Papers

This work develops a reduced order observer framework of monocular visual-inertial odometry (VIO). The key idea consists in considering the gravity vector in the body-frame as an additional state and constructing an appropriate output that relates the monocular bearing measurements and their time derivatives to the body-frame velocity. This eliminates the need for the estimation of the landmark positions, leading to a six-dimensional linear time-varying (LTV) observer for the body-frame velocity and gravity, endowed with uniform global exponential stability guarantees. These estimates are subsequently used to recover the orientation almost globally, up to an unknown constant yaw offset, and the position up to an unknown constant translation. The mixed-bearing framework is further extended to account for the inertial measurement unit (IMU) biases, yielding a twelve-dimensional LTV observer endowed with local exponential stability guarantees. Numerical simulations are provided to illustrate the effectiveness of the proposed observers.

Intelligent reflecting surface (IRS)-assisted mobile Internet of Things uplinks require joint control of channel aging, costly cascaded channel state information (CSI) acquisition, and coupled IRS/radio resources. This investigation develops a unified prediction-aware deterministic framework for partitioned IRS-assisted massive multiple-input multiple-output (mMIMO) uplinks. Direct channels are recursively tracked, whereas cascaded channels are selectively refreshed by differential semi-blind acquisition reusing the same unknown data block across a minimum-reflection baseline and DFT-coded IRS states. Moreover, finite-block acquisition covariances initialize reduced-order beam-domain prediction, which propagates only unit-beam means online and retrieves age-dependent covariances from precomputed tables. A mixed-integer nonconvex formulation captures net throughput, fairness, outage, uncertainty, switching cost, and sliding-window IRS service. The causal controller integrates covariance-floor actionability, feasible equal-size granularity, residual-feasible ownership, isolated-beam one-sweep refinement, same-block fresh-payload accounting, and service-first allocation using recomputed uncertainty-aware ZF rates and bidirectional receiver-consistent risk. Analysis establishes service-feasibility preservation, effective-channel error bounds, distribution-free reliability, and polynomial online scaling. In unseen environments, the predicted acquisition floor matches the practical semi-blind error, actionable recalibration reduces the mean recalibration rate by 31.5%, and IRS-assisted control achieves a 14.09% mean net-rate gain over direct-only transmission. Receiver-consistent allocation improves realized rate, while ablations support equal-size partitioning and single-sweep refinement; zero post-action service violations occur under nominal, mobility, and load-stress conditions.

The future 6G networks are expected to incorporate a proliferation of wireless services in diverse environments, which presents a significant challenge for information security. Conventionally optimization always requires recalculation and learning strategy often suffers poor generalization, which are thus incapable for the security provisioning with wide scenario coverage. In this paper, we propose an adaptive and robust learning framework that leverages a mixture-of-experts (MoE) architecture to achieve cross-scenario physical layer security guarantee. Specifically, we first select a few representative scenarios and establish the scenario-specific generative diffusion model (GDM)-based experts for secure transmission beamforming with artificial noise. The diffusion nature of experts learns the overall probability distribution of security strategy solution landscape and the Transformer-based denoising process enhances the ability to generalize across varying network configurations. Then, a lightweight gating network is constructed to identify the scenarios by engineering the channel features and select the most relevant experts. Finally, an attention-based combiner is introduced to synthesize the security proposals from the top-rated experts to produce a high-fidelity security strategy to cover the unseen scenarios. Simulation results demonstrate that the proposed GDM-based MoE framework can accurately recognize the scenarios and properly select the experts, maintaining near-optimal secrecy rates across a continuum of wireless scenarios and outperforming traditional single-model paradigms.

The growing trend of miniaturization in space, combined with an exponential increase in orbital debris, motivates a new operational paradigm, that is the deployment of multiple small servicers to collectively rendezvous, dock and service large tumbling targets. A single small spacecraft is often limited in thrust or reach for such targets, yet coordinating multiple autonomous spacecraft safely in close proximity remains an open challenge. This paper presents a proof-of-concept decentralized 6-DOF model predictive control (MPC) framework for multiple servicers to safely rendezvous and dock (RVD) to a tumbling target with no knowledge about each others predicted input over the prediction horizon. The proposed framework enforces soft docking conditions, inter-servicer collision avoidance, and obstacle avoidance through embedded constraints, while remaining computationally tractable via a linear time-invariant (LTI) formulation. Validation in MATLAB with three servicers and fixed obstacle arrangements confirms that all constraints are satisfied and successful docking is achieved.

The repeated forward-reverse maneuvers performed by wheel loaders during earthmoving operations make them well suited for automation. However, the nonlinear dynamics of articulated vehicles and complex vehicle-terrain interactions limit the effectiveness of conventional model-based approaches. This paper presents a hierarchical framework that combines long-horizon geometric planning with data-driven predictive control for autonomous wheel-loader operation. A reduced-order articulated kinematic model is used to generate the maneuver geometry, where the forward and reverse trajectories are jointly optimized through a shared intermediate state. To capture the vehicle dynamics, two data-driven deep bilinear Koopman models are learned for the forward and reverse motions using data generated from high-fidelity simulations in Algoryx Dynamics. The learned Koopman representations are subsequently incorporated into a computationally efficient model predictive control (MPC) formulation for trajectory tracking. The resulting controller operates in real time within a 50-ms execution loop. High-fidelity simulation results demonstrate that the proposed end-to-end framework enables accurate and computationally efficient execution of wheel-loader V-cycle maneuvers, providing a promising approach toward autonomous operation of articulated heavy-duty machinery.

Every Hurwitz-stable interval family of monic real polynomials of degree six admits a single real numerator of degree six that makes all the associated transfer functions strictly positive real. We give a constructive proof. The complete existence theorem has been formalized in Lean4.

In LLM-based speech translation, transcription-based chain-of-thought (CoT) suffers from a mismatch between reference transcripts used in supervised fine-tuning (SFT) and model-generated transcripts at inference. To address this, we propose joint recognition and translation fine-tuning via group relative policy optimization (GRPO). We score both transcripts and translations, with translation conditioned on model-generated transcripts, and compare three token advantage strategies. Using Qwen2.5-Omni-3B across four languages, we evaluate CoT against direct speech translation (Direct ST) under SFT and GRPO, training on CoVoST 2 and testing on CoVoST 2 and FLEURS. CoT GRPO outperforms Direct ST GRPO by 1.77 and 0.83 average BLEU points on CoVoST 2 and FLEURS. Compared to CoT SFT, GRPO boosts BLEU by 0.82 and 0.67 points and reduces word error rate (WER) by 8.8% and 7.2% relatively. These results highlight reinforcement fine-tuning as an effective method to mitigate the training-inference mismatch, jointly improving recognition and translation.

Common worst-case analyses for uncertain finite-horizon systems consider quadratic performance metrics based on the strict Bounded Real Lemma. Thus, they assess system performance for bounded inputs, e.g., signals in L2, which exhibit a worst-case shape. Consequently, known disturbance characteristics are left unexploited and uncovered, leading to unnecessarily conservative results. The present paper develops a worst-case analysis covering arbitrarily L2-bounded worst-case signals and partially known disturbances simultaneously. This is achieved by modeling the latter using signal integral-quadratic constraints (IQCs). The resulting analysis condition relies on a dissipation inequality within the IQC framework for finite time horizon problems. This framework also readily allows to incorporate additional system uncertainties in the analysis. The approach's feasibility is demonstrated with the worst-case performance analysis of a small unmanned aerial vehicle in an urban environment.

This paper proposes the design of a single linear parameter-varying (LPV) controller for the combined control and smooth transition between two modes in a spacecraft mission. Current industry practice for transitioning between different controller modes is to use a discrete switching approach. When predefined criteria are satisfied, the controller of one mode is turned off and the controller of the other is initialised, resulting in an undesirable transient behaviour. In addition, each controller must individually undergo a rigorous verification and validation (V&V) process. A single controller synthesised using LPV methods streamlines the V&V process and improves the transient behaviour. The proposed design follows a mixed-sensitivity control scheme with LPV weights that are derived from the performance and robustness requirements of the individual modes. The controller is synthesised by minimising the induced $\mathcal{L}_2$-norm of the closed-loop interconnections between the controller and weighted plant. The performance and robustness of the controller is demonstrated on an acquisition and pointing task of a flexible satellite through a Monte-Carlo campaign.

This paper presents a new approach to the derivation of decentralized plug-and-play stability certificates that can be calculated using black-box admittance spectra, without requiring white-box models of devices. The method is based on analysing the properties of the nodal admittance matrix. The certificates ensure that the devices can be connected at any node of a network of any topology as long as the R/X ratio is within a specified range. The approach is related to the passivity concept, but uses frequency-dependent transformation matrices to check if a device, which is non-passive in a certain frequency range, can still satisfy a plug-and-play stability criterion. We illustrate the method using Grid Forming Inverters (GFMs) connected to IEEE 39 node test-case.

The Fitness-Fatigue model (FFM) was initially designed to gain a better physiological understanding of the impact of training loads on sports performance. For almost 50 years, simulations have been compared with the observed response of sports performance to training loads. Understanding the relationship between training load and performance should have answered some fundamental questions for the physical trainer or sports coach: 1/ how to define the best training to achieve a performance without exhausting an athlete or how to achieve a performance in a limited time; 2/ how to assess the athlete's fatigue and fitness reliably to better understand his performance; 3/ how to prevent the risk of injury. But this was not the case. Studies dedicated to the FFM have been limited to improving somewhat on Banister's initial model without really taking the necessary step back to take advantage of the mathematical framework offered by the state representation, the implicit formalism underlying the FFM. The idea behind this research strategy is that having a valid and accurate model makes it easy to address previous questions through simulation: multiple training scenarios can be simulated until the ideal scenario for a given training question is identified. The main drawback to this approach is the combinatorial nature of the exercise. This paper is not discussing the relevance of the model, but how to use it. The state representation makes it possible to study the controllability, observability and diagnosability of a system (i.e, the athlete) and thus to formally answer the three previous practical questions. It can be considered that sports science studies have missed the richness of the FFM model. Artificial learning approaches are increasingly preferred to the FFM model because they are supposed to better capture observation. However, in the light of the state representation, the FFM model could still be extended naturally while remaining mathematically interpretable, offering mathematical tools to estimate the state of the athlete, and to define the most adequate training and prevent the risk of injury. This article does not aim to solve the question of optimal training in practice, for that it would need to be validated by specialists in physiology and sports science, it just proposes to take a fresh look at training issues and to demystify the mathematical formalism adopted by Banister.

The problem of observing particle motion inside an optically opaque, dense bubbling fluidized bed is addressed. Differential-pressure measurements identify the bulk fluidization state but do not reveal where particles move or how their velocities vary with height. A commercial 60 GHz frequency-modulated continuous-wave radar is mounted above the facility to obtain depth-resolved measurements without optical access or an intrusive probe. Each measured depth cell contains an ensemble of 600 micrometre sand grains in the Rayleigh-Mie scattering transition region. Particle displacement, cell exchange, and changes in shadowing alter echo phases and amplitudes. Their coherent sum is therefore a stochastic complex signal, whose covariance and Doppler spectrum describe ensemble motion. A statistical spectral analysis retrieves total power, mean line-of-sight radial velocity, and Doppler spectral width over range and time. Eight-minute measurements reveal an expanding radar-detectable layer and alternating motion towards and away from the radar. Positive- and negative-mean regions coexist during developed fluidization and exchange their contribution to particle power. Most detectable spectra contain one dominant Doppler lobe; a small subset supports two distinguishable lobes, including cases with one negative and one positive mean velocity. The 5-s total-power trend has a correlation of 0.734 with measured pressure drop, whereas the local fitted spectral-width trend has a weaker correlation of 0.152. This work develops a range-resolved statistical Doppler measurement that reveals bidirectional particle motion and determines when multiple local velocity contributions are distinguishable.

We propose an adaptive predictive sampling and communication framework for real-time remote monitoring over packet-erasure channels. Rather than relying on a prescribed dynamical model or continuous sensing, our model-free method uses Taylor-expansion-based prediction to adaptively estimate local signal dynamics and proactively schedule the next sampling times. An analytical compensation factor accounts for packet losses to satisfy a prescribed reconstruction-error level. We develop adaptive sample-wise and episodic policies and evaluate their performance against event-driven and uniform sampling and communication baselines.

While Chain-of-Thought (CoT) reasoning has improved the capability of language models, directly applying it to Spoken Language Models (SLMs) may introduce long silent intervals under the serial "think-then-speak" paradigm, disrupting real-time spoken interaction. To address this issue, we propose an asynchronous think-aloud framework for reasoning-based SLMs within the Thinker-Talker architecture. The framework maintains a primary reasoning stream for logical deduction and a lightweight think-aloud stream that generates short, task-grounded progress utterances conditioned on the user input and the evolving reasoning state. A dynamic balance strategy coordinates the two streams at runtime, triggering additional think-aloud speech to avoid silent gaps and canceling pending utterances when the final response becomes ready. Experiments on spoken reasoning and question-answering benchmarks show that our approach substantially reduces user-audible silence during reasoning while maintaining answer accuracy comparable to that of a serial "think-then-speak" baseline, demonstrating the potential of asynchronous think-aloud for responsive interaction in SLMs.

Automatic lip-synchronous dubbing requires a speech synthesis model to generate alternating voice and silence patterns in the target language that match the timing of the source clip precisely to ensure an optimal viewing experience. Prior works address this problem by conditioning the speech synthesis process on lip movements extracted from the video signal. In this work, we condition the speech generation on a binary voice-activity signal, which has a lightweight representation and can be produced in multiple ways. We show that the model follows the voice-activity signal with high accuracy while maintaining natural prosody and semantically appropriate pause placement within sentences, as demonstrated through extensive objective and subjective evaluations. By randomly masking this condition during training, we make the feature entirely optional during inference, allowing editors to enforce or relax lip-sync constraints when desired.

Scattered light is common in biomedical images, yet its removal remains challenging. The difficulty arises from three aspects: first, aligned scattered-light-free biomedical ground truth is often unavailable; second, scattering is coupled with weak illumination and sensor-induced noise; and third, many learning-based restoration models are computationally expensive for embedded devices in Internet of Medical Things (IoMT) scenarios. To address these issues, this paper proposes PP-Net, a hybrid physical-prior neural network for biomedical scattered light removal. The proposed method consists of three components: DFN-Net suppresses sensor-induced noise, ASAP estimates the scattering map and recovers a physics-based prior map, and GF-Net refines the prior map by fusing it with the denoised observation. To reduce the dependence on paired biomedical ground truth, a progressive synthetic training and cross-domain transfer strategy is developed. Experiments show that the physical-prior branch improves the peak signal-to-noise ratio (PSNR) by up to 1.26 dB on paired synthetic benchmarks. Under joint noise-and-scattering degradation, PP-Net improves PSNR by more than 10.8 dB and the structural similarity index measure (SSIM) by more than 0.62 compared with representative baseline methods. On real W2S biomedical images, the proposed method reduces the average Natural Image Quality Evaluator (NIQE) score by 43.3%. Edge deployment with RKNN conversion and INT8 quantization achieves an average inference latency of approximately 200 ms per $512\times512$ image over 360 test images. These results demonstrate that PP-Net provides an effective and deployable solution for microscopic imaging, endoscopic inspection, and edge-assisted biomedical analysis in IoMT scenarios.

Recent advances in Artificial Intelligence (AI)-powered Computer-Aided Diagnosis (CAD) systems have substantially improved breast cancer screening, diagnosis, and prognosis. Comparatively, postradiotherapy outcome prediction using paired longitudinal mammograms has received considerably less attention. This is largely due to the limited availability of well-annotated longitudinal datasets. Longitudinal mammograms, coupled with paired pre- and post-treatment information, provide a unique opportunity to characterize treatment-induced breast tissue changes following radiotherapy. The resulting learned representations can serve as a valuable asset for advancing personalized radiotherapy planning and post-treatment management. In this context, we propose Mammo-LIFE, a patient-level multimodal framework for post-radiotherapy outcome prediction that combines longitudinal mammographic features with patient-level clinical variables. The imaging branch processes paired pre- and post-treatment mammograms acquired from the four standard views using a mammography-specific encoder adapted via Low-Rank Adaptation (LoRA). Within each view, preand post-treatment representations are explicitly compared through a longitudinal comparison module to capture treatment-related changes. The resulting view-level embeddings are then aggregated using learned view-attention pooling to form a unified patient-level mammographic representation. Selected clinical variables are subsequently combined with the image-derived prediction probability through a late-fusion strategy. To evaluate the effectiveness of combining paired longitudinal mammograms with clinical information, experiments were conducted on an in-house clinical cohort using patient-level stratified five-fold cross-validation.

As next-generation wireless systems migrate to high-frequency bands, extremely large-scale multiple-input multiple-output (XL-MIMO) is indispensable for combating severe path loss. However, the received path energy along the extremely large array aperture (ELAA) may exhibit spatial non-stationarity, rendering the conventional discrete Fourier transform (DFT) codebook inadequate for channel estimation (CE) due to its full-array angular representation. To address this mismatch, we propose a novel joint angle-subarray (JAS) codebook, where each codeword corresponds to an angular direction and a specific subarray. Spatially non-stationary CE is thus transformed into structured sparse support detection in the JAS domain. However, reliable support recovery remains challenging for conventional model-based methods. We therefore develop a signal processing and deep learning co-design framework, where data-driven JAS parallel support detection is followed by least-squares (LS)-based channel reconstruction. The matched filter response of each JAS grid is embedded into a high-dimensional feature vector. We then propose a Gram-attention module that incorporates the Gram matrix as a physical prior to guide feature purification, enabling the network to suppress leakage-related components while preserving support-relevant information of each grid. The purified features are passed to decoupled heads to infer the hierarchical sparse supports in the JAS domain. Finally, LS is performed over the low-dimensional dictionary constructed from the detected supports to retrieve path gains. Simulation results demonstrate that the proposed framework achieves the highest support detection F1-score and the lowest CE normalized mean square error (NMSE) among all baselines, while ablation studies verify the benefits of integrating the Gram-domain physical prior with data-driven representation learning.

Adaptive regulation can itself perturb the state it is intended to stabilize. In replicated simulations of an adaptive agent, we compare external disturbance, persistent internally generated disturbance, and control-generated disturbance under regulation-first and disturbance-first ordering. Persistent internal disturbance produces the largest exposure and regulatory burden within the tested parameter grid. When positive controller updates generate an immediate disturbance cost, increasing that cost produces a nonmonotonic response: effective disturbance initially rises, variability across stochastic runs increases over an intermediate range, and corrective activity becomes strongly suppressed at higher costs. The results show how disturbance source and timing shape exposure and controller burden in this model. They motivate testing adaptive agents with distinct disturbance sources and assessing regulatory activity alongside exposure.

Revision-capable streaming speech-to-text translation (S2TT) can correct earlier drafts, but process rewards based on visible text may credit content later withdrawn. Persistent Delivery Optimization (PDO) assigns intermediate reward only to content that survives revisions while scoring final quality separately. With 7.49 h of task-specific FLEURS adaptation, PDO achieves the best BLEU on four of five directions and higher COMET than every external streaming baseline in all five directions. Relative to its History-SFT initialization, PDO reduces mean/P90 finalization-aware latency by 10.8%/11.3% and normalized erasure by 15.8%, while emitting at the first permitted 2-s update and improving macro BLEU. Zero-shot evaluation on Europarl-ST and CoVoST 2 confirms that these gains are not confined to the FLEURS training domain.

Detecting emotions is necessary for building systems that can accurately and adaptively interact with humans. Speech Emotion Recognition (SER) has become an important research focus to develop intelligent spoken interfaces. However, most studies predict emotions at the utterance level, ignoring the conversational context, along with the emotional flow and speaker interactions it carries. In this paper, we introduce ACERT (Averaged Contextual Emotion Representation through Time), a module that integrates a flexible-length window of conversational context to better capture emotional evolution in spoken interactions. To evaluate the robustness of this method, we conducted experiments on datasets spanning diverse emotionally expressive styles and contexts. ACERT outperforms current state-of-the-art (SOTA) approaches on IEMOCAP, establishes the first context-aware benchmark on SAFE, and obtains strong results on MELD for unweighted, class-balanced metrics. Ablation studies show that ACERT's gains come from emotional and conversational continuity, rather than from speaker identity or acoustic conditions.

We present a stochastic data-driven predictive control (DPC) framework for discrete-time linear time-invariant systems subject to sub-Gaussian additive disturbances based solely on input–output data. In contrast to related methods that rely on exact disturbance data or at least sample generation for closed-loop guarantees, the proposed approach leverages a disturbance data estimate. By enforcing consistency of the disturbance data estimate with the available input–output data and system class, we first identify data-driven and provably causal subspace predictors for use in DPC. Then, we analyze statistical properties of the corresponding prediction error, yielding tightened constraints for the nominal predictions that guarantee satisfaction of chance constraints. The proposed DPC scheme comes with guarantees on recursive feasibility and conditional chance constraint satisfaction in closed-loop under standard assumptions. A numerical evaluation study demonstrates the performance of the proposed controller.

This paper investigates the impact of in-phase/quadrature (IQ) imbalance on signal detection in affine frequency-division multiplexing (AFDM) systems over doubly dispersive Rayleigh fading channels. The bit error rate (BER) performance of AFDM under various IQ mismatch conditions is evaluated and compared with that of an ideal transceiver. Simulation results show that, under ideal conditions, BER decreases rapidly with increasing signal-to-noise ratio (SNR). However, IQ imbalance introduces image interference that spreads across the affine domain, causing significant performance degradation. The results further highlight the impact of practical IQ imbalance on AFDM systems across different quadrature amplitude modulation (QAM) orders, channel scenarios, and comparisons with orthogonal frequency-division multiplexing (OFDM) and orthogonal time frequency space (OTFS) systems. In particular, higher-order QAM performance degrades significantly, indicating the need for IQ imbalance compensation techniques or impairment-aware detection algorithms to ensure robust operation in practical AFDM systems.

The rapid growth of large language model training and serving is driving AI data centers (AIDCs) toward gigawatt scale. Unlike conventional commercial loads, AIDCs possess significant operational flexibility through dynamic voltage and frequency scaling (DVFS) of training and inference workloads, while periodic model checkpointing can induce abrupt power drops and rebounds that erode operating reserves and increase transmission congestion risks. Coordinating AIDC operation with grid scheduling under these unique operational characteristics is challenging because grid and AIDC operators are generally unwilling to share proprietary data and decision-making authority. This paper proposes a hierarchical privacy-preserving coordinated operation scheme between the power grid and AIDCs to address this gap. The proposed scheme contains three phases. In Phase I, the grid operator computes a certified inner approximation of the AIDCs security region for subsequent coordination. In Phase II, the AIDC operator coordinates training and inference AIDCs to optimize workload allocation within the certified security region and generate power schedules and checkpoint alerts. In Phase III, the grid operator solves a checkpoint-aware two-stage robust optimal power flow (OPF) considering renewable generation and checkpoint uncertainties. By exchanging only compact interface information, the framework preserves the privacy of both grid and AIDCs, avoids frequent iterative communication, and enables secure coordination with guaranteed feasibility. Numerical studies on a modified IEEE 14-bus system and a modified NYISO system demonstrate the effectiveness, robustness, and security of the proposed framework.

Optimal sizing problem (OSP) for battery energy storage system (BESS) under uncertain inputs is often formulated as a two-stage stochastic program (2SP), which typically introduces computational and RAM (memory) bottlenecks for large scenario sets. Benders Decomposition (BD) is a popular approach for tackling this problem, but it suffers from slow convergence to the exact solution. To address this problem, we propose an accelerated online learning-based adaptive hybrid BD algorithm for a risk-averse 2SP formulation. The proposed method avoids getting stuck in the infeasible region during early iterations by explicitly embedding a carefully selected scenario in the master problem, while a tail-relevant scenario selector based on online learning helps to avoid solving the entire scenario set at every iteration. The OSP is formulated to select a behind-the-meter BESS in a multi-site energy community with existing renewables. The use case explores an interesting middle-ground between deterministic acceleration methods and training-heavy ML models, showcasing the potential of ML-assisted decision-making. Compared with vanilla BD, OLAH-BD reduces subproblem evaluations and total wall time by up to 80% under the same tolerance settings.

Tensor-based modulation (TBM) schemes are a promising approach for unsourced random access (URA), where user separation relies on decomposing the received signal tensor via the canonical polyadic decomposition (CPD), typically computed with alternating least squares (ALS). Standard ALS, however, treats the factor matrices as unstructured and fails to exploit the discrete structure of the tensor sub-constellations. We propose DVB-ALS, a discrete variational Bayesian CPD framework with specific priors, tailored to a tensor structure with the corresponding encoding strategy, combined with iterative computation of an approximate posterior distribution. A discrete Gaussian mixture prior on one Grassmannian factor softly aligns the estimates toward the constellation points. The remaining factors are jointly modeled with a structured Gaussian prior whose posterior mean is constrained to the Khatri-Rao product manifold and posterior variance upper-bounded to prevent norm divergence during inference. The resulting closed-form coordinate ascent algorithm jointly estimates all latent factors and their uncertainties. We integrate DVB-ALS into DVB-TBM to design a complete URA receiver with single-user demapping, polar decoding with cyclic redundancy check (CRC) verification, and successive interference cancellation (SIC). Simulation results show significant gains over standard ALS-based decomposition and robust detection performance in URA settings, outperforming state-of-the-art schemes under high system loads.

In-depth characterization of heterogeneous materials has long been a challenge in non-destructive testing. Here, a method is proposed to determine the elastic constants of metallic polycrystalline materials using back-scattered ultrasound. The waves scattered by the microstructure are analyzed to image the effective bulk velocities. To this end, a reflection matrix is acquired with an array of transducers. The projection of this matrix onto a focused basis is used to estimate an average point spread function. Optimizing this function with respect to the propagation model leads to an estimation of the longitudinal velocity. Additional treatments are developed to adapt the method to map the shear wave velocity. The local Poisson's ratio is then deduced from the ratio between those two velocities. Young's modulus and shear modulus can also be obtained assuming known densities. This matrix approach is experimentally validated on different polycrystalline materials. A sample displaying heterogeneous mechanical properties is then simulated to assess the accuracy and the resolution of the method. Its strengths and limitations are discussed, demonstrating its potential for quantitative non-destructive material characterization.

Data-calibrated stochastic MPC typically builds separate risk margins for many events along the horizon, such as times, facets, state/input components, or obstacles, and then combines them with a union bound. This approach is valid, but it does not match the key object used in the tube-MPC recursive-feasibility proof, which shifts a complete error tube. This paper develops collective tube MPC (CT-MPC), where the calibrated uncertainty object is the finite-horizon prediction-error trajectory. A reusable trajectory tube is calibrated offline, its cross-sections define deterministic Pontryagin tightenings online, and the certified violation event is that a fresh prediction-error trajectory leaves the tube. For linear systems with additive uncertainty and fixed ancillary feedback, we prove joint state-input safety over the prediction horizon, one-step recursive feasibility from an explicit shifted candidate, a finite-deployment risk bound, and a practical value-decrease inequality. The finite-sample certificate is distribution-free under split calibration and has beta-binomial form; its complexity is the certified number of residual trajectories that can define the tube, rather than the number of horizon-constraint blocks. We also give implementable shift-compatibility tests for polytopic tubes and a stable-compression fallback for irregular tube designers. Numerical experiments compare CT-MPC with Bonferroni tightening, a sample-envelope tube, and a joint-in-time conformal MPC baseline. The collective tube reduces deterministic tightening while preserving empirical safety and recursive feasibility.

Synthetic multispectral and hyperspectral data are increasingly used to address limited measurements, labels, and acquisition resources in spectral-imaging research. Yet the scope of the evidence produced by these experiments depends not only on the synthesis method, but also on sensing conditions, sample variability, data provenance, evaluation design, and downstream use. We present a literature-informed taxonomy for experiments using synthetic MSI/HSI data through spectral reconstruction and/or data augmentation, with a deliberate focus on agri-food research. The taxonomy is paired with a traversal procedure that connects available evidence, sensing constraints, experimental choices, and evaluation design to the forms of generalization that were actually tested, producing a compact experiment characterization record. Although primarily intended for studies with an existing research question, the same structure can also support earlier-stage planning when only an object of study, dataset, or sensing setup is initially defined. The framework was developed through question-driven reading, practical research experience, and iterative refinement against additional recent studies, and is offered as a basis for further use and refinement.

We present a reference-based protocol for assessing image stability, illustrated with a passive lens support. Pre/post-event wall references separate image-centre displacement from scale change. All 70 trials were retained: 30 baseline and 40 supported, acquired sequentially. Displacement (p = 0.597) and scale spread (p = 0.356) showed no significant difference. Lower median absolute scale deviation in the supported run persisted with scene-only processing, but its cause remains unresolved. Marker-estimation eligibility differed completely between runs for an unknown reason. The contribution is a workflow that reports completerun variability, typical deviations and estimator coverage together; the case study does not establish support efficacy, equivalence or transient damping.