Image and Video Processing
FSDBN: Foreground-Aware EEG-Visual Alignment via Dynamic Brain Networks
Yiheng Liu, Chuhang Zheng, Peiliang Gong et al. (+3 more)
EEG-based visual decoding provides a non-invasive pathway for interpreting visual semantics. However, existing methods often overlook the perceptual asymmetry between foreground and background in complex scenes, leading to background interference and semantic misalignment.
EEG signals also exhibit rapid temporal dynamics and nonstationary spatial patterns, making it difficult to capture the time-varying
brain connectivity associated with focal visual
attention. To address these limitations, we propose FSDBN, a unified framework for robust
EEG-visual decoding. FSDBN introduces Semantic-Consistent Saliency
Alignment to separate semantically relevant foreground regions from background noise under joint saliency and semantic constraints. It further employs Semantic-Prior Dynamic Gating Foreground Fusion to adaptively regulate the contributions of foreground and background features. In parallel,
EEG signals are modeled as adaptive spatiotemporal
brain networks whose functional connectivity dynamically reorganizes to capture neural responses to salient foregrounds. Experiments on zero-shot
brain-to-image
retrieval demonstrate that FSDBN achieves 69.0 percent top-1 accuracy and 92.2 percent top-5 accuracy, outperforming previous state-of-the-art methods. Code is available at https://github.com/LiuYiheng1/FSDBN-
EEG.
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All authors Yiheng Liu, Chuhang Zheng, Peiliang Gong, Jingtao Liu, Daoqiang Zhang, Qi Zhu
Audio and Speech Processing · Cross-listed from cs.SD
Dense-Sparse Dynamic Time Warping for Customizing Piano Concerto Accompaniments
TJ Tsai, Kavi Dey, Yigitcan Ozer et al. (+1 more)
In this study, we explore how pianists can customize Music Minus One (MMO) concerto accompaniments to match their playing style. Bypassing the need for a symbolic score, often not available digitally, we use three types of audio data: solo piano recordings, MMO orchestra-only recordings, and mixed recordings of both piano and orchestra (e.g., from YouTube). The mixed recording serves as an intermediary reference to align the solo and orchestra parts, with only the orchestral part being adjusted through time-scale modification to synchronize with the user's playing. The main challenge with estimating these
alignments is the spectral mismatch between recordings containing different musical parts. Motivated by this application scenario, we introduce Dense-Sparse DTW, a variant of Dynamic Time Warping (DTW) that is designed to improve robustness of
alignments to spectral mismatch by focusing on aligning a selected subset of audio frames containing prominent timing cues. We collect and annotate data from four piano concerto movements and establish a framework for generating and evaluating customized accompaniment recordings. On this
benchmark, we show that Dense-Sparse DTW has better or comparable performance than more complex approaches based on source separation and spectral subtraction techniques.
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All authors TJ Tsai, Kavi Dey, Yigitcan Ozer, Meinard Muller
Systems and Control
LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applications
Daniela Rojas, Abdulwahab Albassam, Aidan G. Leung et al. (+11 more)
Large
language models (LLMs) and agentic AI systems have evolved from natural
language tasks to using external tools to plan, retrieve, and act in technical domains. In smart grids, recent work applies agentic schemes to
forecasting, optimization, and control, wrapping trusted solvers behind
language interfaces and orchestrating multi-step workflows. The literature lacks a unified approach to designing and evaluating such systems. LLMs can produce numerically plausible yet physically infeasible outputs, evaluation protocols vary across tasks, and the boundary between what the model should and should not compute is implicit. This paper presents a solver-grounded design principle: a numerical result is reported only when it originates from a trusted tool and passes explicit verification. We review the building blocks of LLM and agentic AI systems for power systems: prompting strategies and agentic architectures. We instantiate the principle in four case studies: wind power
forecasting, EV charging scheduling, power flow analysis, and contingency diagnosis, each comparing an LLM-only baseline against its solver-grounded counterpart on identical data and metrics. EVAgent reproduces the CVXPY optimum while reducing LLM-only unmet energy by 7.5-9.5x, and GridDebugAgent repairs 17/39 contingency cases while reducing total violations by 52.3%. We propose a four-group evaluation framework spanning task utility, solver-grounded correctness, faithfulness and safe failure, and cost and latency. A consistent division of labor emerges: the agentic system reliably orchestrates, retrieves, and explains, while trusted tools compute and a verification gate decides what is reported.
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All authors Daniela Rojas, Abdulwahab Albassam, Aidan G. Leung, Jett Ngo, Ryan Luo, Peter R. Quawas, Junpyung Kim, Kangkai Liang, Mansi Nanavati, Jonathan Mai, Meng-Chi Tsai, Yun-Tong Tsai, Yize Chen, Yuanyuan Shi
Signal Processing · Cross-listed from cs.IT
Compositional Semantic Communication for Physical AI: Category Theory Meets Game Theory
Christo Kurisummoottil Thomas, Walid Saad and Emilio Calvanese Strinati
Physical artificial intelligence (AI) systems involve distributed sensing
agents with embedded AI models that must coordinate to perceive, reason, and act in networked environments. Transmitting raw sensor data incurs significant communication overhead, latency, and redundancy. While semantic communication (SC) mitigates these challenges by transmitting task-relevant information, existing deep learning-based joint source-channel coding approaches exhibit limited adaptability, poor out-of-distribution generalization, and scalability challenges. To address these limitations, this paper proposes a framework for compositional semantic communication (CSC), enabling heterogeneous physical AI sources to transmit semantic representations (SRs) that compose meaningfully at a base station (BS) or edge server for remote inference. First, a category-theoretic measure of compositional semantics is developed to quantify each device's contribution to inference tasks beyond mutual information. Second, Grothendieck
topologies and presheaves formalize semantic composition across devices, ensuring consistency and task relevance. Building on these foundations, multi-device coordination is formulated as a Stackelberg game in which devices commit to encoding strategies and the BS optimally composes received SRs. An ADMM-based
algorithm computes
equilibrium signaling strategies.
Equilibrium existence is established under mild conditions and is Pareto optimal when compositional information yields increasing collective benefit. Simulation results demonstrate that the proposed approach achieves up to 17% bandwidth reduction and 53% lower end-to-end latency than cooperative multi-
agent, distributed gradient descent, and uniform-selection CSC baselines while maintaining 85% inference accuracy across diverse autonomous driving scenarios.
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Systems and Control
Real-Time Flight Test Maneuver Selection with Monte Carlo Tree Search
Nicholas E. Bostock, Helen Pruitt-Kennett, Marc R. Schlichting et al. (+1 more)
Flight test is shifting toward a data-centric approach in which data contribute to model refinement, reducing reliance on pre-scripted test points. An open problem is how to sequence maneuvers within a sortie to maximize uncertainty reduction under resource limits. We present a real-time planning framework that combines a Gaussian Process (GP) belief model with Monte Carlo Tree Search (MCTS) to select pilot-actionable maneuvers under fuel constraints. Candidate maneuvers are scored using weighted integrated variance reduction (wIVR), and shallow lookahead is performed with a propagated per-evaluation-point variance state to account for downstream coverage redundancy and transition cost. The planner is evaluated in a closed, human-in-the-loop X-Plane simulation against greedy wIVR selection and a fixed test-card baseline. Sortie-summary statistics show significant directional differences, with MCTS-wIVR achieving higher uncertainty reduction per unit fuel over both baselines. The results indicate that posterior-aware adaptive planning is a promising approach to increase efficiency of flight tests.
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All authors Nicholas E. Bostock, Helen Pruitt-Kennett, Marc R. Schlichting, Mykel J. Kochenderfer
Signal Processing
Self-Interference-Aware AS-Assisted Tri-Hybrid Beamforming for Full-Duplex Massive MIMO
Yuanzhe Gong, Cheng-Jie Zhao and Tho Le-Ngoc
This paper proposes a tri-hybrid beamforming (tri-HBF) scheme with
antenna-selection (AS)-based reconfigurable sub-arrays for full-duplex (FD) massive multiple-input multiple-output (mMIMO) systems. A sub-connected HBF architecture is adopted, where AS is performed in a group-wise manner to avoid excessive switch-network and routing complexity. An alternating optimization (AO)
algorithm is developed to jointly optimize the i) active
antenna subsets considering a self-interference (SI)-aware utility, ii) analog beamformers through projected gradient ascent (PGA), iii) digital precoders/combiners via SI-aware regularized zero-forcing (RZF) and minimum mean-square error (MMSE) updates, and iv) DL/UL power allocation by successive convex approximation (SCA). To capture realistic electromagnetic coupling in FD mMIMO operation, experimental SI channels based on an 8x8 Tx-8x8 Rx FD array prototype are incorporated into the study. The proposed AS-aided tri-HBF optimization scheme exhibits robust convergence across various base station configurations and effectively balances desired-signal enhancement, SI mitigation, and multi-user interference suppression in FD mMIMO operation. Illustrative results show that selective activation can outperform full-array activation, achieving a 21.3% higher average sum-rate and a more consistent performance across user realizations, with power-efficiency benefits by reducing the active paths. A comprehensive study is conducted to characterize how the number of activated
antennas affects the achievable rate, user-channel coherence, and SI suppression gain. Compared with various selection baselines, it achieves a 45.1% improvement in average sum-rate, with average DL and UL rate gains of 36.9% and 82.9%, respectively. In addition, beam-level isolation better than 63 dB is achieved, further confirming the effectiveness of the proposed SI-aware design.
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Image and Video Processing · Cross-listed from cs.IR
Remote Awareness of Seafloor Images Collected by AUVs over Low-Bandwidth Communication Links
Adrian Bodenmann, Cailei Liang, Miquel Massot-Campos et al. (+8 more)
This paper introduces a method for real-time processing and transmission of autonomous
underwater vehicle (AUV) imagery over low-bandwidth communication links. It leverages artificial intelligence (AI) techniques to identify a set of images that best represent an entire
dataset, or automatically finds the most similar images to a given query image for transmission to operators. Combined with metadata of a larger set of images, compressed versions of the selected images can be transmitted over satellite communication links or
underwater modems, and provide operators on shore with information about the type of imagery the AUV is collecting while it is still deployed. Data from three deployments off the coast of the UK and in Gran Canaria using different AUVs and imaging systems demonstrate the method in the field. It achieved an almost 400,000-fold reduction in data volume compared to the raw data size, enabling transmission of data summaries of a 2-hour 47-minute-long mapping mission in just over 34 minutes over low-bandwidth satellite communication.
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All authors Adrian Bodenmann, Cailei Liang, Miquel Massot-Campos, Samuel Simmons, Alexander B. Phillips, Alberto Consensi, Matthew Kingsland, Rashiid Sherif, Stan Brown, Adam Riese, Blair Thornton
Signal Processing
System-Level Evaluation of LEO Satellite Communications Under Service-Driven Traffic Dynamics
Miyeon Lee, Dong-Hyun Jung, Sucheol Kim et al. (+2 more)
As low Earth orbit (LEO) satellite communications take shape on a global scale, system-level evaluation has become essential for a rigorous understanding of their performance characteristics. Most existing studies have relied on a full-buffer assumption, which obscures the heterogeneity and intermittency that characterize actual user equipment (UE) traffic. This article evaluates the performance of LEO satellite communications under realistic service conditions through the incorporation of non-full-buffer traffic models. To this end, we develop a system-level simulator that complies with the channel modeling and evaluation methodologies specified in 3rd Generation Partnership Project (3GPP) technical reports 38.811 and 38.821. Three key system-level performance metrics are considered: UE throughput, resource block (RB) allocation ratio, and packet delay, which are evaluated across 3GPP LEO satellite study cases under diverse traffic models. The throughput results characterize the distribution of achievable data rates, which delineates the practical operating boundaries of the system. The RB allocation analysis reveals patterns of resource consumption as UE density varies, which provides a quantitative basis for assessing system capability. Furthermore, the delay analysis characterizes latency behavior, which is of particular importance in satellite environments where substantial propagation delays are inherent and must be examined to ensure service feasibility. These evaluations provide a realistic performance outlook for non-full-buffer LEO satellite communications and provide insight into the user experience under practical operating conditions.
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All authors Miyeon Lee, Dong-Hyun Jung, Sucheol Kim, Hwanjin Kim, Junil Choi
Signal Processing
Radio Map Updating from Streaming Spectrum Measurements via Memory-Based Online Gaussian Processes
Yuanyuan Deng, Bo Zhou, Tian Chen et al. (+5 more)
Radio maps, which estimate spatial
radio-frequency characteristics from spectrum measurements, are essential for applications such as spectrum management and network planning. With the continuous arrival of spectrum measurements, conventional batch processing methods for
radio map reconstruction become computationally prohibitive, as they require reprocessing all accumulated measurements for each
radio map update. To address this, we propose a
memory-based online sparse variational Gaussian process (M-OSVGP) method that efficiently updates
radio maps from streaming spectrum measurements. Our method employs sparse variational inference and updates the posterior online by minimizing a hybrid objective that integrates newly received measurements and a
memory subset of previous ones to mitigate catastrophic forgetting. To further improve posterior approximation as measurements accumulate over spatially diverse regions, we extend M-OSVGP with a grid-assisted online inducing point selection (GOIPS)
algorithm. GOIPS dynamically adapts the number and locations of inducing points based on measurement density and spatial correlation, providing a more informative inducing set while maintaining computational efficiency. Extensive simulations demonstrate the effectiveness of our proposed methods in reconstruction accuracy, computational efficiency, and uncertainty quantification, compared to existing batch and online baselines across various scenarios.
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All authors Yuanyuan Deng, Bo Zhou, Tian Chen, Shijian Gao, Jia Yan, Lantu Guo, Qiuming Zhu, Qihui Wu
Systems and Control · Cross-listed from cs.CR
RT-SHCUA: Real-Time Self-Hosted Computer-Use Agent for UAV Control
Di Lu, Bo Zhang, Xiyuan Li et al. (+5 more)
Natural-
language control offers a promising interface for unmanned aerial vehicles (
UAVs), but directly applying self-hosted computer-use
agents (SHCUAs) to
UAV control introduces a structural mismatch. SHCUAs are designed for interactive host-side tool use, where delayed
agent iterations are often acceptable.
UAV control, however, is coupled with continuously changing physical states, strict timing constraints, safety risks, and security accountability. A stale, unauthorized, or tampered
agent decision may therefore lead to unsafe or untraceable vehicle behavior. This paper proposes a real-time and security-oriented restructuring of SHCUA-based
UAV control. Instead of allowing an SHCUA to directly issue flight commands, we transform its outputs into contract-bound
UAV skill invocations with explicit timing, state, authority, fallback, and evidence semantics. Based on this abstraction, we design an architecture that separates semantic
reasoning from onboard execution and security/safety enforcement. Slow cloud or edge
reasoning is used for mission understanding, while onboard components validate and dispatch only timely, authorized, and state-consistent skills. Security-critical enforcement points can be protected by TEE-style or microcontroller isolation mechanisms without moving the full
language agent or high-frequency flight-control loop into trusted components. Prototype evaluation shows that RT-SHCUA maintains bounded task-level responsiveness while supporting degraded handling, trusted admission, and auditable evidence preservation for SHCUA-mediated
UAV actions.
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All authors Di Lu, Bo Zhang, Xiyuan Li, Yongzhi Liao, Xuewen Dong, Yulong Shen, Zhiquan Liu, Jianfeng Ma
Signal Processing · Cross-listed from cs.IT
Task-Oriented Precoding for Edge Inference over Large-Scale MIMO Systems
Hongru Li, Zeyan Zhuang, Zixin Wang et al. (+4 more)
Future wireless networks are expected to support networked artificial intelligence (AI) services, where multiple devices transmit learned features to an edge server for distributed inference. This setting calls for task-oriented physical-layer optimization, where wireless transmission should preserve useful information for inference rather than only maximize the rate or reconstruct the transmitted signals. A key physical-layer control variable is the multiple-input multiple-output precoder, which determines how device features are shaped and combined over wireless channels. Existing task-oriented precoding methods typically adapt the precoder to instantaneous channel state information at the transmitter (CSIT). However, in multi-device
MIMO systems, acquiring the aggregate channel, feeding back CSI or optimized precoders, and reoptimizing across coherence blocks introduce substantial overhead. This paper develops a random-
matrix-theoretic framework based on statistical CSIT that designs a slow-timescale precoder from channel covariance statistics and training-set feature statistics, without requiring instantaneous CSIT. We adopt maximal coding rate reduction (MCR${^2}$) to measure the class separability of the received features, yielding a task-aware utility for
MIMO precoder design. Since this utility still depends on random small-scale fading, we derive a deterministic approximation that converts it into a fixed-point objective depending only on long-term statistics and large-system dimension ratios via random
matrix theory. A projected block-coordinate ascent and successive convex approximation
algorithm is developed to optimize this deterministic objective under per-device power constraints. Experiments on ModelNet10 verify the approximation and show that the proposed statistical precoder improves task-aware mode allocation and inference performance over competitive
benchmarks.
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All authors Hongru Li, Zeyan Zhuang, Zixin Wang, Hengtao He, Shenghui Song, Jun Zhang, Khaled B. Letaief
Systems and Control
An Augmented Load-Frequency Control Block Diagram for Power Systems
Mahyar Tofighi-Milani, Sajjad Fattaheian-Dehkordi and Matti Lehtonen
The increasing penetration of inverter-based resources has led to a significant reduction in system inertia, resulting in faster and more pronounced frequency deviations in modern low-inertia power systems. In such environments, the dynamic behavior of electrical loads becomes increasingly important in shaping overall system frequency response. This paper presents an enhanced load model that incorporates load-side dynamics in addition to conventional static behavior, thereby augmenting the representation of load-frequency control (LFC) models. This improved formulation increases the accuracy of frequency response studies in power systems. A comparison between the proposed augmented model and the conventional LFC representation demonstrates that relying solely on static load modeling can lead to inaccurate results and potentially misleading conclusions. Therefore, accurate modeling of load-side dynamics is essential for reliable frequency stability assessment in modern power systems.
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Audio and Speech Processing
The tttAI System for the TSA-ASR Task of the SmartGlasses Challenge 2026
Xuanji He, Gaoyang Dong, Xiaoxiao Li et al. (+2 more)
This paper presents the tttAI system submitted to the TSA-ASR task of the SmartGlasses Challenge 2026, evaluated on both two-person dialogues (Track 1) and multi-party meetings (Track 2). The task requires time-stamped speaker-attributed
speech recognition from smart-glasses recordings. This is particularly challenging due to long-form audio, multiple speakers, and frequent overlapping
speech. We proposed a cascaded architecture consisting of speaker diarization, overlap detection, target-speaker extraction, post-processing, and automatic
speech recognition. The diarization module extracts features via WavLM-Large, performs frame-wise speaker classification with a Conformer encoder, and then generates global speaker segments through
embedding clustering. For overlapped regions, we apply a WeSep-based target-speaker extraction model with ECAPA-TDNN speaker
embeddings. When the extraction is unreliable, a dominant-speaker fallback strategy is used. The final system uses FireRedASR2-AED with the first microphone channel. The submitted system has a total parameter count of approximately 1.53B. On Track 1, our system achieves a tcpCER of 7.10%. On Track 2, it achieves a tcpCER of 34.04% and ranks second on the leaderboard.
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All authors Xuanji He, Gaoyang Dong, Xiaoxiao Li, Minchuan Chen, Fengjie Zhu
Signal Processing
Massive MIMO-OFDM ISAC for Sparse ISAR Imaging: Joint Power and Subcarrier Allocation
Hamid Reza Hashempour, Yanjiao Li, Jie Zhang et al. (+2 more)
This paper investigates a massive multiple-input multiple-output (mMIMO) orthogonal frequency-division multiplexing (OFDM) framework for integrated sensing and communication (
ISAC) with inverse synthetic aperture radar (ISAR) imaging, supporting applications such as the Internet of Things (IoT). A dual-function architecture combines communication precoding and dedicated sensing beamforming to enable simultaneous downlink communication and ISAR imaging. Due to intermittent pilot transmission and sparse sensing-subcarrier activation, the received echoes provide incomplete measurements, resulting in a sparse-aperture ISAR reconstruction problem. To address this issue, an adaptive reweighted two-dimensional alternating direction method of multipliers (ADMM)
algorithm is developed for high-resolution image recovery from sparse observations. A joint resource-allocation framework is also proposed to optimize communication-subcarrier assignment, sensing-subcarrier selection, and transmit power allocation subject to communication quality-of-service and sensing constraints. Exploiting channel hardening, analytical full-band sensing
benchmarks based solely on statistical channel state information (CSI) are derived for maximum-ratio (MR) and zero-forcing (ZF) precoding, while a soft actor-critic (SAC)-based method is developed for sparse-sensing resource allocation. Numerical results show that the proposed adaptive ADMM
algorithm improves sparse ISAR reconstruction over conventional methods. The SAC-based design also achieves substantial gains in sum spectral efficiency over the full-band
benchmarks while satisfying communication and sensing constraints, thereby revealing the tradeoff between ISAR reconstruction accuracy and communication spectral efficiency.
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All authors Hamid Reza Hashempour, Yanjiao Li, Jie Zhang, Hyundong Shin, Hien Quoc Ngo
Systems and Control
A Compact 3D-Printed Soft Finger with Cyclic Hydraulic Actuation
Zefang Mao, Yunjia Li, Victoria Bamgboye et al. (+5 more)
Hydraulic soft fingers offer compliant and gentle manipulation, but their practical deployment is limited by bulky fluidic hardware, fabrication complexity, and insufficient design validation. This paper presents a compact 3D-printed soft hydraulic finger driven by a miniature cyclic peristaltic loop. The finger integrates compliant bellows, rigid connectors, and fluidic ports, while an Abaqus fluid-structure model is used to guide selection of wall thickness, pitch angle, and bellows length. The selected design is validated through baseline-corrected chamber-pressure measurements and
vision-based angle tracking. Results show that the quasi-static finite-element model captures the main pressure-angle trends, with remaining offsets mainly attributed to bonding-induced stiffness and hydraulic losses.
Vision-feedback control further enables repeatable angle tracking over a large bending range. Finally, grasping tests on fragile and deformable objects, including tofu and blueberries, demonstrate gentle, slip-free contact without visible damage. Overall, this work establishes a reproducible pipeline from FEA-guided design to closed-loop validation for compact 3D-printed hydraulic soft fingers.
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All authors Zefang Mao, Yunjia Li, Victoria Bamgboye, Sara Ben Haj Hammouda, Aika Ono, Wen Fan, Chao Wu, Dandan Zhang
Signal Processing
Deep Recurrent Q-Learning Based Beam Steering Strategy for Throughput Maximization in WPCNs
Samannaya Adhikari, Navchetan Awasthi and Siddhartha Sarma
In wireless powered communication networks, medium access control protocols for devices using the harvest-then-transmit strategy must be distributed, low-overhead, and capable of handling irregular and infrequent data transmissions to ensure efficient energy utilisation. However, most existing protocols fail to meet one or more of those requirements, leading to wasted scarce harvested energy. We address this by identifying beam steering as a potential mechanism to regulate the charging rate of energy harvesting devices and thus control their access to the shared wireless medium. After formulating a joint problem of energy beam steering and slotted ALOHA-based random access, we leverage a deep learning framework based on an action-specific deep recurrent Q-Network (ADRQN) to learn a beam-steering policy only from the macro-level ternary slot outcomes, namely, idle, success and collision. Additionally, we design an oracle policy with global knowledge of the network to
benchmark our proposed blind adaptive beam-steering approach. The numerical results demonstrate that our approach achieves up to 68% increase in throughput compared to non-learning schemes, while also reaching 75-80% of the oracle policy's performance, all without requiring channel estimation, charge-level reporting, or device-state tracking.
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Systems and Control
Multisine electrochemical impedance spectroscopy for Li-ion battery characterisation
Noël Hallemans, Heiko Seel-Mayer, Peter Keil et al. (+2 more)
Electrochemical impedance spectroscopy (EIS) is a valuable tool for non-invasive battery characterisation, providing a compact representation of physical processes over a wide range of time scales. Commonly, sinusoids at different frequencies are injected sequentially (single-sines). Alternatively, a multisine excitation (a sum of sines) is advantageous for reducing experiment time and allowing impedance to be measured during operation. In this work, we demonstrate high-fidelity multisine EIS measurements on Li-ion
cells over a wide frequency range (20 mHz to 1 kHz), taken with a commercial potentiostat modified to give access to the underlying current and voltage time-series data, and compare these to single-sine EIS. We show how this allows the conditions of linearity and stationarity to be verified directly, which is not possible with impedance data alone. We then measure broadband impedance during charging and discharging, temperature change, and relaxation. Because impedance is a linearisation of the response about an operating point, each of these conditions probes a state that classical EIS excludes by definition: kinetics linearised about a non-zero current, thermally activated reaction rates, and relaxing concentration gradients, respectively. Operando multisine EIS is therefore not simply a faster measurement approach, but one that resolves behaviour that steady-state EIS cannot.
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All authors Noël Hallemans, Heiko Seel-Mayer, Peter Keil, Stephen R. Duncan, David A. Howey
Systems and Control · Cross-listed from cs.RO
A2RL V\textsubscript{max}: The A2RL autonomous racing dataset for long-range, high-speed perception and multi-vehicle interaction
Marvin Klemp, Dominic Ebner, Cornelius Schröder et al. (+15 more)
In autonomous driving development, a perception
dataset is crucial, as it provides fundamental data for training, testing, and validating
algorithms for an autonomous vehicle's
multimodal perception systems. So far, most research has concentrated on providing
datasets for well-structured urban environments. This work introduces the A2RL V\textsubscript{max} open-source
dataset, specifically designed for perception tasks in high-speed autonomous driving and multi-vehicle interaction. The
dataset was captured during the 2024 Abu Dhabi Autonomous Racing League (A2RL), held at the Yas Marina F1
Circuit, with participation from all competing teams. It contains diverse scenarios, including single-vehicle data at varying speeds, multi-vehicle sessions, and the full final four-vehicle race. The
dataset contains almost 30,000 professionally annotated
LiDAR point clouds, along with RADAR point clouds. In particular, it is the first large-scale
dataset in autonomous racing to feature professionally annotated
LiDAR point clouds, enabling deep learning-based perception research. The data is provided in a developer-friendly format, enabling easy implementation and evaluation in future research. We provide implementation and evaluation for off-the-shelf 3D detection and tracking methods. Although baseline methods show promising results for both 3D detection and tracking, specialized methods are required to address the unique challenges of high-speed autonomous driving. For a detailed description of the
dataset, please visit the \href{https://tum-avs.github.io/A2RL_
Dataset_website/}{A2RL V\textsubscript{max}
Dataset Website}
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All authors Marvin Klemp, Dominic Ebner, Cornelius Schröder, Davide Malvezzi, László Turányi, Riccardo Donati, Ilia Schminik, Xia Ning, Yanxin Zhou, Matthew Flagg, Christoph Stiller, Markus Lienkamp, Marko Bertogna, Gergely Bári, Andreas Birk, Ren Jin, Chen Lv, Johannes Betz
Signal Processing
On the Detection Optimality and Exact BER Analysis of NOMA
Tasneem Assaf, Hamad Yahya and Arafat Al-Dweik
Non-orthogonal multiple access (NOMA) detection using successive interference cancellation (SIC) and joint multiuser maximum likelihood (JML) has been extensively studied, although their presumed optimality for individual-user bit error rate (BER) minimization is unestablished. Moreover, existing BER analyses overlook the SIC-induced statistical changes. This letter derives optimal per-user maximum a posteriori (MAP) detectors, identifies when the near-user detector reduces to conventional SIC, and proves the equivalence of conventional SIC and JML. An exact average BER analysis accounting for SIC-induced statistical changes is presented. The results show that the proposed detector achieves modest but consistent BER gains at low-to-moderate signal-to-noise ratio (SNR).
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Image and Video Processing
Luminosity-Adaptive Contrast Enhancement Using CLAHE for Retinal Fundus Images with Quantitative Validation and Comparative Analysis
K. Mithra and Prem Kumar Santhanam
Background: Retinal fundus imaging is central to the early diagnosis of sight-threatening conditions including diabetic retinopathy, glaucoma, and retinal vein occlusion. Clinical utility of fundus images is routinely compromised by non-uniform illumination, motion blur, and low contrast - artefacts that increase the risk of diagnostic error. Effective image enhancement is therefore a prerequisite for reliable computer-aided ophthalmic diagnosis. Methods: This study proposes a two-stage image enhancement pipeline combining luminosity correction via HSV colour space decomposition with Contrast Limited Adaptive Histogram Equalization (CLAHE) applied exclusively to the Value (V) channel. Experiments are conducted on the publicly available DRIVE
dataset (40 retinal fundus images, 584 x 565 pixels, Canon CR5 camera, ophthalmologist-annotated ground truth). Quantitative evaluation employs Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Contrast-to-Noise Ratio (CNR). Baseline comparisons include standard Histogram Equalization (HE) and Adaptive Histogram Equalization (AHE). A binary masking step is subsequently applied to isolate hyper-reflective regions consistent with vascular pathology. Results: The proposed method achieves PSNR = 29.3 dB, SSIM = 0.91, and CNR = 3.12 - outperforming HE (PSNR = 21.4 dB, SSIM = 0.74) and AHE (PSNR = 23.1 dB, SSIM = 0.79) across all metrics, with an average processing time of 0.14 seconds per image. Conclusions: The combined luminosity-CLAHE pipeline yields measurably superior contrast and structural fidelity compared to established baseline methods, with processing speed compatible with clinical screening workflows. Limitations and directions for deep-learning-based comparison are discussed.
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Systems and Control
Graph-Induced Tensor Liftings for Networked SEIR Models: Dimensional Reduction and Residual Analysis
Enrique Baeyens
Networked SEIR models describe epidemic spread within and between interacting subpopulations through contact-supported nonlinear transmission. Standard
polynomial liftings based on complete ordered Kronecker tensors yield linear higher-dimensional representations, but their dimensions grow rapidly because they retain interactions absent from the transmission graph. This paper develops a graph-induced tensor lifting whose observables are selected from the effective transmission support. An exact edge-based quadratic representation separates linear compartmental transitions from nonlinear infection terms. A homogeneous hierarchy is then constructed recursively. The quadratic transmission field generates the next degree. The linear compartmental field saturates the resulting dictionary within that degree. The first edge-closure dynamics are linear up to an explicit cubic truncation residual, and higher-order truncations contain only next-degree terms. The first lifted dimension scales with the numbers of subpopulations and effective transmission channels. At fixed order, graph-induced dictionaries grow linearly with network size under uniformly bounded local connectivity, whereas complete
polynomial liftings retain order-dependent
polynomial growth. Uniform first edge-closure residual bounds depend on the transmission rate and the maximum weighted incoming transmission intensity. Numerical illustrations compare equal intensity per active channel with equal total incoming intensity. They confirm that dictionary dimensions depend only on graph support, whereas residual trajectories also reflect weight accumulation, weight distribution, and nonlinear propagation. These results provide a structured basis for reduced modeling and subsequent model-specific analysis and control.
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Signal Processing
Ambiguity Function Analysis of Pilot-Embedded Random OFDM Signals
Jialin Wu, Fan Liu, Ying Zhang et al. (+3 more)
This paper investigates the statistical ambiguity functions (AFs) of orthogonal frequency division multiplexing (OFDM) waveforms that incorporate deterministic unit-modulus pilot symbols and random data payloads for integrated sensing and communication (
ISAC). We derive analytical expressions for the mean squared discrete periodic ambiguity function (DP-AF) and fast-slow-time ambiguity function (FST-AF) of such pilot-embedded OFDM signals. Our analysis demonstrates that, under a fixed signal length and constellation scheme, the mean squared DP-AF depends jointly on the pilot patterns, pilot symbols and number of pilots, while the mean squared FST-AF relies only on the number of pilots. Numerical simulations closely match the theoretical expressions. Furthermore, in numerical results, we show that different pilot patterns correspond to DP-AF with distinct characteristics, offering relevant considerations for pilot design in communication-centric
ISAC systems.
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All authors Jialin Wu, Fan Liu, Ying Zhang, Yifeng Xiong, Jie Yang, Shi Jin
Signal Processing
DMSNet: Cross-Band Learning for Multi-Target Sensing in Multi-Band ISAC
Haotian Liu, Zhiqing Wei, Quanjiang Zhao et al. (+4 more)
Multi-band integrated sensing and communication (
ISAC) offers complementary high- and low-frequency echo information for multi-target sensing. However, existing dual-band
ISAC sensing methods have a limited ability to exploit deep complementary information across heterogeneous bands and often incur high computational costs. To address these limitations, we propose a Dual-Band Multi-Target Sensing Neural Network (DMSNet) for joint target number and parameter estimation. Under representative simulation conditions, DMSNet outperforms the best baseline in target number estimation, increasing count accuracy from 89.01 % to 91.74 % and Macro-F1 from 90.80 % to 93.07 %. For parameter estimation, compared with the best baselines, DMSNet reduces the median absolute errors of range, velocity, and angle by 82.2%, 56.9%, and 73.2%, respectively. Moreover, DMSNet reduces runtime by 68.7 % relative to the fastest existing dual-band
ISAC sensing method.
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All authors Haotian Liu, Zhiqing Wei, Quanjiang Zhao, Lin Wang, Yunxin Geng, Xingwang Li, Zhiyong Feng
Systems and Control · Cross-listed from cs.MA
A Digital Twin-Based Method for Evaluating Local Collective Tariffs in Distribution-Level Energy Systems
Kristoffer Christensen, Bo Nørregaard Jørgensen and Zheng Grace Ma
This work addresses the need for engineering-grounded evaluation of implement-ed tariff mechanisms in distribution-level energy systems. A digital twin-based method is proposed for assessing local collective tariffs under realistic behavioral and infrastructural conditions. The approach integrates
agent-based modeling of household consumption and generation, virtual aggregation through a shared metering abstraction, and explicit representation of tariff logic within a unified simulation environment. The method is demonstrated through its application to the Danish Local Collective Tariff across representative residential energy community configurations, including scenarios with photovoltaic generation, battery storage, and electric vehicle charging. Results indicate that aggregation of heterogeneous demand profiles reduces peak coincidence and enables more efficient allocation of tariff components, leading to measurable cost reductions at the community level. At the same time, the outcomes reveal sensitivity to the temporal
alignment of consumption and generation, influencing the degree of cost neutrality across participants. The findings illustrate how digital twin-based evaluation can support systematic assessment of tariff mechanisms by capturing the interaction between infrastructure, user behavior, and regulatory design. The proposed approach provides a basis for analyzing and comparing tariff structures in distribution-level energy systems beyond the specific case considered.
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Systems and Control
On Optimal Event-Triggered Distributed Control for Stochastic Multi-Agent Systems via Reinforcement Learning
Ziming Wang, Bingbing Li, Karl H. Johansson et al. (+1 more)
We propose a reinforcement learning (RL) based optimal distributed control
algorithm for the multi-
agent systems (MASs) with
stochastic uncertainties. Unlike existing methods, during the optimized backstepping design process, we use the actor-critic-identifier structure. The actor neural network is used to reflect control behavior, the critic neural network works to evaluate control performance and the unknown
stochastic uncertainties are handled by identifier neural network. Furthermore, a low-pass filter effectively suppresses problems stemming from non-affine nonlinear faults and a hybrid event-triggered control (ETC) strategy is proposed to reduce control frequency. We analyze our
algorithm's operation, and we provide a Lyapunov-based stability proof that guarantees all errors are bounded, ensuring precise tracking between the leader and followers. We validate its correctness in a single-axis robotic manipulator simulation and finally, we compare against the non-optimal control
algorithm highlighting our optimal control
algorithm's operational advantages.
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All authors Ziming Wang, Bingbing Li, Karl H. Johansson, Apostolos I. Rikos
Signal Processing
Waveform Design for OTFS Assisted Simultaneous Acoustic Information and Power Transfer
Jinheng Kang, Yizhe Zhao, Kun Yang et al. (+1 more)
Simultaneous acoustic information and power transfer (SAIPT) is a promising technique for supporting self-sustainable Internet of
Underwater Things (IoUT) networks through concurrent data transmission and energy supplement. However, existing OFDM-based SAIPT studies are vulnerable to severe multipath propagation and Doppler effects in dynamic
underwater acoustic channels. To address this issue, this paper proposes an orthogonal time frequency space (OTFS)-based SAIPT waveform design for dynamic
underwater acoustic channels. The acoustic information transfer (AIT) and acoustic power transfer (APT) symbols are jointly designed, while the transducer conversion efficiencies and nonlinear rectifier characteristics are incorporated into the system model. Based on the derived achievable data rate and DC output expressions, a waveform optimization problem is formulated to maximize the harvested DC output under transmit power and minimum data-rate constraints. To solve the resulting non-convex problem, a successive convex approximation (SCA)-based
algorithm is developed. Simulation results show that the proposed OTFS-based design outperforms the OFDM-based scheme in terms of DC output in the dynamic transmission scenarios. The effects of key system parameters are also analyzed, confirming the effectiveness of the proposed design in improving acoustic energy transfer efficiency.
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All authors Jinheng Kang, Yizhe Zhao, Kun Yang, Jun Liu
Systems and Control
Online learning of neural state-space models
Bendegúz Györök, Tamás Péni, Maarten Schoukens et al. (+1 more)
Recent advances in deep-learning-based nonlinear system identification have led to encoder-based estimation of neural state-space (ANN-SS) models that achieve state-of-the-art performance in offline settings by estimating initial model states from past input-output data. These methods are typically used in multiple-shooting-based offline identification, and online learning of these models remains largely unexplored. This paper presents a batch-wise learning pipeline and a direct recursive identification
algorithm for subspace encoder-based ANN-SS models. We provide convergence analysis of the recursive formulation and validate its performance through extensive simulation studies. The results demonstrate that the proposed approach enables computationally efficient online adaptation with high model accuracy.
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All authors Bendegúz Györök, Tamás Péni, Maarten Schoukens, Roland Tóth
Systems and Control · Cross-listed from cs.RO
Stability and Comfort in Mobile Robot-Pedestrian Interactions
Alireza Jafari, Hong-Son Nguyen and Yen-Chen Liu
Mobile
robots in public spaces must ensure pedestrians' comfort, and yet empirical studies of walkers' subjective safety are rare. Many classical
navigation algorithms do not distinguish the walkers from dynamic obstacles and do not explicitly model subjective human factors. Moreover, most studies focus on holonomic mobile
robots, whereas applications demand Nonholonomic Mobile
Robots (NMR). This paper develops socially aware
algorithms for NMRs, proves the stability, verifies the performance experimentally, and statistically analyzes the reported comfort. We design a framework for NMRs using Social Force Model (SFM) and the projected Time-to-collision Social Force Model (TSFM). We formalize the NMR-pedestrians' and NMR-obstacles' interactions and prove the system's stability, assuming boundedly nonpassive pedestrians. Simulations calibrate the models by maximizing a hybrid cost function of comfort and speed. Pedestrian-
robot interaction experiments compare SFM and TSFM to two remote-controlled baselines and collect walkers' reported comfort. Statistical tools analyze survey results collected during the experiments. Benchmarking the
algorithms against previous studies highlights the proposed methods' advantage with respect to the studied metrics. Overall, the models are stable and improve pedestrian comfort when an NMR navigates through a pedestrian crowd.
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Systems and Control · Cross-listed from cs.LG
Concentration and Mean-Square Bounds for Contractive Stochastic Approximation: A Unified Elementary Approach
Siddharth Chandak
We establish mean-square and concentration bounds for
stochastic approximation (SA) with arbitrary norm contractive mappings, under a multiplicative noise model where the noise may scale affinely with the norm of the iterates, and the iterates are potentially unbounded. These settings arise in reinforcement learning, where operators are often contractive in the $\ell_\infty$ norm and the noise scales with the iterates. To address the arbitrary norm, earlier works replace the non-smooth squared norm with a smooth Lyapunov function constructed via the generalized Moreau envelope. For concentration analysis, these works handle multiplicative noise and unbounded iterates through a multi-stage bootstrapping argument that starts from a time-varying worst-case bound and iteratively refines it. We instead present a unified and elementary analysis that yields both bounds. Using an averaged noise sequence and corresponding auxiliary iterates, we obtain a one-step Lyapunov drift inequality for the normed error directly, without smoothing the norm or constructing an envelope. For the mean-square bound, we combine this drift inequality with an induction argument showing that the iterates remain bounded in expectation. For the concentration bound, we develop a probabilistic induction over a sequence of "good" events on which the iterates are controlled, allowing the standard Azuma-Hoeffding bound to be applied. Our approach yields the first sub-Gaussian tailed maximal (all-time) concentration bound for SA under multiplicative noise, by allowing the stepsize to depend logarithmically on the confidence level. Beyond the specific setting considered here, we discuss the generalizability of these proof techniques to other noise models and iterative
algorithms.
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Signal Processing
Tensor-Based Dynamic Channel Estimation for mmWave Movable Antenna MIMO Systems
Zhendong Li, Linchu Chen, Lin Chen et al. (+5 more)
This paper investigates the dynamic channel estimation
algorithm in mmWave movable
antenna (MA) multiple-input multiple-output (
MIMO) systems. To achieve highly accurate channel estimation, we propose a tensor decomposition-based channel estimation
algorithm. First, by leveraging the path response model and utilizing the intrinsic sparsity of mmWave channels, the channel corresponding to MA pairs at the base station and mobile station is transformed into a superposition of channels from sparse paths. Next, the received signal is constructed as a fourth-order tensor to fully capture the high-dimensional structural information of the MA
MIMO channel. Then, two tensor decomposition schemes are adopted to extract the factor
matrices, and our analysis reveals that the uniqueness of the decomposition can be guaranteed in our model. Subsequently, the propagation loss, frequency offset, angle of arrival/departure, and time delay are obtained based on these factor
matrices and the channel
matrix can be rebuilt. Additionally, Cramér-Rao bound (CRB) is also derived as a performance evaluation standard, proving that the proposed
algorithm achieves a higher estimation accuracy and nearly approaches this minimum bound. Moreover, normalized mean square error (NMSE) is selected as the evaluation metrics for estimation accuracy. Finally, simulation results reveal a notable reduction in the estimation error of the proposed
algorithm when compared to the baseline
algorithms, confirming its estimation advantage.
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All authors Zhendong Li, Linchu Chen, Lin Chen, Zhou Su, Ruoyu Zhang, Guangji Chen, Ying Wang, Wen Chen