Papers

This paper considers a satellite-to-ground communication system in which a ground station (GS) equipped with independently rotatable antenna (RA) elements jointly decodes independent streams from multiple low-Earth-orbit (LEO) satellites over a shared time–frequency resource. Specifically, we formulate a two-timescale throughput maximization problem under exogenous cochannel interference, capturing serving-set composition, RA-enabled channel shaping, time-varying satellite geometry, and mechanically constrained inter-epoch reconfiguration. We first characterize the joint effects of interference-whitened channel strength and spatial separability on multi-satellite reception, motivating the joint design of satellite selection and RA control. With the RA trajectory fixed, we establish the monotone submodularity of the epoch-level selection objective and construct an incumbent-tight modular lower-bound surrogate, leading to an efficient discrete Minorization-Maximization (MM) selection algorithm. For fixed serving sets, we develop slew-feasible RA updates based on Riemannian gradients and organize them into a two-color parallel update scheme. The two blocks are integrated into a monotone alternating algorithm with guaranteed objective convergence. Simulations demonstrate consistent gains over benchmark schemes and reveal an optimal balance between channel strength and spatial separability. The results further show that satellite selection is particularly important in underloaded and actuator-limited regimes, whereas RA shaping becomes more influential near full spatial loading.

An $M$-channel discrete Fourier transform (DFT) channelizer routes an on-grid sinusoid to a single output. When each frame reports only one energy-proportional channel index, signed sub-bin frequency estimation becomes nonregular: dark-channel probability is quadratic in the offset, whereas orientation enters cubically. We study $N$ independent labeled reports under a known uniform-replacement probability $\epsilon_N$ and a single deterministic unitary shared by all frequencies and frames, constrained to routing defect $\tau/N$. If $\sqrt{N}\epsilon_N \to \lambda < \infty$, we establish an attained global-in-frequency minimax limit at the critical scales $N^{-1/4}$ for frequency offset, $N^{-1/2}$ for unitary perturbation and replacement, and $N^{-1}$ for routing defect. The effective unitary tangent is a symmetric complete-graph edge field modulo one centering nuisance, and its first variation is a radius-dependent weighted divergence. For $\lambda>0$, evaluation at $k$ distinct normalized offset magnitudes yields an exact Fourier-Cauchy nullity spectrum: $\lfloor(M-1)/2\rfloor$ evaluations are necessary and sufficient, for every choice of distinct magnitudes, to certify neutrality at all magnitudes. The terminal nullspace has a greatest-common-divisor dimension formula and positive-definite aggregate curvature. Consequently, exact DFT routing is uniquely minimax within the complete critical tangent class for $M=3$, whereas every positive critical defect budget strictly improves the minimax constant for $M\geq4$. The analysis also yields a smallest-prime curvature-visibility law and, for $M\geq5$, discontinuous compatibility geometry but a continuous minimax value at the zero critical replacement floor $\lambda=0$.

Conventional link adaptation typically relies on scalar link-quality indicators such as signal-to-noise ratio (SNR), while richer channel state information (CSI) can improve adaptation at the cost of higher processing complexity. This paper investigates a compact alternative for modulation selection in a real-time multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system using channel frequency response (CFR) magnitude descriptors. A dataset of 87,817 over-the-air (OTA) samples is collected using a USRP-based testbed, with CFR measurements extracted at the base station (BS) from received uplink pilots. Decision tree (DT), random forest (RF), and k-nearest neighbours (KNN) classifiers are evaluated using BS-side SNR, CFR features, and their combination. SNR-only classifiers achieve 35%-42% test accuracy, whereas CFR-only features achieve 73.6%, 81.4%, and 80.0% for DT, RF, and KNN, respectively. CFR-based performance is maintained near the 10% BLER reliability thresholds, with RF reaching 82.8%. A depth-7 DT with 123 leaves is further integrated into the LabVIEW C Node for real-time inference. The results show that compact BS-side CFR descriptors provide more discriminative information than the available scalar BS-side SNR while remaining suitable for lightweight SDR implementation.

Coarse quantization in massive multiple-input multiple-output (MIMO) systems reduces power but causes clipping distortions. The Bayesian Expectation-Maximization (BEM) algorithm can recover clipped signals, but its matrix inversion and dynamic step-size evaluation are hardware bottlenecks. We propose a hardware-friendly one-step correction that uses the initial Jacobi-preconditioned Conjugate Gradient (PCG) direction with a fixed relaxation parameter. The resulting symbol-level update has an ultra-lightweight $\mathcal{O}(U)$ feed-forward datapath and approaches high-resolution reference detectors in the evaluated massive-MIMO setting. Our finite-dimensional analysis establishes the exact one-step descent law, proves that Jacobi normalization cancels the raw multiplicative near-far scaling while confining the loaded-system dependence to bounded attenuation factors, and gives verifiable sufficient conditions for fixed-step descent in terms of normalized channel coherence. System-level results indicate projected power savings for energy-efficient massive MIMO uplinks.

This article addresses the output tracking control problem for a class of high-order uncertain highly-coupled MIMO nonlinear systems subject to input saturation and performance constraints. To resolve the problem, a bidirectional modification mechanism is constructed, which is able to not only relax the constraints when saturation occurs to alleviate potential conflict, but also accelerate the recovery of original constraints after saturation ceases, and further tighten the constraints to enhance control performance if saturation remains inactive at the steadystate phase. Based on the mechanism, a model-, approximationand complexity-explosion-free control scheme is proposed. To bypass the obstacle in Lyapunov analysis, a novel stability analysis framework is developed, which, given that two parameter selection conditions are met, ensures satisfaction of modified constraints and boundedness of all closed-loop signals. Simulation results validate the effectiveness and superiority of the methodology.

Onboard charger (OBC) is essential part of Electric Vehicle (EV). High-performance EVs are preferring three phase charging to achieve higher power level. However, the ability to charge from a single phase supply is also required. A dualmode OBC for EVs is proposed, which is capable of operating from both three-phase supply and single-phase supply. The single phase charging comes with the requirement of bulky DC link capacitance due to double frequency current in DC link. The proposed topology eliminates this by achieving Active Power Decoupling (APD) using only one additional relay switch and a small capacitor. The proposed topology is verified under different conditions in a detailed simulation, which shows more than an order of magnitude reduction in DC link capacitance during single phase operation.

Remote laboratory systems improve accessibility in engineering education and research by enabling Internet-based interaction with physical equipment. This paper presents a modular IoT-enabled remote laboratory platform for hybrid energy system studies, combining renewable energy emulators, battery storage, and programmable loads within a three-interface architecture based on a web HMI, TIA Portal, and MATLAB/Simulink, all connected through a Talk2M VPN cloud. An industrial PLC and IoT gateway provide deterministic local control as well as secure remote access and monitoring. A hierarchical energy-management algorithm is validated by comparing local and remote executions under identical wind and irradiance profiles. The results show small differences in the energy balances of the renewable sources, battery, and load, while typical communication delays are on the order of 100 ms. Consequently, the platform supports research-grade remote experimentation and project-based learning in control and energy systems engineering.

Dominant audio classification pipelines rely either on compact handcrafted summaries or on fixed time-frequency frontends such as log-mel representations prior to deep modeling. While highly successful, these representations do not explicitly expose the physical dynamics of the underlying sound-generating event. We introduce MADS (Multi-view Acoustic Descriptor Set), a compact 19-dimensional physics-informed descriptor set de- signed to capture complementary spectral, temporal, mechanical, and stochastic structure in audio signals. Rather than treating sound only as a spectral pattern, MADS encodes properties related to excitation, damping, periodicity, impulsiveness, and structural consistency within a unified multi-view representation. We evaluate MADS using standard classical machine learning models on ESC-10, ESC-50, and MSoS, and compare it against two conventional handcrafted baselines: a compact 26D MFCC- based baseline and an expanded 38D spectral-summary baseline. Across ESC-10 and ESC-50, MADS achieves the strongest peak results overall, reaching 81.00% and 52.78%, respectively, while using roughly half the dimensionality of the 38D baseline. On MSoS, MADS again delivers the strongest top-end performance, reaching 67.48%. These results establish MADS not merely as a competitive standalone descriptor set, but as the foundational descriptor layer of a broader acoustically grounded representation program for future frame-level and deep-learning-compatible audio modeling.

Deep joint source and channel coding (JSCC) transmits a source by mapping it directly to channel symbols through an end-to-end deep neural network (DNN) and reconstructing it at the receiver. Taking image transmission as an application, this DNN pipeline behaves as a black box: the receiver cannot readily detect security attacks when the transmitted images are corrupted, thereby introducing a new security vulnerability. In this letter, we study defense against input-patch backdoor attacks on deep JSCC, in which a small trigger patch attached to the input forces the decoder to emit an attacker-chosen target image. Most existing patch-trigger defenses are designed for classification, leaving the reconstruction setting of deep JSCC unaddressed. We adapt the gradient mask defense to this reconstruction setting as a baseline and then propose mask-region optimized purification (MROP), which operates at inference and requires no retraining of the JSCC model. Unlike the baseline, which localizes the trigger from the input–output gradient, MROP instead places a per-pixel mask at the encoder input and optimizes it via a Gumbel-sigmoid relaxation to localize the trigger, then refines the trigger region to reconstruct the pure images better. In numerical results, we evaluate the proposed method on CIFAR-10 and STL-10 datasets along with the DeepJSCC and SwinJSCC models. By doing so, we show that the proposed method substantially lowers the attack success rate (ASR) while preserving the peak signal-to-noise ratio (PSNR) of clean reconstructions.

Target sound extraction (TSE) aims to isolate a sound source of interest from a mixture, given a semantic query. Existing TSE systems are conditioned on fixed class representations tied to individual sound categories, limiting their ability to handle the hierarchical relationships that naturally organize environmental sounds. In this paper, we introduce ontology-based TSE, a new task formulation in which a single model extracts sounds queried at any level of a sound ontology, from fine-grained leaf classes such as cat and dog to high-level categories such as animal. We propose a learnable class embedding table defined over all nodes of an AudioSet-derived ontology, regularized with a Cophenetic Correlation Coefficient (CPCC) loss that aligns embedding distances with shortest-path distances in the ontology tree. Our experiments across different approaches show the benefit of considering the ontology structure when training TSE systems.

Accurate tilt angle estimation is important in many engineering applications, such as robotics, motion tracking, and embedded control systems. However, measurements from low-cost inertial sensors are often degraded by noise and drift. This paper presents a single-axis tilt angle estimation system based on the MPU6050 inertial measurement unit, implemented on an RP2040 microcontroller platform, with sensor fusion achieved through a Kalman filter. The accelerometer provides a direct estimate of tilt angle from gravity but is sensitive to noise and short-term fluctuations. The gyroscope provides smooth angular rate measurements, but integration over time introduces drift. To overcome these limitations, a Kalman filter is used to combine measurements from both sensors, leveraging the long-term stability of the accelerometer and the short-term smoothness of the gyroscope. Both simulation and hardware experiments are performed. In simulation, sensor noise and drift are modeled to evaluate the filter performance under control conditions. In the hardware implementation, real-time MPU6050 data is acquired and processed by the RP2040 platform, and the estimated tilt angle is compared with accelerometer-only and gyroscope-only outputs. The results show that the proposed method effectively reduces noise measurements and suppresses long-term drift while preserving good dynamic response. Overall, the system provides more stable and accurate tilt estimation than either sensor alone, demonstrating a practical and accessible approach for Kalman filter based sensor fusion in embedded application. This manuscript is a preprint version of the work. Keywords: Kalman Filter, Accelerometer, Gyroscope, Noise Reduction, Angle Tracking

Feedback-based coding schemes have demonstrated substantial performance gains over today's open-loop coding schemes. Unfortunately, these gains are usually achieved in idealized settings with perfect feedback. Over the last few years, machine learning-based schemes have been shown to be promising solutions for implementing feedback-based codes, particularly when combined with short-block-length open-loop error correcting codes (ECCs) in a concatenated coding structure. However, existing ML-based feedback schemes remain agnostic to the outer code's structure, potentially misallocating feedback resources on error patterns already correctable by the outer ECC. To address this, we propose MaskCode, a Transformer-based inner feedback code for concatenated coding systems, which explicitly incorporates structural knowledge of the outer linear block code into the inner feedback encoder design via two synergistic mechanisms: 1) a soft syndrome-based input that informs the encoder about potential parity constraint violations, and 2) a code-aware attention mask derived from the Tanner graph. We further show that end-to-end training with a differentiable belief propagation (BP) decoder offers no additional gain, as MaskCode's structure-aware design already internalizes the structural knowledge of the outer code; in fact, backpropagation through the iterative BP decoder introduces gradient explosion, which degrades rather than improves performance. Extensive evaluations on BCH and LDPC outer codes demonstrate that MaskCode consistently outperforms all baselines, achieving up to 1.5 dB SNR gain.

As modern data-centric applications such as neural inference and sensor-edge analytics expand, they increasingly encounter the von Neumann memory wall, suffering from excessive data movement overhead and stringent energy constraints. In-Memory Computing (IMC) utilizing emerging non-volatile technologies, such as Magnetic Tunnel Junctions (MTJs), promises to mitigate these bottlenecks. However, conventional binary radix-based IMC architectures suffer from excessive vulnerability to process-induced variations, restricted operating margins, and thermal noise. To bridge the gap between energy efficiency and computational reliability, this work proposes FALCON, a fault-tolerant, MTJ-based in-memory arithmetic architecture integrated with Stochastic Computing (SC). By encoding numerical values into uniform bit-streams, SC naturally absorbs localized soft errors and enables the execution of an essential suite of arithmetic operations using highly compact logic primitives directly within the memory arrays. FALCON integrates a deterministic bit mapping mechanism with reconfigurable logic-in-memory (LIM) structures, eliminating the need to transfer data to external processors or area- and power-hungry random number generators. Experimental results using 14 nm FinFET technology validate the correct functionality of FALCON even under aggressive voltage scaling, severe process variation, and noise injection levels up to 30%, making it a robust framework for reliability-critical edge AI applications. We investigate the proper functionality of FALCON on morphological closing as a realistic noise-tolerant image processing case study.

We address a structural tradeoff in distributed dynamic coordination: when the target complexity is unknown, a low controller order saves states but may leave a persistent tracking error, whereas a high order improves tracking but may burden every agent with unnecessary dynamics. To remove this choice without resorting to computationally more involved nonlinear feedback or chattering-prone nonsmooth feedback, we develop an order-adaptive distributed integral controller (OADIC). Specifically, we start with proportional feedback and add integral states only when locally measurable relative errors show that the current order is inadequate. Meanwhile, we organize the candidate controllers in a nested form, thereby preserving the existing states and gains and maintaining continuous control inputs during order transitions. To provide a theoretical basis for this design, we first characterize the consistency of prescribed relative displacements on the augmented agent–target graph. Next, we construct gains that stabilize all admissible fixed-order subsystems and establish a uniform input-to-state stability bound for the variable-dimension closed loop. Furthermore, we prove that OADIC rejects every insufficient order after finitely many decision intervals and explicitly bound the rejection time of the critical order.

Intelligent vehicles increasingly support adaptive applications beyond driving themselves, ranging from context-aware ADAS and automated driving to in-cabin monitoring and fleet management, all under tight requirements on accuracy, latency, cost, and reliability. Meeting these requirements is challenging because vehicles operate in complex, uncertain, and rapidly changing environments while running on resource-constrained computing platforms. This paper argues that context-situational factors that give meaning to sensor signals and constrain decisions-should be treated as a first-class principle for next-generation vehicle systems, and operationalized as a unified, shared state for learning, risk assessment, and closed-loop control across the software stack. We systematically review state-of-the- art (SOTA) context-aware methods spanning (i) environment understanding, (ii) planning and control, (iii) safety and security, and (iv) connected vehicles. Based on a trend analysis of context-aware design, we identify four key technical challenges in building a general contextual engine for future intelligent vehicles: multi-modal context fusion, temporal context modeling, handling rare events, and collaborative context sharing. We hope this survey will motivate the development of robust and efficient context-aware vehicle applications.

Physical human–robot interaction software often couples desired-behavior specification with constrained realization; we treat these as separate layers. A behavior layer supplies a desired contact-port acceleration $a_k^{\mathrm{id}}=f_\theta(e_k,\dot e_k,F_{h,k})$. A realization layer converts it into constrained robot commands and reports total desired-versus-realized acceleration error instead of hiding it in saturation. A same-objective unconstrained counterfactual separates regularization from constraint intervention, while plant data expose model error. This paper implements a receding-horizon quadratic program realizing memoryless affine behaviors. Changing the behavior modifies objective coefficients through $(C_\theta,G_\theta)$ while the robot-command variable and feasible set remain unchanged. A planar study instantiates impedance and admittance; the same running layer accepts an impedance–admittance–impedance reassignment without reconstruction, under its existing rate limit. On a torque-controlled 7-DOF Franka FR3 in MuJoCo, the runtime freezes task-space dynamics per solve and enforces torque feasibility across its horizon. Under a sustained 20~N push, it holds a slack-relaxed workspace boundary to within approximately 0.1–0.2~mm, versus 4.4~cm (impedance) and 4.7~cm (admittance) overshoot from instantaneous clipping. A derated actuator budget then activates the torque constraint: horizon-wide enforcement keeps its frozen-model plan feasible to $2.1\times10^{-4}$~N$\cdot$m, whereas a first-step-only ablation plans up to 11.329~N$\cdot$m beyond budget; on the executed nonlinear plant, where both share the same local-model error, the gap is smaller but still favors horizon-wide enforcement (0.161 vs.\ 0.380~N$\cdot$m). These results are a focused proof of behavior–realization separation.

Wind turbines require regular maintenance and the resulting costs are a substantial component of a wind farm's cost of electricity production. As a result, there has been ongoing interest in improving wind turbine maintenance scheduling to find an optimal balance between maintenance costs and the risk of failure or unplanned repairs. What remains understudied is the opportunity for wind farms to schedule maintenance in the context of grid conditions and electricity market clearing. This paper contributes to closing this gap by modeling and studying wind farm maintenance planning in a grid and electricity market context. We also review current U.S. practice of wind farm maintenance scheduling, which motivates this paper and its models. Our focus is the derivation of a strategic maintenance planning problem, alongside an efficient solution approach, in which the wind farm operator aims to submit a derated wind farm capacity such that the resulting market clearing and electricity prices maximize its profit while ensuring that all required maintenance can be performed. We derive and study both deterministic and stochastic versions of the model, with the latter considering environmental and operational uncertainties. We conduct numerical experiments using the IEEE RTS 96-bus testbed with real-world offshore wind farm data and investigate the roles of farm and turbine size, as well as forecast quality. We observe an alignment between strategic and grid-serving maintenance planning and find that wind farms can improve their bottom line with low impact on system costs.

Bluetooth Low Energy Channel Sounding (BLE CS), standardized in Bluetooth Core Specification 6.0 (September 2024), enables distance estimation via phase-based ranging (PBR) and round-trip time (RTT). Although prior work has studied CS accuracy in configurations with a fixed orientation, no published work has studied how device orientation affects ranging error on commercial hardware. We present the first study of orientation-induced CS ranging error and a Machine Learning correction using IMU features. This study used the EFR32xG24 Channel Sounding Development Kit, the only commercial CS platform with an integrated six-axis Inertial Measurement Unit (IMU). Our results show that device orientation has a substantial effect on CS ranging accuracy; we found that a Random Forest model trained on the IMU derived orientation achieved a 74.6% Mean Absolute Error (MAE) reduction under a Leave One Orientation Out Evaluation, demonstrating that IMU readings have potential to improve ranging accuracy.

This paper investigates environmental-sensing-aided proactive base station (BS) selection and beam prediction for millimeter-wave (mmWave) vehicle-to-infrastructure (V2I) wireless systems. We exploit onboard panoramic street-view images and a preloaded satellite map to predict communication-relevant environmental information around the vehicle, including nearby building footprints and heights. The predicted height map provides a compact environmental prior and is combined with historical mobility information to jointly predict the next-slot line-of-sight (LoS) state, transmission rate, and transmit and receive beam selections. On our dataset covering different real-world regions across New South Wales, Australia, the proposed framework achieves 91.4% LoS classification accuracy, 0.638 bps/Hz mean absolute error of data rate prediction, and more than 40% higher transmission rate than the conventional reactive baseline in geographically unseen regions, outperforming all evaluated deployable learning-based baselines. The dataset and code will be released at https://github.com/Huzijiao/Cross-view_V2I

The evolution toward sixth-generation (6G) wireless networks is driving larger antenna arrays and highly directional multi-beam transmission, making accurate knowledge of beam-dependent spatial coverage important for beam management and environment-aware network operation. Radio maps (RMs) provide such a representation, yet conventional RM prediction assumes omnidirectional or transmitter-level radiation. In beamformed multiple-input multiple-output (MIMO) systems, one propagation scene instead gives rise to many configuration-dependent beam radio maps (BeamRMs), creating challenges in beam representation and generalization. Existing methods either condition prediction on beam descriptors or use beam maps as auxiliary inputs to generic architectures. We propose BeamRMX, which, to the best of our knowledge, is the first dedicated framework to treat the spatial radiation pattern as the primary BeamRM query and learn how scene geometry transforms it into the received power field. XBase learns multiscale interactions between the radiation query and scene geometry, while an optional Evidence Adapter uses a few cross-configuration BeamRMs from the same scene. Matched-domain and zero-shot experiments show consistent gains over deterministic and diffusion baselines, including mean absolute error reductions of 26.1% on unseen scenes and 47.8% on an unseen configuration. Cross-configuration evidence further improves reconstruction and intra-sector beam refinement.

We present the Real-Time Neuromorphic Spectrum Intelligence Simulator (RT-NuSIS), a modular framework to study spiking neural network (SNN) and memristor-inspired agents for dynamic spectrum access under constrained energy budgets and adversarial conditions. RT-NuSIS couples leaky integrate-and-fire neuronal dynamics, memristive synaptic models, physics-informed energy-harvesting models (triboelectric and RF), and adversary models including jamming and Byzantine behavior. We formalize the simulator mathematically, prove boundedness, present a mean-field adversary threshold, analyze per-step complexity, and provide a reproducible benchmark harness for energy-per-inference, latency, and robustness metrics. The codebase is modular, deterministic by seed, and designed for large-scale event-driven simulations.

At the water-land interface, amphibious fishes encounter wet flowable substrates made of granular solid-water mixtures, which can stay solid or flow like a fluid. As these substrates become wetter or drier, their yield strength (at which solid-fluid transition occurs) and cohesion (how sticky they are) both change, challenging locomotion. Despite substantial understanding of tetrapod locomotion on flowable substrates (mostly dry sand), we know little about how amphibious fishes cope with wet flowable substrates of various wetness. Here, we studied mudskippers on clay mud of controlled, variable wetness over the range where solid-fluid transition occurs. As mud became wetter, its strength decreased by 100-fold, leading the animal to sink deeper, with larger areas of body and fins contacting mud. By contrast, mud stuck most easily at intermediate wetness. The increased sinkage and contact and stickiness change caused more mud to stick to and pull against the animal on wetter mud. We also tested dry mud, which stuck to animal fins as its mucus dried. Despite these challenges, the mudskipper predominately used a conserved crutching gait on all except the wettest mud tested, with a modest performance reduction. When normal crutching became less effective, the animal assisted it with tail thrusting, by bending and straightening it to push downward and backward to generate additional thrust and lift, or even thrusting the tail to jump. These observations suggest that mudskipper's crutching motor program is well adapted to its native muddy substrates but inflexible, with most novelty in tail use.

Animals and robots encounter mud at the water-land interface. Like sand, mud can stay solid or flow like a fluid. Unlike sand, the yield strength of mud at which solid-fluid transitions occur depends on not only the amount of solid relative to fluid (water in mud, air in dry sand), but also how much coarse grains and fine clay are within the solid. Despite understanding of locomotion on/within dry sand dominated by coarse grains with repulsive normal forces and friction, little is known for mud dominated by fine clay with strong cohesion. Here, we developed methods to prepare uniform mud of controlled, variable yield strength and characterize and track its drift from water evaporation. Compared to other flowable substrates, mud strength measured by upward force during penetration is weaker and can vary more, and mud sticks more during extraction to pull downward, making it more challenging for locomotion.

In communication-centric integrated sensing and communication (ISAC), delay-Doppler sensing reuses data-bearing orthogonal frequency division multiplexing (OFDM) signals rather than dedicated radar probing waveforms. Consequently, the resulting range-Doppler map (RDM) is shaped not only by target parameters, but also by communication-symbol randomness and, in multi-antenna transmissions, by spatial beamforming. While existing analyses have largely focused on single-antenna OFDM-ISAC, the delay-Doppler sensing behavior of multi-antenna OFDM-ISAC remains insufficiently understood. This paper analyzes a multi-input multi-output (MIMO)-OFDM-ISAC system in which multiple data streams jointly illuminate a sensing target. We derive second-order moment expressions for the RDM under matched filtering (MF) and reciprocal filtering (RF), and use them to characterize the dynamic range (DR). The analysis reveals two key multi-stream effects. First, under MF, the random superposition of multiple beamformed streams creates an additional RDM floor beyond the modulation-dependent and receiver-noise terms; therefore, constant-modulus signaling no longer eliminates the data-induced pedestal as in single-antenna OFDM-ISAC. Second, under RF, the matched-angle data-induced floor is removed, but the noise floor is amplified according to the reciprocal-power statistics of the beamformed target illumination. These results show that the user-target angular geometry directly governs the MF/RF tradeoff: MF is more robust under weak illumination, whereas RF can achieve a higher DR when reciprocal-noise amplification is mild. Numerical results validate the analysis and demonstrate the distinct geometry-dependent behaviors of MF and RF.

This paper presents an adaptive stochastic spectral embedding (ASSE) method to solve the probabilistic AC optimal power flow (AC-OPF), a critical aspect of power system operation. The proposed method can efficiently and accurately estimate the probabilistic characteristics (e.g., mean, variance, median, and quantile-based metrics) of AC-OPF solutions while minimizing power losses. Based on estimated AC-OPF decisions (i.e., generator outputs), the confidence interval (CI)-based production cost index can be determined. Specially, an adaptive domain partition strategy is adopted to guide refinement domain selection and partition. The Bayesian compressive sensing-based coefficient calculation algorithm is integrated to enhance its performance. Numerical studies on modified IEEE 9-bus and IEEE 118-bus systems demonstrate that the proposed ASSE method offers accurate and fast evaluations compared to Monte Carlo simulations. Comparisons with a sparse polynomial chaos expansion, Gaussian process regression, and deep neural networks, further illustrate its efficacy in accurately assessing the responses with strongly localized behavior and non-symmetric distributions, providing practical decision-making bounds for generator outputs and operating costs under uncertainty.

In a network with ramp-limited generators and inaccurate net-demand forecasts, practical rolling-window dispatch can drive locational marginal prices (LMPs) below generators' bid-in offers. In such cases, out-of-market (OOM) settlements are used to compensate generators and maintain dispatch-following incentives, but OOM can have negative consequences, including nontransparent real-time price signals, discriminatory compensation, and incentives for untruthful bidding. This paper presents an optimal uniform pricing rule that minimizes demand payments, eliminates OOM make-whole payments, preserves LMP-based congestion charges, and ensures revenue adequacy. We derive the proposed pricing rule in closed form and relate it to existing pricing schemes. Numerical comparisons demonstrate favorable generator profits and reduced price volatility. However, higher generator profits are accompanied by increased demand payments, reflecting the in-market, uniform allocation of ramping costs while preserving the LMP-based congestion charges widely used in real-time market settlements. The numerical results also show that, under LMP with OOM settlement, a price-taking generator has an incentive to inflate its offer, whereas this incentive is absent under the proposed pricing rule within the tested bid range.

In this work, we study radically uncoupled learning in discounted general-sum Markov games. Assuming “$\mathsf{ETH}$ for $\mathsf{PPAD}$", we show that, for every fixed discount factor, there is no polynomial-time algorithm for computing inverse-polynomially accurate coarse correlated equilibria in discounted general-sum Markov games when players learn independently in decentralized settings. Complementing this hardness result, we provide what appears to be the first radically uncoupled algorithm with sub-exponential convergence guarantees to coarse correlated equilibria in discounted general-sum Markov games without imposing any structural restrictions on the game. Our algorithm is a layered variant of optimistic mirror descent with an increasing step-size schedule tailored to the multi-agent setting. Finally, we develop both full-feedback and partial feedback versions of the aforementioned algorithm and establish sub-exponential convergence guarantees for each case.

The complexity of time-domain simulation of modern power systems has increased significantly because converter-based resources introduce control dynamics that must be simulated alongside slower system-level and fast electromagnetic dynamics. The resulting wide range of timescales may force classical time-domain solvers to use small timesteps, complicate the solution of nonlinear equations at each timestep, and reduce solver reliability under strongly nonlinear and multi-timescale transient conditions. This paper introduces RAPTOR, a first-of-its-kind time-domain simulation framework for power system dynamic simulations. At its core, RAPTOR introduces a new integration technique that represents the unknown trajectory of hybrid differential-algebraic equations (DAEs) over a time interval using a physics-informed random-projection neural network (PIRPNN) built from fixed Gaussian radial basis functions (RBFs). A nonlinear solver, e.g., Newton-Raphson, determines how to combine the fixed RBFs so that the resulting trajectory satisfies the governing equations. By combining RBFs with broad and localized shapes, RAPTOR can represent complex multi-timescale behavior over comparatively long time intervals. This enables larger effective simulation advances, fewer overall nonlinear solver iterations, and faster integration over long time horizons. Numerical studies on stiff RMS models, IEEE 9-, 14-, 39-, 57-, and 118-bus RMS simulation benchmarks, and EMT test cases show that RAPTOR accurately finds solutions with substantially fewer sequential simulation advances while offering superior accuracy-runtime performance. With speedups of over 10x in certain cases, these results showcase RAPTOR's potential to outperform long-standing and widely used integration methods, such as the Radau method and the trapezoidal rule.

Autonomous vehicles are expected to improve road safety and efficiency, but passengers often remain uncertain about what the vehicle perceives and why it acts as it does. Virtual reality (VR) offers a safe and repeatable medium for presenting this information, yet most passenger-facing VR studies rely on fully simulated vehicles or pre-scripted scenarios, so the motion and perception shown to the user do not originate from a physically operating autonomous system. This paper presents a video-augmented VR framework that couples a physical ROS 2 autonomous robot vehicle to a Unity 6 application deployed on a Meta Quest 3S headset. The vehicle state and live onboard camera stream are transmitted over two independent communication channels, allowing the virtual vehicle to mirror the physical robot's motion while the passenger simultaneously views the vehicle's first-person camera feed and its navigation decisions through an in-vehicle dashboard interface. We evaluate the framework over 20 repeated closed-loop navigation trials. The system achieves a mean state-update latency of 29.63 ms, a mean relative route-progress error of 2.28% between the physical and virtual vehicles, and video delivery at 10.006 frames per second with 0.25% frame loss. All monitored navigation decisions were correctly reflected in the VR interface with no missed or incorrect notifications. The results indicate that the framework can support temporally synchronized, semantically consistent, and accurate route-progress representation for immersive observation of physical autonomous-vehicle behavior.

Conventional power-system stability analysis is largely centralized and centered on a single equilibrium point, which becomes increasingly restrictive in the presence of large-scale fluctuating renewable generation. This paper develops a decentralized framework for stability analysis and control that certifies the asymptotic stability of an equilibrium set rather than that of a given single operating point. To this end, we introduce a new notion termed input–output differential passivity (IODP), which decomposes equilibrium-set stability of the interconnected system into local requirements imposed on individual devices. These requirements are formulated without embedding a particular operating equilibrium into the local conditions; once the certified regions are constructed, stability verification for a given operating scenario reduces to checking whether its equilibrium lies in the certified set. The proposed conditions require each bus to possess a sufficient level of IODP, quantified by an IODP index. To compensate for an IODP shortage, we further develop an I/O-transformation-based passivation controller that reshapes the local input–output behavior of the corresponding device. In this way, all grid-connected components can be made to satisfy the decentralized conditions for system-wide stability. The proposed framework is validated on a modified IEEE 39-bus system. Simulation results demonstrate that it provides a scalable and equilibrium-set-oriented solution for stability certification and control under highly variable operating conditions.