Signal Processing
BCI-Based Assessment of Ocular Response Time Using Dynamic Time Warping Leveraging an RDWT-Driven Deep Neural Framework
Shantanu Sarkar, Sai Shashank Gandavarapu, Jeff Feng et al. (+3 more)
Mild traumatic
brain injury (mTBI) is a prevalent condition that remains difficult to diagnose in its early stages. Oculomotor dysfunction is a well-established marker of mTBI, motivating the development of portable tools that capture both eye-movement behavior and underlying neurophysiology. In this work, we present an initial framework that integrates electroencephalogram (
EEG) with augmented-reality (AR)-based Vestibular/Ocular Motor Screening (VOMS) tasks to estimate subject-specific ocular response times. Pre-processed
EEG signals, obtained through band-pass filtering and average referencing, are analyzed using a Redundant Discrete Wavelet Transform (RDWT)-driven deep neural framework. The RDWT coefficients are subjected to trainable zero-phase convolutional filtering and reconstructed into the time domain via inverse RDWT, followed by channel-wise temporal and spatial filtering using 2D convolution layers and convolutional-LSTM-based decoding. An ablation study demonstrates that wavelet-domain filtering serves as an effective denoising strategy, improving prediction performance. Sliding-window predictions were validated using Pearson correlation (> 0.5), and Dynamic Time Warping (DTW) was subsequently used to estimate ocular response times. DTW-derived metrics revealed VOM-task-dependent differences in ocular response time between participants, characterized using Mann-Whitney U tests. Cross-correlation analysis further revealed task-dependent temporal behaviors: pursuit tasks exhibited reactive tracking, whereas saccades showed anticipatory responses. Overall, the results highlight pursuit tasks as particularly informative for distinguishing timing differences and demonstrate the potential of RDWT-based
EEG features combined with DTW metrics for
multimodal mTBI assessment.
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All authors Shantanu Sarkar, Sai Shashank Gandavarapu, Jeff Feng, Saurabh Prasad, Reza Khanbabaie, Jose L. Contreras-Vidal
Signal Processing · Cross-listed from cs.LG
Exploitation of Hidden Context in Dynamic Movement Forecasting: A Neural Network Journey from Recurrent to Graph Neural Networks and General Purpose Transformers
Lukas Schelenz, Shobha Rajanna, Denis Gosalci et al. (+6 more)
Forecasting within signal processing pipelines is crucial for mitigating delays, particularly in predicting the dynamic movements of objects such as NBA players. This task poses significant challenges due to the inherently interactive and unpredictable nature of sports, where abrupt changes in velocity and direction are prevalent. Traditional approaches, including (S)ARIMA(X), Kalman filters (KF), and Particle filters (PF), often struggle to model the non-linear dynamics present in such scenarios. Machine learning (ML) methods, such as long short-term
memory (LSTM) networks, graph neural networks (GNNs), and
Transformers, offer greater flexibility and accuracy but frequently fail to explicitly capture the interplay between temporal dependencies and contextual interactions, which are critical in chaotic sports environments. In this paper, we evaluate these models and assess their strengths and weaknesses. Experimental results reveal key performance trade-offs across input history length, generalizability, and the ability to incorporate contextual information. ML-based methods demonstrated substantial improvements over linear models across forecast horizons of up to 2s. Among the tested architectures, our hybrid LSTM augmented with contextual information achieved the lowest final displacement error (FDE) of 1.51m, outperforming temporal convolutional neural network (TCNN), graph
attention network (GAT), and
Transformers, while also requiring less data and training time compared to GAT and
Transformers. Our findings indicate that no single architecture excels across all metrics, emphasizing the need for task-specific considerations in trajectory prediction for fast-paced, dynamic environments such as NBA gameplay.
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All authors Lukas Schelenz, Shobha Rajanna, Denis Gosalci, Lucas Heublein, Jonas Pirkl, Jonathan Ott, Felix Ott, Christopher Mutschler, Tobias Feigl
Signal Processing
An integration-free approach for particle flow filtering
Domonkos Csuzdi, Tamás Bécsi and Olivér Törő
Log-homotopy particle flow filters realize nonlinear
Bayesian estimation by continuously migrating samples from the prior to the posterior distribution. This transport is governed by a pseudo-time ordinary differential equation (ODE). A major practical challenge of these filters is the need for numerical integration, which suffers from high computational cost and susceptibility to stiffness. This paper develops an exact, integration-free closed-form solution for the exact Daum–Huang deterministic particle flow under vector linear Gaussian measurements. By transforming the ODE into a specific eigenspace, we derive closed-form algebraic expressions for both the homogeneous state transition
matrix and the inhomogeneous forcing term. We prove that this analytic solution is equivalent to the exact Kalman measurement update. We embed this closed-form evaluation within an $N$-step piecewise method for nonlinear measurement models. We further propose a constant contraction rate substep schedule that equalizes the per-step contraction along the eigendirection of $D$ associated with the largest
eigenvalue $\alpha_{\max}$. The result is a stiffness-mitigating, integration-free particle update for highly nonlinear measurement models. On a bearings-only tracking
benchmark, it achieves the lowest error among the compared filters, at a per-update cost comparable to deterministic particle flow baselines and substantially lower than
stochastic flows.
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Signal Processing · Cross-listed from cs.LG
GenAI for Energy-Efficient and Interference-Aware Compressed Sensing of GNSS Signals on a Google Edge TPU
Thorben Wegner, Lucas Heublein, Tobias Feigl et al. (+3 more)
Traditional methods for classifying global
navigation satellite system (GNSS) jamming signals typically involve post-processing raw or spectral data streams, requiring complex and costly data transmission to cloud-based interference classification systems. In contrast, our proposed approach efficiently compresses GNSS data streams directly at the hardware receiver while simultaneously classifying jamming and spoofing attacks in real time. Given the growing prevalence of GNSS jamming, there is a critical need for real-time solutions suitable for power-constrained environments. This paper introduces a novel method for compressing and classifying GNSS jamming threats using generative artificial intelligence (GenAI), specifically variational autoencoders (VAEs), deployed on Google Edge tensor processing units (TPUs). The study evaluates various autoencoder (AE) architectures to compress and reconstruct GNSS signals, focusing on preserving interference characteristics while minimizing data size near the receiver hardware. The pipeline adapts large-scale AE models for Google Edge TPUs through 8-bit quantization to ensure energy-efficient deployment. Tests on raw in-phase and quadrature-phase (IQ) data, Fast
Fourier Transform (FFT) data, and handcrafted features show the system achieves significant compression (>42x) and accurate classification of approximately 72 interference types on reconstructed signals (F2-score 0.915), closely matching the original signals (F2-score 0.923). The hardware-centric GenAI approach also substantially reduces jammer signal transmission costs, offering a practical solution for interference mitigation. Ablation studies on conditional and factorized VAEs (i.e., FactorVAE) explore latent feature disentanglement for data generation, enhancing model interpretability and fostering trust in machine learning (ML) solutions for sensitive interference applications.
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All authors Thorben Wegner, Lucas Heublein, Tobias Feigl, Felix Ott, Christopher Mutschler, Alexander Rügamer
Signal Processing
Making AFDM Secure Against Eavesdroppers: A Phase Function Design Approach
Hengxuan Liu, Vincent Savaux and Arman Farhang
Affine frequency division multiplexing (AFDM) has recently emerged as a promising waveform for high-mobility communications due to its resilience to Doppler effects and its advantages for integrated sensing and communication (
ISAC). AFDM modulates transmit data symbols using chirp subcarriers with two adjustable parameters. One is used for dealing with the Doppler effect and the second parameter can be used for physical layer security (PLS). In this paper, we focus on designing the second chirp parameter in the form of a generic phase function to enhance the robustness of the waveform against brute-force demodulation by the eavesdropper. In particular, we first derive a design criterion that reveals the brute-force demodulation complexity depends on the first derivative of the phase function. Then, we introduce a family of phase functions that can increase the brute-force demodulation complexity in an unbounded and controllable manner, while preserving chirp structure of AFDM. Our simulation results demonstrate that the proposed phase function design enhances the PLS performance of AFDM by several orders of magnitude compared with the conventional AFDM in terms of brute-force demodulation complexity.
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Systems and Control
Hybrid Metaheuristic Optimization of Distributed Control System Hardware Architecture with Model-Based Verification
Ruslan Zakirzyanov
Large-scale chemical plants rely on distributed process control systems (PCS) comprising numerous processing units, communication modules, and I/O devices interconnected via industrial networks. The design of a cost-efficient and reliable hardware architecture under partial uncertainty in plant parameters remains a challenging combinatorial optimization problem. This paper proposes a formal model for distributed control system hardware architecture synthesis. A hybrid ant colony-based metaheuristic framework is developed to construct feasible hierarchical architectures. The proposed approach is validated on a large-scale sulfuric acid plant control system case study. Plant parameters are identified from operational data, system stability is analyzed, and a controller synthesis is performed based on the optimized architecture. The results demonstrate the feasibility of the approach and confirm that the obtained architecture satisfies structural and dynamic performance requirements.
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Audio and Speech Processing · Cross-listed from cs.CL
Streaming Speech-to-Text Translation with a SpeechLLM
Titouan Parcollet, Shucong Zhang, Xianrui Zheng et al. (+1 more)
Normally, a system that translates
speech into text consists of separate modules for
speech recognition and text-to-text translation. Combining those tasks into a SpeechLLM promises to exploit paralinguistic information in the
speech and to reduce cascaded errors. But existing SpeechLLM systems are slow since they do not work in a real streaming fashion: they wait for a complete utterance of audio before outputting a translation, or output
tokens at fixed intervals, which is not suitable for real applications. This work proposes an LLM-based architecture for real streaming
speech-to-text translation. The LLM learns not just to emit output
tokens, but also to decide whether it has seen enough audio to do so. The system is trained using automatic
alignments of the input
speech and the output text. In experiments on different
language pairs, the system achieves a translation quality close to the non-streaming baseline, but with a latency of only 1-2 seconds.
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All authors Titouan Parcollet, Shucong Zhang, Xianrui Zheng, Rogier C. van Dalen
Signal Processing
ChannelAgent-Empowered Electromagnetic Space World Model: A Case Study on Agent-Driven Channel Generation for 6G AI-Native Air Interface
Mingyue Li, Li Yu, Yuxiang Zhang et al. (+4 more)
As sixth-generation (6G) wireless networks evolve toward increasingly heterogeneous scenarios, tasks, and service requirements, conventional artificial intelligence (AI) models remain limited in task-aware decision-making and autonomous adaptation. To address this issue, this paper first proposes a ChannelAgent-empowered electromagnetic space world model, in which wireless intelligence is organized into a closed-loop process consisting of multi-modal sensing, ChannelAgent as the intelligent core, and execution with feedback update. As a case study,
agent-driven channel generation is instantiated through path loss prediction. Specifically, a task-oriented intelligent feature selection mechanism is designed by integrating reinforcement-learning-inspired policy adaptation with evolutionary search, enabling the
agent to iteratively derive compact and task-suitable feature subsets according to the current scenario and performance feedback. Simulation results demonstrate superior performance in both single-scenario and multi-scenario tasks, highlighting the potential of the proposed model for autonomous, adaptive, task-oriented, and closed-loop wireless intelligence.
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All authors Mingyue Li, Li Yu, Yuxiang Zhang, Heng Wang, Jianhua Zhang, Ping Zhang, Guangyi Liu
Systems and Control
Addressing Terminal Constraints in Data-Driven Demand Response Scheduling
Maximilian Bloor, Martha White, Ehecatl Antonio del Rio Chanona et al. (+1 more)
Electrified chemical processes are incentivized by exposure to time-varying electricity
markets to operate flexibly, but participating in demand response schemes can require satisfying terminal constraints over long horizons. Specifically, terminal constraints may be required when computing optimal schedules in order to preserve dynamic stability. Model-based optimization methods are computationally costly, and data-driven scheduling via reinforcement learning (RL) faces severe credit-assignment challenges. We integrate Goal-Space Planning (GSP) with Deep Deterministic Policy Gradient (DDPG), using learned temporally abstract models over discrete subgoals to propagate value across extended horizons. Using a simulated air separation
benchmark, we demonstrate the proposed approach improves sample efficiency over standard DDPG while satisfying terminal storage constraints, mitigating myopic control behavior.
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All authors Maximilian Bloor, Martha White, Ehecatl Antonio del Rio Chanona, Calvin Tsay
Image and Video Processing
Denoising for Neuromorphic Cameras Based on Graph Spectral Features
Shimpei Harada, Junya Hara, Hiroshi Higashi et al. (+1 more)
Neuromorphic cameras, also known as event-based cameras, can detect changes in the environmental brightness asynchronously and independently for each pixel. They output the brightness changes, i.e., events, as 3-D (2-D pixel coordinates + time) streaming data. While event-based cameras are used in many applications because of their desirable characteristics, e.g., high temporal resolution, low latency, low power consumption, and high dynamic range, their measurements contain considerable noise due to their high sensitivity. In this paper, we propose a denoising method for event-based cameras based on graph spectral features. In the proposed method, we first construct a graph where nodes represent events and edges represent the spatiotemporal distance between the events. To calculate the graph-specified parameter that controls the connectivities of a constructed graph, we utilize the prior on the density of 3-D events. We then calculate the eigenvectors of the graph
Laplacian. The obtained eigenvectors are used to extract noiseless events directly. In the calculation of the eigenvectors, we customize the graph
Laplacian to reorder its
eigenvalues. This allows us to leverage fast eigensolver
algorithms instead of the naive eigendecomposition and thereby reduce computational complexity. In experiments on synthetic and real-world event data, we demonstrate that the proposed method effectively removes noise events from the raw events compared to alternative methods.
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All authors Shimpei Harada, Junya Hara, Hiroshi Higashi, Yuichi Tanaka
Signal Processing
Joint Phase Noise and Channel Estimation for OTFS
Stephen McWade and Arman Farhang
This paper investigates the effect of oscillator phase noise in orthogonal time frequency space (OTFS) systems. The paper provides in-depth analysis of the interference due to phase noise in the delay-Doppler domain and derives expressions for SINR for three different oscillator types, namely free-running oscillators, continuous-time phase locked loops (PLLs) and discrete-time PLLs. The analysis demonstrates the OTFS is sensitive to phase noise and requires appropriate estimation and compensation. In particular, the analysis shows phase noise imposed inter-Doppler-interference (IDI) is severe and that existing phase noise estimation techniques which only consider the common-phase-error (CPE) can not compensate this IDI effectively. Additionally, the existing methods in the OTFS literature on phase noise assume the channel to be a known single tap channel. Hence, in this paper, we propose a method for joint channel and phase noise estimation using a Wiener filtering approach. Our proposed method exploits the statistical nature of both the phase noise and the Doppler spread channel. Our numerical results demonstrate the superior performance of our proposed technique, with gains of up to 8~dB in terms of bit error rate (BER) over existing methods in the literature.
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Systems and Control
Flexibility-Aware Framework for Efficient Planner-Initiated Siting of Data Center
Dongjoo Kim, Lin Dong and Le Xie
Explosive growth in energy-intensive AI data centers is outstripping the pace of power grid interconnection and transmission expansion. While operational flexibility has been proposed to mitigate this stress, existing processes are often reactive and evaluate projects only after they enter a multi-year interconnection queue. To address this, we introduce a planner-initiated siting framework that integrates (i) reliability-gated screening, (ii) system-wide
market-impact assessment under standardized flexibility envelopes (firm, pause, and shift), and (iii)
entropy-weighted multi-criteria scoring to produce ranked, pre-certified catalogues of interconnection-ready locations. Applied to a synthetic 2,000-bus Texas power system, the framework demonstrates that operational flexibility expands the siting frontier by 9-17% at 1 GW and 19-21% at 2 GW compared to firm operation. Median all-hour average prices remain essentially unchanged (USD 24.32/MWh for the 2 GW cases), and the shift envelope attenuates peak-hour price dispersion by approximately 3.4% with minimal side effects during off-peak hours. Utilizing pre-certified envelopes to bypass major transmission reinforcements, this workflow enables first energization in 12-18 months, a conservative reduction of 3.5-4 years versus the conventional 5-8 year project-led process. This technology-agnostic framework provides a proactive decision-making tool for system operators and regulators to fast-track large flexible loads while preserving grid reliability and
market stability.
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Systems and Control
Dynamic Event-Triggered Control of Discrete-Time Nonlinear Systems based on Difference-Algebraic Representations
Vitoriano Casas, Gabriela Reis, Pedro Henrique Coutinho et al. (+2 more)
This paper addresses the dynamic event-triggered control for a class of discrete-time nonlinear systems described by a difference-algebraic representation (DAR), using a gain-scheduled controller. An outstanding aspect of the proposed method is the incorporation of information about the system's nonlinearities into the control law and the trigger function. The proposed event-triggered mechanism also incorporates information on the asynchronous terms induced by the event-based sampling. All these ingredients enable the derivation of a less conservative co-design condition for the simultaneous design of the gain-scheduled control law and the dynamic triggering mechanism to ensure the asymptotic stability of the closed-loop system. An estimate of the region of attraction of the origin of the closed-loop system is obtained to guarantee the closed-loop system's operation within the domain of validity of the DAR. Then, an optimization problem is formulated to reduce the number of events and enlarge the estimated region of attraction. Finally, the effectiveness of the proposed condition is illustrated by a numerical example.
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All authors Vitoriano Casas, Gabriela Reis, Pedro Henrique Coutinho, Iury Bessa, Rodrigo Araújo
Systems and Control · Cross-listed from cs.RO
SeaVis: Modeling and Control of a Remotely Operated Towed Vehicle for Seabed Visualization and Mapping
Abdelhakim Amer, Aske Alstrup, Frederik Rasmussen et al. (+3 more)
High-resolution seafloor mapping necessitates stable and precise positioning for
underwater robots. This paper introduces a novel mathematical model for SeaVis remotely operated towed vehicles (ROTVs) and develops a gain-scheduled linear-quadratic regulator (LQR) for robust depth and attitude control. We validate the approach in a high-fidelity simulation, benchmarking the LQR against a conventional PID controller over a challenging seabed profile. The presented results demonstrate the LQR's superior performance, with significantly enhanced robustness to disturbances, greater control efficiency, and substantially reduced flap actuation. The gain scheduling also confirms the controller's effectiveness across the full operational velocity range. The complete simulation environment and controller are open-sourced.
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All authors Abdelhakim Amer, Aske Alstrup, Frederik Rasmussen, Yury Brodskiy, Andriy Sarabakha, Erdal Kayacan
Image and Video Processing
An Attention-Enhanced Network with Joint Dehazing and Retinex-Based Enhancement for Underwater Images
Sahana Ray, Bibhabasu Debnath and Sanjay Ghosh
Underwater images suffer from severe wavelength-dependent light absorption and scattering, and turbidity due to suspended particles, degrading visual quality for applications in autonomous
underwater vehicles (AUVs), marine biology, archaeology, and offshore infrastructure inspection. Classical IFM inadequately capture nonlinear
underwater light behavior, while purely data-driven methods lack physical interpretability. This paper proposes a three-stage network named ADR, that extends the
underwater image formation model with additional terms to perform
underwater dehazing, followed by Retinex-based enhancement and
attention-enabled U-Net++ refinement. Experiments on UIEB and UFO-120
benchmark datasets demonstrate competitive performance with state-of-the-art methods.
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Signal Processing
Multimodal Learning for MIMO Beam Prediction Based on Variational Inference
Zijian Zheng, Wenqiang Yi, Hyundong Shin et al. (+1 more)
Accurate beam prediction is essential for mitigating signalling overhead and latency in integrated sensing and communication-enabled massive multi-input multi-output systems. With the aid of
multimodal learning, the prediction accuracy can be enhanced by leveraging the complementary information from other existing sensors, but the practical deployment is often constrained by the high cost of acquiring semantically aligned
multimodal datasets. This paper proposes a variational-inference-based
multimodal framework that decouples the optimization problem into modular feature extraction and cross-modal semantic
alignment. Specifically, we develop a two-stage training strategy where the model utilises abundant unimodal data for representation learning before performing refined
alignment on limited
multimodal samples. This design enhances data efficiency and ensures robust feature fusion under sensing uncertainties. Experimental results on the DeepSense6G
dataset demonstrate that the proposed framework achieves competitive beam prediction accuracy and maintains high reliability, while only requiring 20% of the
multimodal training data compared to conventional end-to-end
benchmarks.
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All authors Zijian Zheng, Wenqiang Yi, Hyundong Shin, Arumugam Nallanathan
Systems and Control
Distributionally Robust Model Predictive Control for Virtual Power Plants
Nikolas Recke and Mathias Hudoba de Badyn
This paper presents a distributionally robust model predictive control (DRMPC) framework for the optimal Virtual Power Plant (VPP) operation under electricity price uncertainty. A unified VPP model is formulated that captures the interaction between buildings, battery storage, and renewable generation, all influenced by exogenous weather and
market signals. The proposed approach integrates data-driven
forecasting with
quantile-based uncertainty quantification to construct time-varying Wasserstein ambiguity sets that adapt to forecast dispersion and distributional shifts. This yields a tractable DR-MPC formulation that incorporates predictive distribution information directly into real-time decision making. The method is evaluated using real weather and
market data from a Nordic case study across two seasonal scenarios. The results show that DR-MPC improves economic performance relative to standard forecast-based MPC when the ambiguity radius is chosen appropriately, with consistent gains of up to 0.8% for small radii across both seasonal scenarios. Larger radii become overly conservative and reduce revenue, underscoring the importance of proper radius selection. These findings demonstrate the practical value of distributionally robust optimization for uncertainty-aware VPP operation.
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Image and Video Processing
Efficient Dense Matching for Enhanced Gaussian Splatting Using AV1 Motion Vectors
Julien Zouein, Vibhoothi Vibhoothi, François Pitié et al. (+1 more)
3D Gaussian Splatting (3DGS) has emerged as a prominent framework for real-time, photorealistic scene reconstruction, offering significant speed-ups over Neural Radiance Fields (NeRF). However, the fidelity of 3DGS representations remains heavily dependent on the quality of the initial point cloud. While standard Structure-from-Motion (SfM) pipelines using COLMAP provide adequate initialisation, they often suffer from high computational costs and sparsity in textureless regions, which degrades subsequent reconstruction accuracy and convergence speed. In this work, we introduce an AV1-based feature detection and matching pipeline that significantly reduces SfM processing overhead. By leveraging motion vectors inherent to the AV1
video codec, we bypass computationally expensive exhaustive matching while maintaining
geometric robustness. Our pipeline produces substantially denser point clouds, with up to eight times as many points as classical SfM. We demonstrate that this enhanced initialisation directly improves 3DGS performance, yielding an 9-point increase in VMAF and a 63% average reduction in training time required to reach baseline quality. The project page: https://sigmedia.tv/AV1-3DGS.github.io/
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All authors Julien Zouein, Vibhoothi Vibhoothi, François Pitié, Anil Kokaram
Signal Processing · Cross-listed from stat.ME
Parametrically Adaptive Transition Polynomial: a Signed-Parity Continuous-alpha Extension of Kunchenko Stochastic Polynomials
Serhii Zabolotnii
Kunchenko's method of
polynomial maximization provides a semiparametric apparatus for parameter estimation under non-Gaussian errors, but its classical power basis relies on finite higher-order integer moments. This paper introduces the Parametrically Adaptive Transition
Polynomial (PATP), a signed-parity fractional-power family controlled by a continuous parameter alpha in [0,1]. The quadratic exponent map p_i(alpha) connects the fractal regime p_i(0)=1/i, the degenerate linear point p_i(1/2)=1, and the signed-parity integer-power regime p_i(1)=i. For the degree-S=2 case we derive a closed-form variance-reduction coefficient g_2(alpha) in terms of signed and absolute fractional moments, identify the singular behavior at alpha=1/2, and state the moment and regularity conditions under which the formula is meaningful. The construction should be read as a Form-B PATP analogue within Kunchenko's generalized apparatus, not as an exact recovery of the canonical even-power PMM basis at alpha=1. Numerical illustrations on canonical distributions are used to examine the finite-sample behavior of the signed-parity
estimator and to mark the boundary of applicability for extremely heavy-tailed cases such as Cauchy.
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Signal Processing
Antenna Tilt Failure Detection and Estimation via Integrated Sensing and Communications
Samed Kesir, Batuhan Kaplan, Emre Arslan et al. (+1 more)
This paper addresses the critical sensitivity issue of narrow-beam communication systems to physical misalignments and exploits the potential of Integrated Sensing and Communications (
ISAC) technology to propose a sensor-free
antenna tilt failure detection and estimation framework. The proposed methods utilize environmental static clutter as
geometric anchors to monitor systematic gain shifts in clutter heat maps. The proposed methods are introduced for precise
antenna tilt detection and estimation using the standard 5G NR frame structure and two different waveforms. Numerical results show the potential of the proposed framework to enable autonomous, self healing network maintenance without the need for external sensors.
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All authors Samed Kesir, Batuhan Kaplan, Emre Arslan, Ahmet Faruk Coskun
Systems and Control
Admittance-Guided Inverter Dispatch Command Manipulation Attack: A Grid Stability-Oriented Approach
Hongwei Zhen, Ze Yu, Xin Xiang et al. (+2 more)
The high penetration of voltage source converters in modern smart microgrids enhances operational flexibility while introducing complex cyber-physical vulnerabilities. Existing cyber-attack studies either require detailed knowledge of system
topology and controller dynamics or depend on repeated online interactions, which may compromise practicality by generating operationally infeasible or limit-violating commands. This article investigates a dispatch command manipulation attack and develops an admittance-guided framework to identify the vulnerable inverter and the worst-case dispatch command that most severely degrades system stability. A compromised inverter is utilized to inject controlled harmonic perturbations for sparse admittance measurement, and a physics-informed neural network is then employed to reconstruct the operating-point-dependent admittance of target inverters over the feasible dispatch region. Based on the reconstructed admittance, a stability-margin-oriented optimization is formulated to locate the most vulnerable inverter and the corresponding worst-case dispatch command. Controller hardware-in-the-loop experiments on a five-inverter microgrid demonstrate that the identified command can drive the system into severe sub-synchronous oscillations while remaining within nominal dispatch bounds, highlighting the need for stability-aware command screening beyond static limit checking.
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All authors Hongwei Zhen, Ze Yu, Xin Xiang, Mingyang Sun, Wuhua Li
Systems and Control
Quantifying Cyber-Vulnerability in Power Electronics Systems via an Impedance-Based Attack Reachable Domain
Hongwei Zhen, Ze Yu, Xin Xiang et al. (+2 more)
Power electronics systems are increasingly exposed to cyber threats due to their integration with digital controllers and communication networks. However, an attacker-oriented metric is still lacking to quantify the extent to which a node can be pushed toward instability within a privilege-constrained action space. This letter proposes an impedance-based Attack Reachable Domain (ARD) framework that maps feasible
adversarial actions to critical-
eigenvalue migration through impedance reshaping. Based on the ARD, an Attack Penetration Index is defined to quantify node-level cyber-vulnerability by jointly characterizing the penetration of the nominal stability margin and the accessibility of successful destabilizing attacks within a privilege-constrained action space. To make the proposed assessment computable when inverter models are unavailable, a practical gray-box workflow is further established by integrating existing impedance identification and differentiable surrogate tools. Case studies on a 4-bus system and a modified IEEE 39-bus system show that coordinated cross-layer manipulations are markedly more damaging than isolated single-layer attacks, and that the proposed metric reveals vulnerability patterns that cannot be inferred from grid-strength indicators.
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All authors Hongwei Zhen, Ze Yu, Xin Xiang, Wuhua Li, Mingyang Sun
Systems and Control
Fully Dynamic Rebalancing in Dockless Bike-Sharing Systems via Deep Reinforcement Learning
Edoardo Scarpel, Alberto Pettena, Matteo Cederle et al. (+3 more)
This paper proposes a fully dynamic Deep Reinforcement Learning (DRL) method for rebalancing dockless bike-sharing systems, overcoming the limitations of periodic, system-wide interventions. We model the service through a graph-based simulator and cast rebalancing as a Markov decision process. A DRL
agent routes a single truck in real time, executing localized pick-up, drop-off, and charging actions guided by spatiotemporal criticality scores. Experiments on real-world data show significant reductions in availability failures with a minimal fleet size, while limiting spatial inequality and mobility deserts. Our approach demonstrates the value of learning-based rebalancing for efficient and reliable shared micromobility.
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All authors Edoardo Scarpel, Alberto Pettena, Matteo Cederle, Federico Chiariotti, Marco Fabris, Gian Antonio Susto
Image and Video Processing · Cross-listed from cs.SD
Physics-Based iOCT Sonification for Real-time Interaction Awareness in Subretinal Injection
Luis D. Reyes Vargas, Veronica Ruozzi, Andrea K. M. Ross et al. (+7 more)
Subretinal injection is a delicate vitreoretinal procedure requiring precise needle placement within the subretinal space while avoiding perforation of the retinal pigment epithelium (RPE), a layer directly beneath the target with extremely limited regenerative capacity. To enhance depth perception during cannula advancement, intraoperative optical coherence
tomography (iOCT) offers high-resolution cross-sectional visualization of needle-tissue interaction; however, interpreting these images requires sustained visual
attention alongside the en face microscope view, thereby increasing cognitive load during critical phases and placing additional demands on the surgeon's proprioceptive control. In this paper, we propose a structured, real-time sonification framework designed for extensible mapping of iOCT-derived anatomical features into perceptual auditory feedback. The method employs a physics-inspired acoustic model driven by segmented retinal layers from a stream of iOCT B-scans, with needle motion and injection-induced retinal layer displacements serving as excitation inputs to the sound model, enabling perception of tool position and retinal deformation. In a controlled user study (n=34), the proposed sonification achieved high retinal layer identification accuracy and robust detection of retinal deformation-related events, significantly outperforming a state-of-the-art baseline in overall event identification (83.4% vs. 60.6%, p < 0.001), with gains driven primarily by enhanced detection of injection-induced retinal deformation. Evaluation by experts (n=4) confirmed the clinical relevance and potential intraoperative applicability of the method. These results establish structured iOCT sonification as a viable complementary modality for real-time
surgical guidance in subretinal injection.
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All authors Luis D. Reyes Vargas, Veronica Ruozzi, Andrea K. M. Ross, Shervin Dehghani, Michael Sommersperger, Koorosh Faridpooya, Mohammad Ali Nasseri, Merle Fairhurst, Nassir Navab, Sasan Matinfar
Systems and Control · Cross-listed from cs.LG
A Novel Schur Decomposition-Based Weight Projection Method for Stable State-Space Neural-Network Architectures
Sergio Vanegas, Lasse Lensu and Fredy Ruiz
Building black-box models for dynamical systems from data is a challenging problem in machine learning, especially when asymptotic stability guarantees are required. In this paper, we introduce a novel stability-ensuring and backpropagation-compatible projection scheme based on the Schur decomposition for the state
matrix of linear discrete-time state-space layers, as well as an alternative pre-factorized formulation of the methodology. The proposed methods dynamically project the quasi-triangular factor of the state
matrix's real Schur decomposition onto its nearest stable peer, ensuring stable dynamics with minimal overparameterization. Experiments on synthetic linear systems demonstrate that, despite a marginal increase in computational complexity, the method achieves accuracy and convergence rates comparable to those of state-of-the-art stable-system identification techniques. Furthermore, the lower weight count facilitates convergence during training without sacrificing accuracy in stacked neural-network architectures with static nonlinearities targeting real-world
datasets. These results suggest that the Schur-based projection provides a numerically robust framework for identifying complex dynamics on par with the state of the art while satisfying strict asymptotic-stability requirements.
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Signal Processing
WHTDM: Walsh-Hadamard Transform Division Multiplexing for Doubly-Selective Channels
Wang Hao, Yuan Zhonghao, Chi Yonggang et al. (+3 more)
We propose Walsh-Hadamard Transform Division Multiplexing (WHTDM), a multicarrier waveform that replaces the conventional IFFT/FFT pair in OFDM with a real-valued,
unitary Walsh-Hadamard transform (WHT). WHTDM inherits the CP-OFDM transceiver structure while eliminating all complex multiplications from the transform stage, yielding a transmitter with zero real multipliers in the core modulation block. For detection under doubly-selective channels, we adopt a cross-domain
memory approximate message passing (CD-MAMP) equalizer that operates on the banded structure of the equivalent WHT-domain channel
matrix. Simulation results under the 3GPP TDL-C channel model at 28 GHz demonstrate that WHTDM with CD-MAMP significantly outperforms conventional OFDM 1-tap MMSE at high mobility, achieving over an order of magnitude lower BER at 120 km/h. Among the compared CD-MAMP-equalized new waveforms, WHTDM achieves the best BER performance while maintaining a transmitter complexity 2.5 $\times$ lower than OFDM and completely eliminating complex multipliers from the transform stage, making it well-suited for low-power IoT terminals.
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All authors Wang Hao, Yuan Zhonghao, Chi Yonggang, Zhang Kuang, Tan Chenxing, Yu Jiaxing
Signal Processing
Weight Hybrid Architecture of Rydberg-Atomic Sensors
Hao Wu, Xinyuan Yao, Shanchi Wu et al. (+3 more)
Rydberg atomic
quantum receivers have been seen as novel
radio frequency measurements and the high sensitivity to a large range of frequencies makes it attractive for communications reception. However, their performance can be significantly degraded by hardware-induced noise, particularly the noise from
laser, which impacts the overall system noise floor and exhibits correlation. To address this challenge, this paper proposes a weight hybrid (WH) architecture for Rydberg-atomic sensors, a novel four-channel combining scheme designed for atomic sensors operating in correlated noise environments. By jointly processing dual signal channels and dual noise reference channels, the WH architecture effectively mitigates noise contributions from
lasers and other hardware components. All channels are optimally combined via maximum likelihood estimation within an expectation maximization framework, enabling robust signal extraction under correlated noise. Moreover, the proposed WH architecture is universal and can be readily extended to other types of Rydberg receivers to achieve consistent performance improvements.
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All authors Hao Wu, Xinyuan Yao, Shanchi Wu, Rui Ni, Chen Gong, Kaibin Huang
Signal Processing · Cross-listed from cs.IT
CP-OFDM Achieves Lower Ranging CRB Than Frequency-Spread Waveforms in the Large-Sample Regime
Fan Liu, Yifeng Xiong, Ya-Feng Liu et al. (+3 more)
The inherent randomness of communication symbols creates a fundamental tension in Integrated Sensing and Communications (
ISAC). On the one hand, they enable data transmission while allowing sensing to fully reuse communication resources. On the other hand, their randomness induces waveform-dependent fluctuations that directly affect sensing accuracy. This paper investigates a foundational question arising from this tradeoff: How does the modulation waveform affect the ranging Cramér–Rao Bound (CRB) when sensing reuses random data symbols? We address this question by revealing a structural factorization of the Fisher information
matrix (FIM) for joint delay-amplitude estimation, which separates the deterministic Jacobian of the target geometry from the random frequency-domain signal power induced by the data symbols. This structure yields a Jensen-type universal lower bound on the CRB, which is exactly attained by CP-OFDM under PSK constellations. For QAM and broader sub-Gaussian constellations, we develop an asymptotic perturbation analysis of the inverse FIM and prove that, when the number of transmitted symbols $N$ grows large, CP-OFDM achieves a lower ranging CRB than any frequency-spread orthogonal waveform over the almost-sure event where the random FIM is invertible. This superiority is further extended to amplitude estimation and full joint delay-amplitude estimation. We also characterize the local geometry of the
stochastic CRB minimization problem over the
unitary group. The analysis reveals that CP-OFDM is a stationary point for finite $N$, and its Riemannian Hessian is positive semidefinite for sufficiently large $N$, establishing its asymptotic local optimality. Numerical results confirm that OFDM outperforms representative waveforms including SC, OTFS, and AFDM.
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All authors Fan Liu, Yifeng Xiong, Ya-Feng Liu, Jie Yang, Christos Masouros, Shi Jin
Signal Processing · Cross-listed from cs.CR
Model Forensics in AI-Native Wireless Networks: Taxonomy, Applications, and Case Study
Pengyu Chen, Weiyang Li, Jin Xu et al. (+4 more)
As artificial intelligence (AI) is increasingly embedded in wireless networks, models are becoming core components that influence signal processing, resource scheduling and network control. However, model anomalies, tampering and malicious functions also introduce new security risks. In this article, we focus on model forensics in AI-native wireless networks. Specifically, we first discuss key problems including model authenticity verification, malicious function identification and accountability tracing, and summarize the main categories of model forensics. We then explain the role of model forensics in AI-native wireless networks and review representative application scenarios. In the case study, we use RF fingerprinting as an example and present two concrete workflows based on watermark authentication and
backdoor detection, illustrating how provenance authentication and malicious behavior identification can be implemented in practice. The results show that model forensics can provide important support for anomaly assessment, provenance tracing and trustworthy operation in AI-native wireless networks. Finally, we outline several promising directions for future research in this emerging area.
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All authors Pengyu Chen, Weiyang Li, Jin Xu, Jiacheng Wang, Ning Wang, Dusit Niyato, Tao Xiang
Systems and Control
Automated Curriculum Design for High-dimensional Human Motor Learning
Ankur Kamboj, Rajiv Ranganathan, Xiaobo Tan et al. (+1 more)
Designing effective practice schedules for high-dimensional motor learning tasks remains a challenge, especially when skill states are unobservable and task performance may not reflect the true learning. We propose an automated curriculum design framework that combines a human motor learning model and personalized real-time skill estimation with
Stochastic Nonlinear Model Predictive Control in de-novo (novel) motor learning paradigms. We validated our framework both through simulations and human-subject studies (N = 36) using a hand exoskeleton. Our proposed approach accelerates skill acquisition by $\sim23\%$, and ${\sim17\%}$ when compared to a random curriculum and a performance heuristics-based curriculum, respectively. These significant gains in learning efficiency highlight the potential of model-based, individualized curricula for motor rehabilitation and complex skill training.
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All authors Ankur Kamboj, Rajiv Ranganathan, Xiaobo Tan, Vaibhav Srivastava