remain useful budget-free references. In low-activity regimes, chart regularization further reduces high-percentile localization errors, suggesting that geometry-aware latent representations are more robust under sparse reporting.
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All authors Rodrigo Oliver, Ricardo Vazquez Alvarez, Alejandro Lancho, Stefano Rini