Papers

Shadows cast by terrain and tall structures remain a major obstacle for high-resolution satellite image analysis, degrading classification, detection, and 3D reconstruction performance. Public resources offering geometry-consistent paired shadow/shadow-free satellite imagery are essentially missing, and most Earth-observation datasets are designed for shadow detection or 3D modelling rather than removal. Existing deep shadow-removal datasets either target ground-level or aerial scenes or rely on unpaired and weakly supervised formulations rather than explicit satellite pairs. We address this gap with deSEO, a geometry-aware and physics-informed methodology that, to the best of our knowledge, is the first to derive paired supervision for satellite shadow removal from the S-EO shadow detection dataset through a fully replicable pipeline. For each tile, deSEO selects a minimally shadowed acquisition as a weak reference and pairs it with shadowed counterparts using temporal and geometric filtering, Jacobian-based orientation normalisation, and LoFTR-RANSAC registration. A per-pixel validity mask restricts learning to reliably aligned regions, enabling supervision despite residual off-nadir parallax. In addition to this paired dataset, we develop a DSM-aware deshadowing model that combines residual translation, perceptual objectives, and mask-constrained adversarial learning. In contrast, a direct adaptation of a UAV-based SRNet/pix2pix architecture fails to converge under satellite viewpoint variability. Our model consistently reduces the visual impact of cast shadows across diverse illumination and viewing conditions, achieving improved structural and perceptual fidelity on held-out scenes. deSEO therefore provides the first reproducible, geometry-aware paired dataset and baseline for shadow removal in satellite Earth observation.

Adapting pre-trained deep learning segmentation models to new clinical domains is a persistent challenge in medical image analysis, particularly when annotated data at the target site are scarce. Parameter-efficient fine-tuning strategies offer a principled solution by selectively updating a controlled subset of model parameters, preserving previously acquired representations while reducing the risk of overfitting on small datasets. This paper introduces DAfNe TrainEr (Dante), an open-source module integrating with the Dafne federated segmentation ecosystem as a dedicated training and fine-tuning backend. Dante supports training from scratch with automatic architecture configuration, configurable layer freezing schedules, and Low-Rank Adaptation (LoRA) extended to N-dimensional convolutional layers through channel-wise factorization. To validate the module, Gradual Unfreezing (GU) and LoRA are assessed across realistic cross-domain MRI transfer scenarios covering abdominal organ segmentation and brain white matter lesion segmentation, under full-data and few-shot conditions. GU reduced the epochs required to reach 85% of peak performance by up to 63.6% compared to training from scratch, while LoRA achieved Dice Similarity Coefficients up to 0.957 in data-rich scenarios. Both strategies outperformed the baseline across all tested domains, with gains amplified by richer pre-training datasets. These results validate Dante as a domain-agnostic fine-tuning module for medical image segmentation in real clinical deployment conditions. Dante code is available at https://github.com/dafne-imaging/dafne-torch-trainer while Dafne ecosystem project is available at https://github.com/dafne-imaging.

We provide certificates for almost sure reachability of continuous-time stochastic systems governed by stochastic differential equations (SDEs). We first show that a standard Euler-Maruyama discretization may fail to preserve almost sure reachability property of the system using a double-well Langevin system. This observation motivates us to develop certificates for almost sure reachability directly on the continuous-time system. We introduce a pair of certificates, a drift function and a variant function, and prove necessity and sufficiency for almost sure reachability of an open bounded target set. Using these certificates, for linear SDEs, we give a characterization of almost sure reachability in terms of the spectral structure of the system matrices. For polynomial SDEs, we fix a polynomial template for the drift function and choose the variant function template as an exponential function composed with a polynomial. This allows us to translate the conditions in the certificates into sum-of-squares (SOS) constraints. We then propose an alternating scheme to resolve bilinearities. We illustrate the approach on the double-well Langevin example, showing that continuous-time SOS certificates recover almost sure reachability that is lost under time discretization. Moreover, we verify the SOS approach on a polynomial system.

Industrial Internet of Things (IIoT) networks demand reliable anomaly detection under harsh wireless conditions, yet most detectors fail on four fronts: hostile fading, stealthy non-Gaussian faults, discarded spatial structure, or constrained edge hardware. We propose Graph WPT+HOS, a classical label-free detector that fuses three complementary views: the Graph Fourier Transform (GFT) for spatial inconsistency, the Wavelet Packet Transform (WPT) for transient time-frequency localization, and Higher-Order Statistics (HOS) for non-Gaussian shape. The fused features are scored by a Mahalanobis distance with Ledoit-Wolf shrinkage and converted to alarms by a one-sided CUSUM. The pipeline is asymptotically optimal at the decision stage, requires no labeled anomalies, and runs on ARM-class edge hardware without GPU acceleration. Across six baselines and four domain-shift regimes under Rayleigh fading, Graph WPT+HOS attains the highest ROC-AUC and PR-AUC and reduces CUSUM detection latency.

Dynamic Metasurface Antennas (DMAs) constitute a promising solution for extremely large antenna arrays, requiring lower power consumption and reduced hardware cost as compared to conventional phased arrays. In this paper, we consider a cell-free Orthogonal Frequency Division Multiplexing (OFDM) system comprising multiple Base Stations (BSs) equipped with parallel-plate-waveguided DMAs, which aims to serve multiple users in the downlink direction. Focusing on a realistic frequency-selective model for the response-tunable elements of each DMA panel, and targeting to surpass the necessity of centralized designs that rely on a central processing unit with high computational power, we present a distributed optimization framework with minimal control information exchange for the frequency-selective analog and digital beamforming matrices of the multiple BSs, having the system spectral efficiency maximization as the design objective. Considering imperfect Channel State Information (CSI) availability at each BS, we devise a parallel decomposition framework for the configuration of the tunable parameters of each DMA-based BS. Our numerical results showcase the robustness of the proposed distributed beamforming design over different CSI conditions, and quantify the critical role of taking into account mutual coupling during the DMA design process.

This paper presents a novel physically consistent analytical model for two-dimensional (2D) waveguide-fed metasurface antennas that is based on the discrete dipole approximation. The proposed framework extends previous works deriving power-consistent constraints on the magnetic polarizability tensor, leading to closed-form expressions for the effective polarizabilities. The model is validated through full-wave simulations for multi-feed settings, and is extended to a near-field compatible~formulation enabling accurate predictions in the radiating near-field.

The operation of the pyro process in cement production significantly affects the energy efficiency and sustainability of the cement plant, especially for reductions in carbon dioxide emissions. Hence, pyro process control is essential to obtain efficient and sustainable operation of cement plants. In this paper, we demonstrate how simulations and models can be utilized to evaluate and design control strategies for the pyro section in cement plants. We apply a novel differential algebraic equation (DAE) model for dynamic simulation of the pyro-section in cement plants to design decentralized PI controllers for the pyro-section. We utilize the pyro-process model to evaluate the control structure design. Through linearization of the pyro-process model, we apply the Relative Gain Array (RGA) method to choose and evaluate the pairings of the manipulated variables (MVs) and the controlled variables (CVs). Using simulations of the pyro-section, we generate step responses to estimate transfer models and apply Internal Model Control (IMC) for the tuning of the individual decentralized single-input single-output (SISO) PI controllers. Closed-loop simulations of the PI controllers demonstrate that PI controllers with IMC parameters provide smoother and faster responses compared with manually tuned PI parameters.

Two-dimensional (2D) waveguide-fed metasurfaces enable scalable antenna apertures through guided wave excitation of distributed radiating elements. However, the resulting non-uniform excitation challenges classical interpretations of near-field characteristics. Using a physics-compliant model, this paper analyzes the near-field beam focusing behavior of such architectures. We derive asymptotic scaling laws for the beamforming gain, showcasing that the power-normalized gain scales linearly with the number of radiating elements. Furthermore, we introduce a normalized beam-depth formulation and obtain a compact analytic expression that characterizes the transition to far-field-like behavior. The presented analysis is validated against simulations based on the full electromagnetic model, confirming the accuracy of the derived scaling laws and beam-depth limits.

The paper addresses the critical problem of identifying unknown parameters of an atomic clock ensemble. The ensemble model is considered as a set of individual clock models, where each clock is described by a second-order linear stochastic state-space model. The paper presents identification procedure for model unknown parameters based solely on the availability of differential measurements - that is, the measured pairwise phase differences between a designated pivot clock and all other clocks within the ensemble. Specifically, each clock model is defined by the following set of unknown parameters: the variances characterizing the white frequency noise and random walk frequency noise, the drift, and the (co)variance of the measurement noise. Two distinct identification methods are designed to estimate the unknown clock model parameters. The accuracy of the identified sets of parameters are demonstrated on a simulation scenario/real data combining atomic H-maser (AHM) clocks.

Movable antennas (MAs) have attracted significant attention in wireless communications due to their ability to reconfigure channel conditions by flexibly adjusting the antenna positions within a confined region. However, MA movement generally incurs a non-negligible delay, which may significantly limit the data transmission time at optimized positions. To tackle this challenge, this paper investigates a new joint communication and trajectory optimization problem, where each MA transmits while moving along an optimized trajectory to prolong the effective data transmission time. Focusing on a single-MA system, our goal is to maximize the average data rate by optimizing the MA's positions over time, subject to its maximum velocity constraints. However, this continuous-time antenna position optimization problem is highly non-convex and challenging to solve. To tackle this challenge, we first consider a special case with two channel paths and derive the optimal MA trajectory in closed form. For other general cases, we ingeniously reformulate the average rate maximization problem into a fixed-hop shortest path problem in graph theory by sampling the antenna movement region into a multitude of discrete points, and solve it optimally. Simulation results demonstrate that our proposed algorithm can significantly improve the data rate compared to other baseline schemes.

This paper proposes a deep learning based method for wideband near-field multi-user localization. In particular, the proposed approach utilizes the Zadoff-Chu (ZC) sequence based pilots to mitigate the inter-user interference, which in turn aids the estimation of the multi-tap channel matrix. From this channel matrix, we extract the line-of-sight (LoS) array response based on the delay-tap energy profile. The LoS delay-tap is further refined using parabolic interpolation to obtain the coarse estimate of range parameter. Next, the extracted LoS array response is used to obtain the coarse angle estimate using 2D MUSIC algorithm. These coarse estimates are further refined using the near-field music refinement network (NFMR-Net), which involves separate sub-networks for range and angle estimations. Through numerical analysis, the proposed NFMR-Net is demonstrated to outperform conventional 2D MUSIC algorithm.

Pumped-storage hydropower scheduling couples discrete operating-mode transitions with continuous dispatch decisions through intertemporal reservoir and ramping constraints. Existing convexification methods do not simultaneously accommodate multiple operating modes, persistent continuous reservoir and ramping states, and interval costs that depend on both incoming and outgoing states of the operating modes. We develop an event-based dynamic programming formulation in which mode transitions define variable-length events and mode-specific linear programs optimize continuous within-event operation. We prove that this formulation is equivalent to the conventional time-indexed mixed-integer linear program. Enumerating the finite set of discrete event paths and scaling their boundary-coupled polyhedra then yields an exact finite-dimensional convex-hull LP without state discretization. For tractability, we develop a finite-grid formulation that discretizes reservoir and ramping states only at event boundaries while retaining continuous within-event trajectories. Its feasible set contains that of the corresponding stagewise discretization. Moreover, the resulting LP provides an exact convex-hull representation of the grid-restricted model and admits an error bound linear in the grid resolutions. An event-based branch-and-bound method provides an independent computational verification of the continuous-state reformulation. Standalone experiments verify the theoretical results, while tests on a modified IEEE 24-bus Reliability Test System show that the finite-grid LP substantially reduces computation time with a negligible increase in system cost.

In this work, we propose a hybrid hierarchical control framework for reactive dexterous grasping that explicitly decouples high-level spatial intent from low-level joint execution. We introduce a multi-agent reinforcement learning architecture, specialized into distinct arm and hand agents, that acts as a high-level planner by generating desired task-space velocity commands. These commands are then processed by a GPU-parallelized quadratic programming controller, which translates them into feasible joint velocities while strictly enforcing kinematic limits and collision avoidance. This structural isolation not only accelerates training convergence but also strictly enforces hardware safety. Furthermore, the architecture unlocks zero-shot steerability, allowing system operators to dynamically adjust safety margins and avoid dynamic obstacles without retraining the policy. We extensively validate the proposed framework through a rigorous simulation-to-reality pipeline. Real-world hardware experiments on a 7-DoF arm equipped with a 20-DoF anthropomorphic hand demonstrate highly robust zero-shot transferability for dexterous grasping to a diverse set of unseen objects, highlighting the system's ability to reactively recover from unexpected physical disturbances in unstructured environments.

Integrated sensing and communication (ISAC) has emerged as a key technology for 6G wireless networks. In this paper, wireless sensing for the indoor multi-person tracking is explored with 6G mmWave ISAC systems. To limit the sensing overhead, a sparse deployment of sensing reference signals (RS) is applied in the orthogonal frequency-division multiplexing (OFDM) frame, where the channel state information (CSI) at the sensing RS is extracted for the multi-person tracking. To enable a robust tracking of multiple persons in a complex indoor environment, three key mechanisms are proposed: 1) a modified moving target indicator (MTI) scheme is proposed to remove the static environmental clutter under a sparse RS time spacing; 2) an effective target identification mechanism is developed to exclude false target points; 3) the Kalman filter with a penalty association algorithm is designed to associate the detected points with the right tracks, especially handling the crossover case of two tracks. With the above mechanisms, multiple persons can be effectively tracked with an extremely low sensing overhead. An mmWave bistatic ISAC prototype system at 26 GHz with 500 MHz bandwidth has been developed to validate our design, where the overhead of the sensing RS is less than 0.005%. Experimental results demonstrate that our proposed design achieves a median position error of 12 cm for multi-person tracking with path-crossing in the indoor environment with a single receiver.

The Six-Dimensional Movable Antenna (6DMA) system has emerged as a promising technology to enhance wireless capacity by fully exploiting spatial degrees of freedom. However, applying 6DMA to high-mobility Internet of Vehicles (IoV) scenarios faces significant challenges, primarily due to the difficulty of acquiring instantaneous Channel State Information (CSI) and the risk of service interruptions caused by mechanical reconfiguration delays. To address these issues, this paper proposes a low-complexity, CSI-free single-step reconfiguration framework. First, we design a deterministic discrete position generation scheme based on a latitude-longitude grid with inherent topological structures. Leveraging graph theory, we explicitly model and theoretically derive the lower bounds of movement and time costs for antenna reconfiguration. Subsequently, utilizing the directional sparsity of 6DMA channels, we develop an adaptive optimization strategy that fuses offline environmental priors with online historical feedback. Furthermore, a periodic reconfiguration mechanism based on predicted cumulative vehicle distributions is introduced. By strictly restricting antenna adjustments to the first-order spatial neighborhood, the proposed single-step method effectively eliminates service interruptions. Simulation results demonstrate that the proposed scheme significantly outperforms traditional fixed and global-search-based benchmarks in terms of uplink sum rate, while incurring negligible mechanical overhead and latency, thereby validating its feasibility and robustness in highly dynamic vehicular networks.

The European Union's Carbon Border Adjustment Mechanism (CBAM) creates a complex challenge for the interconnected European electricity market. Traditional static analyses often miss the cross-border spillover effects that are vital for understanding this policy. This paper addresses this gap by developing a spatio-temporal Graph Neural Network (GNN) framework. It quantifies how CBAM affects electricity prices and carbon intensity (CI) at the same time. We modeled a subgraph of eight European countries. Our results suggest that CBAM is not just a uniform tax. Instead, it acts as a tool that transforms the market and creates structural differences. In our simulated scenarios, we observe that low-carbon countries like France and Switzerland can gain a competitive advantage. This suggests a potential decrease in their domestic electricity prices. Meanwhile, high-carbon countries like Poland face a double burden of rising costs. We identify the primary driver as a fundamental shift in the market's merit order.

Domain randomization (DR) is widely used in policy learning to improve robustness to modeling error, but remains underexplored in contact-rich sampling-based predictive control (SPC), where rollout quality is highly sensitive to uncertainty. In this work, we take the first step by studying risk-aware DR in predictive sampling on a simple yet representative Push-T task, comparing average, optimistic, and pessimistic rollout aggregations under randomized model instances. Our initial results suggest that DR affects not only robustness to model error, but also the effective cost landscape seen by the sampling-based optimizer, by reshaping the basin of attraction around contact-producing actions. This opens up potential for exploring better grounded risk-aware contact-rich SPC under model uncertainty. Video: https://youtu.be/f1F0ALXxhSM

This letter studies an uplink over-the-air computation (AirComp) framework in which multiple user equipments are equipped with fluid-antenna (FA) arrays and operate over spatially correlated fading channels. By explicitly modeling channel dependence using the Gumbel copula, closed-form analytical expressions are derived for the cumulative distribution function (CDF) of the mean-squared error (MSE) of the aggregated function. The proposed analysis provides a quantitative performance characterization of AirComp under spatial correlation and provides analytical insights into the role of FA-assisted transmission in correlated wireless environments. Numerical results validate the derived expressions and show that FA deployment can substantially reduce the MSE compared with conventional fixed-antenna systems, although the achievable gain decreases as spatial correlation becomes stronger.

This paper extends the Model Predictive Static Programming (MPSP) framework for nonlinear systems evolving on Euclidean spaces to simple mechanical systems evolving on Lie groups. Classical optimal control approaches based on Pontryagin's Maximum Principle (PMP) lead to nonlinear two-point boundary value problems (TPBVPs), whose numerical solution becomes particularly challenging on nonlinear configuration spaces. To overcome this difficulty, the proposed Lie-group MPSP framework reformulates the finite-horizon optimal control problem as a sequence of static quadratic programs that admit closed-form control updates, thereby avoiding the need to solve TPBVPs directly. The development relies on left-trivialized variations, intrinsic linearization on Lie groups, and a recursive computation of terminal sensitivity matrices, which together enable computationally efficient real-time implementation. The proposed method is demonstrated through optimal flipping maneuvers of a variable-pitch quadrotor (VPQ) and a single-main-rotor helicopter (SMRH), both of which are capable of generating negative thrust. For validation, continuous-time necessary and sufficient optimality conditions are derived, and the corresponding TPBVP solutions are compared against the trajectories generated by the proposed MPSP method in numerical simulations. In addition, the proposed algorithm is systematically compared with the iterative Linear Quadratic Regulator (iLQR) method, and a detailed numerical study is presented to highlight the relative performance and computational features of the two approaches.

Recent advances in generative speech have increased the need for automatic detection of obviously failed synthetic outputs. This is particularly important in clinical settings such as AVATAR therapy, in which schizophrenia patients engage with a computer-generated representation of their hallucinated voices and degraded synthesis may disrupt immersion and therapeutic engagement. We investigate whether low-dimensional, interpretable source-output acoustic features can provide a lightweight first-pass detector of degraded voice-cloning outputs. Motivated by source-filter models of speech, we first test median fundamental frequency (f0) as a source-related consistency measure, and compare it with vocal tract length (VTL) as a filter-related measure and Harmonics-to-Noise Ratio (HNR) as a noise-related descriptor. Human-labeled voice-cloning samples generated with two vocoder families, WaveRNN (n=54) and HiFi-GAN (n=40), were evaluated using an asymmetric thresholding procedure in the input-output feature space. For WaveRNN, f0 and HNR both achieved 85.2% accuracy, outperforming VTL (64.8%). For HiFi-GAN, HNR achieved 80.0% accuracy, followed by f0 at 77.5% and VTL at 67.5%. Sample-level overlap and spectrographic inspection showed that f0 and HNR capture partly distinct failure patterns, rather than providing redundant rankings of the same samples. These results show that simple source-output acoustic consistency measures can provide useful first-pass detection of degraded voice clones, and support the use of interpretable threshold-based screening in applications where failed synthetic speech must be rejected quickly.

Time-resolved facade pressure fields are essential for the wind-resistant design and aerodynamic assessment of high-rise buildings. However, dense instrumentation is costly and often impractical, and sensor outages can further reduce data availability. This study proposes a sensor-outage-aware spatio-temporal graph reconstruction framework for completing facade pressure fields from sparse measurements. The method couples temporal feature extraction with graph-based spatial propagation on a unified facade-domain representation and uses an explicit observation-availability indicator to handle temporarily unavailable sensor signals while reconstructing both missing instrumented channels and non-instrumented locations. The framework is evaluated using wind-tunnel pressure coefficient data for a high-rise building across windward, lateral, and leeward facades under multiple wind directions. The results show reliable outage-tolerant reconstruction at instrumented sensors and accurate full-field completion at non-instrumented nodes, with reconstruction generally most accurate on the windward facade and more challenging on the lateral and leeward facades. Time-domain, spectral, and spatial validations further show that the framework preserves the dominant temporal evolution, principal dynamic content, and coherent large-scale pressure-field organization, while the largest residual discrepancies remain localized in higher-frequency or intermittent components. A two-stage predictive extension is also outlined, in which future sensor signals are forecast at available instrumented locations and then mapped to future full-field pressure estimates through the proposed reconstruction model.

Purpose: Access to electroencephalography (EEG) remains limited across low- and middle-income countries (LMICs) due to cost, infrastructure requirements, and a shortage of trained staff. This study evaluated the feasibility and clinical utility of a smartphone-based EEG system in a real-world setting. Methods: We conducted a multicenter observational study (November 2023 to April 2026) across 29 clinical sites in Kenya. A smartphone-based 27-lead EEG system enabled trained healthcare workers to acquire standardized recordings with remote expert interpretation. Results: 3,036 EEG sessions were performed. Male patients constituted 57.8% of the cohort, with representation across pediatric and adult populations. The most common referral indication was seizures or convulsions (68.5%). Overall, 2,915 (96%) recordings were interpretable, while 121 (4%) were uninterpretable, primarily due to high electrode impedance and insufficient recording duration. Uninterpretable recordings were significantly shorter than interpretable recordings (mean 18.5 vs. 33.8 minutes; median 15.1 vs. 31.6 minutes; p < 0.0001). Mean turnaround time for interpretation was 107 minutes. Among interpretable recordings, 917 (30.2%) were abnormal, including 701 (76.4%) with epileptiform abnormalities, 215 (23.4%) with non-epileptiform findings, and 1 (0.1%) indeterminate finding. Epileptiform abnormalities were highest in children aged 4-9 years (33.1%) and less frequent in adults (14-21%). Non-epileptiform abnormalities were more common in patients aged 60+ years (19.2%) compared to younger age groups (3-9%). Conclusion: Large-scale, point-of-care EEG acquisition by non-specialist operators in a resource-limited setting is feasible. Expansion of smartphone-based EEG systems may improve equitable access to neurological diagnosis and care in LMICs.

Electrodermal activity (EDA) is widely used in wearable Internet of Medical Things (IoMT) systems for continuous health monitoring, including autonomic assessment. However, EDA signals are highly vulnerable to motion artifacts and environmental noise, limiting reliable deployment in harsh operating conditions such as underwater. This study proposes a robust, deployable EDA denoising framework that generalizes across multiple measurement locations and harsh environments. The framework integrates a hybrid CNN-Transformer teacher model with a lightweight depth-wise separable CNN student model via a knowledge distillation (KD) strategy. To further improve robustness, a realistic data augmentation scheme is introduced to simulate diverse motion artifacts and environmental distortions. The KD-based student model significantly reduces model size (7.87 MB to 0.51 MB) and computational cost (105.1M to 11.61M FLOPs) while maintaining denoising performance (MAE: 0.144, SNR improvement: 12.08 dB) using the public dataset validation. In real-world underwater conditions (UMAC dataset) testing, the proposed method substantially improves skin conductance response reconstruction, reducing mean absolute error from 2.809 to 0.215. Furthermore, on independent testing using the CNS-OT dataset, the denoised signals enhanced downstream CNS-OT prediction performance, achieving the highest AUROC (0.806) compared to prior denoising methods. The proposed method also improved the early prediction rate (sensitivity) from 0.550 to 0.767, enabling CNS-OT prediction up to a median of 6.9 minutes before symptom onset. These results demonstrate that the proposed framework not only improves EDA signal quality but also enhances clinically relevant prediction performance while remaining suitable for deployment in resource-constrained wearable Internet of Things systems operating in harsh environments.

Resource allocation in the multiple-input multiple-output (MIMO) multiple access channel (MAC) is a fundamental problem in multiuser communications, yet it is increasingly treated as non-convex and computationally intractable. This has motivated a large body of heuristic machine learning and successive-approximation methods. Results here show that the MIMO MAC admits canonical convex formulations and present four solvers that together characterize its capacity region. maxRMAC performs weighted sum-rate maximization under per-user energy constraints, minPMAC finds the minimum weighted energy required to support target rates, maxRESMAC performs weighted sum-rate maximization under a total energy constraint, and admMAC tests rate-region feasibility. The solvers exploit the polymatroid structure of the MAC rate region and the separability of the dual Lagrangian across frequency tones, which reduces the problem to parallel per-tone covariance optimizations solved via limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) over Cholesky-like covariance factors. Experiments on spatially correlated MIMO orthogonal frequency-division multiplexing (OFDM) channels show that the proposed solvers match a commercial convex solver in solution quality while running up to two orders of magnitude faster and scaling to regimes where the commercial solver times out. Through broadcast channel (BC) to MAC duality, the same solvers also enable optimal precoder design for the MIMO BC. All solvers are open-sourced and available at https://github.com/muhd-umer/canonical-mac.

Accurate electromagnetic field (EMF) exposure mapping is critical for wireless network planning, environmental monitoring, and the deployment of next generation communication systems. The mapping results can be converted into the form of a radio map, a key technology in digital twin communication systems, used to describe the wireless signal propagation characteristics at every location in a specific area. Existing deep learning approaches treat propagation estimation as a pure regression problem and do not enforce physical consistency in the predicted fields. In this paper, we propose Phy2-ExposNet, a novel neural network framework that decouples exposure mapping into a physics-informed estimation stage and a transformer-based residual refinement stage. It first estimates the fields under two physical constraints and then refines the resulting exposure map by capturing long range interactions and complex spatial propagation patterns. Experiments demonstrate that the proposed method achieves lower estimation error while significantly reducing model complexity compared to existing approaches. It achieves around 15% relative error reduction over strong baselines, while using over 80% fewer parameters than conventional physics-informed models. Ablation results further reveal that the physics-informed design is crucial for capturing complex propagation effects, particularly in boundary and shadow regions.

Evaluating canine electrocardiograms (ECGs) is challenging due to noise that can obscure clinically relevant cardiac electrical activity. Common sources of interference include respiration, muscle activity, poor lead contact, and external electrical artifacts. Classical signal denoising techniques, such as filtering and wavelet-based methods, struggle to suppress diverse noise patterns while preserving morphological features critical for accurate ECG delineation. We propose an autoencoder-based neural network model and training strategy for ECG denoising as a preprocessing step for canine ECG analysis. The model is trained to reconstruct clean cardiac signals from noisy inputs, enabling effective noise reduction without degrading diagnostically important waveforms. Our approach demonstrates strong performance across both noisy and clean ECG recordings, indicating robustness to varying signal conditions and suitability for downstream delineation tasks.

This paper develops a guidance law for nonlinear interception using input-output feedback linearization (IOL). The engagement between a pursuer and an evader is modeled using point-mass dynamics, and a baseline IOL-based guidance law is constructed by regulating the angular rates of the line-of-sight (LOS) vector. While this approach yields stable input-output behavior, it does not constrain the internal (zero) dynamics of the system, which can result in non-intercepting trajectories despite successful regulation of the LOS rates. To address this limitation, a modified IOL-based guidance law is proposed that incorporates a correction mechanism to enforce convergence of the range. The resulting formulation ensures that LOS alignment corresponds to a closing trajectory, thereby enabling convergence of the pursuer to the evader for a broad class of initial engagement geometries. The proposed method retains the computational simplicity and real-time implementability of feedback linearization while improving closed-loop performance relative to classical guidance laws. Extensive Monte Carlo simulations over a wide range of initial conditions are conducted to evaluate the proposed method. The results demonstrate improved reliability, reduced miss distance, and consistent convergence compared to the baseline IOL and classical proportional navigation.

Minute-scale photovoltaic (PV) ramps caused by rapidly evolving cloud occlusion pose challenges to short-timescale power balancing and require reliable advance warning. This paper develops an event-oriented ultra-short-term PV forecasting framework that predicts future cloud evolution as an intermediate representation linking sky observations to PV output. PhyDiffNet combines physics-informed cloud-motion modeling with generative video prediction, while RaPVFormer integrates observed and predicted sky states, sun-location information, and historical PV measurements to forecast PV trajectories and ramp events up to 16 minutes ahead. Evaluation on the SKIPP'D dataset shows that the proposed framework achieves a Critical Success Index of 96.2% for ramps beginning 1–4 minutes ahead and retains 84.9% for those beginning 13–16 minutes ahead, outperforming all deployable baselines across the evaluated lead-time intervals. At a validation-calibrated 95% reliability target, it reduces the required fast upward reserve margin by 34.2% relative to the Persistence Model and by 11.6% relative to the strongest deployable baseline under the adopted reserve metric. These results show that explicit future-cloud prediction improves advance warning of PV ramps and translates forecasting gains into lower short-timescale balancing margins under a common reliability target.

In cinematography, visual attributes such as color grading, contrast, and brightness are manipulated to reinforce the emotional narrative of a scene. However, conventional Image Signal Processors (ISPs) prioritize scene fidelity, effectively neglecting this expressive dimension. To bring this cinematic capability to real-time camera pipelines during video capture, we introduce EMOVIS (EMotion-Optimized VISual processing). We establish a systematic mapping between a compact set of high-level emotional states (Happy, Calm, Angry, Sad) and low-level ISP controls - including color saturation, local tone mapping, and sharpness - supported by a calibration user study with statistically significant effects across parameters. We propose a control framework that integrates these emotion-driven adjustments into standard ISP hardware without altering the underlying processing stages. Validation via blind A/B testing shows that viewers prefer the emotion-optimized rendering in 87% of trials when the target emotion matches the scene context, indicating that emotion-aligned ISP control improves perceived suitability for expressive visual content.

This paper proposes a pilot-aware, degeneracy-driven Agent-Based Modelling (ABM) framework for distributed resource allocation in RSMA-enabled multi-user MIMO systems under imperfect Channel State Information (CSI) and residual Successive Interference Cancellation (SIC) error. The centralized RSMA power allocation problem is reformulated as a distributed multi-agent system, where users operate as autonomous agents that iteratively adapt transmit powers based on locally observed feasibility conditions. To capture the joint impact of interference coupling, CSI estimation errors, pilot overhead, and residual SIC error, a novel degeneracy index defined as the ratio of target to achieved signal-to-interference-plus-noise ratio (SINR) is introduced as a unified feasibility metric. This enables a scalable fixed-point power control mechanism that characterizes the feasible operating region without requiring global CSI. Analytical expressions for user-level and system-level outage probabilities are derived under spatially correlated fading, providing insights into reliability under practical impairments. The fundamental interplay between degeneracy, outage probability, and effective throughput is established, revealing that system performance is governed by the feasibility of the bottleneck user. To further enhance resilience, Degeneracy-Weighted Path Robustness (DWPR) and Functional Substitution Score (FSS) are incorporated to exploit path diversity and functional redundancy. Numerical results show that the proposed framework achieves near-centralized performance in sparse networks, while providing notable throughput gains and improved scalability in dense deployments, highlighting its effectiveness for robust and distributed resource management in next-generation wireless systems.