Papers

We study feasibility guarantees for safety filters developed using Control Barrier Functions (CBFs) when a safe set is defined using the pointwise minimum of continuously differentiable functions, a construction that is common for the backup CBF (BCBF) method and typically nonsmooth. We replace the minimum by its log-sum-exp (soft-min) smoothing and show that, under a strict safety condition, the smooth function becomes a CBF (or extended CBF) for a range of the smoothing parameter. For compact safe sets, we derive an explicit lower bound on the smoothing parameter that makes the smooth function a CBF and hence renders the corresponding safety constraint feasible. For unbounded sets, we introduce tail conditions under which the smooth function satisfies an extended CBF condition uniformly. Finally, we apply these results to BCBFs. We show that safety of a compact (terminal) backup set under a backup controller, together with a condition ensuring safety of the backup trajectories on the relevant boundary of the safe set, is sufficient for constraint feasibility for BCBFs. These results provide a recipe for a priori feasibility guarantees for smooth inner approximations of nonsmooth safe sets without the need for additional online certification.

This study presents a systematic evaluation of time-frequency feature design for binaural sound source localization (SSL), focusing on how feature selection influences model performance across diverse conditions. We investigate the performance of a convolutional neural network (CNN) model using various combinations of amplitude-based features (magnitude spectrogram, interaural level difference - ILD) and phase-based features (phase spectrogram, interaural phase difference - IPD). Evaluations on in-domain and out-of-domain data with mismatched head-related transfer functions (HRTFs) reveal that carefully chosen feature combinations often outperform increases in model complexity. While two-feature sets such as ILD + IPD are sufficient for in-domain SSL, generalization to diverse content requires richer inputs combining channel spectrograms with both ILD and IPD. Using the optimal feature sets, our low-complexity CNN model achieves competitive performance. Our findings underscore the importance of feature design in binaural SSL and provide practical guidance for both domain-specific and general-purpose localization.

Pixel antenna is a promising technology to enhance the wireless communication data rate by adaptively reconfiguring each antenna's radiation pattern via a so-called antenna coding technique which controls the states of switches connected to multiple pixel ports. This paper studies a multiple-input multiple-output (MIMO) system where both the transmitter and the receiver are equipped with multiple pixel antennas. We aim to characterize the fundamental capacity limit of this MIMO system by jointly optimizing the transmit covariance matrix and the antenna coders at both the transmitter and the receiver. This problem is a mixed-integer non-linear program (MINLP) which is non-convex and particularly challenging to solve due to the binary-valued optimization variables corresponding to the antenna coders. We first propose an exhaustive search based method to obtain the optimal solution to this problem, which corresponds to the fundamental capacity limit. Then, we propose a branch-and-bound based iterative algorithm aiming to find a high-quality suboptimal solution with lower complexity than exhaustive search as the number of pixel ports becomes large. Finally, we devise an alternating optimization (AO) based algorithm with polynomial complexity. Numerical results show that our proposed algorithms achieve a flexible trade-off between performance and complexity. Moreover, equipping the transceivers with pixel antennas can enhance the achievable rate of MIMO communications.

In this paper, we study a cellular-connected unmanned aerial vehicle (UAV) which aims to fly between two predetermined locations while maintaining ultra-reliable low-latency communications (URLLC) for command-and-control (C2) links with terrestrial base stations (BSs). Long-range flights often trigger frequent inter-cell handovers, which may introduce delays and synchronization overhead. We jointly optimize the continuous trajectory and BS association to minimize handovers, path length, and flying time, subject to communication reliability and kinematic constraints. To address this problem, we reformulate it as an optimization based on the graph of convex sets (GCS). First, the URLLC requirement is translated into spatially feasible regions in the flight plane for each BS. And an intersection graph is constructed including the start and goal points. Each graph node is associated with a smooth and dynamically feasible trajectory segment. The trajectory is parameterized in space by Bézier curves and in time by a monotonic Bézier scaling, together with convex constraints that ensure continuity and enforce speed bounds. Next, we impose unit-flow constraints to enforce a single path, and by coupling the resulting binary edge-selection variables with the convex constraints, we obtain a mixed-integer convex program (MICP). Applying a convex relaxation and rounding to the mixed-integer convex program produces nearly globally optimal routes, and a final refinement yields smooth, dynamically feasible trajectories. Simulations verify that the method preserves URLLC connectivity while achieving a clear trade-off between fewer handovers and flight efficiency.

Accurate performance modeling of PV systems in urban environments is a significant challenge due to complex partial shading. This study introduces a high-resolution, hierarchical modeling framework that provides detailed insights from the solar cell to the system level. Rigorously validated against field-test data from calibrated equipment, the model demonstrates high accuracy in predicting minute-wised dynamic electrical characteristics (R2 > 0.90). A key finding is the critical shortcoming of conventional, coarser-resolution models under realistic shading; these are shown to overestimate the actual string operating power by up to 163% and the monthly energy yield by up to 54%. The proposed framework avoids these errors by precisely capturing mismatch losses and the time-varying phenomena of system components, such as bypass diode activations. Furthermore, the model accurately quantifies the effectiveness of mitigation technologies, showing that Module-Level Power Electronics (MLPEs) can increase the monthly energy yield of a heavily shaded string by over 20%. This research provides a crucial tool for reliable system design, accurate power forecasting, and the optimization of PV systems in complex urban settings.

Electrical isolation is critical to ensure safety and minimize electromagnetic interference (EMI), yet existing methods struggle to simultaneously transmit power and signals through a unified channel. Here we demonstrate a mechanically-isolated gate driver based on microwave-frequency surface acoustic wave (SAW) device on lithium niobate that achieves galvanic isolation of 2.75 kV with ultralow isolation capacitance (0.032 pF) over 1.25 mm mechanical propagation length, delivering 13.4 V open-circuit voltage and 44.4 mA short-circuit current. We demonstrate isolated gate driving for a gallium nitride (GaN) high-electron-mobility transistor, achieving a turn-on time of 108.8 ns comparable to commercial drivers and validate its operation in a buck converter. In addition, our SAW device operates over an ultrawide temperature range from 0.5 K (-272.6 {\deg}C) to 544 K (271 {\deg}C). The microwave-frequency SAW devices offer inherent EMI immunity and potential for heterogeneous integration on multiple semiconductor platforms, enabling compact, high-performance isolated power and signal transmission in advanced power electronics.

MIRI is the imager and spectrograph covering wavelengths from $4.9$ to $27.9$ $\mu$m onboard the James Webb Space Telescope (JWST). The Medium-Resolution Spectrometer (MRS) consists of four integral field units (IFU), each of which has three sub-channels. The twelve resulting spectral data cubes have different fields of view, spatial, and spectral resolutions. The wavelength range of each cube partially overlaps with the neighboring bands, and the overlap regions typically show flux mismatches which have to be corrected by spectral stitching methods. Stitching methods aim to produce a single data cube incorporating the data of the individual sub-channels, which requires matching the spatial resolution and the flux discrepancies. We present Haute Couture, a novel stitching algorithm which uses non-negative matrix factorization (NMF) to perform a matrix completion, where the available MRS data cubes are treated as twelve sub-matrices of a larger incomplete matrix. Prior to matrix completion, we also introduce a novel pre-processing to homogenize the global intensities of the twelve cubes. Our pre-processing consists in jointly optimizing a set of global scale parameters that maximize the fit between the cubes where spectral overlap occurs. We apply our novel stitching method to JWST data obtained as part of the PDRs4All observing program of the Orion Bar, and produce a uniform cube reconstructed with the best spatial resolution over the full range of wavelengths.

High communication costs create a major bottleneck for distributed resource allocation over unbalanced directed networks. Conventional dual gradient tracking methods, while effective for problems on unbalanced digraphs, rely on periodic communication that creates significant overhead in resource-constrained networks. This paper introduces a novel event-triggered dual gradient tracking algorithm to mitigate this limitation, wherein agents communicate only when local state deviations surpass a predefined threshold. We establish comprehensive convergence guarantees for this approach. First, we prove sublinear convergence for non-convex dual objectives and linear convergence under the Polyak-Łojasiewicz condition. Building on this, we demonstrate that the proposed algorithm achieves sublinear convergence for general strongly convex cost functions and linear convergence for those that are also Lipschitz-smooth. Numerical experiments confirm that our event-triggered method significantly reduces communication events compared to periodic schemes while preserving comparable convergence performance.

The sixth-generation (6G) wireless networks will rely on ultra-dense multi-cell deployment to meet the high rate and connectivity demands. However, frequency reuse leads to severe inter-cell interference, particularly for cell-edge users, which limits the communication performance. To overcome this challenge, we investigate a beyond diagonal reconfigurable intelligent surface (BD-RIS) aided multi-cell multi-user downlink MIMO communication system, where a BD-RIS is deployed to enhance desired signals and suppress both intra-cell and inter-cell interference. We formulate the joint optimization problem of the transmit beamforming matrices at the BSs and the BD-RIS reflection matrix to maximize the weighted sum rate of all users, subject to the challenging unitary constraint of the BD-RIS reflection matrix and transmit power constraints at the BSs. To tackle this non-convex and difficult problem, we apply the weighted minimum mean squared error (WMMSE) method to transform the problem into an equivalent tractable form, and propose an efficient alternating optimization (AO) based algorithm to iteratively update the transmit beamforming and BD-RIS reflection using Lagrange duality theory and manifold optimization. Numerical results demonstrate the superiority of the proposed design over various benchmark schemes, and provide useful practical insights on the BD-RIS deployment strategy for multi-cell systems.

Programmable metasurfaces and adjustable antennas are promising technologies. The security of a rotatable array system is investigated in this paper. A dual-base-station (BS) architecture is adopted, in which the BSs collaboratively perform integrated sensing of the eavesdropper (the target) and communication tasks. To address the security challenge when the sensing target is located on the main communication link, the problem of maximizing the secrecy rate (SR) under sensing signal-to-interference-plus-noise ratio requirements and discrete constraints is formulated. This problem involves the joint optimization of the array pose, the antenna distribution on the array surface, the multi-layer transmitting RIS phase matrices, and the beamforming matrices, which is non-convex. To solve this challenge, an two-stage online algorithm based on the generalized Rayleigh quotient and an offline algorithm based on the Multi-Agent Deep Deterministic Policy Gradient are proposed. Simulation results validate the effectiveness of the proposed algorithms. Compared to conventional schemes without array pose adjustment, the proposed approach achieves approximately 22% improvement in SR. Furthermore, array rotation provides higher performance gains than position changes.

This article presents an analytical account of the capacity limits and operational challenges of the main airports in the São Paulo Macrometropolis. Drawing on international examples, such as London Heathrow, it discusses how large hubs combine high traffic generation with severe physical constraints, highlighting how saturation intensifies delays, operating costs, and pressures for expansion. It analyzes capacity scarcity as a central economic problem, in which runways, aprons, boarding gates, and terminals become critical resources whose use requires administrative and market mechanisms, including slot coordination, prioritization rules, and regulatory incentives. It discusses the limitations imposed by high earthmoving costs, environmental impacts, and expropriation costs, which restrict the physical expansion of central airports such as Congonhas and increase dependence on efficiency gains. Demand projections indicate that the combined capacity of Congonhas, Guarulhos, and Viracopos is likely to be exceeded, even in conservative scenarios, reinforcing the urgency of integrated planning. The effects of regulatory restrictions on Congonhas, the challenges of expanding Guarulhos, and the structural difficulties of Viracopos are evaluated, highlighting the strategic relevance of this multi-airport system for sustaining national connectivity.

Computed tomography perfusion (CTP) and magnetic resonance perfusion (MRP) are widely used in acute ischemic stroke assessment and other cerebrovascular conditions to generate quantitative maps of cerebral hemodynamics. While commercial perfusion analysis software exists, it is often costly, closed source, and lacks customizability. This work introduces PyPeT, an openly available Python Perfusion Tool for head CTP and MRP processing. PyPeT is capable of producing cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), time-to-peak (TTP), and time-to-maximum (Tmax) maps from raw four-dimensional perfusion data. PyPeT aims to make perfusion research as accessible and customizable as possible. This is achieved through a unified framework in which both CTP and MRP data can be processed, with a strong focus on modularity, low computational burden, and significant inline documentation. PyPeT's outputs can be validated through an extensive debug mode in which every step of the process is visualized. Additional validation was performed via visual and quantitative comparison with reference perfusion maps generated by three FDA-approved commercial perfusion tools and a research tool. These comparisons show a mean SSIM around 0.8 for all comparisons, indicating a good and stable correlation with FDA-approved tools. The code for PyPeT is openly available at our GitHub https://github.com/Marijn311/CT-and-MR-Perfusion-Tool

Generative models have shown remarkable performance in speech enhancement (SE), achieving superior perceptual quality over traditional discriminative approaches. However, existing generative SE approaches often overlook the risk of hallucination under severe noise, leading to incorrect spoken content or inconsistent speaker characteristics, which we term linguistic and acoustic hallucinations, respectively. We argue that linguistic hallucination stems from models' failure to constrain valid phonological structures and it is a more fundamental challenge. While language models (LMs) are well-suited for capturing the underlying speech structure through modeling the distribution of discrete tokens, existing approaches are limited in learning from noise-corrupted representations, which can lead to contaminated priors and hallucinations. To overcome these limitations, we propose the Phonologically Anchored Speech Enhancer (PASE), a generative SE framework that leverages the robust phonological prior embedded in the pre-trained WavLM model to mitigate hallucinations. First, we adapt WavLM into a denoising expert via representation distillation to clean its final-layer features. Guided by the model's intrinsic phonological prior, this process enables robust denoising while minimizing linguistic hallucinations. To further reduce acoustic hallucinations, we train the vocoder with a dual-stream representation: the high-level phonetic representation provides clean linguistic content, while a low-level acoustic representation retains speaker identity and prosody. Experimental results demonstrate that PASE not only surpasses state-of-the-art discriminative models in perceptual quality, but also significantly outperforms prior generative models with substantially lower linguistic and acoustic hallucinations.

This paper presents a novel method for transient stability analysis (TSA) that circumvents the limitations of sequential numerical integration and energy functions. The proposed method begins by constructing a trajectory-dependent stability indicator function to distinguish the system's destiny. To overcome the difficulty in analyzing the asymptotic behavior at infinite time, a strategic time contraction mapping is then applied. This allows TSA to be recast as a pole-placement detection problem for the indicator function. By leveraging high-order derivatives at the initial state, a rational function approximation is derived, yielding a mathematically direct and computationally efficient prediction. Numerical validations on benchmark systems demonstrate that the method not only provides a direct mathematical shortcut for TSA in power systems but also establishes a promising new methodology for evaluating the transient stability of a broad class of nonlinear dynamical systems.

This paper investigates a pinching-antenna (PA)-enabled cognitive radio network, where both the primary transmitter (PT) and secondary transmitter (ST) are equipped with a single waveguide and multiple PAs to facilitate simultaneous spectrum sharing. Under a general Ricean fading channel model, a closed-form analytical expression for the average spectral efficiency (SE) achieved by PAs is first derived. Based on this, a sum-SE maximization problem is formulated to jointly optimize the primary and secondary pinching beamforming, subject to system constraints on the transmission power budgets, minimum antenna separation requirements, and feasible PA deployment regions. To address this non-convex problem, a three-stage optimization algorithm is developed to sequentially optimize both the PT and ST pinching beamforming, and the ST power control. For the PT and ST pinching beamforming optimization, the coarse positions of PA are first determined at the waveguide-level. Then, wavelength-level refinements achieve constructive signal combination at the intended user and destructive superposition at the unintended user. For the ST power control, a closed-form solution is derived. Simulation results demonstrate that i) PAs can achieve significant SE improvements over conventional fixed-position antennas; ii) the proposed pinching beamforming design achieves effective interference suppression and superior performance for both even and odd numbers of PAs; and iii) the developed three-stage optimization algorithm enables nearly orthogonal transmission between the primary and secondary networks.

This paper proposes a hybrid-gain finite-time sliding-mode control (HG-FTSMC) strategy for a class of perturbed nonlinear systems. The controller combines a finite-time reaching law that drives the sliding variable to a predefined boundary layer with an inner mixed-power or exponential law that guarantees rapid convergence within the layer while maintaining smooth and bounded control action. The resulting control design achieves finite-time convergence and robustness to matched disturbances, while explicitly limits the control effort. The control framework is first analyzed on a perturbed first-order integrator model, and then extended to Euler-Lagrange (EL) systems, representing a broad class of robotic and mechanical systems. Comparative simulations demonstrate that the proposed controller achieves settling times comparable to recent finite-time approaches [1], while substantially reducing the control effort. Finally, trajectory-tracking simulations on a two-link manipulator further validate the robustness and practical feasibility of the proposed HG-FTSMC approach.

Video-to-audio generation (V2A) is of increasing importance in domains such as film post-production, AR/VR, and sound design, particularly for the creation of Foley sound effects synchronized with on-screen actions. Foley requires generating audio that is both semantically aligned with visible events and temporally aligned with their timing. Yet, there is a mismatch between evaluation and downstream applications due to the absence of a benchmark tailored to Foley-style scenarios. We find that 74% of videos from past evaluation datasets have poor audio-visual correspondence. Moreover, they are dominated by speech and music, domains that lie outside the use case for Foley. To address this gap, we introduce FoleyBench, the first large-scale benchmark explicitly designed for Foley-style V2A evaluation. FoleyBench contains 5,000 (video, ground-truth audio, text caption) triplets, each featuring visible sound sources with audio causally tied to on-screen events. The dataset is built using an automated, scalable pipeline applied to in-the-wild internet videos from YouTube-based and Vimeo-based sources. Compared to past datasets, we show that videos from FoleyBench have stronger coverage of sound categories from a taxonomy specifically designed for Foley sound. Each clip is further labeled with metadata capturing source complexity, UCS/AudioSet category, and video length, enabling fine-grained analysis of model performance and failure modes. We benchmark several state-of-the-art V2A models, evaluating them on audio quality, audio-video alignment, temporal synchronization, and audio-text consistency. Samples are available at: https://gclef-cmu.org/foleybench

Modeling and congestion mitigation of mixed-autonomy traffic systems consisting of human-driven vehicles (HVs) and autonomous vehicles (AVs) have become increasingly critical with the rapid development of autonomous driving technology. This paper develops an event-triggered control (ETC) framework for mitigating congestion in such systems, which are modeled using an extended Aw-Rascle-Zhang (ARZ) formulation consisting of coupled 4 x 4 hyperbolic partial differential equations (PDEs). Ramp metering is employed as the boundary actuation mechanism. To reduce computational and communication burdens while avoiding excessive ramp signal changes, we design the ETC strategy based on the backstepping method, together with an observer-based ETC formulation for practical implementation under limited sensing. Rigorous Lyapunov analysis ensures exponential convergence and avoidance of Zeno behavior. Extensive simulations validate the proposed approach under diverse traffic scenarios, including varying AV penetration rates, different spacing policies, multiple demand levels, and non-recurrent congestion patterns. Results show that ETC not only stabilizes mixed traffic flows but also significantly reduces control updates, improving driver comfort, and roadway safety. Higher AV penetration rates lead to longer release time and fewer triggering events, indicating the positive impact of AVs in mitigating traffic congestion while reducing computational resource usage. Compared to continuous backstepping controllers, the proposed ETC achieves near-equivalent stabilization performance with far fewer controller updates, resulting in longer signal release time that reduces driver distraction, which demonstrates great potential for ETC applications in traffic management.

This paper presents an integrated navigation framework for Autonomous Mobile Robots (AMRs) that unifies environment representation, trajectory generation, and Model Predictive Control (MPC). The proposed approach incorporates a quadtree-based method to generate structured, axis-aligned collision-free regions from occupancy maps. These regions serve as both a basis for developing safe corridors and as linear constraints within the MPC formulation, enabling efficient and reliable navigation without requiring direct obstacle encoding. The complete pipeline combines safe-area extraction, connectivity graph construction, trajectory generation, and B-spline smoothing into one coherent system. Experimental results demonstrate consistent success and superior performance compared to baseline approaches across complex environments.

Self-play reinforcement learning has demonstrated significant success in learning complex strategic and interactive behaviors in competitive multi-agent games. However, achieving such behaviors in continuous decision spaces remains challenging. Ensuring adaptability and generalization in self-play settings is critical for achieving competitive performance in dynamic multi-agent environments. These challenges often cause methods to converge slowly or fail to converge at all to a Nash equilibrium, making agents vulnerable to strategic exploitation by unseen opponents. To address these challenges, we propose DiffFP, a fictitious play (FP) framework that estimates the best response to unseen opponents while learning a robust and multimodal behavioral policy. Specifically, we approximate the best response using a diffusion policy that leverages generative modeling to learn adaptive and diverse strategies. Through empirical evaluation, we demonstrate that the proposed FP framework converges towards $\epsilon$-Nash equilibria in continuous- space zero-sum games. We validate our method on complex multi-agent environments, including racing and multi-particle zero-sum games. Simulation results show that the learned policies are robust against diverse opponents and outperform baseline reinforcement learning policies. Our approach achieves up to 3$\times$ faster convergence and 30$\times$ higher success rates on average against RL-based baselines, demonstrating its robustness to opponent strategies and stability across training iterations

This paper presents an autonomous sensing framework for identifying and localizing multiple User Equipments (UEs) in Fifth Generation (5G) cellular networks using a non-serving Unmanned Aerial Vehicle (UAV). A complete onboard processing chain is developed to perform synchronization, multi-UE identification, and localization directly from standard 3GPP-compliant uplink Sounding Reference Signals (SRS). Unlike conventional UAV-assisted approaches relying on serving nodes or infrastructure support, the proposed platform operates as a passive sensing UAV, requiring only limited initial coordination with the network and no mission-time control-plane interaction. The approach exploits the structured and periodic nature of SRS transmissions together with a tailored protocol configuration to ensure robust operation under realistic multi-UE interference. The system operates with narrowband SRS (1.4 MHz), reducing UE power consumption and hardware complexity while enabling high multiplexing through cyclic shifts and frequency resources. Reliable synchronization and multi-UE identification are achieved even when multiple UEs share the same resources. The UAV autonomously collects measurements along its trajectory and estimates UE positions using a trajectory-based localization strategy. The proposed framework is validated through extensive simulations and a full-scale experimental campaign, achieving localization errors below 8 m in urban scenarios and below 3 m in rural conditions, outperforming state-of-the-art Angle of Arrival (AoA)- and Time Difference of Arrival (TDoA)-based methods by about 5-6 m. These results demonstrate the feasibility of infrastructure-independent sensing UAVs for Low-Altitude Wireless Networks (LAWN), enabling scalable and rapidly deployable situational awareness in emergency and connectivity-limited environments.

Cyber-attacks jeopardize the safe operation of inverter-based resource-dominated microgrids (IBR-dominated microgrids). At the same time, existing diagnostic methods either depend on expensive multi-point instrumentation or stringent modeling assumptions that are untenable under single-point measurement constraints. This paper proposes a Fractional-Order Memory-Enhanced Attack-Diagnosis Scheme (FO-MADS) that achieves timely fault localization and cyber-resilient fault diagnosis using only one VPQ (voltage, active power, reactive power) measurement point. FO-MADS first constructs a dual fractional-order feature library by jointly applying Caputo and Grünwald-Letnikov derivatives, thereby amplifying micro-perturbations and slow drifts in the VPQ signal. A two-stage hierarchical classifier then pinpoints the affected inverter and isolates the faulty IGBT switch, effectively alleviating class imbalance. Robustness is further strengthened through Progressive Memory-Replay Adversarial Training (PMR-AT), whose attack-aware loss is dynamically re-weighted via Online Hard Example Mining (OHEM) to prioritize the most challenging samples. Experiments on a four-inverter IBR-dominated microgrid testbed comprising 1 normal and 24 fault classes under four attack scenarios demonstrate diagnostic accuracies of 96.6% (bias), 94.0% (noise), 92.8% (data replacement), and 95.7% (replay), while sustaining 96.7% under attack-free conditions. These results establish FO-MADS as a cost-effective and readily deployable solution that markedly enhances the cyber-physical resilience of IBR-dominated microgrids.

Partially observable Markov decision processes (POMDPs) are a central model for uncertainty in sequential decision making. The most basic objective is the reachability objective, where a target set must be eventually visited, and the more general parity objectives can model all omega-regular specifications. For such objectives, the computational analysis problems are the following: (a) qualitative analysis that asks whether the objective can be satisfied with probability 1 (almost-sure winning) or probability arbitrarily close to 1 (limit-sure winning); and (b) quantitative analysis that asks for the approximation of the optimal probability of satisfying the objective. For general POMDPs, almost-sure analysis for reachability objectives is EXPTIME-complete, but limit-sure and quantitative analyses for reachability objectives are undecidable; almost-sure, limit-sure, and quantitative analyses for parity objectives are all undecidable. A special class of POMDPs, called revealing POMDPs, has been studied recently in several works, and for this subclass the almost-sure analysis for parity objectives was shown to be EXPTIME-complete. In this work, we show that for revealing POMDPs the limit-sure analysis for parity objectives is EXPTIME-complete, and even the quantitative analysis for parity objectives can be achieved in EXPTIME.

Unmanned Aerial Vehicles (UAVs) or drones have witnessed a spectacular surge in applications for military, commercial, and civilian purposes. However, their potential for flight is always limited by the finite power budget of their onboard power supplies. The limited flight time problem has led to intensive research into new sources of power and innovative charging strategies to enable protracted, autonomous flight. This paper gives a comparative summary of the current state-of-the-art in UAV power and refuelling technology. The paper begins with an analysis of the variety of energy sources, from classical batteries to fuel cells and hybrid systems, based on their relative advantages and disadvantages in energy density, weight, and safety. Subsequently, the review explores a spectrum of replenishment options, from simple manual battery swapping to sophisticated high-tech automatic docking stations and smart contact-based charging pads. Most of the review is dedicated to the newer technology of wireless power transfer, which involves near-field (inductive, capacitive) and far-field (laser, microwave) technology. The article also delves into the most important power electronic converter topologies, battery management systems, and control approaches that form the core of these charging systems. Finally, it recapitulates the most significant challenges in technical, economic, and social aspects for promising avenues of future research. The comprehensive review is a valuable guide for researchers, engineers, and policymakers striving to enhance UAV operational performance.

Constructing clean and low-carbon rural integrated energy system (RIES) is a fundamental requirement for supporting China's rural modernization and new-type urbanization. Existing research on RIES decarbonization primarily focuses on the optimal low-carbon operation of system-level energy devices at the macro level, while the synergistic carbon-reduction effects of demand-side flexible loads and external carbon trading mechanisms have not been fully explored. Meanwhile, at the micro level, the carbon sensitivity of device parameters and their potential contribution to emission reduction remain insufficiently investigated. To address these gaps, this study integrates macro- and micro-level analyses. At the macro level, a multi-energy-coupled low-carbon optimal operation framework is developed, incorporating coordinated electric-thermal demand response (DR) and carbon trading. At the micro level, a carbon emission model for RIES components is established, and sensitivity analysis is conducted on 28 carbon-related parameters to identify highly sensitive determinants of emission reduction. Case studies based on typical operation data from a rural region in northern China demonstrate that coordinated electric-thermal DR and carbon trading can achieve maximum carbon-reduction potential. Furthermore, the identified high-sensitivity parameters provide essential theoretical guidance for enhancing the decarbonization potential of RIES.

In practical applications, the efficacy of a control algorithm relies critically on the accurate knowledge of the parameters and states of the underlying system. However, obtaining these quantities in practice is often challenging. Adaptive observers address this issue by performing simultaneous state and parameter estimation using only input-output measurements. While many adaptive observer designs exist for continuous-time systems, their discrete-time counterparts remain relatively unexplored. This paper proposes an initial excitation (IE)-based adaptive observer for discrete-time linear time-invariant systems. In contrast to conventional designs that rely on the persistence of excitation condition, which requires continuous excitation and infinite control effort, the proposed method does not require excitation for infinite time, thus making it more practical for stabilization tasks. We employ a two-layer filtering structure and a normalized gradient descent-based update law for learning the unknown parameters. We also propose modifying the regressors to enhance information extraction, leading to faster convergence. Rigorous theoretical analysis guarantees bounded and exponentially converging estimates of both states and parameters under the IE condition, and simulation results validate the efficacy of the proposed design.

Integrated Sensing and Communications (ISAC) will become a service in future mobile communication networks. It enables the detection and recognition of passive objects and environments using radar-like sensing. The ultimate advantage is the reuse of the mobile network and radio access resources for scene illumination, sensing, data transportation, computation, and fusion. It enables building a distributed, ubiquitous sensing network that can be adapted for a variety of radio sensing tasks and services. In this article, we develop the principles of multi-sensor ISAC (MS-ISAC). MS-ISAC corresponds to multi-user MIMO communication, which in radar terminology is known as distributed MIMO radar. \ First, we develop basic architectural principles for MS-ISAC and link them to example use cases. We then propose a generic MS-ISAC architecture. After a brief reference to multipath propagation and multistatic target reflectivity issues, we outline multilink access, coordination, precoding and link adaptation schemes for MS-ISAC. Moreover, we review model-based estimation and tracking of delay~/~Doppler from sparse OFDMA~/~TDMA frames. We emphasize Cooperative Passive Coherent Location (CPCL) for bistatic correlation and synchronization. Finally, issues of multisensor node synchronization and distributed data fusion are addressed.

Multi-agent reinforcement learning (MARL) has shown promise for large-scale network control, yet existing methods face two major limitations. First, they typically rely on an exponential decay property of agent interactions on far-away nodes, which can be exploited to develop more efficient and tractable MARL algorithms. When this exponential decay property does not hold, these algorithms do not account for long-range interactions such as epidemic outbreaks or cascading power failures. Second, existing approaches lack network generalizability, or the ability to generalize to networks of different topological structure and scale than those seen during training. In this work, we first present a mean-field stability analysis and empirical study investigating the conditions for long-range network interactions. These results motivate our primary contribution: STACCA (Shared Transformer Actor-Critic with Counterfactual Advantage), a transformer-based MARL framework that addresses both long-range interactions and network generalizability. STACCA employs a centralized Graph Transformer Critic to model long-range dependencies and provide system-level feedback, while its shared Graph Transformer Actor learns a generalizable policy capable of adapting across diverse network topologies. To improve credit assignment during training, STACCA integrates a novel counterfactual advantage estimator that is compatible with state-value critic estimates. We evaluate STACCA on epidemic containment and rumor-spreading network control tasks, demonstrating improved performance and network generalizability. These results highlight the potential of transformer-based MARL architectures to achieve generalizable control in large-scale networked systems.

Emergency Medical Technicians (EMTs) operate in high-pressure environments, making rapid, life-critical decisions under heavy cognitive and operational loads. We present EMSGlass, a smart-glasses system powered by EMSNet, the first multimodal multitask model for Emergency Medical Services (EMS), and EMSServe, a low-latency multimodal serving framework tailored to EMS scenarios. EMSNet integrates text, vital signs, and scene images to construct a unified real-time understanding of EMS incidents. Trained on real-world multimodal EMS datasets, EMSNet simultaneously supports up to five critical EMS tasks with superior accuracy compared to state-of-the-art unimodal baselines. Built on top of PyTorch, EMSServe introduces a modality-aware model splitter and a feature caching mechanism, achieving adaptive and efficient inference across heterogeneous hardware while addressing the challenge of asynchronous modality arrival in the field. By optimizing multimodal inference execution in EMS scenarios, EMSServe achieves 1.9x – 11.7x speedup over direct PyTorch multimodal inference. A user study evaluation with six professional EMTs demonstrates that EMSGlass enhances real-time situational awareness, decision-making speed, and operational efficiency through intuitive on-glass interaction. In addition, qualitative insights from the user study provide actionable directions for extending EMSGlass toward next-generation AI-enabled EMS systems, bridging multimodal intelligence with real-world emergency response workflows.

We propose a reinforcement learning (RL) framework for multi-objective decision-making, where the agent seeks to optimize a vector of rewards rather than a single scalar value. The objective is to ensure that the time-averaged reward vector converges asymptotically to a predefined target set. Since standard RL algorithms operate on scalar rewards, we introduce a dynamic scalarization mechanism guided by Blackwell's Approachability Theorem. This theorem enables adaptive updates of the scalarization vector to guarantee convergence toward the target set. Assuming ergodicity, the Markov chain induced by the learned policies admits a stationary distribution, ensuring all states recur with finite return times. Our algorithm exploits this property by defining an inner loop that applies a policy gradient method (with baseline) between successive visits to a designated recurrent state, enforcing Blackwell's condition at each iteration. An outer loop then updates the scalarization vector after each recurrence. We establish theoretical convergence of the long-run average reward vector to the target set and validate the approach through a numerical example.