Papers

Underwater flow sensing is critical for unmanned underwater vehicles (UUVs) and environmental monitoring, yet existing sensors often suffer from low responsiveness, high detection thresholds, limited directional discrimination, complex packaging, and poor long-term stability, especially for navigation and target perception in turbid and cluttered waters. Previous solutions based on traditional strain gauges with limited detection accuracy or doped silicon sensors with limited detection height have shown feasibility but still face challenges in scalability, robustness under harsh aquatic conditions, and calibration complexity. This work presents a bio-inspired whisker sensor that provides a balanced solution by embedding high-gauge-factor silicon strain gauges into a flexible PDMS base, mimicking seal whiskers to offer both high sensitivity and simplified packaging. The device exhibits a linear force-resistance response with a limit of detection of 0.27 mN, maintains stability after 10,000 loading cycles, and shows minimal offset drift of less than 2 percent. It also demonstrates frequency matching in underwater dipole tests with clear longitudinal and transverse spatial response patterns. These results indicate a robust and scalable route for underwater flow sensing on UUV platforms in practical deployments.

Power Flow (PF) calculation based on machine learning (ML) techniques offer significant computational advantages over traditional numerical methods but often struggle to maintain full physical consistency. This paper introduces a physics-informed test-time training (PI-TTT) framework that enhances the accuracy and feasibility of ML-based PF surrogates by enforcing AC power flow equalities and operational constraints directly at inference time. The proposed method performs a lightweight self-supervised refinement of the surrogate outputs through few gradient-based updates, enabling local adaptation to unseen operating conditions without requiring labeled data. Extensive experiments on the IEEE 14-, 118-, and 300-bus systems and the PEGASE 1354-bus network show that PI-TTT reduces power flow residuals and operational constraint violations by one to two orders of magnitude compared with purely ML-based models, while preserving their computational advantage. The results demonstrate that PI-TTT provides fast, accurate, and physically reliable predictions, representing a promising direction for scalable and physics-consistent learning in power system analysis.

Nonholonomic constraints restrict feasible velocities without reducing configuration-space dimension, which makes collision-free geometric paths generally non-executable for car-like robots. Ackermann steering further imposes curvature bounds and forbids in-place rotation, so escaping from narrow dead ends typically requires tightly sequenced forward and reverse maneuvers. Classical planners that decouple global search and local steering struggle in these settings because narrow passages occupy low-measure regions and nonholonomic reachability shrinks the set of valid connections, which degrades sampling efficiency and increases sensitivity to clearances. We study nonholonomic narrow dead-end escape for Ackermann vehicles and contribute three components. First, we construct a generator that samples multi-phase forward-reverse trajectories compatible with Ackermann kinematics and inflates their envelopes to synthesize families of narrow dead ends that are guaranteed to admit at least one feasible escape. Second, we construct a training environment that enforces kinematic constraints and train a policy using the soft actor-critic algorithm. Third, we evaluate against representative classical planners that combine global search with nonholonomic steering. Across parameterized dead-end families, the learned policy solves a larger fraction of instances, reduces maneuver count, and maintains comparable path length and planning time while under the same sensing and control limits. We provide our project as open source at https://github.com/gitagitty/cisDRL-RobotNav.git

In this paper, we consider a flexible-antenna architecture, referred to as a pinching-antenna (PA) system, in which multiple PAs realized by activating small dielectric particles along a dielectric waveguide are jointly employed to serve a single-antenna user. We investigate antenna placement and power allocation optimization in PA-assisted non-orthogonal multiple access (NOMA) systems using a convolutional neural network (CNN). An optimization strategy is developed to determine the PA locations that maximize achievable NOMA performance while satisfying physical and spatial constraints. The proposed method adopts a two-stage structure, combining a user-aware initialization with a gradient-based refinement, enabling near-optimal performance with significantly lower computational cost. A max-min fairness formulation is introduced for power allocation to balance the power budget among users with varying channel strengths, solved efficiently via quasi-linear programming and bisection search. Finally, a CNN-based learning framework is employed to capture the nonlinear mapping between channel conditions and the corresponding optimal power coefficients. This framework can infer near-optimal power allocations for unseen network configurations without retraining, offering scalability and adaptability. Simulation results show that the proposed CNN-based NOMA approach for PA systems improves sum rate and user fairness while reducing computational complexity.

Video-on-demand streaming has benefitted from content-adaptive encoding (CAE), i.e., adaptation of resolution and/or quantization parameters for each scene based on convex hull optimization. However, CAE is very challenging to develop and deploy for interactive game streaming (IGS). Commercial IGS services impose ultra-low latency encoding with no lookahead or buffering, and have extremely tight compute constraints for any CAE algorithm execution. We propose the first CAE approach for resolution adaptation in IGS based on compact encoding metadata from past frames. Specifically, we train a convolutional neural network (CNN) to infer the best resolution from the options available for the upcoming scene based on a running window of aggregated coding block statistics from the current scene. By deploying the trained CNN within a practical IGS setup based on HEVC encoding, our proposal: (i) improves over the default fixed-resolution ladder of HEVC by 2.3 Bjøntegaard Delta-VMAF points; (ii) infers using 1ms of a single CPU core per scene, thereby having no latency overhead.

Recent advances in diffusion models have positioned them as powerful generative frameworks for speech synthesis, demonstrating substantial improvements in audio quality and stability. Nevertheless, their effectiveness in vocoders conditioned on mel spectrograms remains constrained, particularly when the conditioning diverges from the training distribution. The recently proposed GLA-Grad model introduced a phase-aware extension to the WaveGrad vocoder that integrated the Griffin-Lim algorithm (GLA) into the reverse process to reduce inconsistencies between generated signals and conditioning mel spectrogram. In this paper, we further improve GLA-Grad through an innovative choice in how to apply the correction. Particularly, we compute the correction term only once, with a single application of GLA, to accelerate the generation process. Experimental results demonstrate that our method consistently outperforms the baseline models, particularly in out-of-domain scenarios.

Estimating 3D geometry from monocular colonoscopy images is challenging due to non-Lambertian surfaces, moving light sources, and large textureless regions. While recent 3D geometric foundation models eliminate the need for multi-stage pipelines, their performance deteriorates in clinical scenes. These models are primarily trained on natural scene datasets and struggle with specularity and homogeneous textures typical in colonoscopy, leading to inaccurate geometry estimation. In this paper, we present ColonAdapter, a self-supervised fine-tuning framework that adapts geometric foundation models for colonoscopy geometry estimation. Our method leverages pretrained geometric priors while tailoring them to clinical data. To improve performance in low-texture regions and ensure scale consistency, we introduce a Detail Restoration Module (DRM) and a geometry consistency loss. Furthermore, a confidence-weighted photometric loss enhances training stability in clinical environments. Experiments on both synthetic and real datasets demonstrate that our approach achieves state-of-the-art performance in camera pose estimation, monocular depth prediction, and dense 3D point map reconstruction, without requiring ground-truth intrinsic parameters.

This paper addresses the joint scheduling problem of stochastic workloads and a hydrogen-enabled distributed energy system in a low-carbon Internet data centers (IDC). Although such workloads can be shifted over temporal and spatial horizons, it poses challenges when they cannot be accurately predicted, resulting in significant efficiency degradation and high operational cost of the energy system. The problem becomes even more difficult when the workload shifting decisions would influence their randomness, which is natural for the IDC workloads. To tackle these issues, we propose a workload classification model based on the decision-dependent uncertainty set, where the spatiotemporal elasticity of different types of random workloads are clearly identified and the decision dependencies are explicitly described as linear constraints. Thus, a mixed integer program is then established for the optimal scheduling of both workload demand and energy supply. To enhance system resilience against high volatility scenarios, a rolling horizon algorithm is developed to ensure nonanticipativity and full-scenario feasibility. Numerical tests demonstrate that the proposed method exhibits effective workload scheduling decisions with dramatic energy operational cost reductions compared to the benchmarks under most of the uncertain scenarios.

This paper investigates the rate-energy trade-off for pinching-antenna systems (PASS)-assisted simultaneous wireless information and power transfer (SWIPT) systems. Both the single information user (IU)/energy user (EU) and multiple IUs/EUs scenarios are considered.1) For the single IU/EU scenario, a pinching beamforming optimization problem is formulated for simultaneously maximizing data rate and harvested energy. To tackle this problem, a two-stage algorithm is proposed. Specifically, the successive convex approximation (SCA) method is first invoked for minimizing the large-scale path loss, which is followed by the fine-tuning method for the phase alignment. 2) For the multiple IUs/EUs scenario, three multiple access schemes are considered, i.e., frequency division multiple access (FDMA), time division multiple access (TDMA), and non-orthogonal multiple access (NOMA). The corresponding multi-objective optimization problem (MOOP) that simultaneously maximizes the minimum data rate and minimum harvested energy is formulated for ensuring users' fairness. To address this problem, we adopt the $\epsilon$-constraint method to first convert the intractable MOOPs to single-objective optimization problems (SOOPs). Then, for the SOOP under each multiple access protocol, the particle swarm optimization (PSO) and convex optimization methods are adopted for solving the pinching beamforming and resource allocation problems, respectively. Simulation results unveil that: i) PASS can achieve a significantly superior rate-energy region compared to conventional fixed-position antenna systems for pinching beamforming; and ii) by exploiting the time-switching feature, TDMA can outperform both NOMA and FDMA for the multiple IUs/EUs scenario.

Emerging sixth-generation wireless systems are increasingly heterogeneous, with compatibility across diverse configurations, ubiquitous coverage, and expanded functionalities. Although deep learning has substantially benefited wireless system design, existing approaches are typically trained for specific system settings and scenarios with limited generalizability. Here we present WiFo-2, a space-time-frequency foundation model for unified wireless communications and sensing system design. Pretrained on a heterogeneous dataset of 11.6 billion channel state information (CSI) points, WiFo-2 learns generalized wireless representations across scenarios, configurations, and tasks, and exhibits scaling-law behavior. WiFo-2 achieves reliable and accurate zero-shot channel reconstruction, outperforming fully supervised task-specific models. With only 1% of the training samples required by supervised AI models, WiFo-2 achieves state-of-the-art performance across 9 distinct wireless tasks. A functional hardware prototype further demonstrates its real-world deployability and superior capability across diverse wireless tasks. This work provides a versatile wireless design framework and advances understanding of wireless channels.

In modern radar systems, target detection and parameter estimation face significant challenges when confronted with mainlobe jamming. This paper presents a Diffusion-based Model and Data Dual-driven (DMDD) approach to estimate and detect multitargets and suppress structured jamming. In DMDD, the jamming prior is modeled through a score-based diffusion process with its score learned from the pure jamming data, enabling posterior sampling without requiring detailed knowledge of jamming. Meanwhile, the target signal is usually sparse in the range space, which can be modeled via a sparse Bayesian learning (SBL) framework, and hyperparameter is updated through the expectation-maximization (EM) algorithm. A single diffusion process is constructed for the jamming, while the state of targets are estimated through direct posterior inference, enhancing computational efficiency. The noise variance is also estimated through EM algorithm. Numerical experiments demonstrate the effectiveness of the proposed method in structured jamming scenarios. The proposed DMDD algorithm achieves superior target detection performance, compared with existing methods.

Coordinated optimization dispatch (COD) of transmission system operator (TSO) and distribution system operator (DSO) can effectively ensure system security and efficiency under high-penetration distributed energy resource (DER) integration. Researches of large-scale COD problem can be categorized into iterative approaches that allow DSO to dispatch independently, and non-iterative methods based on projections of feasible regions (FR). However, the iterative methods suffer from low computational convergence and efficiency, while non-iterative methods struggle to solve equivalent projections with high-dimensional FR. To address these issues, this paper proposes a TSO-DSO coordinated dispatch approach based on an accelerated non-iterative Equality Set Projection (ESP) algorithm. First, ESP algorithm is employed to overcome the bottleneck of high-dimensional FR construction. Second, an regularization-based accelerated method is proposed to reduce computational burden when degeneracy occurs. Accelerated ESP algorithm constructs projection of FR via adjacent facet searching. Therefore, it is less sensitive to the increase of vertices and could efficiently construct the projection of high-dimensional FR. Case studies on a polyhedron dataset, IEEE 33-Bus System and T118D10 TSO-DSO system demonstrate the effectiveness and computational efficiency of the proposed COD approach.

This note proposes a data-driven output-feedback stabilizing policy iteration for unknown linear discrete-time systems with unmeasurable states. Existing policy iteration methods for optimal control must start from a stabilizing control policy, which is particularly challenging to obtain for unknown systems, especially when states are unavailable. In such cases, it is more difficult to guarantee stability and convergence performance. To address this problem, an output-feedback stabilizing policy iteration framework is developed to learn closed-loop stabilizing control policies while ensuring convergence performance. Specifically, cumulative scalar parameters are introduced to compress the original system to a stable scale. Then, by integrating modified policy iteration with parameter update rules, the system is gradually amplified/restored to the original system while preserving stability such that the stabilizing control policy is obtained. The entire process is driven solely by input-output data. Moreover, a stability analysis is provided for output-feedback. The proposed approach is validated by simulations.

Passive object sensing with communication signals is a key enabler of perceptive mobile networks and integrated sensing and communication. In practical bistatic deployments, transmitter-receiver asynchrony and hardware impairments introduce time-varying random phase offsets in Channel State Information (CSI). Together with limited bandwidth and small antenna arrays, these effects degrade sensing accuracy. This work proposes a lightweight bistatic passive tracking and sensing framework that operates in the CSI-power domain. CSI power suppresses these offsets without explicit phase calibration, while preserving target-induced sensing cues. We show that physically admissible constraints in the spatial-frequency domain induced by transmitter-receiver geometry can resolve the mirror ambiguity inherent to real-valued CSI power. Building on these properties, we develop a real-time 3D Fourier-domain processing pipeline that jointly recovers spectral (delay), spatial (angle), and temporal (Doppler) signatures. The resulting features are integrated into an online framework with adaptive motion detection, outlier suppression, and extended Kalman filter tracking with deterministic initialization, followed by position-refined micro-Doppler feature extraction for micro-motion sensing. Extensive experiments, including simulations, a real-world prototype using 3.1 GHz LTE signals, and an open-source gait recognition dataset, demonstrate the effectiveness of the proposed CSI-power-based framework for bistatic passive tracking and sensing.

In the field of autonomous driving, camera-based perception models are mostly trained on clear weather data. Models that focus on addressing specific weather challenges are unable to adapt to various weather changes and primarily prioritize their weather removal characteristics. Our study introduces a semantic-enabled network for object detection in diverse weather conditions. In our analysis, semantics information can enable the model to generate plausible content for missing areas, understand object boundaries, and preserve visual coherency and realism across both filled-in and existing portions of the image, which are conducive to image transformation and object recognition. Specific in implementation, our architecture consists of a Preprocessing Unit (PPU) and a Detection Unit (DTU), where the PPU utilizes a U-shaped net enriched by semantics to refine degraded images, and the DTU integrates this semantic information for object detection using a modified YOLO network. Our method pioneers the use of semantic data for all-weather transformations, resulting in an increase between 1.47% to 8.80% in mAP compared to existing methods across benchmark datasets of different weather. This highlights the potency of semantics in image enhancement and object detection, offering a comprehensive approach to improving object detection performance. Code will be available at https://github.com/EnisZuo/SemOD.

With an ever-growing urban population, the need for transportation is increasing at an alarming rate. Thus, the massive increase in the number of vehicles is creating traffic congestion which creates various environmental, societal, and economic problems. To tackle traffic-related issues, several Smart Traffic Systems (STS) have been proposed and implemented. As a result, a comprehensive review of STS has become necessary. The main objective of this paper is to provide an overview and a thorough review of the existing STSs in terms of various technological approaches, traffic detection technologies using different sensors, various networking/communication tools, and their pros and cons. The paper also provides information on major STS services. In addition, challenges related to modern STS are identified. Therefore, the taxonomy of STSs provided in this paper will aid researchers, urban planners, and policymakers to recognize and install the best-suited STSs for their settings.

We present a framework for optimal trajectory generation in flow-driven systems governed by the Navier-Stokes equations, combining a Proper Orthogonal Decomposition (POD) reduced0order model (ROM) with Model Predictive Control (MPC). The approach (i) approximates the velocity field from data via snapshot POD and orthogonal projection, (ii) derives a Galerkin-projected dynamical model in reduced coordinates, and (iii) employs MPC to plan control inputs that steer an agent through the predicted flow while satisfying state and actuation constraints. By leveraging reduced-order modeling, the method enables real-time control in high-dimensional flow environments. Simulations demonstrate accurate flow-field reconstruction and efficient trajectory generation within realistic wind environments.

Temporal point processes (TPPs) provide a natural mathematical framework for modeling heartbeats due to capturing underlying physiological inductive biases. In this work, we apply density-based neural TPPs to model heartbeat dynamics from 18 subjects. We adapt a goodness-of-fit framework from classical point process literature to Neural TPPs and use it to optimize hyperparameters, identify appropriate training sequence lengths to capture temporal dependencies, and demonstrate zero-shot predictive capability on heartbeat data.

Quantitative MRI (qMRI) offers tissue-specific biomarkers that can be tracked over time or compared across populations; however, its adoption in clinical research is hindered by significant computational demands of parameter estimation. Images acquired at high spatial resolution or requiring fitting multiple parameters often require lengthy processing time, constraining their use in routine pipelines and slowing methodological innovation and clinical translation. We present GACELLE, an open source, GPU-accelerated framework for high-throughput qMRI analysis. GACELLE provides a stochastic gradient descent optimiser and a stochastic sampler in MATLAB, enabling fast parameter mapping, improved estimation robustness via spatial regularisation, and uncertainty quantification. GACELLE prioritises accessibility: users only need to provide a forward signal model, while GACELLE's backend manages computational parallelisation, automatic parameter updates, and memory-batching. The stochastic solver performs fully vectorised Markov chain Monte Carlo with identical likelihood on CPU and GPU, ensuring reproducibility across hardware. Benchmarking demonstrates up to 451-fold acceleration for the stochastic gradient descent solver and 14,380-fold acceleration for stochastic sampling compared to CPU-based estimation, without compromising accuracy. We demonstrated GACELLE's versatility on three representative qMRI models and on an image reconstruction task. Across these applications, GACELLE improves parameter precision, enhances test-retest reproducibility, and reduces noise in quantitative maps. By combining speed, usability and flexibility, GACELLE provides a generalisable optimisation framework for medical image analysis. It lowers the computational barrier for qMRI, paving the way for reproducible biomarker development, large-scale imaging studies, and clinical translation.

By introducing two polar coordinates transformations, the marine vessel's original two-input-three-output second-order tracking model can be reduced to a two-input-two-output feedback form. However, the resulting system does not confirm to the strict-feedback structure, leading to potential singularity when designing the stabilizing function for the virtual input in the recursive controller design. Moreover, the polar coordinate transformation itself inherently introduces singularities. To address these singularity issues, this paper employs a control barrier function (CBF) based approach and formulates the trajectory tracking problem as a quadratic program (QP) solved via a QP optimizer. Numerical simulations are carried out to demonstrate the effectiveness of the proposed method.

Due to the limited permissions for upgrading dualside (i.e., server-side and client-side) loss tolerance schemes from the perspective of CDN vendors in a multi-supplier market, modern large-scale live streaming services are still using the automatic-repeat-request (ARQ) based paradigm for loss recovery, which only requires server-side modifications. In this paper, we first conduct a large-scale measurement study with up to 50 million live streams. We find that loss shows dynamics and live streaming contains frequent on-off mode switching in the wild. We further find that the recovery latency, enlarged by the ubiquitous retransmission loss, is a critical factor affecting live streaming's client-side QoE (e.g., video freezing). We then propose an enhanced recovery mechanism called AutoRec, which can transform the disadvantages of on-off mode switching into an advantage for reducing loss recovery latency without any modifications on the client side. AutoRec allows users to customize overhead tolerance and recovery latency tolerance and adaptively adjusts strategies as the network environment changes to ensure that recovery latency meets user demands whenever possible while keeping overhead under control. We implement AutoRec upon QUIC and evaluate it via testbed and real-world commercial services deployments. The experimental results demonstrate the practicability and profitability of AutoRec.

Although quadrotor navigation has achieved high performance in trajectory planning and control, real-time adaptability in unknown complex environments remains a core challenge. This difficulty mainly arises because most existing planning frameworks operate in an open-loop manner, making it hard to cope with environmental uncertainties such as wind disturbances or external perturbations. This paper presents a unified real-time navigation framework for quadrotors in unknown complex environments, based on the online construction of guiding vector fields (GVFs) from discrete reference path points. In the framework, onboard perception modules build a Euclidean Signed Distance Field (ESDF) representation of the environment, which enables obstacle awareness and path distance evaluation. The system first generates discrete, collision-free path points using a global planner, and then parameterizes them via uniform B-splines to produce a smooth and physically feasible reference trajectory. An adaptive GVF is then synthesized from the ESDF and the optimized B-spline trajectory. Unlike conventional approaches, the method adopts a closed-loop navigation paradigm, which significantly enhances robustness under external disturbances. Compared with conventional GVF methods, the proposed approach directly accommodates discretized paths and maintains compatibility with standard planning algorithms. Extensive simulations and real-world experiments demonstrate improved robustness against external disturbances and superior real-time performance.

Accurate path loss (PL) modeling is essential for evaluating and optimizing cell-free massive MIMO systems, especially in dense urban environments where traditional models fail to capture the complexity of real-world propagation. This paper introduces CUNEC (Cell-free massive MIMO for Urban Non-stationary Environments with Correlations, a novel PL model that accounts for spatial non-stationarity, inter-access point (AP)/user equipment (UE) correlations, and urban-specific propagation phenomena such as corner diffraction and street canyon waveguiding.bCUNEC segments AP-UE paths by street order, models PL as a stochastic function of urban geometry, and integrates spatially correlated shadowing. The parameters are derived from large-scale ray tracing and validated against both additional ray tracing in New York, NY and real-world channel measurements in Los Angeles, CA. Compared to the conventional alpha-beta model, CUNEC significantly improves accuracy in the considered urban propagation scenarios. An open-source dataset comprising over 30,000 AP locations and 128 UE positions is also released to support reproducible research and future system development.

We propose analytical mean square error (MSE) expressions for the Kalman filter (KF) and the Kalman smoother (KS) for benchmark studies, where the true system dynamics are unknown or unavailable to the estimator. In such cases, as in benchmark evaluations for target tracking, the analysis relies on deterministic state trajectories. This setting introduces a model mismatch between the estimator and the true system, causing the covariance estimates to no longer reflect the actual estimation errors. To enable accurate performance prediction for deterministic state trajectories without relying on computationally intensive Monte Carlo simulations, we derive recursive MSE expressions with linear time complexity. The proposed framework also accounts for measurement model mismatch and provides an efficient tool for performance evaluation in benchmark studies involving long trajectories. Simulation results confirm the accuracy and computational efficiency of the proposed method.

Large vision foundation models have been widely adopted for retinal disease classification without systematic evidence justifying their parameter requirements. In the present work we address two critical questions: First, are large domain-specific foundation models essential, or do compact general-purpose architectures suffice? Second, does specialized retinal pretraining justify its computational cost? To answer this, we benchmark initialization strategies across four retinal imaging classification tasks spanning Optical Coherence Tomography (OCT) and Color Fundus Photography (CFP) modalities: 8-class OCT classification, 3-class diabetic macular edema (DME), 5-class diabetic retinopathy (DR), and 3-class glaucoma (GL) detection. We evaluate 12-13 model configurations per task, including vision transformers (22.8M-86.6M parameters), Swin Transformers (27.6M-28.3M), ConvNeXt (28.6M), and the domain-specific RETFound models (303M), under identical training conditions. Our results challenge prevailing assumptions: First, we demonstrate that pretraining provides universal benefits (5.18-18.41% improvement), scaling with task difficulty. Second, compact architectures (27-29M) dominate Pareto frontiers; SwinV2-tiny achieves top-1 performance on three datasets. Third, RETFound (303M) justifies its computational cost only for challenging DR grading (accuracy of 71.15%), while ImageNet pretraining proves to be sufficient with all other tasks (DME accuracy: 99.24%, OCT accuracy: 97.96%). CFP tasks show larger pretraining accuracy gains (9.13-18.41%) than OCT (5.18%). Thus, the evidence suggests that compact general-purpose models deliver near-optimal performance for most retinal classification tasks; specialized foundation models warranted only for fine-grained discrimination under extreme class imbalance.

Real-time sea state estimation is vital for applications like shipbuilding and maritime safety. Traditional methods rely on accurate wave-vessel transfer functions to estimate wave spectra from onboard sensors. In contrast, our approach jointly estimates sea state and vessel parameters without needing prior transfer function knowledge, which may be unavailable or variable. We model the wave-vessel system using pseudo mass-spring-dampers and develop a dynamic model for the system. This method allows for recursive modeling of wave excitation as a time-varying input, relaxing prior works' assumption of a constant input. We derive statistically consistent process noise covariance and implement a square root cubature Kalman filter for sensor data fusion. Further, we derive the Posterior Cramer-Rao lower bound to evaluate estimator performance. Extensive Monte Carlo simulations and data from a high-fidelity validated simulator confirm that the estimated wave spectrum matches methods assuming complete transfer function knowledge.

This work extends a previous study that introduced an algorithm for state estimation on manifolds within the framework of the Kalman filter. Its objective is to address the limitations of the earlier approach. The reversible Kalman filter was designed to provide a methodology for evaluating the accuracy of existing Kalman filter variants with arbitrary precision on synthetic data. It has favorable numerical properties on synthetic data, achieving arbitrary precision without relying on the small-velocity assumption and depending only on sensor noise. However, its application to real data encountered difficulties related to measurement noise, which was mitigated using a heuristic. In particular, the heuristic involved an event detection step switching between reversible Kalman filter and classical Kalman variant at chosen moments. In the present work, we propose a study of this detection step and propose a methodology to prove at which moment the reversible Kalman approach improves on classical multiplicative variant.

This study investigates the potential of hydrogen as a sustainable energy carrier for mobility applications in island territories, which are traditionally dependent on fossil fuel imports. Green hydrogen is identified as a key component of the energy transition. A Mixed Integer Linear Programming (MILP) model with a multi-period, multi-objective framework is used to optimize the hydrogen supply chain based on system costs, greenhouse gas (GHG) emissions, and a risk index. The model incorporates critical island-specific factors such as water resource availability, renewable energy sources, tourism flow, and geographic constraints. A multi-criteria decision making tool based on a modified version of TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) aids the identification of optimal solutions. Results suggest a decentralized Hydrogen Supply Chains (HSC) structure with minimized transport. The levelized cost of hydrogen (LCOH) is estimated at 6.54 ___/kg, and GHG emissions range from 1.32 to 1.75 kgCO 2 e/kg H 2. This study highlights the impact of tourism on energy demand and the crucial role of water resources, offering a novel approach to optimizing island-specific HSC.

This paper presents a group-theoretic framework for structured channel estimation in Orthogonal Frequency Division Multiplexing (OFDM). By modeling subcarriers as the cyclic group \(\mathbb{Z}_N\), we show that nulling a subgroup \(H \subseteq \mathbb{Z}_N\) constrains the channel impulse response to its annihilator \(H^\perp\) in the dual domain. A low-complexity estimator is proposed that detects such structure by evaluating energy concentration across candidate annihilators. Simulations demonstrate consistent gains in mean squared error, bit error rate, and throughput compared with least-squares and linear minimum mean square error baselines, achieving competitive performance with substantially lower complexity and preserved interpretability.

Real-world biosignal data is frequently corrupted by various types of noise, such as motion artifacts, and baseline wander. Although digital signal processing techniques exist to process such signals; however, heavily degraded signals cannot be recovered. In this study, we aim to classify two things: first, a binary classification of noisy and clean biosignals, and next, to categorize various kinds of noise such as motion artifacts, sensor failure, etc. We implemented K-means clustering, and our results indicate that the algorithm can most reliably group clean segments from noisy ones, particularly strong performance in identifying clean data compared to various categories of noise. This approach enables the selection of only high-quality bio-signal segments and provides accurate results for feature engineering that may enhance the precision of machine learning models trained on biosignals.