Systems and Control
How to Capture Human Preference: Commissioning of a Robotic Use-Case via Preferential Bayesian Optimisation
Sander De Witte, Jeroen Taets, Andras Retzler et al. (+2 more)
The popularity of
Bayesian Optimization (BO) to automate or support the commissioning of engineering systems is rising. Conventional BO, however, relies on the availability of a scalar objective function. The latter is often difficult to define and rarely captures the nuanced judgement of expert operators in industrial settings. Preferential
Bayesian Optimization (PBO) addresses this limitation by relying solely on pairwise preference feedback of a human expert, so-called duels. In this paper, we study PBO's capacity to commission a particular setup where a manipulator needs to push a block towards a target position. We
benchmark state-of-the-art
algorithms in both simulations and in the real world. Our results confirm that PBO can commission the set-up to the satisfaction of an expert operator whilst relying solely on binary preference feedback. To evaluate to what extend the same result can be achieved using conventional BO we investigate the experts decision consistency against an expert-designed cost function. Our study reveals that the experts fail to define a cost function that is in full agreement with their own decision process as witnessed in the PBO experiments. We then show that the auxiliary cost function that is constructed as a by-product of the PBO
algorithms outperforms the expert-designed cost function in terms of decision consistency. Furthermore we demonstrate that this cost function can be used with conventional BO
algorithms in an effort to reproduce the optimal design. This proofs the preference based cost function captures the experts' preferences perhaps more effectively than the experts could articulate preference themselves. In conclusion, we discuss downsides and propose directions for future research.
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All authors Sander De Witte, Jeroen Taets, Andras Retzler, Guillaume Crevecoeur, Tom Lefebvre
Signal Processing · Cross-listed from cs.RO
AgriLiRa4D: A Multi-Sensor UAV Dataset for Robust SLAM in Challenging Agricultural Fields
Zhihao Zhan, Yuhang Ming, Shaobin Li et al. (+1 more)
Multi-sensor Simultaneous Localization and Mapping (SLAM) is essential for Unmanned Aerial Vehicles (
UAVs) performing agricultural tasks such as spraying, surveying, and inspection. However, real-world, multi-modal agricultural
UAV datasets that enable research on robust operation remain scarce. To address this gap, we present AgriLiRa4D, a multi-modal
UAV dataset designed for challenging outdoor agricultural environments. AgriLiRa4D spans three representative farmland types-flat, hilly, and terraced-and includes both boundary and coverage operation modes, resulting in six flight sequence groups. The
dataset provides high-accuracy ground-truth trajectories from a Fiber Optic Inertial
Navigation System with Real-Time Kinematic capability (FINS_RTK), along with synchronized measurements from a 3D
LiDAR, a 4D Radar, and an Inertial Measurement Unit (IMU), accompanied by complete intrinsic and extrinsic calibrations. Leveraging its comprehensive sensor suite and diverse real-world scenarios, AgriLiRa4D supports diverse SLAM and localization studies and enables rigorous robustness evaluation against low-texture crops, repetitive patterns, dynamic vegetation, and other challenges of real agricultural environments. To further demonstrate its utility, we
benchmark four state-of-the-art multi-sensor SLAM
algorithms across different sensor combinations, highlighting the difficulty of the proposed sequences and the necessity of multi-modal approaches for reliable
UAV localization. By filling a critical gap in agricultural SLAM
datasets, AgriLiRa4D provides a valuable
benchmark for the research community and contributes to advancing autonomous
navigation technologies for agricultural
UAVs. The
dataset can be downloaded from: https://zhan994.github.io/AgriLiRa4D.
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All authors Zhihao Zhan, Yuhang Ming, Shaobin Li, Jie Yuan
Signal Processing
Multimodal Mixture-of-Experts for ISAC in Low-Altitude Wireless Networks
Kai Zhang, Wentao Yu, Hengtao He et al. (+3 more)
Integrated sensing and communication (
ISAC) is a key enabler for low-altitude wireless networks (LAWNs), providing simultaneous environmental perception and data transmission in complex aerial scenarios. By combining heterogeneous sensing modalities such as visual, radar,
lidar, and positional information,
multimodal ISAC can improve both situational awareness and robustness of LAWNs. However, most existing
multimodal fusion approaches use static fusion strategies that treat all modalities equally and cannot adapt to channel heterogeneity or time-varying modality reliability in dynamic low-altitude environments. To address this fundamental limitation, we propose a mixture-of-experts (MoE) framework for
multimodal ISAC in LAWNs. Each modality is processed by a dedicated expert network, and a lightweight gating module adaptively assigns fusion weights according to the instantaneous informativeness and reliability of each modality. To improve scalability under the stringent energy constraints of aerial platforms, we further develop a sparse MoE variant that selectively activates only a subset of experts, thereby reducing computation overhead while preserving the benefits of adaptive fusion. Comprehensive simulations on three typical
ISAC tasks in LAWNs demonstrate that the proposed frameworks consistently outperform conventional
multimodal fusion baselines in terms of learning performance and training sample efficiency.
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All authors Kai Zhang, Wentao Yu, Hengtao He, Shenghui Song, Jun Zhang, Khaled B. Letaief
Image and Video Processing · Cross-listed from cs.LG
A unified framework for geometry-independent operator learning in cardiac electrophysiology simulations
Bei Zhou, Cesare Corrado, Shuang Qian et al. (+9 more)
Learning neural operators on heterogeneous and irregular geometries remains a fundamental challenge, as existing approaches typically rely on structured discretisations or explicit mappings to a shared reference domain. We propose a unified framework for geometry-independent operator learning that reformulates the learning problem in an intrinsic coordinate space defined on the underlying
manifold. By expressing both inputs and outputs in this shared coordinate domain, the framework decouples operator learning from mesh discretisation and
geometric variability, while preserving meaningful spatial organisation and enabling faithful reconstruction on the original geometry. We demonstrate the framework on cardiac electrophysiology, a particularly challenging setting due to extreme anatomical variability across heart geometries. Leveraging a GPU-accelerated simulation pipeline, we generate large-scale
datasets of high-fidelity electrophysiology simulations across diverse patient-specific anatomies and train customised neural operators to predict full-field local activation time maps. The proposed approach outperforms established neural operators on both atrial and ventricular geometries. Beyond cardiac electrophysiology, we further show that the same representation enables operator learning in cardiac biomechanics, a distinct problem involving volumetric deformation, highlighting the generality of the proposed framework. Together, these results establish intrinsic coordinate representations as a principled and extensible pathway for neural operator learning on complex physical systems characterised by heterogeneous geometry.
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All authors Bei Zhou, Cesare Corrado, Shuang Qian, Maximilian Balmus, Angela W. C. Lee, Cristobal Rodero, Caroline Roney, Marco J.W. Gotte, Luuk H.G.A. Hopman, Gernot Plank, Mengyun Qiao, Steven Niederer
Systems and Control
A Modified Boost Converter Topology for Dynamic Characterization of Hot Carrier and Trap Generation in GaN HEMTs
Moshe Azoulay, Gilad Orr and Gady Golan
Modern microelectronic systems require long term operational stability, necessitating precise reliability models to predict device lifecycles and identify governing failure mechanisms. This is particularly critical for high power GaN High-Electron-Mobility Transistors (HEMTs), where reliability research has historically trailed behind low power digital counterparts. This study introduces a novel application of a modified boost converter
circuit designed to investigate GaN failure mechanisms, specifically targeting the determination of reliability factors for the MTOL model. By utilizing a high duty cycle, the
circuit stresses the device at maximum rated voltages and currents with minimal input requirements, accelerating hot carrier and trap generation without immediate detrimental failure. Experimental validation was conducted using an EPC 2038 GaN transistor under a constant drain current of 400 mA and a duty cycle of 0.7. The results confirmed that the increase in Drain-Source on-resistance ($R_{DS(on)}$) follows a logarithmic trend over time, consistent with the EPC Phase 12 reliability model. While initial tests at 40V did not successfully validate the longitudinal optical
phonon scattering energy ($\hbar\omega_{LO}$), but were reasonably acceptable, subsequent stress tests at 70V and 100V yielded $\hbar\omega_{LO}$ values that were successfully validated against existing theoretical and experimental data. This methodology provides a robust framework for predicting performance and lifetime across varying operational parameters in modern power electronics.
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Systems and Control · Cross-listed from cs.RO
Dynamic Log-Gaussian Process Control Barrier Function for Safe Robotic Navigation in Dynamic Environments
Xin Yin, Chenyang Liang, Yanning Guo et al. (+1 more)
Control Barrier Functions (CBFs) have emerged as efficient tools to address the safe
navigation problem for
robot applications. However, synthesizing informative and obstacle motion-aware CBFs online using real-time sensor data remains challenging, particularly in unknown and dynamic scenarios. Motived by this challenge, this paper aims to propose a novel Gaussian Process-based formulation of CBF, termed the Dynamic Log Gaussian Process Control Barrier Function (DLGP-CBF), to enable real-time construction of CBF which are both spatially informative and responsive to obstacle motion. Firstly, the DLGP-CBF leverages a logarithmic transformation of GP regression to generate smooth and informative barrier values and gradients, even in sparse-data regions. Secondly, by explicitly modeling the DLGP-CBF as a function of obstacle positions, the derived safety constraint integrates predicted obstacle velocities, allowing the controller to proactively respond to dynamic obstacles' motion. Simulation results demonstrate significant improvements in obstacle avoidance performance, including increased safety margins, smoother trajectories, and enhanced responsiveness compared to baseline methods.
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All authors Xin Yin, Chenyang Liang, Yanning Guo, Jie Mei
Systems and Control · Cross-listed from math.OC
Bayesian Ambiguity Contraction-based Adaptive Robust Markov Decision Processes for Adversarial Surveillance Missions
Jimin Choi and Max Z. Li
Collaborative Combat Aircraft (CCAs) are envisioned to enable autonomous Intelligence, Surveillance, and Reconnaissance (ISR) missions in contested environments, where adversaries may act strategically to deceive or evade detection. These missions pose challenges due to model uncertainty and the need for safe, real-time decision-making. Robust Markov Decision Processes (RMDPs) provide worst-case guarantees but are limited by static ambiguity sets that capture initial uncertainty without adapting to new observations. This paper presents an adaptive RMDP framework tailored to ISR missions with CCAs. We introduce a mission-specific formulation in which the CCA alternates between movement and sensing states.
Adversarial uncertainty is represented by finite threat prototypes defining observation, exposure, and state-action transition models. Our approach incrementally refines policies by eliminating inconsistent threat models, allowing the CCA to move from worst-case protection toward more efficient mission policies while maintaining robustness. We establish fixed point stability, agreement with nominal planning as credible sets identify the true node-specific threat prototypes, and a conservative expected value guarantee while those prototypes remain credible. Experiments under Gaussian and non-Gaussian threat models across diverse network
topologies show higher mission rewards and fewer exposure events compared to nominal and static robust planners.
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Signal Processing
Cuffless Blood Pressure Estimation from Six Wearable Sensor Modalities in Multi-Motion-State Scenarios
Yiqiao Chen, Fazheng Xu, Zijian Huang et al. (+2 more)
Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide, and sustained hypertension is an often silent risk factor, making cuffless continuous blood pressure (BP) monitoring with wearable devices important for early screening and long-term management. Most existing cuffless BP estimation methods use only photoplethysmography (PPG) and electrocardiography (ECG) signals, alone or in combination. These models are typically developed under resting or quasi-static conditions and struggle to maintain robust accuracy in multi-motion-state scenarios. In this study, we propose a six-modal BP estimation framework that jointly leverages ECG, multi-channel PPG, attachment pressure, sensor temperature, and triaxial acceleration and angular velocity. Each modality is processed by a lightweight branch encoder, contrastive learning enforces cross-modal semantic
alignment, and a mixture-of-experts (MoE) regression head adaptively maps the fused features to BP across motion states. Comprehensive experiments on the public Pulse Transit Time PPG
Dataset, which includes running, walking, and sitting data from 22 subjects, show that the proposed method achieves mean absolute errors (MAE) of 3.60 mmHg for systolic BP (SBP) and 3.01 mmHg for diastolic BP (DBP). From a clinical perspective, it attains Grade A for SBP, DBP, and mean arterial pressure (MAP) according to the British Hypertension Society (BHS) protocol and meets the numerical criteria of the Association for the Advancement of Medical Instrumentation (AAMI) standard for mean error (ME) and standard deviation of error (SDE).
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All authors Yiqiao Chen, Fazheng Xu, Zijian Huang, Juchi He, Zhenghui Feng
Systems and Control · Cross-listed from cs.RO
Integrated YOLOP Perception and Lyapunov-based Control for Autonomous Mobile Robot Navigation on Track
Mo Chen
This work presents a real-time autonomous track
navigation framework for nonholonomic differential-drive mobile
robots by jointly integrating multi-task visual perception and a provably stable tracking controller. The perception pipeline reconstructs lane centerlines using 2D-to-3D camera projection, arc-length based uniform point resampling, and cubic
polynomial fitting solved via robust QR least-squares optimization. The controller regulates
robot linear and angular velocities through a Lyapunov-stability grounded design, ensuring bounded error dynamics and asymptotic convergence of position and heading deviations even in dynamic and partially perceived lane scenarios, without relying on HD prior maps or global satellite localization. Real-world experiments on embedded platforms verify system fidelity, real-time execution, trajectory smoothness, and closed-loop stability for reliable autonomous
navigation.
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Systems and Control · Cross-listed from cs.CR
Towards a Multi-Layer Defence Framework for Securing Near-Real-Time Operations in Open RAN
Hamed Alimohammadi, Samara Mayhoub, Sotiris Chatzimiltis et al. (+2 more)
Securing the near-real-time (near-RT) control operations in Open
Radio Access Networks (Open RAN) is increasingly critical, yet remains insufficiently addressed, as new runtime threats target the control loop while the system is operational. In this paper, we propose a multi-layer defence framework designed to enhance the security of near-RT RAN Intelligent Controller (RIC) operations. We classify operational-time threats into three categories, message-level, data-level, and control logic-level, and design and implement a dedicated detection and mitigation component for each: a signature-based E2 message inspection module performing structural and semantic validation of signalling exchanges, a telemetry poisoning detector based on temporal anomaly scoring using an LSTM network, and a runtime xApp attestation mechanism based on execution-time hash challenge-response. The framework is evaluated on an O-RAN testbed comprising FlexRIC and a commercial RAN emulator, demonstrating effective detection rates, low latency overheads, and practical integration feasibility. Results indicate that the proposed safeguards can operate within near-RT time constraints while significantly improving protection against runtime attacks, introducing less than 80 ms overhead for a network with 500 User Equipment (UEs). Overall, this work lays the foundation for deployable, layered, and policy-driven runtime security architectures for the near-RT RIC control loop in Open RAN, and provides an extensible framework into which future mitigation policies and threat-specific modules can be integrated.
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All authors Hamed Alimohammadi, Samara Mayhoub, Sotiris Chatzimiltis, Mohammad Shojafar, Muhammad Nasir Mumtaz Bhutta
Systems and Control · Cross-listed from physics.ins-det
Combined Effects of Transient Ionizing and Electromagnetic Pulse on Vertical NPN Bipolar Transistor
Meiqing Zhong, Cui Meng, Yinong Liu et al. (+4 more)
Combined effects of transient ionizing and electromagnetic pulse on vertical NPN bipolar transistor were experimentally investigated under pulsed X-ray irradiation. Technology computer-aided design (TCAD) simulation method was also employed to explore the underlying physical mechanisms. The results demonstrate that the combined effect of a positive pulse injected into the collector (CEMP) and pulsed X-ray irradiation exceeds the linear superposition of their individual effects. Conversely, the combined effect of a positive pulse injected into the base (BEMP) and pulsed X-ray irradiation aligns closely with the results observed under BEMP acting alone. Mechanism analysis reveals that when CEMP and pulsed X-ray irradiation act simultaneously, there is a significant increase in both the drift photocurrent at the collector junction and the
diffusion photocurrent near the collector junction. However, when BEMP and pulsed X-ray irradiation act simultaneously, these photocurrent components remain small, leading to a combined effect similar to the results observed when BEMP acts alone. These findings provide critical insights for the radiation-hardening design of bipolar
circuits in harsh radiation environments.
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All authors Meiqing Zhong, Cui Meng, Yinong Liu, Lanfeng Yuan, Chicheng Liu, Bolun Feng, Maoxing Zhang
Signal Processing
In-Context Learning for Deep Joint Source-Channel Coding Over MIMO Channels
Meng Hua, Wenjing Zhang, Chenghong Bian et al. (+1 more)
Large
language models have demonstrated the ability to perform in-context learning (ICL), whereby the model performs predictions by directly mapping the query and a few examples from the given task to the output variable. In this paper, we study ICL for deep joint source-channel coding (DeepJSCC) in image transmission over multiple-input multiple-output (
MIMO) systems, where an ICL denoiser is employed for
MIMO symbol estimation. We first study the transceiver without any hardware impairments and explore the integration of
transformer-based ICL with DeepJSCC in both open-loop and closed-loop
MIMO systems, depending on the availability of channel state information (CSI) at the transceiver. For both open-loop and closed-loop scenarios, we propose two
MIMO transceiver architectures that leverage context information, i.e., pilot sequences and their outputs, as additional inputs, enabling the DeepJSCC encoder, DeepJSCC decoder, and the ICL denoiser to jointly learn encoding, decoding, and estimation strategies tailored to each channel realization. Next, we extend our study to a more challenging scenario where the transceiver suffers from in-phase and quadrature (IQ) imbalance, resulting in nonlinear
MIMO estimation. In this case, the context information is also exploited, facilitating joint learning across the DeepJSCC encoder, decoder, and the ICL denoiser under hardware impairments and varying channel conditions. Numerical results demonstrate that the ICL denoiser for
MIMO estimation significantly outperforms the conventional least-squares method, with even greater advantages under IQ imbalance. Moreover, the proposed
transformer-based ICL framework, integrated with contextual information, achieves significant improvements in end-to-end image reconstruction quality under transceiver IQ imbalance.
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All authors Meng Hua, Wenjing Zhang, Chenghong Bian, Deniz Gunduz
Signal Processing
Spectral-Energy Efficiency Tradeoff of Nearly-Passive RIS in MIMO URLLC Downlink: Diagonal vs. Beyond Diagonal
Mohammad Soleymani, Alessio Zappone, Eduard Jorswieck et al. (+2 more)
This paper investigates the spectral and energy efficiency (EE) tradeoff of nearly-passive (NP), both locally and globally NP (GNP), reconfigurable intelligent surface (RIS), considering diagonal and beyond-diagonal (BD) implementations in multi-user multiple-input multiple-output (MU-
MIMO) broadcast channels designed for ultra-reliable low-latency communication (URLLC). We demonstrate that while all RIS architectures enhance the spectral efficiency, GNP BD-RIS achieves the highest gains. However, its EE is highly sensitive to the static
circuit power consumption since BD-RIS has many more
circuit elements than diagonal architectures. Furthermore, we demonstrate that the benefits of BD-RIS over diagonal RIS diminish as the number of data streams per user increases due to enhanced channel diversity in
MIMO systems.
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All authors Mohammad Soleymani, Alessio Zappone, Eduard Jorswieck, Marco Di Renzo, Ignacio Santamaria
Signal Processing · Cross-listed from cs.SD
Two-Dimensional Quantization for Geometry-Aware Audio Coding
Tal Shuster and Eliya Nachmani
Recent neural audio codecs have achieved impressive reconstruction quality, typically relying on quantization methods such as Residual Vector Quantization (RVQ), Vector Quantization (VQ) and Finite Scalar Quantization (FSQ). However, these quantization techniques limit the
geometric structure of the latent space, make it harder to capture correlations between features leading to inefficiency in representation learning, codebook utilization and
token rate. In this paper we introduce Two-Dimensional Quantization (Q2D2), a quantization scheme in which feature pairs are projected onto structured 2D grids, such as hexagonal, rhombic, or rectangular tiling and quantized to the nearest grid values, yielding an implicit codebook defined by the product of grid levels, with codebook sizes comparable to conventional methods. Despite its simple
geometric formulation, Q2D2 improves audio compression efficiency, with low
token rates and high codebook utilization while maintaining state of the art reconstruction quality. Specifically, Q2D2 achieves competitive to superior performance in various objective and subjective reconstruction metrics, across extensive experiments in
speech, audio and music domains compared to state of the art models. Comprehensive ablation studies further confirm the effectiveness of our design choices.
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Systems and Control · Cross-listed from math.OC
On robotic manipulators with time-dependent inertial parameters: From physical consistency to boundedness of the mass matrix
Tom Kaufmann and Johann Reger
We generalize the robotics equation describing the dynamics of open kinematic chains by including the effect of time-dependent change of inertial parameters as well as the effects of causative mass-density redistribution, triggered by internal movement of mass-carrying particles relative to their body-fixed frames. Time dependency of inertial parameters that results from the sole addition of mass to the
robot prominently occurs during the loading of end-effectors–a scenario covered by our model without restriction from the restraint that kinematic parameters of the
robot must remain constant. Further, our model also includes internal mass-density redistributions that adhere to this kinematic restraint such as trolleys attached to the
robot or the movement of passengers. To accompany the generalized robotics equation with some theoretical infrastructure, we then introduce the concepts of uniform physical consistency and upper boundedness of inertial parameters under which desirable, structural properties regarding the existence of finite, positive uniform bounds of the mass
matrix can be shown to carry over to the more involved case of time-dependent inertial parameters. These findings have implications for adaptive control, as they facilitate more realistic testing for robustness against unforeseen time dependencies. Moreover, the results in this paper also provide a pathway to ensuring the desirable existence of finite, positive uniform bounds of the estimated mass
matrix under upper bounded, uniformly physically consistent estimation regimes.
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Systems and Control
A Unified Bayesian Framework for Data-Driven Smoothing, Prediction, and Control
Mingzhou Yin, Andrea Iannelli, Seyed Ali Nazari et al. (+1 more)
Extending data-driven
algorithms based on Willems' fundamental lemma to
stochastic data often requires empirical and customized workarounds. This work presents a unified
Bayesian framework for linear systems that provides a systematic and general method for handling
stochastic data-driven tasks, including smoothing, prediction, and control, via maximum a posteriori estimation. This framework formulates a unified trajectory estimation problem for the three tasks by specifying different types of trajectory knowledge. Then, a
Bayesian problem is solved that optimally combines trajectory knowledge with a data-driven characterization of the trajectory from offline data for correlated input-output uncertainties with elliptical distributions. Under specific conditions, this problem is shown to generalize existing data-driven prediction and control
algorithms. Numerical examples demonstrate the performance of the unified approach for all three tasks against other data-driven and system identification approaches.
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All authors Mingzhou Yin, Andrea Iannelli, Seyed Ali Nazari, Matthias A. Müller
Audio and Speech Processing
Identifiability Conditions for Acoustic Feedback Cancellation with the Two-Channel Adaptive Feedback Canceller Algorithm
Arnout Roebben, Toon van Waterschoot, Jan Wouters et al. (+1 more)
In audio signal processing applications with a microphone and a loudspeaker within the same acoustic environment, the loudspeaker signals can feed back into the microphone, thereby creating a closed-loop system that potentially leads to system instability. To remove this acoustic coupling, prediction error method (PEM) feedback cancellation
algorithms aim to identify the feedback path between the loudspeaker and the microphone by assuming that the input signal can be modelled by means of an autoregressive (AR) model. It has previously been shown that this PEM framework and resulting
algorithms can identify the feedback path correctly in cases where the forward path from microphone to loudspeaker is sufficiently time-varying or non-linear, or when the forward path delay equals or exceeds the order of the AR model. In this paper, it is shown that this delay-based condition can be generalised for one particular PEM-based
algorithm, the so-called two-channel adaptive feedback canceller (2ch-AFC), to an invertibility-based condition, for which it is shown that identifiability can be achieved when the order of the forward path feedforward filter exceeds the order of the AR model. Additionally, the condition number of inversion of the correlation
matrix as used in the 2ch-AFC
algorithm can serve as a measure for monitoring the identifiability.
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All authors Arnout Roebben, Toon van Waterschoot, Jan Wouters, Marc Moonen
Systems and Control · Cross-listed from cs.IT
Value of Communication in Goal-Oriented Semantic Communications: A Pareto Analysis
Jiping Luo, Bowen Li and Nikolaos Pappas
Emerging cyber-physical systems increasingly operate under stringent communication constraints that preclude reliable transmission of all available machine-type data. Motivated by this challenge, goal-oriented semantic communication advocates a minimalist design principle: transmit only what is necessary to achieve the system's goal. In this work, we formulate optimal semantic communication design as a bi-objective Markov decision process (MDP) that trades off two competing objectives: system performance and communication cost. In contrast to classical approaches that seek to optimize system performance by exhausting a prescribed communication budget, we propose a minimalist design that answers: What is the marginal value of communication, and what is the minimum communication required to achieve the goal? Our approach is based on a Pareto analysis that characterizes the complete set of policies achieving optimal tradeoffs between these two objectives. The value of communication is defined as the absolute slope of the resulting Pareto front. A key result of this paper shows that this front admits a tractable structure: it is convex and piecewise linear, and its corner points correspond to simple deterministic policies. The entire front can be constructed by mixing the deterministic policies at neighboring corner points. Leveraging these
geometric properties, we introduce SPLIT, an efficient and provably optimal
algorithm for computing the Pareto front. Numerical results demonstrate the efficiency of SPLIT, the diminishing returns of over-provisioning in communication, and the effectiveness of Pareto-optimal semantic communication design.
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Signal Processing
RadioPiT: Radio Map Generation with Pixel Transformer Driven by Ultra-Sparse Real-World Data
Zeyao Sun, Bohao Fan, Qingyu Liu et al. (+2 more)
As wireless communication networks rapidly evolve, spectrum resources are increasingly scarce, making effective spectrum management critically important.
Radio map is a spatial representation of signal characteristics across different locations in a given area, which serves as a key tool for enabling precise spectrum management. To generate accurate
radio maps, extensive research efforts have been made. However, most existing studies are conducted on simulation data, which differs significantly from real-world data and cannot accurately reflect the spectrum characteristics of practical environments. To tackle this problem, we construct a
dataset of real-world
radio map with a self-developed measurement system. Due to the limited volume of real-world data and the distributional discrepancies between simulation and real-world data, we propose a Pixel
Transformer (PiT)- based model enhanced with the test-time adaptation (TTA) strategy, named RadioPiT, for real-world
radio map generation. Experimental results demonstrate that our proposed RadioPiT significantly outperforms baseline methods in real-world scenarios, yielding a 21.9% decrement in the root mean square error (RMSE) compared to RadioUNet.
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All authors Zeyao Sun, Bohao Fan, Qingyu Liu, Shuhang Zhang, Lingyang Song
Signal Processing
zea: A Toolbox for Cognitive Ultrasound Imaging
Tristan S.W. Stevens, Wessel L. van Nierop, Ben Luijten et al. (+7 more)
We present zea (pronounced ze-yah), a Python package for cognitive ultrasound imaging that offers a flexible, modular, and differentiable pipeline for ultrasound data processing. Additionally, it includes a collection of pre-defined models for ultrasound image and signal processing. The toolbox is designed to be easy to use, with a high-level interface that enables users to define custom ultrasound reconstruction pipelines and integrate deep learning models seamlessly. Built on top of Keras 3, it supports all three major deep learning backends: TensorFlow, PyTorch, and JAX, making it straightforward to incorporate custom ultrasound processing pipelines into machine learning workflows. Documentation is available at https://zea.readthedocs.io/.
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All authors Tristan S.W. Stevens, Wessel L. van Nierop, Ben Luijten, Vincent van de Schaft, Oisín Nolan, Beatrice Federici, Louis D. van Harten, Simon W. Penninga, Noortje I.P. Schueler, Ruud J.G. van Sloun
Signal Processing
Masked Symbol Modeling for Demodulation of Oversampled Baseband Communication Signals in Impulsive Noise-Dominated Channels
Oguz Bedir, Nurullah Sevim, Mostafa Ibrahim et al. (+1 more)
Recent breakthroughs in natural
language processing show that
attention mechanism in
Transformer networks, trained via masked-
token prediction, enables models to capture the semantic context of the
tokens and internalize the grammar of
language. While the application of
Transformers to communication systems is a burgeoning field, the notion of context within physical waveforms remains under-explored. This paper addresses that gap by re-examining inter-symbol contribution (ISC) caused by pulse-shaping overlap. Rather than treating ISC as a nuisance, we view it as a deterministic source of contextual information embedded in oversampled complex baseband signals. We propose Masked Symbol Modeling (MSM), a framework for the physical (PHY) layer inspired by Bidirectional Encoder Representations from
Transformers methodology. In MSM, a subset of symbol aligned samples is randomly masked, and a
Transformer predicts the missing symbol identifiers using the surrounding "in-between" samples. Through this objective, the model learns the latent syntax of complex baseband waveforms. We illustrate MSM's potential by applying it to the task of demodulating signals corrupted by impulsive noise, where the model infers corrupted segments by leveraging the learned context. Our results suggest a path toward receivers that interpret, rather than merely detect communication signals, opening new avenues for context-aware PHY layer design.
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All authors Oguz Bedir, Nurullah Sevim, Mostafa Ibrahim, Sabit Ekin
Systems and Control · Cross-listed from cs.LG
RE-LLM: Integrating Large Language Models into Renewable Energy Systems
Ali Forootani, Mohammad Sadr, Danial Esmaeili Aliabadi et al. (+1 more)
Energy system models are increasingly employed to guide long-term planning in multi-sectoral environments where decisions span electricity, heat, transport, land use, and industry. While these models provide rigorous quantitative insights, their outputs are often highly technical, making them difficult to interpret for non-expert stakeholders such as policymakers, planners, and the public. This communication gap limits the accessibility and practical impact of scenario-based modeling, particularly as energy transitions grow more complex with rising shares of renewables, sectoral integration, and deep uncertainties. To address this challenge, we propose the Renewable Energy Large
Language Model (RE-LLM), a hybrid framework that integrates Large
Language Models (LLMs) directly into the energy system modeling workflow. RE-LLM combines three core elements: (i) optimization-based scenario exploration, (ii) machine learning surrogates that accelerate computationally intensive simulations, and (iii) LLM-powered natural
language generation that translates complex results into clear, stakeholder-oriented explanations. This integrated design not only reduces computational burden but also enhances inter-pretability, enabling real-time
reasoning about trade-offs, sensitivities, and policy implications. The framework is adaptable across different optimization platforms and energy system models, ensuring broad applicability beyond the case study presented. By merging speed, rigor, and interpretability, RE-LLM advances a new paradigm of human-centric energy modeling. It enables interactive,
multilingual, and accessible engagement with future energy pathways, ultimately bridging the final gap between data-driven analysis and actionable decision-making for sustainable transitions.
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All authors Ali Forootani, Mohammad Sadr, Danial Esmaeili Aliabadi, Daniela Thraen
Signal Processing
Joint CFO-Channel Estimation under Strong Inter-Cell Interference for Low-Altitude Radio Mapping
Bowen Li, Haotian Zhang, Mu Jia et al. (+2 more)
Extending terrestrial networks into low-altitude airspace is a practical way to support aerial services, and accurate low-altitude
radio maps are essential for characterizing terrestrial base station (BS) coverage and guiding system design. This work targets per-
cell per-beam
radio mapping from 5G new
radio (NR) synchronization signal (SS) burst sets. Conventional processing treats interference as noise and focuses on the strongest link, which is insufficient to comprehensive awareness of the
radio environment and ineffective in dense multi-
cell low-altitude scenarios. We propose a successive waveform reconstruction and cancellation framework that iteratively estimates, reconstructs, and subtracts the SSs of stronger BSs, thereby enabling reliable detection and estimation of ultra-weak signals. To support this, we introduce the notion of a carrier frequency offset (CFO)-coherent block within which a common-CFO/per-synchronization signal block (SSB)-channel model holds and design a joint CFO-channel
estimator that coherently aggregates multiple SSBs within each CFO-coherent block. We further derive closed-form scaling laws that relate estimation accuracy to unmanned aerial vehicle (
UAV) speed, motion geometry, burst periodicity, and the length of the CFO-coherent block. Simulations show that the proposed framework can detect and estimate SSs at signal-to-interference-and-noise ratio (SINR) levels down to -30 dB. Field tests at 150 m altitude demonstrate per-beam coverage maps for more than ten overlapping BSs and reveal that, despite strong received power, the measured SINR rarely exceeds 10 dB, underscoring the need for careful interference management in low-altitude airspace.
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All authors Bowen Li, Haotian Zhang, Mu Jia, Junting Chen, Nikolaos Pappas
Signal Processing · Cross-listed from stat.ME
Inferring Dynamic Hidden Graph Structure in Heterogeneous Correlated Time Series
Jeshwanth Mohan, Bharath Ramsundar and Sandya Subramanian
Modeling heterogeneous correlated time series requires the ability to learn hidden dynamic relationships between component time series with possibly varying periodicities and generative processes. To address this challenge, we formulate and evaluate a windowed variance-correlation metric (WVC) designed to quantify time-varying correlations between signals. This method directly recovers hidden relationships in an specified time interval as a weighted adjacency
matrix, consequently inferring hidden dynamic graph structure. On simulated data, our method captures correlations that other methods miss. The proposed method expands the ability to learn dynamic graph structure between significantly different signals within a single cohesive dynamical graph model.
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Signal Processing
Experimental Methods, Health Indicators, and Diagnostic Strategies for Retired Lithium-ion Batteries: A Comprehensive Review
Song Zhang, Ruohan Guo, Xiaohua Ge et al. (+2 more)
Reliable health assessment of retired lithium-ion batteries is essential for safe and economically viable second-life deployment, yet remains difficult due to sparse measurements, incomplete historical records, heterogeneous chemistries, and limited or noisy battery health labels. Conventional laboratory diagnostics, such as full charge-discharge cycling, pulse tests, Electrochemical Impedance Spectroscopy (EIS) measurements, and thermal characterization, provide accurate degradation information but are too time-consuming, equipment-intensive, or condition-sensitive to be applied at scale during retirement-stage sorting, leaving real-world
datasets fragmented and inconsistent. This review synthesizes recent advances that address these constraints through physical health indicators, experiment testing methods, data-generation and augmentation techniques, and a spectrum of learning-based modeling routes spanning supervised, semi-supervised, weakly supervised, and unsupervised paradigms. We highlight how minimal-test features, synthetic data, domain-invariant representations, and uncertainty-aware prediction enable robust inference under limited or approximate labels and across mixed chemistries and operating histories. A comparative evaluation further reveals trade-offs in accuracy, interpretability, scalability, and computational burden. Looking forward, progress toward physically constrained generative models, cross-chemistry generalization, calibrated uncertainty estimation, and standardized
benchmarks will be crucial for building reliable, scalable, and deployment-ready health prediction tools tailored to the realities of retired-battery applications.
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All authors Song Zhang, Ruohan Guo, Xiaohua Ge, Perter Mahon, Weixiang Shen
Signal Processing
Retroreflective Optical ISAC for 6G: Technologies, Applications and Future Directions
Tiantian Chu, Chen Chen, Jia Ye et al. (+7 more)
Integrated sensing and communication (
ISAC) has emerged as a key technological paradigm for sixth generation (6G) mobile networks, aiming to unify sensing and communication in a spectrally efficient and hardware lightweight manner.
Radio frequency
ISAC (RF-
ISAC) is constrained by spectrum crowding, limited sensing resolution, and susceptibility to electromagnetic interference. In contrast, optical
ISAC (O-
ISAC) leverages the large bandwidth and short wavelength of optical carriers and is regarded as an important complement to RF-
ISAC. However, conventional O-
ISAC relies on natural optical reflections from target surfaces, which generate weak echoes that are highly dependent on surface materials, thereby limiting the achievable sensing range and sensing accuracy. This article introduces retroreflective optical
ISAC (RO-
ISAC), which alleviates these limitations by equipping targets with compact retroreflective modules. These modules return incident light approximately back to the source over a useful range of incidence angles, forming a well controlled double pass optical path with strong and stable echoes, and thereby further unlocking the application potential of O-
ISAC. The conceptual architecture of RO-
ISAC is presented together with the underlying retroreflection mechanism. Key enabling technologies for RO-
ISAC systems are discussed, including channel modeling, waveform design, bidirectional transmission, and multi-target sensing and communication, with representative details and experimental validation. The suitability of RO-
ISAC is analyzed in indoor, aerial,
underwater, and satellite scenarios, and challenges and research directions related to mobility, cooperative networking, intelligent operation, and sustainable deployment are outlined, pointing toward robust and scalable RO-
ISAC deployment in future 6G networks.
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All authors Tiantian Chu, Chen Chen, Jia Ye, Xin Xiong, Sihua Shao, Zhihong Zeng, Dengke Wang, Fengli Yang, Guanjun Xu, Harald Haas
Systems and Control · Cross-listed from cs.NE
Pascal-Weighted Genetic Algorithms: A Binomially-Structured Recombination Framework
Otman A. Basir
This paper introduces a new family of multi-parent recombination operators for Genetic
Algorithms (GAs), based on normalized Pascal (binomial) coefficients. Unlike classical two-parent crossover operators, Pascal-Weighted Recombination (PWR) forms offsprings as structured convex combination of multiple parents, using binomially shaped weights that emphasize central inheritance while suppressing disruptive variance. We develop a mathematical framework for PWR, derive variance-transfer properties, and analyze its effect on schema survival. The operator is extended to real-valued, binary/logit, and permutation representations. We evaluate the proposed method on four representative
benchmarks: (i) PID controller tuning evaluated using the ITAE metric, (ii) FIR low-pass filter design under magnitude-response constraints, (iii) wireless power-modulation optimization under SINR coupling, and (iv) the Traveling Salesman Problem (TSP). We demonstrate how, across these
benchmarks, PWR consistently yields smoother convergence, reduced variance, and achieves 9-22% performance gains over standard recombination operators. The approach is simple,
algorithm-agnostic, and readily integrable into diverse GA architectures.
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Signal Processing
Bayesian Optimization for Non-Cooperative Game-Based Radio Resource Management
Yunchuan Zhang, Jiechen Chen, Junshuo Liu et al. (+1 more)
Radio resource management in modern cellular networks often calls for the optimization of complex utility functions that are potentially conflicting between different base stations (BSs). Coordinating the resource allocation strategies efficiently across BSs to ensure stable network service poses significant challenges, especially when each utility is accessible only via costly, black-box evaluations. This paper considers formulating the resource allocation among spectrum sharing BSs as a non-cooperative game, with the goal of aligning their allocation incentives toward a stable outcome. To address this challenge, we propose PPR-UCB, a novel
Bayesian optimization (BO) strategy that learns from sequential decision-evaluation pairs to approximate pure Nash
equilibrium (PNE) solutions. PPR-UCB applies martingale techniques to Gaussian process (GP) surrogates and constructs high probability confidence bounds for utilities uncertainty quantification. Experiments on downlink transmission power allocation in a multi-
cell multi-
antenna system demonstrate the efficiency of PPR-UCB in identifying effective
equilibrium solutions within a few data samples.
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All authors Yunchuan Zhang, Jiechen Chen, Junshuo Liu, Robert C. Qiu
Systems and Control
Ensuring Stability of Non-Minimal Modes in Input-Output Data-Driven Representation
Joowon Lee, Nam Hoon Jo, Hyungbo Shim et al. (+2 more)
Many recent data-driven control approaches for linear time-invariant systems are based on output trajectory prediction using input-output data
matrices. The system dynamics described by this predictor, which we refer to as the input-output data-driven representation, yields non-unique autoregressive with exogenous inputs (ARX) models having possibly unstable non-minimal modes. In this note, we show that the stability of these non-minimal modes is ensured by a certain choice of ARX model, which coincides with the minimum-norm least-squares predictor using the Moore-Penrose inverse of the data
matrix. This stability guarantee holds regardless of the underlying system's stability. Moreover, the stability persists under sufficiently small noise in data when a suitably truncated Moore-Penrose inverse is used. Consequently, the ARX model need not be reduced to the true system order in order to avoid unstable additional modes.
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All authors Joowon Lee, Nam Hoon Jo, Hyungbo Shim, Florian Dörfler, Jinsung Kim
Systems and Control
Physics-Constrained Neural Dynamics: A Unified Manifold Framework for Large-Scale Power Flow Computation
Xuezhi Liu
Power flow analysis is a fundamental tool for power system analysis, planning, and operational control. Traditional Newton-Raphson methods suffer from limitations such as initial value sensitivity and low efficiency in batch computation, while existing deep learning-based power flow solvers mostly rely on supervised learning, requiring pre-solving of numerous cases and struggling to guarantee physical consistency. This paper proposes a neural physics power flow solving method based on
manifold geometry and gradient flow, by describing the power flow equations as a constraint
manifold, and constructing an energy function \(V(\mathbf{x}) = \frac{1}{2}\|\mathbf{F}(\mathbf{x})\|^2\) and gradient flow \(\frac{d\mathbf{x}}{dt} = -\nabla V(\mathbf{x})\), transforming power flow solving into an
equilibrium point finding problem for dynamical systems. Neural networks are trained in an unsupervised manner by directly minimizing physical residuals, requiring no labeled data, achieving true "end-to-end" physics-constrained learning.
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