Papers

Over the past few decades, continuous quaternion-based attitude control has been proven highly effective for driving rotational systems that can be modeled as rigid bodies, such as satellites and drones. However, methods rooted in this approach do not enforce the existence of a unique closed-loop (CL) equilibrium attitude-error quaternion (AEQ); and, for rotational errors about the attitude-error Euler axis larger than {\pi}rad, their proportional-control effect diminishes as the system state moves away from the stable equilibrium of the CL rotational dynamics. In this paper, we introduce a new type of attitude control law that more effectively leverages the attitude-error Euler axis-angle information to guarantee a unique CL equilibrium AEQ and to provide greater flexibility in the use of proportional-control efforts. Furthermore, using two different control laws as examples-through the construction of a strict Lyapunov function for the CL dynamics-we demonstrate that the resulting unique equilibrium of the CL rotational system can be enforced to be uniformly asymptotically stable. To assess and demonstrate the functionality and performance of the proposed approach, we performed numerical simulations and executed dozens of real-time tumble-recovery maneuvers using a small quadrotor. These simulations and flight tests compellingly demonstrate that the proposed axis-angle-based method achieves superior flight performance-compared with that obtained using a high-performance quaternion-based controller-in terms of stabilization time.

Self-supervised learning (SSL) has significantly advanced acoustic representation learning. However, most existing models are optimised for either speech or audio event understanding, resulting in a persistent gap between these two domains. We address this gap with SPEAR (SPEech and Audio Representations), a self-supervised framework that distils complementary knowledge from a speech-focused SSL teacher and a general-audio SSL teacher into a single unified model. SPEAR applies multi-codebook vector quantisation to continuous teacher representations to produce fine-grained discrete tokens that capture both semantic and acoustic information. To effectively integrate these heterogeneous representations, SPEAR jointly predicts them given a masked input with an asymmetric pre-training loss. We further improve robustness in complex sound scenes through a novel token mixing mechanism. Extensive experiments demonstrate that SPEAR consistently outperforms existing unified speech and audio models. SPEAR establishes a new state-of-the-art on the SUPERB benchmark, surpassing WavLM Large on 12 of 15 tasks, while achieving competitive performance on the HEAR benchmark. These results position SPEAR as a versatile foundation for general-purpose speech and audio representation learning. The code and pre-trained models will be released.

Accurate, real-time wireless signal prediction is essential for next-generation networks. However, existing vision-based frameworks often rely on computationally intensive models and are also sensitive to environmental interference. To overcome these limitations, we propose a novel, physics-guided and light-weighted framework that predicts the received signal strength indicator (RSSI) from camera images. By decomposing RSSI into its physically interpretable components, path loss and shadow fading, we significantly reduce the model's learning difficulty and exhibit interpretability. Our approach establishes a new state-of-the-art by demonstrating exceptional robustness to environmental interference, a critical flaw in prior work. Quantitatively, our model reduces the prediction root mean squared error (RMSE) by 50.3% under conventional conditions and still achieves an 11.5% lower RMSE than the previous benchmark's interference-eliminated results. This superior performance is achieved with a remarkably lightweight framework, utilizing a MobileNet-based model up to 19 times smaller than competing solutions. The combination of high accuracy, robustness to interference, and computational efficiency makes our framework highly suitable for real-time, on-device deployment in edge devices, paving the way for more intelligent and reliable wireless communication systems.

The performance of federated learning (FL) over wireless networks critically depends on accurate and timely channel state information (CSI) across distributed devices. This requirement is tightly linked to how rapidly the channel gains vary, i.e., the coherence intervals. In practice, edge devices often exhibit unequal coherence times due to differences in mobility and scattering environments, leading to unequal demands for pilot signaling and channel estimation resources. Conventional FL schemes that overlook this coherence disparity can suffer from severe communication inefficiencies and training overhead. This paper proposes a coherence-aware, communication-efficient framework for joint channel training and model updating in practical wireless FL systems operating under heterogeneous fading dynamics. Focusing on downlink impairments, we introduce a resource-reuse strategy based on product superposition, enabling the parameter server to efficiently schedule both static and dynamic devices by embedding global model updates for static devices within pilot transmissions intended for mobile devices. We theoretically analyze the convergence behavior of the proposed scheme and quantify its gains in expected communication efficiency and training accuracy. Experiments demonstrate the effectiveness of the proposed framework under mobility-induced dynamics and offer useful insights for the practical deployment of FL over wireless channels.

Robotic systems navigating in real-world settings require a semantic understanding of their environment to properly determine safe actions. This work aims to develop the mathematical underpinnings of such a representation – specifically, the goal is to develop safety filters that are risk-aware. To this end, we take a two step approach: encoding an understanding of the environment via Poisson's equation, and associated risk via Laplace guidance fields. That is, we first solve a Dirichlet problem for Poisson's equation to generate a safety function that encodes system safety as its 0-superlevel set. We then separately solve a Dirichlet problem for Laplace's equation to synthesize a safe guidance field that encodes variable levels of caution around obstacles – by enforcing a tunable flux boundary condition. The safety function and guidance fields are then combined to define a safety constraint and used to synthesize a risk-aware safety filter which, given a semantic understanding of an environment with associated risk levels of environmental features, guarantees safety while prioritizing avoidance of higher risk obstacles. We demonstrate this method in simulation and discuss how a priori understandings of obstacle risk can be directly incorporated into the safety filter to generate safe behaviors that are risk-aware.

The increasing frequency and severity of High Impact and Low Probability events such as hurricanes and windstorms pose significant challenges to the resilience of electrical power distribution systems, particularly in regions of New England where there is a significant amount of overhead infrastructure in areas where vegetation is predominant. Traditional reliability-focused planning is insufficient to address the systemic vulnerabilities exposed by such extreme events. This paper presents a novel risk based framework for long term resilience planning of active overhead distribution systems, with a specific focus on mitigating the impacts of high wind and hurricane induced outages.

This paper proposes a noncoherent low probability of detection (LPD) communication system based on direct sequence spread spectrum (DSSS) and Grassmannian signaling. Grassmannian constellations enhance covertness because they tend to follow a noise-like distribution. Simulations showed that Grassmannian signaling provides competitive bit error rates (BER) at low signal-to-noise ratio (SNR) regimes with low probability of detection at the unintended receiver compared to coherent schemes that use QPSK or QAM modulation formats and need pilots to perform channel estimation. The results suggest the practicality and security benefits of noncoherent Grassmannian signaling for LPD communications due to their improved covertness and performance.

We revisit the problem of symmetric private information retrieval (SPIR) in settings where the database replication is modeled by a simple graph. Here, each vertex corresponds to a server, and a message is replicated on two servers if and only if there is an edge between them. To satisfy the requirement of database privacy, we let all the servers share some common randomness, independent of the messages. We aim to quantify the improvement in SPIR capacity, i.e., the maximum ratio of the number of desired and downloaded symbols, compared to the setting with graph-replicated common randomness. Towards this, we develop an algorithm to convert a class of PIR schemes into the corresponding SPIR schemes, thereby establishing a capacity lower bound on graphs for which such schemes exist. This includes the class of path and cyclic graphs for which we derive capacity upper bounds that are tighter than the trivial bounds given by the respective PIR capacities. For the special case of path graph with three vertices, we identify the SPIR capacity to be $\frac{1}{2}$.

A conditional latent-diffusion based framework for solving the electromagnetic inverse scattering problem associated with microwave imaging is introduced. This generative machine-learning model explicitly mirrors the non-uniqueness of the ill-posed inverse problem. Unlike existing inverse solvers utilizing deterministic machine learning techniques that produce a single reconstruction, the proposed latent-diffusion model generates multiple plausible permittivity maps conditioned on measured scattered-field data, thereby generating several potential instances in the range-space of the non-unique inverse mapping. A forward electromagnetic solver is integrated into the reconstruction pipeline as a physics-based evaluation mechanism. The space of candidate reconstructions form a distribution of possibilities consistent with the conditioning data and the member of this space yielding the lowest scattered-field data discrepancy between the predicted and measured scattered fields is reported as the final solution. Synthetic and experimental labeled datasets are used for training and evaluation of the model. An innovative labeled synthetic dataset is created that exemplifies a varied set of scattering features. Training of the model using this new dataset produces high quality permittivity reconstructions achieving improved generalization with excellent fidelity to shape recognition. The results highlight the potential of hybrid generative physics frameworks as a promising direction for robust, data-driven microwave imaging.

We report over 3 kV breakdown voltage and ultra-low leakage (011) e̱ta}-Ga2O3 power devices utilizing Schottky barrier engineering and high-permittivity (ąppa}) dielectric (ZrO2) field plate. The (011) orientation of e̱ta}-Ga2O3 enabled low background doping and thick drift layers which are promising to support kV-class vertical e̱ta}-Ga2O3 power switches. The Schottky barrier engineering was performed with a composite Pt cap/PtOx/Pt (1.5 nm) anode contact to take advantage of the enhanced reverse blocking capabilities enabled by PtOx while allowing low turn-on voltage by the interfacing thin Pt layer. We also performed a systematic study using a co-processed Pt/(011) e̱ta}-Ga2O3 Schottky barrier diodes (SBDs) on the same wafer. The bare SBDs revealed a breakdown voltage of ~1.5 kV, while the field-plate Pt/(011) e̱ta}-Ga2O3 SBDs achieved an increased breakdown voltage of 2.75 kV owing to the edge field management. Further enhancement of the breakdown voltage was achieved by tunneling leakage management using composite Pt cap/PtOx/Pt (1.5 nm) Schottky contacts that ultimately enabled breakdown voltage of 3.7 kV for the field-plate diodes. Remarkably, the Pt cap/PtOx/Pt (1.5 nm) Schottky contacts maintained similar turn-on voltage as the Pt/(011) e̱ta}-Ga2O3 SBDs. The combination of efficient tunneling leakage management by composite Pt cap/PtOx/Pt (1.5 nm) contacts with similar turn-on voltage, edge field reduction by high-ąppa} dielectric ZrO2 field plate, as well as the advantageous material properties offered by (011) e̱ta}-Ga2O3 demonstrate a promising strategy for developing ultra-low leakage and multi-kV class vertical (011) e̱ta}-Ga2O3 power devices.

State-space models (SSMs) are a widely used tool in time series analysis. In the complex systems that arise from real-world data, it is common to employ particle filtering (PF), an efficient Monte Carlo method for estimating the hidden state corresponding to a sequence of observations. Applying particle filtering requires specifying both the parametric form and the parameters of the system, which are often unknown and must be estimated. Gradient-based optimisation techniques cannot be applied directly to standard particle filters, as the filters themselves are not differentiable. However, several recently proposed methods modify the resampling step to make particle filtering differentiable. In this paper, we present an implementation of several such differentiable particle filters (DPFs) with a unified API built on the popular PyTorch framework. Our implementation makes these algorithms easily accessible to a broader research community and facilitates straightforward comparison between them. We validate our framework by reproducing experiments from several existing studies and demonstrate how DPFs can be applied to address several common challenges with state space modelling.

The increasing integration of renewable energy, particularly offshore wind, introduces significant uncertainty into hybrid AC-HVDC systems due to forecast errors and power fluctuations. Conventional control strategies typically rely on fixed setpoints and neglect frequency deviations, which can compromise system stability under rapid renewable variations. To address this challenge, this paper presents a forecast-integrated, optimal power flow (OPF)-based adaptive control framework. Wind speed forecasts generated using a Random Forest model are incorporated into a time-coupled OPF to determine baseline converter setpoints in anticipation of wind fluctuations, which are further adjusted in real time based on actual operating conditions. An adaptive droop control scheme is developed that jointly considers DC voltage and AC frequency deviations. The effectiveness of the proposed control framework is validated through hardware-in-the-loop (HIL) simulations, demonstrating its capability to ensure stable and robust operation of hybrid AC-HVDC systems under high penetration of renewable energy.

Estimating subsurface dielectric properties is essential for applications ranging from environmental surveys of soils to nondestructive evaluation of concrete in infrastructure. Conventional wave inversion methods typically assume few discrete homogeneous layers and require dense measurements or strong prior knowledge of material boundaries, limiting scalability and accuracy in realistic settings where properties vary continuously. We present a physics informed machine learning framework that reconstructs subsurface permittivity as a fully neural, continuous function of depth, trained to satisfy both measurement data and Maxwells equations. We validate the framework with both simulations and custom built radar experiments on multilayered natural materials. Results show close agreement with in-situ permittivity measurements (R^2=0.93), with sensitivity to even subtle variations (Delta eps_r=2). Parametric analysis reveals that accurate profiles can be recovered with as few as three strategically placed sensors in two layer systems. This approach reframes subsurface inversion from boundary-driven to continuous property estimation, enabling accurate characterization of smooth permittivity variations and advancing electromagnetic imaging using low cost radar systems.

We approach generative adversarial network (GAN) training from its original real-versus-fake classification viewpoint, replacing the cross-entropy discriminator objective with an upper bound on the Bayes error rate (BER). Using the Bayes optimal learning threshold (BOLT) loss, we formulate an adversarial game in which the discriminator tightens this bound and the generator maximizes it. We characterize the unrestricted optimal discriminator and show that, under balanced class priors, the population game is equivalent to minimizing total variation between the data and generator distributions. Restricting the bounded discriminator to be 1-Lipschitz yields BOLT-GAN. For balanced priors and the full discriminator class, we establish upper and lower bounds relating its population discrepancy to Wasserstein-1 distance on bounded domains. Controlled experiments on four image-generation benchmarks show improved sample quality and coverage over WGAN-GP under matched architectures, optimization settings, and gradient penalties. At a common 20-epoch budget, BOLT-GAN reduces Fréchet Inception Distance (FID) from 60.0 to 44.2 on CIFAR-10 and from 102.5 to 28.4 on LSUN Bedroom-64.

Motivated by Industry 4.0 applications, we consider quickest change detection (QCD) of an abrupt change in a process when its measurements are transmitted by a sensor over a lossy wireless link to a decision maker (DM). The sensor node samples measurements using a Bernoulli sampling process, and places the measurement samples in the transmit queue of its transmitter. The transmitter uses a retransmit-until-success transmission strategy to deliver packets to the DM over the lossy link, in which the packet losses are modeled as a Bernoulli process, with different loss probabilities before and after the change. We pose the QCD problem in the non-Bayesian setting under Lorden's framework, and propose a CUSUM algorithm. By defining a suitable Markov process, involving the DM measurements and the queue length process, we show that the problem reduces to QCD in a Markov process. Characterizing the information measure per measurement sample at the DM, we establish the asymptotic optimality of our algorithm when the false alarm rate tends to zero. Further, when the DM receives incomplete data due to channel loss, we present asymptotically optimal QCD algorithms by suitably modifying the CUSUM algorithm. We then explore the last-come-first-served (LCFS) queuing discipline at the sensor transmit queue to lower detection delay in the non-asymptotic case. Next, we consider the case of multiple sensors, each with its own wireless transmitter queue, and show that our analysis extends to the case of multiple homogeneous sensors. When the sensors are heterogeneous, we present a sensor scheduling algorithm that minimizes detection delay by balancing the trade-off between the age of the observations and their information content. Numerical analysis demonstrate trade-offs that can be used to optimize system design parameters in the non-asymptotic regime.

This paper presents a decentralized control framework that incorporates social awareness into multi-agent systems with unknown dynamics to achieve prescribed-time reach-avoid-stay tasks in dynamic environments. Each agent is assigned a social awareness index that quantifies its level of cooperation or self-interest, allowing heterogeneous social behaviors within the system. Building on the spatiotemporal tube (STT) framework, we propose a real-time STT framework that synthesizes tubes online for each agent while capturing its social interactions with others. A closed-form, approximation-free control law is derived to ensure that each agent remains within its evolving STT, thereby avoiding dynamic obstacles while also preventing inter-agent collisions in a socially aware manner, and reaching the target within a prescribed time. The proposed approach provides formal guarantees on safety and timing, and is computationally lightweight, model-free, and robust to unknown disturbances. The effectiveness and scalability of the framework are validated through simulation and hardware experiments on a 2D omnidirectional

Recent advances in Speech Foundation Models (SFMs) enable direct processing of raw audio, allowing models to respond to subtle paralinguistic variation. However, how these models interpret non-lexical cues remains largely unstudied. We introduce VQ-Bench, a controlled evaluation suite featuring a parallel dataset of synthesized modal, breathy, creaky, and end-creak phonation types. We evaluate SFM sensitivity through open-ended generation across four ecologically valid domains, alongside speech emotion recognition. Our results reveal performance gaps: while a leading commercial API failed basic biometric sanity checks, other models exhibited systematic shifts in agency, empathy, and leadership based on phonation. Our findings also highlight gender asymmetries in salary and leadership endorsements, demonstrating that SFMs may mirror or amplify human social biases. This work establishes a reproducible framework for ensuring responsible paralinguistic interpretation in speech-based AI.

We present PitchFlower, a flow-based neural audio codec with explicit pitch controllability. Our approach promotes pitch disentanglement through a simple perturbation: during training, F0 contours are flattened and randomly shifted at the input, while the true F0 is provided as conditioning to regenerate the original audio. A vector-quantization bottleneck prevents pitch recovery, and a flow-based decoder generates high quality audio. Experiments show that PitchFlower achieves accurate pitch control at the level of DSP baselines but at much higher audio quality, and performs on par with state-of-the-art neural approaches. Notably, despite using WORLD-transformed audio for training, our method filters out the vocoder's inherent artifacts, revealing a strong resilience of deep generative modeling to input degradation. This finding suggests that our framework provides a simple and extensible path that could be extended to other speech attributes.

Efficient truck platooning is a key strategy for reducing freight costs, lowering fuel consumption, and mitigating emissions. Deadlines are critical in this context, as trucks must depart within specific time windows to meet delivery requirements and avoid penalties. In this paper, we investigate the optimal formation and dispatch of truck platoons at a highway station with finite capacity \(L\) and deadline constraints \(T\). The system operates in discrete time, with each arriving truck assigned a deadline of \(T\) slot units. The objective is to leverage the efficiency gains from forming large platoons while accounting for waiting costs and deadline violations. We formulate the problem as a Markov decision process and analyze the structure of the optimal policy \(\pi^\star\) for \(L = 3\), extending insights to arbitrary \(L\). We prove certain monotonicity properties of the optimal policy in the state space \(\mathcal{S}\) and identify classes of unreachable states. Moreover, since the size of \(\mathcal{S}\) grows exponentially with \(L\) and \(T\), we propose heuristics–including conditional and deep-learning based approaches–that exploit these structural insights while maintaining low computational complexity.

Reliable downlink communication in satellite-to-underground networks remains challenging due to severe signal attenuation caused by underground soil and refraction in the air-soil interface. To address this, we propose a novel cooperative rate-splitting (CRS)-aided transmission framework, where an aboveground relay decodes and forwards the common stream to underground devices (UDs). Based on this framework, we formulate a max-min fairness optimization problem that jointly optimizes power allocation, message splitting, and time slot scheduling to maximize the minimum achievable rate across UDs. To solve this high-dimensional non-convex problem under uncertain channels, we develop a deep reinforcement learning solution framework based on the proximal policy optimization (PPO) algorithm that integrates distribution-aware action modeling and a multi-branch actor network. Simulation results under a realistic underground pipeline monitoring scenario demonstrate that the proposed approach achieves average max-min rate gains exceeding $167\%$ over conventional benchmark strategies across various numbers of UDs and underground conditions.

Ambiguities in data and problem constraints can lead to diverse, equally plausible outcomes for a machine learning task. In beat and downbeat tracking, for instance, different listeners may adopt various rhythmic interpretations, none of which would necessarily be incorrect. To address this, we propose a contrastive self-supervised pre-training approach that leverages multiple hypotheses about possible positive samples in the data. Our model is trained to learn representations compatible with different such hypotheses, which are selected with a knowledge-based scoring function to retain the most plausible ones. When fine-tuned on labeled data, our model outperforms existing methods on standard benchmarks, showcasing the advantages of integrating domain knowledge with multi-hypothesis selection in music representation learning in particular.

In this paper, we present a computational approach to certify almost sure reachability for discrete-time polynomial stochastic systems by turning drift–variant criteria into sum-of-squares (SOS) programs solved with standard semidefinite solvers. Specifically, we provide an SOS method based on two complementary certificates: (i) a drift certificate that enforces a radially unbounded function to be non-increasing in expectation outside a compact set of states; and (ii) a variant certificate that guarantees a one-step decrease with positive probability and ensures the target contains its nonpositive sublevel set. We transform these conditions to SOS constraints. For the variant condition, we enforce a robust decrease over a parameterized disturbance ball with nonzero probability and encode the constraints via an S-procedure with polynomial multipliers. The resulting bilinearities are handled by an alternating scheme that alternates between optimizing multipliers and updating the variant and radius until a positive slack is obtained. Two case studies illustrate the workflow and certifies almost-sure reachability.

This paper presents a new neural network (NN) paradigm for scalable and generalizable stability analysis of power systems. The paradigm consists of two parts: the neural stability descriptor and the sample-augmented iterative training scheme. The first part, based on system decomposition, constructs the object (such as a stability function or condition) for stability analysis as a scalable aggregation of multiple NNs. These NNs remain fixed across varying power system structures and parameters, and are repeatedly shared within each system instance defined by these variations, thereby enabling the generalization of the neural stability descriptor across a class of power systems. The second part learns the neural stability descriptor by iteratively training the NNs with sample augmentation, guided by the tailored conservativeness-aware loss function. The training set is strategically constructed to promote the descriptor's generalizability, which is systematically evaluated by verification and validation during the training process. Specifically, the proposed NN paradigm is implemented for large-disturbance stability analysis of the bulk power grid and small-disturbance stability conditions of the microgrid system. Finally, numerical studies for the two implementations demonstrate the applicability and effectiveness of the proposed NN paradigm.

Integrated Sensing and Communication (ISAC) is a key enabler in 6G networks, where sensing and communication capabilities are designed to complement and enhance each other. One of the main challenges in ISAC lies in resource allocation, which becomes computationally demanding in dynamic environments requiring real-time adaptation. In this paper, we propose a Deep Reinforcement Learning (DRL)-based approach for dynamic beamforming and power allocation in ISAC systems. The DRL agent interacts with the environment and learns optimal strategies through trial and error, guided by predefined rewards. Simulation results show that the DRL-based solution converges within 2000 episodes and achieves up to 80% of the spectral efficiency of a semidefinite relaxation (SDR) benchmark. More importantly, it offers a significant improvement in runtime performance, achieving decision times of around 20 ms compared to 4500 ms for the SDR method. Furthermore, compared with a Deep Q-Network (DQN) benchmark employing discrete beamforming, the proposed approach achieves approximately 30% higher sum-rate with comparable runtime. These results highlight the potential of DRL for enabling real-time, high-performance ISAC in dynamic scenarios.

In this paper, we present a Newton-Euler formulation of the equations of motion for underwater vehicles with an interntal moving mass actuator. Furthermore, the moving mass dynamics are expressed as an extension to the manoeuvring model for underwater vehicles, originally introduced by Fossen (1991). The influence of the moving mass is described in body-frame and included as states in both an additional kinematic equation and as part of the coupled rigid-body kinetics of the underwater vehicle. The Coriolis-centripetal effects are derived from Kirchhoff's equations and the hydrostatics are derived using first principals. The proposed Newton-Euler model is validated through simulation and compared with the traditional Hamiltonian internal moving mass actuator formulation.

This paper presents a unified modeling, estimation, and feedback framework for reconfigurable intelligent surface RIS-assisted optical wireless links. The key modeling element is a long-exposure pixel gain that extends the classical diffraction-limited response by statistically averaging angular jitter and mispointing; it admits an exact real-integral form and captures boresight attenuation and progressive sidelobe filling. The end-to-end system couples free-space path loss, Beer–Lambert atmospheric extinction, pixel-level diffraction, and optical efficiency with a unitary-pilot least-squares channel estimator and quantized phase feedback. Analysis closely matches Monte Carlo simulations and yields concrete design rules: with a surface of N=64 pixels, pilot length $M=2N$, and pilot SNR=20 dB, the normalized mean-squared error is0.005, implying an effective-SNR loss of about 0.5 and a capacity penalty of 0.007bits-s. Six-bit phase quantization introduces no measurable additional penalty at these operating points, setting a practical benchmark for feedback resolution. Training overhead scales strongly with pixel geometry: halving pixel width (quartering pixel area) increases the pilot length required to maintain the same NMSE by roughly fourfold. The framework reconciles physical-optics modeling with estimation-and-feedback design and provides a principled basis for scalable link budgeting in RIS-assisted optical networks.

In this paper, we consider a dynamic radio frequency sensing system aiming to spatially track multiple targets over time. We develop a conditional denoising diffusion probabilistic model (C-DDPM)-assisted framework that learns the temporal evolution of target parameters by leveraging the noisy echo observations as conditioning features. The proposed framework integrates a variational autoencoder (VAE) for echo compression and utilizes classifier-free guidance to enhance conditional denoising. In each transmission block, VAE encodes the received echo into a latent representation that conditions DDPM to predict future target states, which are then used for codebook beam selection. Simulation results show that the proposed approach outperforms classical signal processing, filtering, and deep learning benchmarks. The C-DDPM-assisted framework achieves significantly lower estimation errors in both angle and distance tracking, demonstrating the potential of generative models for integrated sensing and communications.

In this work, we study data-driven stabilization of linear time-invariant systems using prior knowledge of system-theoretic properties, specifically stabilizability and controllability. To formalize this, we extend the concept of data informativity by requiring the existence of a controller that stabilizes all systems consistent with the data and the prior knowledge. We show that if the system is controllable, then incorporating this as prior knowledge does not relax the conditions required for data-driven stabilization. Remarkably, however, we show that if the system is stabilizable, then using this as prior knowledge leads to necessary and sufficient conditions that are weaker than those for data-driven stabilization without prior knowledge. In other words, data-driven stabilization is easier if one knows that the underlying system is stabilizable. We also provide new data-driven control design methods in terms of linear matrix inequalities that complement the conditions for informativity.

Terahertz (THz) band is considered a promising candidate to meet the high-throughput requirement for future sixth-generation (6G) wireless communications due to its ultrawide bandwidth. However, due to the high penetration loss at high-frequencies, blockage becomes a serious problem in THz communications, especially in near-field indoor communications with numerous obstacles. To address this issue, this paper investigates blockage-resilient near-field beam training based on self-accelerating Airy beam, which can propagate along a curved trajectory to circumvent obstacles. Specifically, we first analyze the trajectory of the Airy beam and the beam pattern at the receiver using a discrete Fourier transform (DFT) codebook in the presence of obstacles. Interestingly, we reveal that the beam pattern not only captures the receiver's location information but also implicitly encodes the spatial relationship between the receiver and obstacle, which facilitates identifying the optimal Airy beam configuration. Based on this insight, we formulate the blockage-resilient beam training task as a multitask learning problem and propose a lightweight attention-based multi-parameter beam training network (AMPBT-Net) to jointly predict the angle, distance, and curvature parameters of the optimal Airy beam based on the beam pattern. Finally, simulation results demonstrate that the Airy beam effectively mitigates blockage effects and the proposed scheme achieves comparable performance to exhaustive beam sweeping while significantly reducing training overhead.

Diffusion models have emerged as powerful priors for single-image restoration, but their application to zero-shot video restoration suffers from temporal inconsistencies due to the stochastic nature of sampling and complexity of incorporating explicit temporal modeling. In this work, we address the challenge of improving temporal coherence in video restoration using zero-shot image-based diffusion models without retraining or modifying their architecture. We propose two complementary inference-time strategies: (1) Perceptual Straightening Guidance (PSG) based on the neuroscience-inspired perceptual straightening hypothesis, which steers the diffusion denoising process towards smoother temporal evolution by incorporating a curvature penalty in a perceptual space to improve temporal perceptual scores, such as Fréchet Video Distance (FVD) and perceptual straightness; and (2) Multi-Path Ensemble Sampling (MPES), which aims at reducing stochastic variation by ensembling multiple diffusion trajectories to improve fidelity (distortion) scores, such as PSNR and SSIM, without sacrificing sharpness. Together, these training-free techniques provide a practical path toward temporally stable high-fidelity perceptual video restoration using large pretrained diffusion models. We performed extensive experiments over multiple datasets and degradation types, systematically evaluating each strategy to understand their strengths and limitations. Our results show that while PSG enhances temporal naturalness, particularly in case of temporal blur, MPES consistently improves fidelity and spatio-temporal perception–distortion trade-off across all tasks.