Papers

Deep reinforcement learning has evolved from classical dynamic programming, temporal-difference learning, and tabular control into a broad framework for sequential decision-making under uncertainty. This book provides a structured introduction to that evolution, emphasizing not only how reinforcement learning algorithms work, but also why they were developed, which problems they address, where they fail, and how they connect to real-world systems. It combines textbook foundations, research-oriented discussion, and a systems perspective. Early chapters introduce reinforcement learning, Markov decision processes, dynamic programming, Monte Carlo methods, temporal-difference learning, and the transition from tabular to deep approaches. The middle chapters cover major algorithmic families, including DQN, advanced value-based methods, policy gradients, actor-critic methods, PPO, SAC, model-based reinforcement learning, MuZero, offline reinforcement learning, and sequence-modeling approaches. Later chapters extend the discussion to multi-agent and hierarchical learning, safe reinforcement learning, reinforcement learning from human feedback, reasoning-oriented AI systems, communication networks, UAV applications, implementation pipelines, experimental methodology, failure analysis, and future research directions. Throughout the book, examples from UAV-assisted networks, SD-WAN traffic engineering, safe control, and reasoning-based AI connect mathematical concepts to practical challenges such as partial observability, competing objectives, safety constraints, deployment drift, and uncertain evaluation. The book is intended for advanced students, researchers, and engineers with basic knowledge of probability, linear algebra, calculus, and programming.

In non-terrestrial network deployments, local obstructions may block line-of-sight satellite links before the satellite reaches the geometric elevation mask, causing abrupt and unplanned handovers. To address this limitation, this paper proposes a map-free method for learning the local LoS skyline, defined as the obstruction elevation over azimuth, from binary availability labels derived from passive satellite signal observations at the terminal. The problem is formulated as a binary classification task in the azimuth-elevation space, where the skyline is extracted as the decision boundary of the learned blockage probability surface. Two complementary estimators are investigated, namely a Gaussian Process (GP) classifier and a neural multilayer perceptron (MLP) with circular azimuth encoding and Monte Carlo Dropout uncertainty indicators. The learned obstruction surface is then combined with satellite ephemeris information through EphemerisWindow, a trajectory-level prediction method that estimates future LoS termination events before the serving link is lost. The results show that both learned estimators improve the skyline reconstruction compared with empirical bracketing and enable proactive handover preparation without requiring 3D building maps, sky cameras, or additional environmental sensing.

Cybersecurity is a fundamental requirement for protecting wearable devices used in healthcare Internet of Things (H-IoT) systems. Security failures in these resource-constrained systems directly compromise patient safety. Physiological data and network traffic are frequent targets of cyberattacks in H-IoT environments. To address these risks, deep learning-based cybersecurity mechanisms for H-IoT often involve complex architectures with large parameter counts. Existing datasets are also rarely assessed for quality, limiting their applicability. However, this research addresses these challenges by developing multiple realistic datasets and proposing lightweight deep learning models, namely the Temporal Convolutional Network (TCN) and Residual TCN (Res-TCN), for H-IoT. It includes two binary classification datasets for Distributed Denial of Service (DDoS) attacks and a multiclass dataset representing Selective Forwarding (SF), Man-in-the-Middle (MITM), and DDoS attacks. The datasets UL-ECE-MQTT-DDoS-H-IoT2025 and UL-ECE-UDP-DDoS-H-IoT2025 are generated in Cooja and ns-3 to capture transmission behaviours and protocol variations. The third dataset, UL-ECE-MultiAttack-H-IoT2025, integrates physiological and network features to represent multiple cyber threats in H-IoT. Building on this, the TCN model is designed to detect and mitigate DDoS attacks over the MQTT and UDP-based datasets. It incorporates a monitoring frequency-based detection mechanism and a dynamic threshold-based mitigation strategy. To enable edge deployment, the model is quantised and converted into TensorFlow Lite (TFLite) for real-time DDoS detection on Raspberry Pi 4, achieving low latency and power-efficient operation in H-IoT. This thesis establishes a deep learning-based cybersecurity defence mechanism encompassing realistic dataset generation, lightweight model design, and edge deployment for securing H-IoT systems.

Electroencephalography (EEG) foundation models aim to learn reusable representations from large-scale unlabeled recordings. A common pretraining strategy is masked waveform reconstruction, but applying supervision directly to noisy EEG may encourage models to recover predictable background activity, acquisition effects, and artifacts rather than neural structure that transfers across tasks. This raises a central question: what should an EEG foundation model predict to learn transferable representations? We introduce EEG-JEPA a structured latent-prediction framework for EEG foundation modeling. Rather than reconstructing masked voltage samples, a masked context encoder and predictor infer contextual latent states produced by an exponential-moving-average target encoder that observes the complete input. EEG-JEPA organizes target design along three complementary dimensions: target content specifies what representation is predicted, target support specifies where prediction occurs over structured electrode–time regions through Neurotopology-Aware Multi-scale Electrode-Temporal Masking (N-MET), and target depth specifies at which encoder layers supervision is applied. Together, these designs shift EEG pretraining from recovering missing measurements to inferring latent states from structured electrode–time context. We evaluate EEG-JEPA through controlled objective comparisons, frozen multitask transfer, and full fine-tuning. Under the same backbone, pretraining corpus, and training duration, EEG-JEPA improves the 14-task frozen macro balanced accuracy from 40.49% to 50.42% over CBraMod-style masked waveform reconstruction. Multi-source continuation further raises this result to 52.94%, the highest average among the EEG foundation models evaluated on EEG-FM-Bench. Under protocol-matched full fine-tuning, EEG-JEPA also improves the nine-task average balanced accuracy from 68.98% to 70.65%.

Image restoration quality can be evaluated along three complementary facets: pixel-level fidelity, human perception, and downstream machine preference. However, existing lossy compression restoration methods optimize for at most one of these criteria: fidelity-oriented models often regress toward conditional means and produce over-smoothed outputs, while generative approaches hallucinate plausible but factually incorrect textures that degrade both ground-truth fidelity and downstream task accuracy. To navigate this three-way tradeoff, we propose FDIR, a two-stage architecture that decouples the conflicting demands through complementary inductive biases: Quality-Guided One-Step Flow Matching (QO-Flow) recovers global semantic structure in latent space via a single forward pass, while Flow-Conditioned Detail Refinement (FCDR) deterministically restores high-frequency textures and suppresses generative hallucinations in pixel space. Extensive experiments demonstrate that FDIR achieves superior fidelity, with a favorable perceptual-fidelity balance and competitive machine preference.

This paper presents a method for computing inner polytopic approximations of admissible sets for continuous-time linear control systems subject to multiple affine state constraints, with a particular concern on computational tractability for large dimensional problems. In place of globally computing the admissible set and the part of its boundary called the barrier, we compute the so-called individual admissible sets and the corresponding barriers for each single constraint. We then use the exact sampling of linear systems and generate polytopes in half-space representation that provide an approximation of the individual admissible sets, to finally intersect them. We provide a complexity analysis of the whole procedure to evaluate its efficiency. The approach is illustrated by two examples -a triple integrator and a mass-spring-damper chain considered in 4, 6, 8, and 10 dimensions- with corresponding runtimes evaluated for both.

Deploying state-of-the-art deep neural networks (DNNs) at the wireless edge is severely bottlenecked by the strict energy and resource constraints of mobile devices. Although federated split learning (FSL) alleviates on-device computational burdens by offloading workloads to an edge server, this may introduce systemic overheads, while the continuous exchange of intermediate activations, gradients, and submodels still incurs significant energy consumption (EC). To address this, we propose a green quantized FSL (GQ-FSL) framework that incorporates stochastic quantization for both local collaborative training and wireless transmissions. Notably, GQ-FSL supports asymmetric precision levels for the client- and server-side submodels, effectively decoupling device energy constraints from global convergence degradation. To quantify these tradeoffs, we develop parameterized energy models for the split architecture and derive a theoretical convergence bound under statistically heterogeneous data. Building on that, we formulate a joint optimization problem to configure the DNN split point and precision levels, minimizing the total system EC while satisfying strict latency and target accuracy constraints. Ultimately, we demonstrate that GQ-FSL enables large-scale DNN deployment on resource-constrained devices, achieving superior energy efficiency compared to quantized federated learning and full-precision FSL.

This paper studies a multi-tenant resource allocation problem in a satellite open radio access network (O-RAN) wholesale setting, where heterogeneous traffic classes share a time-varying limited satellite capacity over a finite planning horizon. We formulate a joint pricing and reserve allocation problem from a service provider perspective, where tenant-specific demand exhibits price elasticity and stochastic service requirements subject to strict service-level agreement (SLA) constraints, leading to a coupled economic and reliability-driven bottleneck. A deterministic reformulation is adopted to approximate probabilistic SLA requirements through tractable margin constraints, enabling coordinated control of horizon-wide contract prices and time-varying reserves. The resulting problem is non-convex due to interdependent decisions across tenants, time windows, and service classes. To address this, an alternating optimization (AO) scheme is developed separating pricing and allocation decisions while preserving feasibility and SLA guarantees. Numerical results show that the proposed method achieves near-optimal profit within approximately $1\%$ of a global benchmark, while reducing runtime by up to $22\times$. In contrast, considered baseline schemes incur profit losses exceeding $15\%$ or fail to satisfy SLA constraints. The proposed approach consistently maintains non-positive empirical SLA gaps and achieves up to $30\%$ higher resource utilization than a price-optimization baseline without adaptive reserve control. These results demonstrate that joint economic and resource control enables the provider to efficiently exploit scarce satellite network capacity with reliable service delivery and scalable computation.

This article presents a modular inference framework that integrates Flow Matching generative models with formal Control Barrier Function (CBF) safety guarantees. Unlike existing methods that apply external safety filters to a model's final output, our approach modifies the Flow Matching denoising process within the model to inherently generate safe trajectories. By employing a smooth Log-Sum-Exponential aggregate barrier, we enforce safety over entire action chunks. This aggregate barrier ensures a minimal increase in computational overhead and does not alter the semantic intent of the model. We show that, within the proposed framework, the 2-Wasserstein distance between the generated distribution and the target distribution remains bounded. Our method eliminates the need for safety-specific datasets or costly model retraining, providing a versatile solution for safe inference. We validate the approach on two robotic manipulation platforms and a 2D navigation benchmark, verifying that our framework achieves reliable safety without degrading the success rate of the model.

Promoting cooperative behaviour amongst decision makers has key implications for the long term sustainability of social systems. Incentives can promote cooperation in situations where defection is more favourable. Previous research has identified optimal decentralised incentives in structured populations where the social environment is described by a generic single-layer networks. Optimality is here intended in the sense of cost minimisation. Here, we provide and optimal incentive strategy for structured populations in general network, a rather unexplored case. Further, we look at populations where, through the network structure, each player interact with one set of neighbours while contributing to an opinion diffusion dynamics from a second set of neighbours. Hence we cover the case of multilayer networks. To fill this gap, we provide a solution to the optimal control problem by solving the Hamilton-Jacobi-Bellman equation and derive an analytic solution for distributing incentives in multilayer networks. By implementing the dynamics of the networked Prisoner's Dilemma Game, we provide a feedback control loop that yields an optimal incentive distribution over time. The dynamic incentive depends on the current state of cooperation, the game-layer network, the strategy-diffusion layer, and the payoff matrix design. We found that the optimal incentive is node-wise, unique to each node on the network, influenced by its relative position according to the adjacency matrices of both layers. We also found that the optimal solution is not exclusively reward or punishment. While one node can receive a reward, the other node may receive punishment at the same time, depending on its current level of cooperation, relative to other nodes. We provide analytic solutions and numerical validations for the cases studied, comparing our results to the existing literature.

Post hurricane distribution system restoration requires rapid repair scheduling subject to feeder topology, field logistics, and electrical feasibility. This paper presents a quantum inspired quadratic unconstrained binary optimization (QUBO) assisted adaptive large neighborhood search (ALNS) framework. At each restoration stage, a local CPU simulated annealing sampler ranks individual repairs and multi job combinations near the energized frontier. A deterministic decoder preserves crew truck logistics, enforces full useful crew utilization, and rejects infeasible batches. Final schedules are validated through OpenDSS replay. The framework is evaluated on the IEEE 123 node test feeder without distributed generation under 80, 90, and 100 m/s wind scenarios. In the 100 m/s stress test, the proposed method reduces mean system average interruption duration index and energy not supplied by 2.24% and restoration makespan by 50.71% relative to classical energized ALNS. Results show that QUBO assistance is most valuable when severe damage creates a larger combinatorial repair space.

This paper introduces metasurface wavefront bending (MWB) to enhance spatial multiplexing in radiative nearfield multi-user multiple-input multiple-output (MU-MIMO) systems. By increasing spherical-wave curvature, MWB strengthens range-dependent phase variations across the receiver array, reduces inter-user channel correlation and improves user separability. The paper develops a curvature-dependent channel analysis, a generalized-sheet-transition-condition (GSTC) synthesisprocedure for MWB and a complete metasurface-assisted MUMIMO channel model. Both a practical common-profile scheme and an ideal user-specific benchmark are evaluated. The results demonstrate that MWB provides substantial improvements in spectral efficiency and effective channel rank. Finally, a threelayer transmissive Huygens metasurface architecture is proposed for physical implementation.

Cable-Driven Parallel Robots (CDPRs) are a type of parallel mechanism in which cables are used as actuators. Due to the two levels of redundancy and numerous constraints within the CDPR actuation, joint and operational spaces (together known as the tri-space), tracking a given trajectory in the operational space while satisfying constraints in tri-space simultaneously is challenging. To the best of the authors' knowledge, there does not exist any tri-space control framework, which is robust, effective, and directly applicable to several architectures of redundantly actuated CDPRs. This paper proposes a tri-space control framework that combines Reactive Control (RC) and Iterative-Learning Control (ILC) to perform repetitive tasks in the operational space. The framework allows the tracking of operational space trajectories online with feasible cable forces, while avoiding undesirable situations such as cable-link interference, joint interference, and loss of manipulability. On the other hand, by finding an optimal parameter in the null space using a novel parameterization of a null space vector, the performance can be improved through ILC when the task is repeatedly executed. Simulation and hardware results on various Multilink Cable-Driven Robot (MCDRs) and Hybrid Cable-Driven Robots (HCDRs) show that the proposed tri-space control framework can be conveniently and effectively applied to the real-time control of different CDPRs.

This paper discusses the data-driven model matching problem. In particular, this paper focuses on two-degree-of-freedom control systems, and consider to design feedforward controller from input-output data. An intuitive solution to this problem would be identifying the optimal controller using data, but this does not give the exact solution to the original problem. A shaping filter is required to compensate for this gap, and the main contribution of this paper is to give the optimal shaping filter. The proposed shaping filter is constructed from available information under reasonable assumptions, and its effectiveness is shown through a numerical example and a practical experiment. The relation between the proposed shaping filter and Estimated Response Iterative Tuning (ERIT) is also discussed, and it is shown that ERIT is optimal for a special case.

Different Length Alignment Sewing (DLAS), which involves stretching the shorter fabric to match the longer one and sewing them together in a straight line, is a challenging task that needs to satisfy several requirements when automating the sewing process. To address the challenges, this research proposes a novel robotic sewing system, Different Length Robotic Sewing System (DLRoSS), which consists of a roller type end-effector, attached to a 6-DoF manipulator. The end-effector composed of active shorter and longer fabric rollers, and a passive press-roller attached to the shorter-fabric roller. Assuming that one end of the two fabric layers are initially positioned under the sewing machine's presser foot, the system automates DLAS by operating in four distinct phases. (P1) Fabric wrapping: Individual fabric layers are picked, held, and wrapped from the other end onto the feed rollers. (P2) Sewing: During the sewing, the shorter fabric is stretched and aligned with the longer fabric in real-time using roller velocity control based on the sewing speed and apriori known length ratio. (P3) Sewing completion: In the final sewing round on the fabric rollers, the press roller is engaged to prevent the stretched fabric from slipping off due to internal tension. (P4) Sewing fabric release: At the end of sewing, the fabric edge moves past the press roller, and the fabric releases from the rollers. Experimental results demonstrate that DLRoSS achieves consistent, high-quality sewing of stretchable fabrics of different materials and lengths.

Adapting deep learning models to profound clinical heterogeneity typically relies on parameter-efficient fine-tuning (PEFT) to avoid the severe overfitting associated with full end-to-end network updates. Although PEFT successfully navigates limited data scenarios, it inherently forces the training of a separate, isolated adapter for every specific diagnostic task. Consolidating these isolated adapters into a single generalist network risks negative transfer, as optimization gradients from conflicting visual domains interfere. To address this, we propose MoPET, a mixture-of-experts (MoE) method that uses a learned sparse router to direct each input through a small subset of low-rank PEFT experts injected into a frozen foundation model, sharing capacity across datasets while limiting cross-domain gradient conflict. Through selected evaluations on the MedMNIST benchmark, we first establish that PEFT outperforms full network updates, improving average accuracy from 86.50% to 88.97%. We then show that a single MoPET model consolidates four heterogeneous datasets into one network, improving average accuracy over the best isolated PEFT adapters (93.46% versus 92.83%). Finally, we show that co-training with auxiliary datasets improves accuracy on data-constrained clinical targets, raising average target accuracy over the strongest isolated adapter from 81.58% to 83.58%. Our source code is publicly available at https://github.com/sdoerrich97/mopet.

With the development of photovoltaic (PV) and motor drive, the coupled eight-switch three-phase three-level inverter (TP-TLI) is receiving more attentions. However, the previous modulation strategies for coupled eight-switch TP-TLI are suitable for high power factor (PF), which cannot satisfy the requirement for PV and motor drive. Aiming at this, this paper proposes a hybrid virtual space vector modulation (HVSVM) for the coupled eight switch TP-TLI with extended PF range. The mechanism of PF limitation is analyzed in deep. Two virtual vectors are constructed in each sector, and three basic VSVMs with different PF range are proposed according to the number of virtual small vectors used. Combined with three VSVMs, the HVSVM is proposed by detecting the three-phase current polarities. The PF angle range of the HVSVM is extended from about [-30{\deg}, 30{\deg}] to [-90{\deg}, 90{\deg}]. Besides, an active neutral-point voltage control (NPVC) is also presented by using two coefficients to adjust the dwell times of the two pairs of redundant small vectors. This approach considers the NP current and the derivation of NPV, and thereby it is accurate to control the NPV. The correctness and feasibility of the proposed HVSVM and active NPVC are verified by experiments through a 3 kVA prototype.

Memristors can achieve up to a 1000-times reduction in energy consumption in neuromorphic and in-memory computing, but their integration into power converter control remains incomplete. Existing implementations either convert memristor outputs into digital signals for DSP-based pulse-width modulation (PWM), reintroducing computational overhead, or use analog PWM circuits with limited programmability. Since PWM generation is the final essential stage of power converter control, a programmable memristor-based PWM circuit is needed to fully realize the energy-efficiency benefits of memristive computing. This paper presents a programmable memristor-based PWM circuit operating at approximately two hundred kilohertz for switching power converters. The design combines digital-controller programmability with the low power consumption of analog PWM generators. Experimental validation using commercially available memristors achieves a programmable switching frequency from 144.7 kHz to 204.2 kHz. Compared with a commonly used DSP implementation in power converter control, the proposed design achieves a 92% reduction in power consumption.

Balancing sequence length, representational capacity, and long-horizon stability is a central problem in autoregressive (AR) speech and audio generation. Representations with higher frame rates or greater capacity can preserve more signal detail, but they also make streaming generation more vulnerable to distribution drift and AR error accumulation. Conversely, shorter and more compressed representations simplify AR modeling, but their limited bandwidth may discard important components and constrain the upper bound of reconstruction fidelity and generation quality. We ask whether a low-frame-rate, high-dimensional, high-bandwidth continuous representation can be co-designed with a streaming generation framework to support robust high-fidelity reconstruction, strong single-token predictability, and superior long-horizon stability. We decompose this goal into two coupled problems: what geometric and statistical properties a high-dimensional representation space should have, and how an AR continuous-token generator should be structured to resist error accumulation. Accordingly, we propose Locodec, a locally encoded codec that shapes its representation space to improve the interpolatability of a lower-dimensional core manifold and the identifiability of the native high-dimensional coordinates, thereby improving the predictability of high-dimensional high-bandwidth tokens. We also propose MP-ELD, a single-token AR flow-matching framework that uses multi-path information routing and residual classifier-free guidance to mitigate error accumulation. Experiments with 8-Hz, 768-dimensional tokens show that our design preserves reconstruction quality, improves single-token predictability, achieves competitive WER, and maintains stable long-form synthesis, without using external SSL/ASR models, pretrained text language models, or post-training stages.

Flexible electronics (FE) platforms rely on analog and mixed-signal (AMS) circuits - biosensors, readout front-ends, and analog-to-digital converters - that dominate both functionality and energy consumption, making on-chip clock generation an essential yet power-critical function. Existing oscillator-based solutions suffer from unbounded process, voltage, and temperature (PVT) drift that degrades signal integrity, while alternative clock sources can consume up to 90% of the total system power budget, rendering them inapplicable to FE platforms and elevating clock generation to a primary power and energy-efficiency design constraint. This paper presents the first phase-locked loop (PLL) architecture designed for n-type-only amorphous indium-gallium-zinc oxide (a-IGZO) thin-film transistor (TFT) technology, addressing FE-specific constraints such as the absence of p-type devices, limited carrier mobility, and strong PVT variability. Rather than targeting high-precision frequency synthesis, the proposed design operates as a low-bandwidth temporal stabilizer: a free-running ring-oscillator-based voltage-controlled oscillator (VCO) is softly regulated by a minimal charge-pump feedback loop to bound long-term frequency drift without requiring a continuous high-quality external reference. The proposed PLL supports frequencies from 1 kHz to 300 kHz while occupying 0.0115-0.0233 mm2 and consuming 0.115-0.153 mW. Compared with prior oscillator-based FE clocking solutions, our architecture reduces power by more than 400x while achieving footprint reductions exceeding 1500x compared to flexible VCOs, and more than 390x with respect to ring-oscillator solutions. Validated across four representative published IGZO AMS systems, the proposed PLL achieves an rms period jitter of 2.24 ns and a long-term frequency accuracy within 1000 ppm, providing reference-anchored clock stability in FE platforms.

With the ever-increasing pervasiveness of smart edge devices, the demand is growing for applications that can be tailored to users (e.g., custom keyword spotting) or patients (e.g., adaptive health monitoring). Yet, most edge devices rely on fixed inference algorithms and thus cannot learn on-device to personalize predictions. When they can, devices typically support only a specific learning scenario, such as few-shot learning (FSL): going beyond this requires resorting either to another specialized device or to cloud-based retraining, which implies significant energy and latency overheads, a lack of real-time capabilities, and privacy concerns. In this work, we introduce embedder-centric learning (ECL), a framework that unifies four different online learning scenarios: FSL for on-the-fly customization, continual learning (CL) for knowledge accumulation, zero-shot learning (ZSL) for leveraging semantic data, and in-context learning (ICL) for adapting beyond classification. We demonstrate in silicon that ECL can be deployed on resource-constrained devices across four real-world use cases representative of the aforementioned learning scenarios. Our approach establishes a new state-of-the-art performance for FSL character recognition (Omniglot: 96.8% for 5-way 1-shot, 83.3% for 32-way 1-shot), and the first hardware baseline for CL in keyword spotting (NeuroBench keyword FSCIL: 71.8% for 200-way 5-shot). Moreover, we present the first hardware demonstrations of ZSL with semantic data (60.6% for 5-way spoken sentence classification) and ICL (46.2% at the 500th token of RegBench) operating at micro-to-milliwatt power budgets. Therefore, by unifying multiple learning scenarios, we pave the way for smart and versatile devices that can adapt right at the edge, without reliance on the cloud.

We propose a conformal prediction framework for quantifying the error of physics-based predictors used in control, where simple models are preferred for synthesis, certification, and real-time use. Because these models are selected for compatibility with the intended application rather than for maximal predictive accuracy, their error combines process noise with a state-dependent discrepancy. A data-driven discrepancy estimate defines an asymmetric nonconformity score: errors consistent with the learned discrepancy are penalized less than equally large in the opposite direction. The sets remain in the nominal model's error coordinates and are physics-consistent, i.e., they contain a ball at the origin. The construction is agnostic to the discrepancy model (kernel, neural-network, or other), preserves finite-sample marginal validity under exchangeability, and provably narrows the interval over a characterizable state-input region. We further show that, for RKHS models, the power function provides a local confidence measure for adaptive score design and we extend the construction to the multivariate case via a Minkowski-gauge score yielding a jointly calibrated disturbance set.

Extremely large-scale antenna array (ELAA) has emerged as a common feature of many key candidate technologies for 6G, where the near-field characteristics become dominant. The quasi-distributed array can further extend the near-field range and utilize the near-field benefits to improve the system performance. However, its typical implementation with modular arrays suffers from severe grating lobes that cause non-negligible inter-user interferences. To solve this problem, we propose the modular minimum-redundancy array (M-MRA) to suppress near-field grating lobes by redesigning the subarray configuration. Specifically, we first characterize the beam pattern of the conventional modular uniform linear array (M-ULA). Contrary to the common belief that grating lobes only exist in the angle domain, we reveal that near-field grating lobes may also occur in the distance domain. We further analyze how to suppress near-field grating lobes for the M-ULA. The results demonstrate that increasing the number of antennas per module can suppress grating lobes. In particular, the number of antennas required grows linearly with the inter-module spacing, thus the grating lobe interferences are severe under a limited number of antennas. This limitation inspires us to propose the M-MRA by redesigning the subarray structure. For M-MRA, the nonuniform antenna spacing within each subarray provides a narrower spatial envelope, allowing it to suppress near-field grating lobes in the angle and distance domains simultaneously. Simulation results verify that the proposed M-MRA can significantly improve the spectrum efficiency of multi-user near-field communications under the same number of antennas.

Depot charging of electric bus fleets must minimize electricity costs, respect grid limits, and remain feasible despite uncertain trip energy demand. While cost-optimal charging is well studied, its value under different electricity prices and grid connection capacities, as well as the economic cost of robustness, remain poorly quantified. We address these gaps with a convex robust formulation in which bounded demand uncertainty is enforced through worst-case state-of-energy constraints. The formulation is evaluated against charge-on-arrival using realistic service schedules for four Swiss depots containing 7-35 buses. In the depots studied, smaller depots achieve the greatest relative benefit from optimization, with total electricity cost reductions exceeding 50%, because optimization mitigates charging peaks that strongly affect their costs. For all depot sizes, the savings from optimization increase with electricity price volatility. Optimization can also lower the grid capacity required for feasible operation by over 40%, although tight limits reduce peak shaving potential. Protection against energy-demand deviations of 10% increases total electricity cost by less than 0.1%. The resulting charging power profiles exhibit interpretable price-threshold and peak-shaping behavior, providing practical guidance for real-world implementations.

To estimate confidence for end-to-end Automatic Speech Recognition (ASR) systems, recent research has proposed Confidence Estimation Modules that incorporate features from the backbone ASR model. Most existing approaches, however, are architecture-dependent. In this paper, we propose the Score-Rank Confidence Estimation Module (SR-CEM), a lightweight module that leverages beam search information to generate token- and word-level confidence scores. Specifically, SR-CEM constructs features by combining the scores and ranks of tokens within a hypothesis. Experiments show that SR-CEM achieves effective calibration on both in-domain and out-of-domain English data. On the in-domain testset, it attains a Maximum Calibration Error of 4.50% and an Expected Calibration Error of 0.30% at the token level, significantly outperforming softmax confidence (20.04% and 1.75%, respectively). At the word level, SR-CEM achieves 8.17% and 0.35%, compared to 17.91% and 1.67% from softmax confidence. Furthermore, we demonstrate its robustness across hybrid and transducer ASR architectures with different decoding strategies, as well as on Dutch, noisy and conversational speech conditions. Our main finding is that SR-CEM is particularly effective in reducing Maximum Calibration Error, which is critical for reliable downstream use of ASR outputs, while maintaining architecture independence and generality across diverse evaluation conditions.

Accurate fault location is critical for distribution network reliability. However, increasing distributed energy resource (DER) penetration complicates fault location due to intermittent generation and bidirectional power flows that reshape fault signatures. Spatio-Temporal Graph Neural Networks (STGNNs) have shown promise by jointly modeling spatial and temporal dependencies, but their behavior under increasing DER penetration has not been studied rigorously. In this paper, we (i) systematically benchmark spatio-temporal graph attention network (STGATv2) against purely temporal (gated recurrent unit, GRU), purely spatial (GATv2) and traditional machine learning baselines, and (ii) evaluate how well models generalize across increasing DER penetration levels (10%, 25%, 50%) on a reconfigured IEEE 123-bus feeder with multiple DER injection points and moderate-to-high impedance faults. Results show that STGATv2 consistently outperforms neural baselines, achieving 92-94% macro F1 in-distribution. Notably, generalization across penetration levels is asymmetric: training at 50% penetration retains near in-distribution F1 score at lower levels, whereas training at 10% degrades considerably at 50% - with STGATv2 retaining 81-84% F1 under these drastic shifts, substantially higher than GATv2 and GRU which drop to 69-74% F1 and 73-75% F1 respectively. Under realistic measurement noise, STGATv2 maintains > 85% F1, while GRU drops as low as 33.5% F1, highlighting the critical role of topological awareness for robust fault location in active distribution networks.

Batteryless overlays couple passive throughput to Wi-Fi sounding overhead and channel state information (CSI) aging. This paper investigates channel sounding for ultra-high reliability (UHR) operation in a Wi-Fi 8/IEEE 802.11bn-inspired downlink multi-user multiple-input multiple-output (MU-MIMO) system with a batteryless passive overlay. We optimize the post-sounding transmission interval to maximize the aggregate throughput of the active Wi-Fi and passive links, while jointly accounting for sounding overhead, CSI aging, modulation and coding scheme (MCS), passive attenuation, and passive data rate. A packet-level cross-layer model evaluates the cycle-average throughput, and a data-driven search identifies the optimal interval under different operating conditions. Simulations demonstrate that passive overlay reshapes the conventional sounding tradeoff: depending on the MCS and passive-link configuration, the additional passive throughput may or may not compensate for the associated Wi-Fi reliability loss, causing the optimal interval to shift. The results provide design guidance for reliable and low-power MU-MIMO WLANs.

In this paper, we construct a Fleming-Viot particle system for a class of subcritical Bellman-Harris processes. We prove that it selects the Yaglom limit at a polynomial rate in the number of particles. Since lifetimes are non-exponential, the population size is not Markov, and the analysis must therefore be carried out on the space of age configurations. In this setting, the Lyapunov functions used for Galton-Watson processes are no longer norm-like. Nevertheless, we establish a Yaglom theorem that strengthens the classical result: the conditional laws converge in total variation at an exponential rate, with decay rate given by the Malthusian parameter. We also prove that the drift condition, which links the hazard rate to the offspring law, is necessary within a natural class of Lyapunov functions, showing that it is a feature of the measure-valued lift rather than a defect of the estimates. Finally, we illustrate the estimator through an application to livestock epidemic surveillance, where the Yaglom limit is the null distribution of a change-detection test.

When multiple wireless edge servers communicate with a common core server, their uplink transmissions create a multiple-access bottleneck that affects the la- tency of distributed edge learning systems. This paper analyzes this bottleneck and investigates the use of fractional power control (FPC) to mitigate it in hierarchi- cal federated learning (HFL). Modeling the spatial deployment of edge servers and wireless channel gains using stochastic wireless models, we analytically character- ize the mean core aggregation latency (CAL) under a TDMA aggregation policy. We formulate the cumulative core aggregation latency as a renewal reward pro- cess, where each learning round constitutes a renewal cycle and task completion defines the stopping time. This representation yields an exact decomposition of the mean cumulative aggregation latency into the product of the expected stopping round and the expected per-round aggregation latency under iid server selection. We then derive analytical upper bounds on the expected per-round latency under fractional power control (FPC). Numerical results demonstrate that even modest FPC exponents substantially reduce CAL across a wide range of deployment scenar- ios. These results highlight fractional power control as a simple and model-agnostic mechanism for mitigating wireless aggregation bottlenecks in edge learning systems.

Although world-action models (WAMs) enhance long-horizon robot control by predicting visual evolution before acting, long-horizon reliability demands repeated re-grounding in real observations–not recursive rollout. Existing WAMs address this by refreshing history or KV cache with ground-truth data between chunks. However, such chunk-wise feedback operates at a coarse temporal granularity and thus fails to correct prediction errors at the individual time-step level. To address this, we propose Feedback Flow Matching (FBFM), a training-free inference mechanism that pushes re-grounding inside the actively generated chunk. During flow matching, FBFM applies a masked pseudoinverse correction to the conditional velocity field: it leverages the preceding action chunk to guide generation of the next action chunk, and uses the image observed after executing that preceding chunk to guide the next frame prediction. This cross-chunk pairing–where feedback from one chunk arrives in time to shape the next–creates an asynchronous loop that corrects errors without waiting for chunk boundaries. Being training-free, the mechanism improves responsiveness to unexpected events and suppresses drift in long-horizon tasks. We evaluate FBFM on both a joint-generation WAM (DreamZero) and a stage-wise WAM (LingBot-VA). On selected LIBERO and RoboTwin2.0 tasks, it improves success rates by over 5% in favorable settings, and real-world robot observation-prediction diagnostics show notably better tracking. We argue that FBFM offers a new paradigm for fine-grained online correction, bridging open-loop flow generation with closed-loop real-world dynamics.