Systems and Control
Minimum Energy Cruise of All-Electric Aircraft with Applications to Advanced Air Mobility
Steven Li and Luis Rodrigues
Electrified propulsion is expected to play an important role in the sustainable development of Advanced Air Mobility (AAM). However, the limited energy density of batteries motivates the need to minimize energy consumption during flight. This paper studies the minimum total energy problem for an all-electric aircraft in steady cruise flight. The problem is formulated as an optimal control problem in which the cruise airspeed and final cruise time are optimization variables. The battery supply voltage is modeled as an affine function of the battery charge. Pontryagin's Minimum Principle is used to derive the necessary and sufficient conditions for optimality, from which closed-form expressions for the optimal cruise airspeed and optimal final cruise time are obtained. Additional analytical conditions are derived that determine when all-electric operation is feasible, one of which is that sufficient electric charge must be available. Numerical simulations based on the BETA Technologies CX300 all-electric aircraft and a representative AAM scenario illustrate how the aircraft weight, cruising altitude, electrical system efficiency, and initial battery charge influence the optimal airspeed and the feasibility of all-electric cruise.
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Image and Video Processing
Understanding Task Aggregation for Generalizable Ultrasound Foundation Models
Fangyijie Wang, Tanya Akumu, Vien Ngoc Dang et al. (+5 more)
Foundation models promise to unify multiple clinical tasks within a single framework, but recent ultrasound studies report that unified models can underperform task-specific baselines. We hypothesize that this degradation arises not from model capacity limitations, but from task aggregation strategies that ignore interactions between task heterogeneity and available training data scale. In this work, we systematically analyze when heterogeneous ultrasound tasks can be jointly learned without performance loss, establishing practical criteria for task aggregation in unified clinical imaging models. We introduce M2DINO, a multi-organ, multi-task framework built on DINOv3 with task-conditioned Mixture-of-Experts blocks for adaptive capacity allocation. We systematically evaluate 27 ultrasound tasks spanning
segmentation, classification, detection, and regression under three paradigms: task-specific, clinically-grouped, and all-task unified training. Our results show that aggregation effectiveness depends strongly on training data scale. While clinically-grouped training can improve performance in data-rich settings, it may induce substantial negative transfer in low-data settings. In contrast, all-task unified training exhibits more consistent performance across clinical groups. We further observe that task sensitivity varies by task type in our experiments:
segmentation shows the largest performance drops compared with regression and classification. These findings provide practical guidance for ultrasound foundation models, emphasizing that aggregation strategies should jointly consider training data availability and task characteristics rather than relying on clinical taxonomy alone.
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All authors Fangyijie Wang, Tanya Akumu, Vien Ngoc Dang, Amelia Jiménez-Sánchez, Jieyun Bai, Guénolé Silvestre, Karim Lekadir, Kathleen M. Curran
Systems and Control · Cross-listed from cs.AI
Don't Vibe Code, Do Skele-Code: Interactive No-Code Notebooks for Subject Matter Experts to Build Lower-Cost Agentic Workflows
Sriram Gopalakrishnan
Skele-Code is a natural-
language and graph-based interface for building workflows with AI
agents, designed especially for less or non-technical users. It supports incremental, interactive notebook-style development, and each step is converted to code with a required set of functions and behavior to enable incremental building of workflows.
Agents are invoked only for code generation and error recovery, not orchestration or task execution. This
agent-supported, but code-first approach to workflows, along with the context-engineering used in Skele-Code, can help reduce
token costs compared to the multi-
agent system approach to executing workflows. Skele-Code produces modular, easily extensible, and shareable workflows. The generated workflows can also be used as skills by
agents, or as steps in other workflows.
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Image and Video Processing
Dual Agreement Consistency Learning with Foundation Models for Semi-Supervised Fetal Heart Ultrasound Segmentation and Diagnosis
Fangyijie Wang, Guénolé Silvestre and Kathleen M. Curran
Congenital heart disease (CHD) screening from fetal echocardiography requires accurate analysis of multiple standard cardiac views, yet developing reliable artificial intelligence models remains challenging due to limited annotations and variable image quality. In this work, we propose FM-DACL, a semi-supervised Dual Agreement Consistency Learning framework for the FETUS 2026 challenge on fetal heart ultrasound
segmentation and diagnosis. The method combines a pretrained ultrasound foundation model (EchoCare) with a convolutional network through heterogeneous co-training and an exponential moving average teacher to better exploit unlabeled data. Experiments on the multi-center challenge
dataset show that FM-DACL achieves a Dice score of 59.66 and NSD of 42.82 using heterogeneous backbones, demonstrating the feasibility of the proposed semi-supervised framework. These results suggest that FM-DACL provides a flexible approach for leveraging heterogeneous models in low-annotation fetal cardiac ultrasound analysis. The code is available on https://github.com/13204942/FM-DACL.
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Systems and Control · Cross-listed from cs.GT
Token Economy for Fair and Efficient Dynamic Resource Allocation in Congestion Games
Leonardo Pedroso, Andrea Agazzi, W. P. M. H. Heemels et al. (+1 more)
Self-interested behavior in sharing economies often leads to inefficient aggregate outcomes compared to a centrally coordinated allocation, ultimately harming users. Yet, centralized coordination removes individual decision power. This issue can be addressed by designing rules that align individual preferences with system-level objectives. Unfortunately, rules based on conventional monetary mechanisms introduce unfairness by discriminating among users based on their wealth. To solve this problem, in this paper, we propose a
token-based mechanism for congestion games that achieves efficient and fair dynamic resource allocation. Specifically, we model the
token economy as a continuous-time dynamic game with finitely many boundedly rational
agents, explicitly capturing their evolutionary policy-revision dynamics. We derive a mean-field approximation of the finite-population game and establish strong approximation guarantees between the mean-field and the finite-population games. This approximation enables the design of integer tolls in closed form that provably steer the aggregate dynamics toward an optimal efficient and fair allocation from any initial condition.
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All authors Leonardo Pedroso, Andrea Agazzi, W. P. M. H. Heemels, Mauro Salazar
Systems and Control
Robust Global Position and Heading Tracking on SE(3) via Saturated Hybrid Feedback
Luís Martins, Carlos Cardeira and Paulo Oliveira
This letter presents a novel control solution to the robust global position and heading tracking problem for underactuated vehicles, equipped with single-axis thrust and full torque actuation, operating under strict, user-defined actuation limits. The architecture features a saturated position tracking controller augmented with two first-order filters. This formulation ensures the boundedness of the first and second derivatives, yielding less conservative bounds and systematically generating bounded attitude references whose limits are easily tuned via design parameters. To track these dynamic references, the inner loop comprises a saturated, modified Rodrigues parameter (MRP)-based controller paired with a hybrid dynamic path-lifting mechanism. This approach allows the attitude tracking law to be designed on a covering space of the configuration
manifold. By leveraging a stability equivalence framework, the methodology establishes that the resulting interconnected system achieves robust global asymptotic and semi-global exponential tracking on SE(3), while complying with user-defined input saturation bounds. Numerical simulations validate the proposed solution.
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Signal Processing
Overlap-Summation-Based Pulse Shaping Transceiver for Affine Frequency Division Multiplexing
Haojian Zhang, Jiayan Yang, Tingting Zhang et al. (+2 more)
Affine frequency division multiplexing (AFDM) has recently emerged as a promising waveform for doubly-selective channels. A direct-windowing-based pulse shaping transceiver (PS-AFDM) was proposed to suppress the Doppler sidelobes, thus improving the accuracy of channel estimation. We observe that, when the path delays and Doppler shifts are randomly distributed, the legacy PS-AFDM scheme significantly increases the condition number of the effective channel
matrix. The resulting ill-conditioning degrades the numerical stability of channel equalization in noise and consequently increases the BER. To address this issue, this letter applies the existing weighted overlap-summation (WOLA) transceiver to AFDM and proposes a novel channel-aware (CA) receive shaping window design, which simultaneously achieves accurate channel estimation and robust equalization performance, at the cost of additional prefix overhead and receive-window calculation. The resulting scheme is termed CAWOLA-AFDM. Compared with the legacy WOLA scheme, which employs a fixed receive window, the proposed CAWOLA design exploits the channel estimates of slowly varying power gains and Doppler shifts to design a channel-tailored receive shaping window in closed form, thereby further enhancing channel-estimation accuracy while maintaining the channel condition number when a Nyquist prototype window is adopted. The proposed CAWOLA receive-window design aims to produce an effective AFDM channel with more compact support in the DAFT domain. The source code for the simulations is provided at https://github.com/SANIS-HITSZ/Waveform_AFDM.
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All authors Haojian Zhang, Jiayan Yang, Tingting Zhang, Xu Zhu, Qinyu Zhang
Systems and Control
Real-time Coordination of Cascaded Hydropower under Decision-Dependent Uncertainty
Eliza Cohn, Ning Qi, Upmanu Lall et al. (+1 more)
This study proposes a real-time control framework for cascaded hydropower systems that incorporates decision-dependent uncertainty (DDU) to capture the coupling of streamflow uncertainties across reservoirs. The framework jointly models exogenous forecast errors and endogenous uncertainty propagation, characterizing the dependence between upstream releases and downstream inflow variability through a heteroskedastic variance model conditioned on past forecast errors and releases. We formulate a rolling-horizon joint chance-constrained optimization under DDU to ensure reliable cascade hydropower dispatch, with storage-reference tracking to address the future water value and a McCormick relaxation of the nonconvex head. We develop a tractable supporting hyperplane
algorithm that decouples uncertainty from decisions across time steps and directly enforces the joint chance constraint under DDU at the prescribed risk level. We establish the convergence of the proposed method and characterize its adaptive risk allocation behavior. Case studies on the Lower Columbia River demonstrate that incorporating DDU reduces the integrated violation index by up to 34.2% and increases total generation by up to 12.8 GWh over a year relative to decision-independent uncertainty (DIU). Sensitivity and scalability analyses across Monte Carlo inflow scenarios further demonstrate the value of the framework for reliable real-time cascaded hydropower operations.
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All authors Eliza Cohn, Ning Qi, Upmanu Lall, Bolun Xu
Systems and Control · Cross-listed from cs.LG
RHYME-XT: A Neural Operator for Spatiotemporal Control Systems
Marijn Ruiter, Miguel Aguiar, Jake Rap et al. (+2 more)
We propose RHYME-XT, an operator-learning framework for surrogate modeling of spatiotemporal control systems governed by input-affine nonlinear partial integro-differential equations (PIDEs) with localized rhythmic behavior. RHYME-XT uses a Galerkin projection to approximate the infinite-dimensional PIDE on a learned finite-dimensional subspace with spatial basis functions parameterized by a neural network. This yields a projected system of ODEs driven by projected inputs. Instead of integrating this non-autonomous system, we directly learn its flow map using an architecture for learning flow functions, avoiding costly computations while obtaining a continuous-time and discretization-invariant representation. Experiments on a neural field PIDE show that RHYME-XT outperforms a state-of-the-art neural operator and is able to transfer knowledge effectively across models trained on different
datasets, through a fine-tuning process.
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All authors Marijn Ruiter, Miguel Aguiar, Jake Rap, Karl H. Johansson, Amritam Das
Systems and Control
Koopman Generator Decomposition for Port-Hamiltonian System
Victor M. Preciado
We study how the vector-field structure of nonlinear port-
Hamiltonian systems is reflected in the infinitesimal Koopman generator. The generator admits a natural bracket decomposition into a conservative interconnection-bracket derivation, a dissipative metric-bracket derivation, and an input-port derivation. The conservative component is formally skew-adjoint on a test space whenever the conservative flow preserves the reference measure and the relevant boundary terms vanish. The dissipative component is not claimed to be a positive operator on arbitrary observables; rather, the positive semidefinite object is the metric bracket $[f,f]_R=\nabla f^\intercal\mathbf{R}\nabla f\ge 0$, which yields the exact port-
Hamiltonian energy balance for the
Hamiltonian observable: \[ \mathcal{K}_{\mathbf{u}}\mathcal{H} =-[\mathcal{H},\mathcal{H}]_R +\mathbf{y}^\intercal\mathbf{u} \le \mathbf{y}^\intercal\mathbf{u}. \] We use these bracket identities to motivate finite-dimensional weak Galerkin and data-driven lifted models: when the Galerkin measure is conservative for the
Hamiltonian interconnection flow and boundary terms vanish, the conservative contribution is skew in the Galerkin mass metric, while the dissipative bracket induces a positive semidefinite Dirichlet
matrix. These identities motivate structure-preserving lifted port-
Hamiltonian surrogates that are passive and support damping injection in the lifted coordinates, while distinguishing exact bracket identities, projection residuals, finite-data estimation error, and the residual and injectivity assumptions needed to transfer lifted conclusions back to the original nonlinear state.
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Systems and Control · Cross-listed from math.OC
Certainty-equivalent adaptive MPC for uncertain nonlinear systems
Johannes Köhler
We provide a method to design adaptive controllers for nonlinear systems using model predictive control (MPC). By combining a certainty-equivalent MPC formulation with least-mean-square parameter adaptation, we obtain an adaptive controller with strong robust performance guarantees: The cumulative tracking error and violation of state constraints scale linearly with noise energy, disturbance energy, and path length of parameter variation. A key technical contribution is developing the underlying certainty-equivalent MPC that tracks output references, accounts for actuator limitations and desired state constraints, requires no system-specific offline design, and provides strong inherent robustness properties. This is achieved by leveraging finite-horizon rollouts, artificial references, recent analysis techniques for optimization-based controllers, and relaxed soft state constraints. For open-loop stable systems, we derive a semi-global result that applies to arbitrarily large measurement noise, disturbances, and parametric uncertainty. For stabilizable systems, we derive a regional result that is valid within a given region of attraction and for sufficiently small uncertainty. Applicability and benefits are demonstrated with numerical simulations involving systems with large parametric uncertainty: a linear stable chain of mass-spring-dampers and a nonlinear unstable quadrotor navigating obstacles.
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Audio and Speech Processing
The Silent Thought: Modeling Internal Cognition in Full-Duplex Spoken Dialogue Models via Latent Reasoning
Donghang Wu, Tianyu Zhang, Yuxin Li et al. (+4 more)
During conversational interactions, humans subconsciously engage in concurrent thinking while listening to a speaker. Although this internal cognitive processing may not always manifest as explicit linguistic structures, it is instrumental in formulating high-quality responses. Inspired by this cognitive phenomenon, we propose a novel Full-duplex LAtent and Internal
Reasoning method named FLAIR that conducts latent thinking simultaneously with
speech perception. Unlike conventional "thinking" mechanisms in NLP, which require post-hoc generation, our approach aligns seamlessly with spoken dialogue systems: during the user's speaking phase, it recursively feeds the latent
embedding output from the previous step into the next step, enabling continuous
reasoning that strictly adheres to causality without introducing additional latency. To enable this latent
reasoning, we design an Evidence Lower Bound-based objective that supports efficient supervised finetuning via teacher forcing, circumventing the need for explicit
reasoning annotations. Experiments demonstrate the effectiveness of this think-while-listening design, which achieves competitive results on a range of
speech benchmarks. Furthermore, FLAIR robustly handles conversational dynamics and attains competitive performance on full-duplex interaction metrics.
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All authors Donghang Wu, Tianyu Zhang, Yuxin Li, Hexin Liu, Chen Chen, Eng Siong Chng, Yoshua Bengio
Systems and Control
Verification and Validation of Physics-Informed Surrogate Component Models for Dynamic Power-System Simulation
Petros Ellinas, Indrajit Chaudhuri, Johanna Vorwerk et al. (+1 more)
Physics-informed machine learning surrogates are increasingly explored to accelerate dynamic simulation of generators, converters, and other power grid components. The key question, however, is not only whether a surrogate matches a stand-alone component model on average, but whether it remains accurate after insertion into a differential-algebraic simulator, where the surrogate outputs enter the algebraic equations coupling the component to the rest of the system. This paper formulates that in-simulator use as a verification and validation (V&V) problem. A finite-horizon bound is derived that links allowable component-output error to algebraic-coupling sensitivity, dynamic error amplification, and the simulation horizon. Two complementary settings are then studied: model-based verification against a reference component solver, and data-based validation through conformal calibration of the component-output variables exchanged with the simulator. The framework is general, but the case study focuses on physics-informed neural-network surrogates of second-, fourth-, and sixth-order synchronous-machine models. Results show that good stand-alone surrogate accuracy does not by itself guarantee accurate in-simulator behavior, that the largest discrepancies concentrate in stressed operating regions, and that small equation residuals do not necessarily imply small state-trajectory errors.
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All authors Petros Ellinas, Indrajit Chaudhuri, Johanna Vorwerk, Spyros Chatzivasileiadis
Audio and Speech Processing
Multi-Source Evidence Fusion for Audio Question Answering
Aivo Olev and Tanel Alumäe
Large audio
language models (LALMs) can answer questions about
speech, music, and environmental sounds, yet their internal
reasoning is largely opaque and difficult to validate. We describe TalTech's solution to the
Agent Track of the Interspeech 2026 Audio
Reasoning Challenge, in which systems are evaluated on
reasoning process quality, specifically the factual accuracy, logical soundness, and completeness of their
reasoning chains. Our multi-source ensemble pipeline uses two LALMs that generate independent observations, while a separate text-only
reasoning model cross-checks these against outputs from 25 acoustic tools organized into reliability tiers. By grounding every inference step in explicit, reliability-tagged evidence, the system produces dense, verifiable
reasoning chains. Our system ranked first in the challenge, outperforming all competing systems by a wide margin in challenge's
reasoning quality metric.
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Signal Processing
Comparison of 60 GHz and 80 GHz Vehicle-to-Vehicle Channels Using Delay and Doppler Characteristics
Ales Prokes, Tomas Mikulasek, Josef Vychodil et al. (+6 more)
The aim of this paper is to provide a comparison of channel characteristics for vehicle-to-vehicle (V2V) communication at 60 GHz and 80 GHz frequency bands in a high-mobility scenario where two vehicles pass each other in opposite directions. The study is based on measurements of the time-varying channel impulse response capturing the behavior of multi-path propagation during vehicle motion. By directly comparing these two frequency bands under identical measurement conditions, we attempt to quantify the differences in power delay profile, root mean square (RMS) delay spread, RMS Doppler spread, and intervals (regions) of stationarity in time domain. The results show that these bands do not differ significantly, but the 80 GHz band exhibits somewhat greater RMS delay spread and RMS Doppler spread when calculated over the entire delay-Doppler spectrum, and conversely exhibits shorter stationarity regions. However, the characteristics of the measurement setup in the two bands and their influence on comparative measurements must be considered. In particular,
attention must be paid to the impact of
antennas.
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All authors Ales Prokes, Tomas Mikulasek, Josef Vychodil, Radek Zavorka, Jiri Blumenstein, Jaroslaw Wojtun, Jan M. Kelner, Cezary Ziolkowski, Aniruddha Chandra
Systems and Control
An HMDP-MPC Decision-making Framework with Adaptive Safety Margins and Hysteresis for Autonomous Driving
Siyuan Li, Chengyuan Liu and Wen-Hua Chen
This paper presents a unified decision-making framework that integrates Hybrid Markov Decision Processes (HMDPs) with Model Predictive Control (MPC), augmented by velocity-dependent safety margins and a prediction-aware hysteresis mechanism. Both the ego and surrounding vehicles are modeled as HMDPs, allowing discrete maneuver transition and kinematic
evolution to be jointly considered within the MPC optimization. Safety margins derived from the Intelligent Driver Model (IDM) adapt to traffic context but vary with speed, which can cause oscillatory decisions and velocity fluctuations. To mitigate this, we propose a frozen-release hysteresis mechanism with distinct trigger and release thresholds, effectively enlarging the reaction buffer and suppressing oscillations. Decision continuity is further safeguarded by a two-layer recovery scheme: a global bounded relaxation tied to IDM margins and a deterministic fallback policy. The framework is evaluated through a case study, an ablation against a no-hysteresis baseline, and largescale randomized experiments across 18 traffic settings. Across 8,050 trials, it achieves a collision rate of only 0.05%, with 98.77% of decisions resolved by nominal MPC and minimal reliance on relaxation or fallback. These results demonstrate the robustness and adaptability of the proposed decision-making framework in heterogeneous traffic conditions.
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Systems and Control
Data-Driven Predictive Control for Stochastic Descriptor Systems: An Innovation-Based Approach Handling Non-Causal Dependencies
Yunxiang Ma, Yibo Wang, Zhongmei Li et al. (+1 more)
Descriptor systems arise naturally in real-world applications governed by algebraic constraints, such as power networks, robotics and chemical processes. When a descriptor model contains a nontrivial nilpotent block, the discrete-time input–output map may be improper: the current output depends on future inputs and, in the
stochastic case, on future noise terms. This letter proposes a data-driven predictive control framework for
stochastic descriptor systems that handles these non-causal dependencies without explicitly identifying system
matrices. The key idea is to split fast subsystem into noise-driven and input-driven parts, and then combine the former with the slow subsystem such that an innovation-driven Kalman filter can be appropriately defined to reformulate the
stochastic descriptor system into an innovation-driven form. Based on this, a new behavioral system representation is derived, which inspires a data-driven innovation-based multi-step output predictor and a practical Inno-DeePC
algorithm that enables data-driven predictive control design without known system
matrices while implicitly handling algebraic constraints. Numerical experiments on a DC microgrid demonstrate the effectiveness of the proposed approach.
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All authors Yunxiang Ma, Yibo Wang, Zhongmei Li, Chao Shang
Audio and Speech Processing · Cross-listed from cs.SD
Modeling Overlapped Speech with Shuffles
Matthew Wiesner, Samuele Cornell, Alexander Polok et al. (+3 more)
We propose to model parallel streams of data, such as overlapped
speech, using shuffles. Specifically, this paper shows how the shuffle product and partial order finite-state automata (FSAs) can be used for
alignment and speaker-attributed transcription of overlapped
speech. We train using the total score on these FSAs as a loss function, marginalizing over all possible serializations of overlapping sequences at subword, word, and phrase levels. To reduce graph size, we impose temporal constraints by constructing partial order FSAs. We address speaker attribution by modeling (
token, speaker) tuples directly. Viterbi
alignment through the shuffle product FSA directly enables one-pass
alignment. We evaluate performance on synthetic LibriSpeech overlaps. To our knowledge, this is the first
algorithm that enables single-pass
alignment of multi-talker recordings. All
algorithms are implemented using k2 / Icefall.
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All authors Matthew Wiesner, Samuele Cornell, Alexander Polok, Lucas Ondel Yang, Lukáš Burget, Sanjeev Khudanpur
Signal Processing
Vehicle-to-Vehicle Millimeter-Wave Channel Characterization at 60 and 80 GHz
Radek Zavorka, Tomas Mikulasek, Josef Vychodil et al. (+9 more)
This paper presents results from a vehicle-to-vehicle channel measurement campaign conducted in the millimeter-wave (MMW) frequency bands at center frequencies of 60GHz and 80GHz, each with a bandwidth of 2GHz. The measurements were performed in a dynamic oncoming-vehicle scenario using a time-domain channel sounder with high-resolution data acquisition. Power delay profiles were extracted to study the temporal
evolution of multipath components, and the root mean square (RMS) delay spread was analyzed to characterize the temporal dispersion of the channel. The results demonstrate differences between the two frequency bands. At 60GHz, the RMS delay spread is well approximated by a Gaussian distribution with a higher median value, while at 80GHz it follows a lognormal distribution with a lower median. Furthermore, the number of resolvable multipath components was found to be nearly twice as high at 60\,GHz compared to 80GHz, highlighting the impact of
antenna beamwidth and frequency-dependent propagation mechanisms.
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All authors Radek Zavorka, Tomas Mikulasek, Josef Vychodil, Jiri Blumenstein, Hussein Hammoud, Jaroslaw Wojtun, Aniruddha Chandra, Markus Hofer, Jan M. Kelner, Cezary Ziolkowski, Christoph Mecklenbrauker, Ales Prokes
Systems and Control
Robust Dynamic Pricing and Admission Control with Fairness Guarantees
Yingqing Chen, Anni Li, Christos G. Cassandras et al. (+3 more)
Dynamic pricing is commonly used to regulate congestion in shared service systems. This paper is motivated by the fact that in the presence of users with varying price sensitivity (responsiveness), conventional monotonic pricing can lead to unfair outcomes by disproportionately excluding price-elastic users, particularly under high or uncertain demand. We therefore develop a
fairness-oriented mechanism under demand uncertainty. The paper's contributions are twofold. First, we show that when
fairness is imposed as a hard state constraint, the optimal (revenue maximizing) pricing policy is generally non-monotonic in demand. This structural result departs fundamentally from standard surge pricing rules and reveals that price reduction under heavy load may be necessary to maintain equitable access. Second, we address the problem that price elasticity among heterogeneous users is unobservable. To solve it, we develop a robust dynamic pricing and admission control framework that enforces capacity and
fairness constraints for all user type distributions consistent with aggregate measurements. By integrating integral High Order Control Barrier Functions (iHOCBFs) with a robust optimization framework under uncertain user-type distribution, we obtain a controller that guarantees forward invariance of safety and
fairness constraints while optimizing revenue. Numerical experiments demonstrate improved
fairness and revenue performance relative to monotonic surge pricing policies.
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All authors Yingqing Chen, Anni Li, Christos G. Cassandras, Homayoun Hamedmoghadam, Fabian Wirth, Robert Shorten
Signal Processing
Fairness-Aware Beamforming for Polarimetric ISAC Systems with Polarization-Reconfigurable Antennas
Weijie Xiong, Jingran Lin, Di Jiang et al. (+4 more)
Polarization diversity offers significant flexibility for enhancing integrated sensing and communications (
ISAC). However, conventional dual-polarized arrays typically require dedicated
radio-frequency (RF) chains for each polarization branch, leading to prohibitive hardware costs. To address this, polarization-reconfigurable (PR)
antennas have emerged as a cost-effective alternative, enabling polarization flexibility with reduced hardware complexity by driving two polarization branches with a single RF chain. In this paper, we investigate
fairness-aware beamforming for
ISAC systems equipped with PR
antennas. Specifically, we jointly optimize the transmit beamforming and PR control coefficients to maximize the minimum signal-to-interference-plus-noise ratio (SINR) for communication users and the minimum signal-to-clutter-plus-noise ratio (SCNR) for sensing targets. The resulting problem is highly nonconvex and nonsmooth due to the strong coupling among optimization variables in the max-min objective, as well as the nonconvex spherical constraints imposed by the PR
antennas. To tackle this, we derive an equivalent smooth reformulation by introducing auxiliary variables and transforming the minimum operators into inequality constraints. Subsequently, we develop an exact-penalty product Riemannian
manifold gradient descent (EP-PRMGD)
algorithm, which integrates an exact penalty method with Riemannian optimization to guarantee convergence to a Karush-Kuhn-Tucker (KKT) point. Numerical results demonstrate that the proposed PR-enabled
ISAC scheme achieves performance comparable to dual-polarized architectures while utilizing only half the RF chains, thereby validating its effectiveness in balancing
fairness and hardware efficiency.
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All authors Weijie Xiong, Jingran Lin, Di Jiang, Cunhua Pan, Hongli Liu, Kai Zhong, Qiang Li
Systems and Control · Cross-listed from cs.RO
Multisource human-in-the-loop digital twin testbed for connected and autonomous vehicles in mixed traffic flow
Jianghong Dong, Chunying Yang, Mengchi Cai et al. (+5 more)
In the emerging mixed traffic environments, Connected and Autonomous Vehicles (CAVs) have to interact with surrounding human-driven vehicles (HDVs). This paper introduces MSH-MCCT (Multi-Source Human-in-the-Loop Mixed Cloud Control Testbed), a novel CAV testbed that captures complex interactions between various CAVs and HDVs. Utilizing the Mixed Digital Twin concept, which combines Mixed Reality with Digital Twin, MSH-MCCT integrates physical, virtual, and mixed platforms, along with multi-source control inputs. Bridged by the mixed platform, MSH-MCCT allows human drivers and CAV
algorithms to operate both physical and virtual vehicles within multiple fields of view. Particularly, this testbed facilitates the coexistence and real-time interaction of physical and virtual CAVs & HDVs, significantly enhancing the experimental flexibility and scalability. Experiments on vehicle platooning in mixed traffic showcase the potential of MSH-MCCT to conduct CAV testing with multi-source real human drivers in the loop through driving simulators of diverse fidelity. The
videos for the experiments are available at our project website: https://dongjh20.github.io/MSH-MCCT.
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All authors Jianghong Dong, Chunying Yang, Mengchi Cai, Chaoyi Chen, Qing Xu, Jianqiang Wang, Jiawei Wang, Keqiang Li
Signal Processing
Design of Uplink ISAC Systems with Cooperative Sensing: Power Control and Receive Beamforming
Ling He, Vaibhav Kumar, Roberto Bomfin et al. (+3 more)
Integrated sensing and communication (
ISAC) has emerged as a key paradigm for next-generation wireless systems, which allows wireless resources to be used for data transmission and target sensing simultaneously. In this paper, multi-user collaborative target detection in the uplink
ISAC system is investigated. To incorporate the target sensing functionality, the system relies on the reuse of uplink signals from the communication users. Specifically, we analyze an uplink multi-user single-input multiple-output (MU-SIMO) communication system with bistatic sensing. Using the channel statistics, we formulate the problem of joint optimal pilot and data power allocation to maximize the uplink ergodic sum rate while meeting communication and sensing quality-of-service (QoS) requirements. To address this non-convex problem, we propose an alternating optimization (AO)-based iterative framework, where the joint power allocation problem is decomposed into two sub-problems. Specifically, the pilot power allocation is optimized using a penalty dual decomposition (PDD)-based gradient ascent
algorithm, while the data power allocation is solved via successive convex approximation (SCA). Once the long-term power allocation is determined, the base station (BS) estimates the instantaneous channels using a minimum mean-squared error (MMSE)
estimator. Subsequently, based on the estimated instantaneous channel state information (CSI), the receive beamforming for communication users is optimized via another SCA-based method to maximize the sum rate. Meanwhile, the optimal receive beamforming for the target is obtained in closed-form through
eigenvalue decomposition (EVD).
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All authors Ling He, Vaibhav Kumar, Roberto Bomfin, Yingyang Chen, Miaowen Wen, Marwa Chafii
Image and Video Processing · Cross-listed from cs.IT
Cache-enabled Generative Joint Source-Channel Coding for Evolving Semantic Communications
Shunpu Tang, Qianqian Yang, Jihong Park et al. (+3 more)
Learning-based semantic communication (SemCom) has recently emerged as a promising paradigm for improving the transmission efficiency of wireless networks. However, existing methods typically rely on extensive end-to-end training, which is both inflexible and computationally expensive in dynamic wireless environments. Moreover, they fail to exploit redundancy across multiple transmissions of semantically similar content, limiting overall efficiency. To overcome these limitations, we propose a channel-aware generative
adversarial network (GAN) inversion-based joint source-channel coding (CAGI-JSCC) framework that enables training-free SemCom by leveraging a pre-trained SemanticStyleGAN model. By explicitly incorporating wireless channel characteristics into the GAN inversion process, CAGI-JSCC adapts to varying channel conditions without additional training. Furthermore, we introduce a cache-enabled dynamic codebook (CDC) that caches disentangled semantic components at both the transmitter and receiver, allowing the system to reuse previously transmitted content. This semantic-level caching can continuously reduce redundant transmissions as experience accumulates. Extensive experiments on image transmission demonstrate the effectiveness of the proposed framework. In particular, our system achieves comparable perceptual quality with an average bandwidth compression ratio (BCR) of 1/224, and as low as 1/1024 for a single image, significantly outperforming baselines with a BCR of 1/128.
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All authors Shunpu Tang, Qianqian Yang, Jihong Park, Zhaoyang Zhang, Kaibin Huang, Deniz Gunduz
Systems and Control
On maximal positive invariant set computation for rank-deficient linear systems
Bogdan Gheorghe, Daniel Ioan, Cristian Flutur et al. (+2 more)
The maximal positively invariant (MPI) set is obtained through a backward reachability procedure involving the iterative computation and intersection of predecessor sets under state and input constraints. However, standard static feedback synthesis may place some of the closed-loop
eigenvalues at zero, leading to rank-deficient dynamics. This affects the MPI computation by inducing projections onto lower-dimensional subspaces during intermediate steps. By exploiting the Schur decomposition, we explicitly address this singular case and propose a robust
algorithm that computes the MPI set in both polyhedral and constrained-zonotope representations.
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All authors Bogdan Gheorghe, Daniel Ioan, Cristian Flutur, Ionela Prodan, Florin Stoican
Systems and Control · Cross-listed from cs.IT
Physical Layer Security in Finite Blocklength Massive IoT with Randomly Located Eavesdroppers
Tijana Devaja, Milica Petkovic, Sokol Kosta et al. (+2 more)
This paper analyzes the physical layer security performance of massive uplink Internet of Things (IoT) networks operating under the finite blocklength (FBL) regime. IoT devices and base stations (BS) are modeled using a
stochastic geometry approach, while an eavesdropper is placed at a random location around the transmitting device. This system model captures security risks common in dense IoT deployments. Analytical expressions for the secure success probability, secrecy outage probability and secrecy throughput are derived to characterize how
stochastic interference, fading and eavesdropper spatial uncertainty interact with FBL constraints in short packet uplink transmissions. Numerical results illustrate key system behavior under different network and channel conditions.
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All authors Tijana Devaja, Milica Petkovic, Sokol Kosta, Dejan Vukobratovic, Cedomir Stefanovic
Signal Processing
Optimizing Antenna Coding for Pixel Antenna Empowered SISO-OFDM Systems
Tianrui Qiao, Shanpu Shen, Yijun Chen et al. (+1 more)
This work investigates
antenna coding optimization to enhance the channel capacity of single-input single-output orthogonal frequency division multiplexing (SISO-OFDM) systems empowered by highly reconfigurable pixel
antennas. We first introduce the model for pixel
antenna empowered SISO-OFDM systems using a beamspace channel representation. We next formulate the problem to maximize the channel capacity through jointly optimizing
antenna coding and the power allocation across subcarriers and solve it by Successive Exhaustive Boolean Optimization (SEBO) and water-filling (WF)
algorithm. To reduce computational complexity, a codebook-based approach is also proposed for
antenna coding optimization. Simulation results show that the channel capacity of SISO-OFDM system across all signal-to-noise-ratio (SNR) regions considered can be enhanced through leveraging pixel
antennas as compared to using conventional
antenna with fixed configuration. This result demonstrates the effectiveness of
antenna coding technology empowered by pixel
antenna in enhancing SISO-OFDM systems.
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All authors Tianrui Qiao, Shanpu Shen, Yijun Chen, Ross Murch
Systems and Control
Defending the power grid by segmenting the EV charging cyber infrastructure
Kirill Kuroptev, Florian Steinke and Efthymios Karangelos
This paper examines defending the power grid against load-altering attacks using electric vehicle charging. It proposes to preventively segment the cyber infrastructure that charging station operators (CSOs) use to communicate with and control their charging stations, thereby limiting the impact of successful cyber-attacks. Using real German charging station data and a reconstructed transmission grid model, a threat analysis shows that without
segmentation, the successful hack of just two CSOs can overload two transmission grid branches, exceeding the N-1 security margin and necessitating defense measures. A novel defense design problem is then formulated that minimizes the number of imposed
segmentations while bounding the number of branch overloads under worst-case attacks. The resulting IP-MILP bi-level problem can be solved with an exact column and constraint generation
algorithm and with heuristics for fast computation on large-scale instances. For the near-real-world Germany case, the applicability of the heuristics is demonstrated and validated under relevant load and dispatch scenarios. It is found that the simple scheme of segmenting CSOs evenly by their installed capacity leads to only 23% more segments compared to the heuristic optimization result, suggesting potential relevance as a regulatory measure.
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Systems and Control
Hierarchical Decision-Making under Uncertainty: A Hybrid MDP and Chance-Constrained MPC Approach
Siyuan Li, Chengyuan Liu and Wen-Hua Chen
This paper presents a hierarchical decision-making framework for autonomous systems operating under uncertainty, demonstrated through autonomous driving as a representative application. Surrounding
agents are modeled using Hybrid Markov Decision Processes (HMDPs) that jointly capture maneuver-level and dynamic-level uncertainties, enabling the multi-modal environmental prediction. The ego
agent is modeled using a separate HMDP and integrated into a Model Predictive Control (MPC) framework that unifies maneuver selection with dynamic feasibility within a single optimization. A set of joint chance constraints serves as the bridge between environmental prediction and optimization, incorporating multi-modal environment predictions into the MPC formulation and ensuring safety across all plausible interaction scenarios. The proposed framework provides theoretical guarantees on recursive feasibility and asymptotic stability, and its benefits in terms of safety and efficiency are validated through comprehensive evaluations in highway and urban environments, together with comparisons against a rule-based baseline.
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Systems and Control
Real-Time Online Learning for Model Predictive Control using a Spatio-Temporal Gaussian Process Approximation
Lars Bartels, Amon Lahr, Andrea Carron et al. (+1 more)
Learning-based model predictive control (MPC) can enhance control performance by correcting for model inaccuracies, enabling more precise state trajectory predictions than traditional MPC. A common approach is to model unknown residual dynamics as a Gaussian process (GP), which leverages data and also provides an estimate of the associated uncertainty. However, the high computational cost of online learning poses a major challenge for real-time GP-MPC applications. This work presents an efficient implementation of an approximate spatio-temporal GP model, offering online learning at constant computational complexity. It is optimized for GP-MPC, where it enables improved control performance by learning more accurate system dynamics online in real-time, even for time-varying systems. The performance of the proposed method is demonstrated by simulations and hardware experiments in the exemplary application of autonomous miniature racing.
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All authors Lars Bartels, Amon Lahr, Andrea Carron, Melanie N. Zeilinger