Papers

Low-altitude uncrewed aerial vehicles (UAVs) can pose growing risks to airspace safety, security, and privacy. Cellular infrastructure can passively sense them without dedicated radar hardware by exploiting integrated sensing and communication (ISAC) technology. Most prior work exploits monostatic sensing or bistatic/multistatic configurations based on downlink measurements. To the best of our knowledge, this paper presents the first uplink framework, where multiple user equipments (UEs) transmit sounding reference signal (SRS) pilots and the base station (BS) receives the UAV-scattered echoes. Sensing from uplink SRS, however, introduces new challenges. Each UE has its own oscillator and timing loop, so the channel estimate at the BS carries residual timing, frequency, and amplitude impairments that corrupt the UAV delay and Doppler. Moreover, the UAV echo is weaker than both the line-of-sight (LOS) path and urban clutter, so detection from a single UE transmission is not reliable. We address these challenges by designing a LOS-referenced synchronization scheme and a joint detector. The synchronization reuses the existing timing advance (TA) command and an adjacent-occasion conjugate product to remove the residuals without additional signaling. Then the detector searches a shared 3D state space and accumulates evidence across UEs. It leverages a normalized contrast that exploits the bistatic geometry. We evaluate the framework in a cluttered urban scene at frequency range 1 (FR1) with four pedestrian UEs and a 100 MHz 5G New Radio (NR) waveform. The proposed pipeline achieves sub-nanosecond synchronization and a 4.84 m median 3D position error.

Optimal Transmission Switching (OTS) reduces generation cost by strategically opening transmission lines, but its mixed-integer linear program (MILP) formulation scales poorly for large-scale transmission networks. Reinforcement learning (RL) offers a computationally efficient alternative, but existing RL-based OTS approaches rely on soft penalties that permit physical constraint violations. This paper presents a comparison between an RL framework and an MILP-based optimization method for OTS. Case studies were carried out on the IEEE RTS-96 24-bus system; results show that the agent was able to produce near-optimal solutions at low switching budgets and tended to yield suboptimal solutions at high switching budgets. However, the RL agent was able to generate feasible solutions two-to-three orders of magnitude faster than the optimization solver.

Reducing the response time for accurate emergency-caller localization is critical in vehicular and public-safety networks. Although mobile devices commonly use GNSS, Wi-Fi, or cellular positioning, their accuracy and availability can degrade because of poor signal reception, limited infrastructure, and regulatory constraints. UAV-based localization offers a promising alternative by using airborne sensors to cooperatively estimate the target position. However, existing Fisher information matrix (FIM)-based trajectory optimization methods depend on the current target estimate and can perform poorly in the early mission stage, when measurements are limited and uncertainty is high. We propose a rigidity-based UAV trajectory optimization method that maximizes the smallest nonzero singular value of the rigidity matrix associated with the UAV-target sensing graph, improving geometric conditioning and reducing position ambiguity. We also introduce a pruning-based matrix reduction strategy for efficient real-time implementation. Simulations show that the proposed method reduces search time by 32.9% compared with FIM-based methods and satisfies the FCC horizontal emergency-localization requirement sooner. Further results demonstrate scalability, robustness to UAV positioning errors and NLOS path loss, low sensitivity to heading parameters, practical computation and communication costs, and more stable degradation than PPO-based baselines under severe sensing and navigation perturbations.

Generative models often represent signals as dense grids of amplitudes, blurring sharp transients that are crucial for the correctness of physical signals. We introduce Singularity Space, a generative framework that represents signals through complex-plane singularities, rooted in the classical pole-residue representation of meromorphic functions. We learn a latent space of physically constrained, per-signal singularity configurations to solve an inverse problem from degraded or partial observations. The framework has three key properties: interpretability, in which each generated singularity configuration corresponds to a set of physical parameters; structural stability, which mitigates Gibbs artifacts at discontinuities; and resolution-free output reconstruction on arbitrary grids without retraining or interpolation. Our framework employs a transformer-based diffusion model that directly predicts samples at complex-plane singularity coordinates, subject to geometric constraints during sampling. As a controlled test case for sharp-feature recovery, we evaluate our framework on 1D Burgers shocks, where each shock is represented by 32 predicted singularities (an $8\times$ reduction versus a 1024-point grid signal). Our framework preserves signal structure ($\text{TV ratio} \approx 1$) under unseen test-time observation noise, achieves a $4.2\times$ lower reconstruction error in zero-shot sub-resolution generalization than a grid-based baseline, and recovers physical parameters to $10^{-4}$ absolute error in-distribution. These results suggest that singularity-based representations may provide a practical foundation for other transient-dominated signals such as speech and biomedical signals, with potential extension to higher-dimensional domains.

Learning temporal logic specifications from system demonstrations is essential for tasks such as formal verification and controller synthesis, especially in safety-critical domains. Existing approaches typically assume demonstrations are correct or only affected by misclassification errors. In practice, however, system traces are often uncertain or incomplete due to sensor faults, measurement errors, or data loss. We present a framework for learning minimal Linear Temporal Logic (LTL) formulas from demonstrations with uncertainty. Our approach models uncertainty via Hamming distance to generate possible estimates around each observed trace, which are grouped with constraints requiring that at least one trace per group is consistent with the learned formula. Our problem is then reduced to an equivalent Pseudo-Boolean Optimization. We evaluate our method against state-of-the-art LTL learning approaches and show that it recovers specifications that more closely align with ground-truth formulas under uncertainty.

Certifying the safety of a control action in real time, from streaming partial observations of a nonlinear, interconnected system under non-stationary disturbances, is a problem no existing data-driven framework solves. Batch methods such as data-enabled predictive control require a pre-collected dataset and offer no stability certificate for nonlinear dynamics; informativity-based approaches characterize data sufficiency offline and non-recursively; neither exploits the known graph topology of networked systems as a structural prior. This paper addresses both limitations. First, we develop a streaming contraction certificate beta_cert(t) = beta_hat(t) - rho(t), where beta_hat(t) is estimated recursively via integral regression on a sliding window of partial input-output observations, and rho(t) is a data-dependent uncertainty radius mapping estimation error to a conservative bound on the true closed-loop contraction rate. The certificate issues a provably safe deployment signal the moment beta_cert(t) crosses and sustains above zero. Second, we introduce a topology-aware estimator enforcing known graph adjacency as exact zero constraints on the Jacobian, reducing the effective parameter count per row from O(N) to O(d_max) for maximum node degree d_max. On a five-node nonlinear benchmark under heavy-tailed Laplace disturbances with two observed nodes, the streaming certificate achieves certified deployment at t*=2.6s from 130 samples, 17 seconds earlier than an offline batch baseline, with 16x lower accumulated error during the unprotected window. The topology-aware estimator cuts certification time by 59% (1.62s vs 3.98s) and accumulated disturbance cost by 58%, with the advantage persisting across all window sizes below 40 samples. The framework is domain-agnostic and applies to any large-scale nonlinear networked system under streaming data and partial observations.

We introduce a graph bispectrum formulation for characterizing higher-order interactions in graph signals. While conventional graph spectral methods capture only second-order structure, many graph signals exhibit nonlinear interactions that are not reflected in covariance or graph power spectra. Motivated by classical higher-order spectral analysis, we define a graph bispectrum tensor based on third-order moments of graph Fourier coefficients and derive a compact graph bicoherence measure that summarizes nonlinear mode interactions in a low-dimensional and scale-invariant form. We establish key properties of the proposed quantities, including vanishing third-order moments for Gaussian graph signals and a dynamical interpretation in terms of nonlinear mode coupling. Experiments on synthetic random graph signals demonstrate that the proposed measures detect complementary nonlinear dependencies even when second-order statistics are similar. We further apply the method to EEG recordings from the CHB-MIT Scalp EEG Database and show that ictal activity exhibits substantially increased nonlinear graph spectral coupling compared to interictal periods. The proposed approach provides an interpretable and computationally efficient tool for higher-order interaction analysis for graph signals.

We investigate the detection and accommodation of sensor faults for a proton exchange membrane electrolyzer coupled to a DC/DC converter powered by renewable energy sources. The proposed method for detecting and accommodating the sensor fault is model-free and is based on the concept of ultra-local model that is becoming classic in control engineering. The existing literature on active control tolerant to sensor fault dedicated to this question shows that no previous work has addressed this topic. Our approach mitigates the effect of sensor fault on closed-loop behavior and guarantees the stability and performance of the overall system. Numerical simulations under variations in renewable energy sources validate our approach.

We study the problem of joint compression and detection in distributed sensing systems, motivated by applications such as device-to-device connectivity in IoT networks and distributed radar. In such systems, spatially separated sensors must collaboratively decide whether their observations stem from a common underlying signal, while communicating over highly bandwidth-limited links. We consider a fundamental, insightful model in which one sensor (the encoder) observes a continuous-time realization of a stationary bandlimited Gaussian process, while the other sensor (the decoder) observes a delayed and noisy version of that signal, with an unknown delay. The encoder is allowed to transmit only a $k$-bit message to the decoder to assist in making a binary decision: either the observations are statistically independent, or they are time-shifted noisy versions of the same signal. We propose a low-complexity extremum-based scheme that exploits the structure of the signal to enable reliable decision-making under tight communication constraints. We derive nonasymptotic upper bounds on the false alarm and mis-detection probabilities of our method, as well as a simplified asymptotic bound for the latter. Representative simulations demonstrate that the proposed scheme outperforms the prevalent 1-bit-per-sample quantization baseline and a Fisher-information-based compression benchmark, while closely approaching an information-theoretic (nonrealizable) rate-distortion benchmark.

Achieving optimal screw placement for orthopedic surgeries requires frequent alignment checks and multiple anatomical views under X-ray – a process known as "fluoro-hunting" that increases radiation exposure to patients and surgical teams. This work introduces X-GuideAR, an augmented reality (AR) framework for identifying optimal X-ray views, aimed at reducing radiation exposure while ensuring accurate screw placement. To exemplify the benefits of X-GuideAR, we focus on S2 alar-iliac (S2AI) screw placement. Our system provides radiation-free guidance for view acquisition and drilling by generating synthetic X-ray previews that accelerate fluoro-hunting. Once the required anatomical views are identified using these previews, a real X-ray is acquired, and the preview of the drilling trajectory is augmented onto it, facilitating precise screw placement with minimal additional radiation. A preliminary study involving eight S2AI trajectories performed by an expert spine surgeon demonstrated a 62.3% reduction in the number of X-rays. Post-procedure evaluations showed that trajectories done with X-GuideAR supported an average safe screw diameter of 12.95 mm compared to 5.9 mm under the conventional workflow, suggesting improved bony containment and potential biomechanical benefit. X-GuideAR shows great potential to reduce radiation exposure and streamline S2AI screw placement, offering a promising direction toward safer and more efficient surgeries.

Battery electrical tab cooling is effective at reducing internal thermal gradients by exploiting the high thermal conductivity of the current collectors, whereas surface cooling is effective at reducing temperature rise because of its large heat transfer area. Using either strategy alone, however, limits the achievable trade-off between thermal uniformity and temperature rise reduction. This work proposes an integrated tab-surface cooling (ITSC) system in which coolant is dynamically allocated among the lateral surface and tab channels. The allocation is formulated as an optimal control problem in which the battery temperature is regulated towards a desired reference and thermal gradients are minimised. To support this formulation, a first-principles coolant model is developed and coupled with battery and valve-actuation models. The resulting optimal coolant-allocation problem is solved using a computationally efficient real-time iteration model predictive control (RTI-MPC) scheme, with a nonlinear MPC serving as a closed-loop performance benchmark. Evaluation results under realistic driving conditions showed that RTI-MPC reproduces the nonlinear MPC thermal response with absolute errors below 0.0035 degC while reducing the computational cost from several seconds to 19.3 ms, indicating strong potential for real-time implementation. Additionally, evaluation of the proposed ITSC system against conventional cooling configurations demonstrates that ITSC achieves the best overall trade-off between temperature regulation and thermal gradient reduction.

This work is concerned with the development of control strategies for the Direct Internal Recycling Loop (DIRL) system, which is an essential part of the Tritium Fuel Cycle (TFC) for the fueling of fusion reactors. As a first step, a control-oriented model is developed that describes dynamic behavior of DIRL with interactions between the torus reactor, buffer and fuel units, and recirculation streams. This model is used to evaluate the controllability, stability of the DIRL, and interactions between input and output variables. Moreover, the direct recycling of isotopes from the exhaust gases is discussed from a control perspective. It is observed that Gas Distribution and Storage (GDS) within the DIRL is associated with significant control challenges due to input-output interactions and competing process objectives. Three control strategies are developed and evaluated for the GDS of the European Demonstration fusion power plant (EU-DEMO): a Multiple Input Multiple Output (MIMO) control scheme, a redesigned GDS configuration with extended input variables enabling decentralised Single Input Single Output (SISO) control, and an extended-input MIMO control scheme addressing protium dilution. All strategies are assessed against three control objectives: maintaining GDS pressure around a prescribed set-point to ensure process safety; regulating the tritium-deuterium fuelling ratio for optimal reactor operation; and managing protium concentration to prevent fuelling dilution.

A key limitation on the use of diffusion models in robotic planning is their inability to inherently enforce safety or dynamical constraints, which often results in physically infeasible or unsafe outputs. Hybrid approaches that employ model predictive control (MPC) to address this problem can be unstable, as poor trajectory initializations from the diffusion model prevent the MPC from converging to a safe and feasible solution. To overcome these challenges, we propose D-SafeMPC, which enhances the interaction between diffusion and control. Our method guides the reverse diffusion process with control barrier functions (CBFs) and control Lyapunov functions (CLFs) and employs an iterative-projection scheme where an MPC refines the trajectory at each denoising step. This steers sampling toward safe, goal-directed regions and provides reliable MPC warm starts. In simulations on a Franka manipulator across four scenarios (one static-obstacle and three dynamic-obstacle settings) and in a sim-to-real experiment on a physical Franka robot, D-SafeMPC improves safety, task success rates, and planning efficiency over state-of-the-art baselines. To facilitate reproducibility, our source code and experimental configurations are available in a repository at https://github.com/erdiphd/D-SafeMPC

This paper provides two results that are useful in the study of the existence and the stability properties of almost periodic solutions for a given dynamical system. The obtained results are generalizations of recent results for periodic systems and are applied to the global entrainment problem in nonlinear time-invariant control systems. It is shown that local exponential stability for the unforced system and input-to-state stability with respect to small inputs can guarantee global entrainment to small almost periodic inputs. In this way, global entrainment is shown in Lotka-Volterra systems with a Volterra-Lyapunov stable interaction matrix. All results can be extended to the uniformly recurrent case.

Automated assessment of second language (L2) speaking proficiency requires substantial annotated speech data, which are scarce compared to written learner corpora. We investigate whether written L2 responses can be transformed into useful synthetic speech for proficiency assessment using text-to-speech (TTS) and voice cloning. Using COREFL, a corpus of paired spoken and written responses from L2 learners of English, we systematically study two factors: how written responses should be transformed into spoken-style language ("speechification") and how synthetic voices and texts should be paired based on shared learner attributes (proficiency level, first language, both, or neither). We generate speechified responses with a large language model and synthesise them using TTS and voice cloning, then evaluate their utility for audio-based (HuBERT) and text-based (ModernBERT) proficiency grading. Results show that pairing voices and texts based on proficiency provides a principled strategy for synthetic data generation, while speechification substantially improves the match between written and spoken L2 and improves downstream grading performance. Augmenting real training data with synthetic speechified responses improves both HuBERT- and ModernBERT-based graders, demonstrating the potential of synthetic spoken data for L2 speaking proficiency assessment.

A low-overhead site-specific multi-user multiple-input multiple-output (MU-MIMO) beamforming framework is proposed. Conventional limited-feedback MU-MIMO relies on channel state information reference signal (CSI-RS) transmission and user feedback before grouping and beamforming, which requires substantial online overhead when the antenna dimension and candidate-user pool are large. To reduce this burden, the proposed framework exploits site-specific information (SSI), which captures local radio propagation features. By learning the mapping from low-overhead beam-domain observations to effective transmit spatial subspaces of users, the BS can infer inter-user separability before high-resolution CSI acquisition and construct a compact group-level CSI acquisition subspace for the selected users. This site-specific design can be implemented within the standard limited-feedback procedure using synchronization signal block (SSB)-based reference signal received power (RSRP) fingerprints for subspace inference and CSI-RS feedback for low-dimensional CSI refinement. Extensive numerical results demonstrate that the proposed framework can identify compatible user groups before CSI-RS acquisition, preserve most scheduled-user channel energy in a compact group subspace, and achieve higher effective rates than conventional systems with significantly lower overhead and user-side processing burden.

This paper presents the design, fabrication, and experimental validation of a compact monopulse direction-finding (DF) receiver operating in the X-band. The receiver combines a $2 \times 2$ antipodal Vivaldi antenna array with a rat-race (180-degree hybrid) comparator network that simultaneously synthesizes the sum ($\Sigma$), azimuth-difference ($\Delta_{az}$), and elevation-difference ($\Delta_{el}$) channels required for monopulse processing, removing the need for mechanical scanning. The Vivaldi element provides good impedance matching across 8-12 GHz with a simulated realized gain of approximately 6.44 dBi, and the comparator exhibits the 180-degree phase balance at its difference ports required for angle encoding, with the fabricated prototype preserving the matched, balanced response measured across the band. Three LTC5564 envelope detectors convert the channel outputs to DC voltages that are digitized by an on-board microcontroller and processed in software to form the monopulse ratios and estimate the direction of arrival (DoA), with the result displayed in real time on a MATLAB graphical user interface. Azimuth direction finding is experimentally demonstrated over fourteen angles spanning -20 to 19 degrees at 11 GHz, yielding a root-mean-square error (RMSE) of 7 degrees; the architecture is directly extensible to elevation. The result is a passive, low-cost, and fully integrated receiver suitable for radar sensing, electronic warfare, and surveillance.

Radar cross-section (RCS) reduction is a fundamental requirement in modern stealth technology, playing a critical role in the low-observable performance of aerial and naval platforms. Among the various passive RCS reduction strategies, including radar-absorbing materials, absorptive coatings, and artificially engineered surfaces, metasurface-based cross-polarization conversion has emerged as a compelling approach owing to its structural simplicity and low profile. In this work, a single-layer cross-polarization conversion (CPC) metasurface developed on a cost-effective FR4 dielectric substrate (relative permittivity 4.4, loss tangent 0.02) is proposed for multiband RCS reduction. The designed structure achieves a polarization conversion ratio (PCR) exceeding 95% at three distinct operating frequencies of 7.8 GHz, 11.7 GHz, and 18 GHz, spanning the C-, X-, and Ku-bands, which directly translates into a monostatic RCS reduction exceeding 10 dBsm at the corresponding bands. The metasurface further demonstrates stable polarization conversion performance under oblique incidence up to 60 degrees, confirming its suitability for wide-angle illumination conditions encountered in practical deployment scenarios. Experimental validation conducted in an anechoic chamber confirms close agreement with full-wave electromagnetic simulations, substantiating the reliability of the fabricated prototype. The proposed design offers a lightweight, low-cost, and high-performance candidate for multiband stealth and low-observable platform applications.

Frequency-domain analysis based on converter output admittance is a key tool for studying converter-driven stability in power grids. This paper presents a Python-based identification tool built on a completely open-source simulator, eliminating the need for commercial licenses such as MATLAB or PSCAD and improving configurability through an MIT-licensed stack. The identification method uses steady-state signal injection with a sinusoidal sweep, deriving frequency-domain admittance from time-domain simulations. Analytical output-admittance models are developed for both grid-forming (disturbance-observer-based) and grid-following (phase-locked-loop-based) control to verify the numerical results. The tool's results are compared against a commercial PSCAD-based alternative, demonstrating accurate admittance identification across control methods. Code and examples are available online to support reproducibility.

Deep neural networks (DNNs) have shown strong potential for ultrasound computed tomography (USCT) reconstruction in ideal noise-free environments, yet existing DNNs are vulnerable to the noisy conditions in clinical practice, as they equally treat inputs that suffer mild, moderate, or severe noise. More challenging, the distributions of noise shift along with the environment, indicating the less effectiveness of noise-aware training, which injects a specific noise distribution into the training data. We rethink these challenges and observe that the DNN models can become more robust to noise if we know the noise sources and filter them out. This filtering operation is very alike the Multiplexers (or MUX), a fundamental combinational circuit in digital logic design. However, the challenge here is that noise can happen randomly during inference; as a result, the manually predefined MUX cannot work. To address these challenges, we propose MUX-USCT, a novel encoder-decoder DNN architecture that encodes the known acoustic acquisition geometry with an "adaptive MUX" that can automatically identify and filter noise, where the attention mechanism is applied in reconstructing the speed-of-sound map. On the OpenPros benchmark, MUX-USCT reaches 6.88 m/s MAE with 17% fewer parameters than the leading baseline with 7.65 m/s of MAE. Under simulated clinical noise, it remains stable across diverse degradation types that cause geometry-agnostic baselines to fail. Results show that the attention distributions in MUX-USCT provide interpretable indicators of the signal quality between pairs of transducers.

Objective intelligibility metrics (OIMs) enable fast and low-cost evaluation of speech intelligibility and are widely used in speech technology assessment. In this study, we evaluate five reference-based OIMs (STOI, ESTOI, STGI, HASPI, and SIIB) and two deep learning-based no-reference metrics (MOSA-Net+ and W2V-SIP) on SpInt, a new Spanish speech intelligibility dataset. Our results show that reference-based OIMs consistently outperform modern data-driven no-reference approaches, which degrade notably under training-test acoustic mismatches such as language mismatch. This effect is particularly relevant in our scenario, as none of the evaluated metrics were exposed to Spanish speech data during development. Consequently, to foster research on more robust and generalizable no-reference OIMs, SpInt is released publicly.

We consider the recovery of a pair of sparse vectors from a limited number of nonlinear observations of their superposition: $y_i=g(\inner{\ba_i}{\bPhi\bw^\ast+\bPsi\bz^\ast})+e_i$, $i=1,\dots,m$, with $m\ll n$, incoherent orthonormal bases $\bPhi,\bPsi$, a scalar link $g$, and noise $e_i$ that may be heavy-tailed or contaminated. We propose a regularization-based framework combining a Huberized data fidelity with generalized folded-concave penalties (SCAD, MCP), and a two-block proximal alternating algorithm with backtracking (NLD-PALM) whose whole iterate sequence provably converges to critical points under the Kurdyka–Łojasiewicz property, with local linear rates. On the statistical side we establish restricted strong convexity of the Huberized nonlinear loss through an exact sign-definite decomposition, and derive estimation error bounds of order $\sigma\sqrt{s\log(n)/m}$ that hold at every localized stationary point, an oracle rate $\sigma\sqrt{s/m}$ free of $\log n$ and shrinkage bias under a beta-min condition, and a co-equal recovery theorem for unknown monotone links via a linear surrogate and a clipped Plan–Vershynin decoupling. The estimator requires no knowledge of the sparsity levels, and its guarantees hold under symmetric noise with only finite variance. Experiments at $n=512$ under a frozen data-driven regularization rule show an earlier phase transition than convex $\ell_1$ demixing and greedy hard-thresholding baselines, a $35\times$ accuracy advantage over squared-loss estimation under $5\%$ gross outliers, and successful demixing of spike-plus-background signals observed through a saturating amplifier.

Multimodal sentiment analysis relies on language, visual, and acoustic cues, but utterance-level modality quality may vary due to occlusion, background noise, motion blur, or imperfect transcripts, causing conventional fusion to over-trust unreliable modalities. We propose MRUF, a reliability-aware fusion method that combines multi-granularity routing with uncertainty-aware calibration. MRUF summarizes sentiment-relevant representations, performs subspace- and modality-level routing, and supervises modality routing with leave-one-out error increases to estimate utterance-level modality importance. It further predicts modality-wise uncertainty and refines modality gates through inverse-variance reweighting, while modality-invariant contrastive alignment stabilizes the shared representation space. Experiments on CMU-MOSI and CMU-MOSEI under aligned and unaligned settings show consistent improvements over strong baselines, and mechanism analysis verifies that modalities with higher predicted uncertainty receive lower fusion weights.

Underwater dead reckoning estimates vehicle position when vision is unavailable and external positioning cannot be assumed. A single set of filter parameters can work well in many situations, but fixed tuning may be poorly matched during turns, motion transitions, or periods when sensor measurements are less reliable. This paper presents the Situation-Triggered Calibrated Adaptive Robust Extended Kalman Filter for a BlueROV2. An onboard probabilistic trigger identifies the current motion situation while one error-state filter runs continuously. When the trigger is confident, the filter changes only to the corresponding pre-calibrated process- and measurement-noise matrices; the state estimate, covariance history, dynamics, and measurement models are not reset or replaced. The trigger, noise profiles, and a one-time Doppler velocity log yaw-alignment correction are calibrated offline using sparse AprilTag-supervised pool runs. A separate validation set selects the scheduling policy, which is then fixed before held-out testing. Across four held-out pool runs, the method reduces label-weighted mean per-run translation root-mean-square error from 0.488 m to 0.471 m relative to the same filter backbone with one global noise profile, and every held-out run favors the scheduled method. A paired bootstrap over 10-second segments gives a candidate-minus-baseline difference of -0.017 m with a 95% confidence interval of [-0.024, -0.008] m, while orientation error remains essentially unchanged. These results indicate that situation-aware covariance scheduling provides a modest but consistent vision-free dead-reckoning improvement without switching estimators or resetting the filter.

Machine anomalous sound detection (ASD) is an important technology for industrial acoustic monitoring, where robust acoustic representation learning remains challenging due to limited anomalous samples and complex machine sound characteristics. Existing pre-trained acoustic representation models do not fully capture frequency-specific characteristics of machine sounds. To address this, we propose ECHOv2, a frequency-structured pre-trained acoustic representation model. The model learns localized intra-band representations to capture fine-grained spectral patterns while also incorporating a two-level self-distillation strategy with explicit inter-band supervision to model cross-frequency dependencies. The inter-band branch performs global context alignment and masked sub-band reconstruction, and multiple summary tokens are introduced for structured aggregation with controllable frequency granularity, enabling region-aware interaction across sub-bands during training. This design allows ECHOv2 to robustly handle diverse machine types and noisy operating conditions while maintaining stable representation quality. To enable fair and consistent evaluation of pre-trained audio backbones, we establish a unified ASD benchmark over DCASE 2020–2025 with two complementary protocols: embedding-based evaluation for frozen representation discriminability and adaptation-based evaluation for downstream transferability. Ablation studies confirm the effectiveness of intra-band learning, inter-band supervision, and structured aggregation granularity for robust ASD representation learning. These findings demonstrate that structured cross-band modeling improves acoustic representation learning for machine monitoring and provides an effective pre-trained representation model for industrial anomalous sound detection. The model and benchmark are publicly available to promote reproducible research.

Objective. Existing quadratic unconstrained binary optimization (QUBO)-based sparse-view computed tomography (CT) reconstruction neglects photon-counting statistics and anatomical heterogeneity. We address both limitations within the QUBO framework.Approach. We propose a quantum compressed-sensing CT method combining penalized weighted least squares (PWLS) and guided total variation (GTV). PWLS weights projection residuals by photon-count reliability, whereas GTV uses gradients from a prior image reconstructed by the simultaneous algebraic reconstruction technique (SART) to preserve edges and suppress noise in homogeneous regions. After binary encoding, both terms form a unified QUBO model. Experiments used four 40 times 40 CT images under a 10-view fan-beam geometry with Poisson noise. Comparisons included conventional reconstruction methods, QUBO variants, gradient descent, simulated annealing, and a D-Wave hybrid quantum-classical solver.Main results. PWLS-GTV achieved the best reconstruction quality across all cases. In the representative chest case, it reached a peak signal-to-noise ratio (PSNR) of 36.64 dB, compared with 22.48 dB for SART, the best conventional baseline. GTV consistently outperformed conventional total variation. Simulated annealing and the D-Wave hybrid solver produced similar reconstructions, whereas gradient descent was ineffective. Repeated hybrid-solver runs showed stable performance.Significance. The framework incorporates photon-statistical weighting and structure-guided regularization into QUBO-based CT reconstruction without changing its quadratic form, providing a proof of concept for quantum-assisted sparse-view CT reconstruction.

Accurate geometric calibration is essential for fluoroscopy-guided spinal imaging, digitally reconstructed radiograph (DRR) generation, and 2D–3D vertebral registration. Although calibration quality is typically evaluated using reconstruction-based metrics such as reprojection error, its influence on projection-domain consistency remains poorly understood. This study presents a synthetic framework for evaluating how intrinsic calibration perturbations affect vertebral fluoroscopic projections and downstream registration performance. CT-derived vertebral models and controlled cone-beam imaging geometry were used to generate DRRs with both ground-truth and perturbed intrinsic calibration parameters while maintaining identical anatomy and acquisition pose. Projection-domain changes were quantified using anatomical landmark displacement, contour distance, silhouette overlap, image similarity, and landmark-based 2D–3D registration accuracy in anterior–posterior (AP) and lateral (LAT) views. Results show that even small intrinsic calibration perturbations produce measurable changes in vertebral projection geometry, contour morphology, landmark localization, and DRR appearance. Sensitivity is strongly view dependent, with LAT projections exhibiting substantially greater deformation and anatomical displacement than AP projections. These projection inconsistencies also degrade downstream 2D–3D registration, particularly rotational alignment accuracy. The findings demonstrate that projection-domain consistency complements conventional reconstruction-based calibration metrics and provides a practical framework for assessing calibration robustness. This approach may improve the reliability of DRR generation and fluoroscopy-guided vertebral registration in image-guided spinal applications.

Multiband multistatic integrated sensing and communication (ISAC) in fragmented FR3 bands (7-24 GHz) enables high resolution localization via virtual wideband and spatial diversity. However, frequency anisotropy decorrelates target scattering across non-contiguous bands, while large inter-band frequency gaps generate severe grating lobes that create persistent ghost peaks. We propose sidelobe-controlled off-grid profile estimation (SCOPE), a robust localization algorithm that exploits multi-view consistency across distributed receivers and frequency bands to suppress grating-lobe ambiguities. At the transmitter, an iterative minimax precoder suppresses out-of-region sidelobes to reduce false peaks in the coarse likelihood map. At the receiver, SCOPE employs profile likelihood with Top-K inhibition-based peak selection to avoid trapping in ghost basins, followed by derivative-free off-grid refinement. Simulations demonstrate that SCOPE achieves sub-meter localization with 90% probability at -5 dB SNR and 3 mm root mean square error (RMSE) at 25 dB SNR.

Dance-to-music generation is a promising task for applications such as choreography support and automatic accompaniment, where temporal coordination between body movement and sound is essential. In particular, using human joint positions as the motion representation is attractive because they explicitly capture body dynamics while being lightweight, privacy-preserving, and easy to integrate with motion capture and pose-estimation pipelines. A central challenge in this setting, however, is the scarcity of high-quality paired dance-music data, since collecting accurately synchronized pairs is costly and often constrained by copyright and performance rights. This makes it difficult to train end-to-end models solely from paired data. To address this issue, we propose a dance-conditioned music generation framework that efficiently exploits both unpaired and paired data. Our method combines pretrained unimodal encoders for motion and music, beat-guided contrastive pretraining to align their feature spaces, and a ControlNet-style conditioning module on top of a pretrained text-to-audio diffusion model. Experiments on AIST++ demonstrate that the proposed techniques improve both dance-music alignment and audio quality, as confirmed by quantitative and qualitative evaluations. Compared to a state-of-the-art method, our approach achieves superior dance alignment performance and competitive audio quality. Code is available at https://github.com/kmraven/AudioLDM-ControlNet .

Finite knowledge is usually stored extensionally, one code or vector per item. We ask whether a finite collection can instead be stored intensionally, as the decoded orbit of one compact law that returns exactly to its start. For X objects, we encode item i as the i-th phase of a fixed rotation in a learned latent space and decode all phases with a shared network; the latent advances through a bank of rotations at integer harmonics of the cycle, a real discrete Fourier operator, so that R^X equals the identity and exact closure is guaranteed rather than learned. Images are a controlled carrier; looping video is the case where the phase order is the content's own temporal structure. Holding the decoder fixed and varying only the operator, a general learned operator diverges, a norm-preserving but non-periodic one degrades around the loop, and the exactly periodic operator is flat; on real images the gap widens. Capacity is then the decoder's budget: dense decoders carry a structural overhead per crisp image that no size reconciles with compression, while a small convolutional decoder on objects that share a manifold reaches crisp and compressed. A codebook control shows the generative law is free in reconstruction terms while multiplying the latent store many-fold. On seven benchmark clips, against a matched frame-index baseline, the cycle reaches equal or better fidelity at equal parameters while wrapping at machine precision, where the baseline leaves a visible seam; pinning the baseline's frequencies to loop harmonics closes its seam too, confirming that exact periodicity is the operative constraint. Finite cyclic knowledge can be stored as dynamics rather than independent instances, with exact recurrence supplied by algebra and content by a shared decoder.