Papers

Pretrained speech representations are increasingly used for depression detection, but selecting the best encoder layer on the same data used for evaluation biases reported performance. On DAIC-WOZ, with repeated nested cross-validation across five deep encoder families, naive best-of-25-layer probing inflates AUC by up to $0.09$. On shuffled labels over the real latent representations, naive best-of-25 still reaches AUC $0.59$ versus $0.50$ under nested selection, and this bias grows with the number of probed layers and with smaller samples, isolating selection, not signal, as the cause. The effect replicates on a second clinical corpus and language (Androids, Italian). Under leakage-free selection, a fair comparison across representation families identifies a compact, task-aligned affect–prosody model as competitive with substantially more complex SSL, deep-learning, audio-LLM, and codec-based approaches, while using $\sim\!10^{3}\times$ fewer trainable downstream parameters. Extending the analysis to individual PHQ-8 symptoms shows that the same selection bias persists at this finer-grained level. We argue nested model-selection protocols should be standard when probing encoder layer representations on small clinical speech datasets.

Deploying model predictive control (MPC) requires constructing or identifying a predictive model for each target system. Time-series foundation models (TSFMs) offer an attractive option thanks to strong zero-shot forecasting capabilities across systems. However, low forecast error does not guarantee that a TSFM captures the system's response to the alternative actions considered by the controller. We study this gap using residential heat-pump control as a test bed, measuring the agreement between predicted and ground-truth effects of control interventions. Importantly, we find that TSFMs can recover the system's input-response relationship when the context contains sufficient independent control excitation. Common fine-tuning pipelines and feature smoothing reduce, but do not eliminate, the need for in-context excitation. Our results indicate that current TSFMs used for predictive control require sufficiently informative control variation in the inference context. Initial closed-loop results show promise for shorter context windows.

Low-power speech recognition requires reducing audio acquisition and feature-processing costs as well as neural inference. We present Lyric, a speech-recognition front end that uses wave-domain computing to extract features before digitization. Our proposed system uses passive acoustic resonators to separate speech by frequency and supplies their slowly varying envelopes to a compact temporal neural network. This moves spectral filtering into the acoustic structure, reducing the data and processing needed for recognition. We build a prototype and evaluate spoken-digit recognition on a speakerdisjoint AudioMNIST split across nine classifier families. The front end acquires 32 times fewer scalar samples than a 16-kHz waveform. In the EdgeSpeechNet-A comparison on a Raspberry Pi 4, we reduce preprocessing-plus-inference latency and estimated processor energy per inference by 98.6%, with a 3.58-percentage-point decrease in accuracy to 95.70%.

Underwater Wireless Optical Communications (UWOC) provide essential high data rates for Autonomous Underwater Vehicles (AUVs), but reliable connectivity is critically affected by transmitter-receiver misalignment. This work addresses the beam pointing problem for a moving AUV subject to unknown ocean currents through a Deep Reinforcement Learning (DRL) framework. We develop a comprehensive 3D UWOC channel model incorporating depth-dependent attenuation, turbulence, and a discrete-ray method to accurately quantify geometric and misalignment losses. The resulting agent jointly optimizes beam steering and divergence angles, learning a policy that prioritizes continuous link maintenance. Evaluated using real oceanographic data, the framework's performance is assessed via the excess outage metric, which isolates outages occurring exclusively due to pointing errors. Relative to perfect alignment between nodes, the proposed method bounds this metric to 8.5% under ocean current-free tracking conditions and limits it to 16.2% when subjected to current-induced drift. The findings obtained in this work confirms that DRL-enabled beam control is capable of adaptive tracking and mitigating link outages in dynamic underwater environments.

Gliomas are the most common type of primary brain tumors. Diffuse low-grade gliomas (DLGGs) are slow-growing tumors that are often minimally symptomatic for a long period of time. They progress to a higher grade, resulting in the patient's death. Treatments include surgery, chemotherapy and radiation therapy to control tumor progression. Responses to treatments are highly variable among patients. Furthermore, the diffuse component of this tumor entity has been observed but is not yet well measured. Here, we propose a new variable to quantify glioma morphology, called ESVR (Extra Sphere Volume Ratio). We use then a machine learning approach to study the importance of different variables for discriminating patterns of patients: patient-specific variables, genetic alterations of tumor tissue, and some image-based variables like ESVR and the volume of the tumor, obtained by MRI analysis. Our machine learning approach shows that the patient's age and ESVR at diagnosis, as well as the pathology results seem to play an important role. Taking the highlighted variables into account could thus help to model the tumor behavior. In association with other classical biomarkers, our new ESVR feature could help the clinician to manage the treatment, as it carries significant information in terms of aggressivity of the DLGG.

This paper introduces a dense upper mid-band Frequency Range (FR3) Channel Knowledge Map (CKM) dataset for Uncrewed Aerial Vehicles (UAVs) with an on-board base station (UxNB). It contains 16,180 CKM samples from 3,236 urban locations in 66 cities at 7.125 GHz and UxNB heights of up to 500 meters. Every 513x513 sample stores the building geometry, the line-of-sight (LOS) state, attenuation, delay spread, angular spread, and variable transmitter height. As a focused first use-case, we propose an interpretable height-aware attenuation model. Its LOS branch combines free-space loss, two-ray term, and compact data-based height/range calibration. Its non-LOS (NLOS) branch combines a COST 231-Hata basis with local environment-specific linear calibration. The proposed model reaches 1.93 dB overall root mean square error, with 1.74 dB in LOS and 3.54 dB in NLOS on over 500 unique locations in 14 unseen cities (2,590 samples in total). The result provides attenuation data for FR3 UxNB deployments and a physical anchor for future FR3 modeling efforts.

We present a receding-horizon optimal control for fixed-wing aircraft subject to state constraints. These constraints include operational constraints such as altitude, geofencing, and obstacle avoidance, as well as bounds on flight-path angle, roll angle, and airspeed. The state constraints are composed into a single soft-minimum control barrier function (CBF). We use this composite CBF in a constrained-approximate dynamic program to obtain a sequence of analytic closed-form control functions that approximately minimize a quadratic finite-horizon integral cost subject to the CBF constraint. The resulting receding-horizon control is non-myopic in the sense that it approximately optimizes the integral cost while satisfying the state constraint at all times along the entire prediction horizon. We demonstrate constraint satisfaction and performance in simulations of a fixed-wing aircraft navigating an obstacle-filled airspace under wind uncertainty. We also compare this receding-horizon control with 2 other methods.

Sensor placement for stochastically arriving targets is studied using a void probability objective, defined as the probability that no target remains undetected over a finite horizon. Direct optimization is intractable because it requires an expectation over a random intensity field. A common surrogate based on Jensen's inequality replaces the random field with its mean, yielding a tractable objective but discarding distributional information. The proposed Gamma Laplace surrogate approximates cellwise exposure variables with moment matched Gamma distributions and evaluates the objective using their closed form Laplace transforms. Unlike scalar moment corrections or local expansions, this approach operates at the distribution level and retains a Laplace transform structure under an independent Gamma approximation. A log transformation and constant shift yield an objective shown to be monotone submodular, enabling efficient greedy optimization with performance guarantees. Numerical experiments on real ship traffic data show consistency with the Jensen surrogate in low uncertainty regimes. Under moderate uncertainty, the method improves candidate location rankings, leading to more robust sensor selections and reduced sensitivity to variability in target arrivals.

Ultrasound (US)-based auricular vagus nerve stimulation (aVNS) is emerging as a versatile, noninvasive modality for neuromodulation - spanning clinical therapies (e.g., pain management, depression) and general well-being applications. However, most existing US-based aVNS systems remain bulky and require wet coupling media (e.g., ultrasound gel) to achieve both spatially localized stimulation and efficient acoustic transmission. We present a wearable US stimulation system combining a personalized, dry-coupled earpiece with a battery-powered, wireless US probe. The earpiece integrates a 2 MHz piezoelectric transducer fully encapsulated in Humimic material designed for anatomical fit and stable coupling without externally applied gel. Hydrophone measurements of the fully encapsulated earpiece in water give a peak rarefactional pressure of 0.24 MPa at 15 mm for a 15 V unipolar excitation. We further demonstrate in vivo feasibility through fully dry, in-ear 2 MHz pulse-echo acquisitions under realistic wearing conditions, confirming effective US transmission without the need for coupling gel. These results demonstrate the integration of a personalized dry acoustic interface with battery-powered stimulation electronics, providing a wearable platform for investigating US-based aVNS. Future studies will focus on functional outcomes, including heart rate variability and pupillary responses during stimulation.

The ever-growing number of satellites and resident space objects in low Earth orbit keeps increasing the risk of collision between these objects. To mitigate this risk, the development of new space situational awareness tools and a new generation of probabilistic emulators of the thermosphere is increasingly felt by the community of operators and scientists. We previously built a Reduced Order Probabilistic Emulator based on 3 steps: dimensionality reduction, dynamic modeling, and uncertainty quantification. Here we further develop the dynamic modeling side within ROPE using a class of stable and autoregressive nonlinear models inspired by SINDy, a framework for the identification of the nonlinear dynamics. We use this framework, which we call SINDy-AR, to emulate the thermospheric density simulated by TIEGCM. This is also the first time a SINDy-AR framework is applied to such a high dimensional system as the thermospheric emulation because of its complexity. The dynamical emulators are selected on stability and accuracy criteria, so that they can be relied on in operations and space situational awareness applications. Building surrogate models with SINDy-AR can show dynamical differences between the learned emulations. The state-dependent structure of the TIEGCM emulation shows compatibility with the presence of terdiurnal, 27-day linked to solar rotation, semiannual, and annual tidal waves. The execution speedup of the whole SINDy-AR pipeline with respect to TIEGCM 2.0 is about 90,000 times, which makes the new framework capable of being deployed on board a satellite given the low computational constraints it would be subjected to.

Compressive Synthetic Aperture Radar (SAR) imaging, typically formulated as an inverse problem and solved with traditional iterative optimisation methods, can be very computationally expensive. We investigate the use of denoising diffusion probabilistic models (DDPMs) for compressive SAR image reconstruction, where the diffusion model is guided by a poor initial reconstruction from sub-sampled data obtained via standard imaging methods. We augment this data-driven method with model-driven interleaved refinement processes, inspired by traditional compressed sensing (CS) methods, to enhance sparsity and heavy tails in SAR images. Experimental results on real ERS-1 SAR datasets demonstrate consistent improvements over baseline DDPM and state-of-the-art methods, with notable gains in reconstruction fidelity and structural similarity, while incurring only modest computational overhead. The proposed hybrid framework effectively combines the representational power and efficiency of diffusion models with the interpretability and robustness of model-based optimisation, enabling accurate compressive SAR imaging.

Voice anonymization (VA) is used to conceal the voice identities in speech data. Performance is usually estimated using automatic speaker verification (ASV) as a proxy to judge the capability of an attacker to re-identify speakers after anonymization. The defender anonymizes test utterances; the attacker compares them to equally anonymized reference utterances to infer voice identity, with degraded ASV performance indicating successful anonymization. Thus, the defender's protection is one-sided and only applied to test utterances via VA. Though unexplored so far, there is an opportunity to enhance anonymization by protecting reference utterances too. We present a new, preemptive VA paradigm: reference utterances are protected using adversarial noise to degrade their potential to infer voice identity. Preemptive protection does not degrade the perceived quality of reference utterances and is independent of the specific ASV system used for identity inference. By combining preemptive protection and regular test-side VA, ASV equal error rates can be increased from 14% to 45% (near-perfect privacy).

Investigating the statistics of the sum of random variables (RVs) is fundamental in wireless communication systems, as it can be used to accurately estimate their performance. In this paper, exact closed-form expressions are derived for the probability density function (PDF) and cumulative distribution function (CDF) of the sum of inverse-gamma (IG) RVs. The IG distribution is especially suitable for performance analysis purposes because it provides a tractable yet accurate model of shadowing/local mean power variations. Unlike prior studies on sums of shadowing RVs, the resulting formulas are simple to evaluate and numerically stable. Their utility is demonstrated in two practical settings: unmanned aerial vehicle (UAV)-assisted networks and high-speed railway communications. Across all considered scenarios, the analytical results are in excellent agreement with the Monte Carlo simulation results, while the asymptotic approximations closely track the exact results

Magneto-mechanical resonators (MMRs) have emerged as promising passive sensors for wireless localization and remote inductive sensing in industrial and medical environments. However, fundamental limits on achievable localization accuracy using the spatial sensitivity profiles of receive coils remain a key challenge. In this work, we demonstrate that an MMR - comprising two permanent magnets in an anti-parallel arrangement - exhibits a measurable shift in its mechanical resonance frequency in response to an externally applied magnetic field, a property we exploit for spatial encoding. We derive and experimentally validate a method that maps frequency shifts of 1D positions within a known magnetic gradient field, demonstrating accurate localization along a linear trajectory. This gradient-field frequency encoding approach constitutes a conceptually distinct alternative to conventional sensitivity encoding with receiver coil arrays, with the potential to simplify hardware requirements while improving localization accuracy.

Urban environments are increasingly exposed to complex, dynamic, and interdependent risks, ranging from traffic incidents and infrastructure failures to large-scale crises such as extreme weather events and emergency response scenarios. In such conditions, decision-makers and operators are required to act under time-critical constraints while relying on fragmented, heterogeneous, and often incomplete information. The lack of unified situational awareness, combined with limited visibility and disconnected systems, significantly affects response time, coordination efficiency, and overall operational effectiveness. In parallel, modern cities have deployed extensive sensing infrastructures, including surveillance camera networks, IoT devices, connected vehicles, and satellite-based observation systems. Although these technologies generate vast amounts of data, their exploitation remains limited due to the absence of integrated platforms capable of real time data fusion, intelligent interpretation, and intuitive visualization. As a result, a substantial gap persists between data availability and actionable intelligence, particularly in safety-critical and crisis management applications. To address this challenge, this paper presents an AI-driven XR situational awareness and operational platform designed for urban crisis management and smart mobility operations. The proposed system integrates data from city infrastructure, connected vehicles, and VRUs, enabling a comprehensive and real-time understanding of the urban environment. Through a unified operational dashboard and AR interfaces, the platform supports both centralized monitoring and field-level interaction. By enhancing perception, enabling cooperative awareness, and delivering context-aware information, the proposed approach improves decision-making, coordination, and safety across diverse urban scenarios.

We consider the fusion of lung ultrasound images with routinely-collected clinical and demographic data for the purpose of automated tuberculosis (TB) screening using deep-learning. Such deep-learning based screening tools for TB could meaningfully support the health care system in Africa, where the burden of disease is severe and resources are constrained. Beginning with an established ResNet baseline for classification of lung ultrasound images, which achieves an area under the receiver operating characteristic (AUROC) curve of 0.91 [0.86,0.96] (95% CI), we consider the incorporation of the clinical and demographic data using three fusion approaches. We find that a simple average-based fusion of the output scores of separately-trained image and clinical data classifiers consistently matches or outperforms a more complex approach where the data is fused earlier and a combined classifier is trained. Fusing the image and the clinical classifiers in this way leads to a classifier with an overall AUROC of 0.95 [0.91,0.99] (specificity of 0.76 at sensitivity 0.93) which is an improvement of 4% absolute over the image-only baseline. We also find that greedy feature selection can be used to reduce the number of clinical and demographic inputs without sacrificing classification performance. Finally, when we differentiate between clinical and demographic data that are self-reported, that require some basic measurement or calculation, and that require a point-of-care (POC) test, we find the inclusion of the POC tests included in this study to be of minimal benefit to classification performance. We conclude that the incorporation of routinely-collected clinical and demographic data is a promising way to improve the performance of lung ultrasound based automatic classification.

A dubbed character keeps its identity while the voice actor changes, so character identity and speaker identity are distinct properties of one recording, yet speaker verification measures only the latter. To address this gap, we propose KyaraBench, a benchmark that scores character voice directly instead of speaker voice, built from 85 human-audited identities in a dubbed anime corpus. It poses two challenging conditions: one that swaps the performer under a fixed character, and one that fixes the performer under changing characters. Listening studies with 78 participants provide human reference scores for cross-performer verification and same-actor discrimination. Speaker-verification baselines show increased errors under these character-specific conditions. We then train KyaraEmbed, a compact encoder using multilingual character supervision, same-actor negatives, and a language-alignment term. The model achieves the best performance on all character-specific conditions in the main comparison. We release the benchmark, protocol, and encoder publicly.

Radio frequency fingerprint identification (RFFI) enables device identification and authentication by exploiting transmitter-specific hardware impairments embedded in received RF signals. Existing RFFI methods are typically designed for a specific operating regime, such as closed-set identification, open-set recognition (OSR), or discovery of new devices, and often treat these tasks as separate algorithmic components. In this paper, we propose a holistic RFFI framework that jointly supports closed-set identification, OSR, and novel class discovery (NCD) within a single expandable architecture. The proposed method formulates RFFI as vector quantization in a learned latent feature space, where registered devices are represented by labeled codewords. This representation enables closed-set identification through codeword assignment, OSR through uncertainty measures induced by the codebook geometry, and NCD by using the latent representations and open-set uncertainty of rejected samples to organize them into new device identities. We further develop a multi-stage training procedure that combines supervised representation learning, latent-space refinement, codebook initialization, and vector-quantization-aware optimization. Experimental results on LoRa and WiFi RFFI datasets demonstrate that the proposed framework achieves accurate identification of registered devices, reliable detection of unseen transmitters, and effective discovery and registration of new device classes, highlighting the benefits of coupling the different RFFI tasks.

Smart ports increasingly depend on IoT-enabled digitalization to turn heterogeneous operational processes into measurable, optimizable workflows. Accordingly, this paper first provides a concise review of IoT-enabler technologies for port digital transformation, emphasizing how sensing, connectivity, and data integration capabilities support real-time decision-making in port environments. Motivated by the growing emphasis on operational decarbonization reflected in the European Green Deal and the Sustainable Development Goals (SDGs), we then present a data-driven fleet scheduling framework for landside port operations at the Port of HaminaKotka (Mussalo terminal), Finland. The scheduling framework reallocates vehicle operating hours to minimize total CO2eq emissions while preserving the same aggregate workload. The study uses a two-year dataset from 77 port-operating vehicles, analyzed at monthly and annual aggregation levels, to derive vehicle-specific intensity metrics (CO2eq per operating hour) and identify reallocation opportunities. We formulate the scheduling task as a linear programming workload allocation problem that minimizes the weighted sum of assigned hours while keeping total required hours fixed, enforcing fleet-level caps on CO2eq and energy use, and respecting per-vehicle operating limits. The optimization is solved using the HiGHS linear programming solver via SciPy's linprog. Results show that shifting utilization away from high-intensity vehicles and toward more efficient units yields substantial system-level improvements with no reduction in total operating hours: the optimized schedule achieves an approximately 31% reduction in total CO2eq and a simultaneous 9.4% reduction in total energy consumption.

Dysarthric speech reconstruction (DSR) typically relies on linguistic or phonetic representations extracted from impaired speech to generate a more intelligible waveform. We investigate a complementary approach that instead intervenes in a representation related to speech production. Starting from a neural analysis–synthesis framework, we introduce a residual mapper that modifies an articulatory-aligned latent space while preserving speaker and prosodic information. The mapper is pretrained on parallel synthetic healthy and artificially dysarthric speech, then adapted to natural dysarthric speech using phoneme-guided and adversarial objectives. We further compare the articulatory bottleneck with a dimension-matched unsupervised representation to assess the benefit of explicit articulatory supervision.

One of the major transformative factors in 6G will be the integration of Artificial Intelligence (AI) to become a native part of Radio Access Network (RAN). While most physical-layer AI features have so far been evaluated in isolation using link-level simulations, their combined behavior in a realistic multi-cell, multi-UE deployment has remained largely unexplored. In this paper, we present system-level performance results when multiple uplink AI features are enabled together, achieved by integrating accurate link-level and system-level simulators. To infer state-of-the-art deep-learning-aided Multiple Input Multiple Output (MIMO) receivers under the dynamic allocations produced by a realistic uplink scheduler, we propose a mirrored data augmentation method that decouples receiver performance from scheduled allocation size. In addition to these Physical Layer (PHY) receiver features, we combine several recent advances in deep reinforcement learning to train uplink power control and link adaptation that outperform a heuristic baseline and further boost the gains obtainable from the AI receiver alone. The system-level results show that the combined AI features improve the mean uplink user throughput by roughly 27% compared to a non-AI baseline, confirming that the individual PHY and Medium Access Control (MAC) AI features provide complementary gains when deployed jointly.

Electric vehicles and heat pumps add large, time-flexible loads to low-voltage distribution feeders. When each household independently minimizes its own charging cost against the same electricity price, this flexible demand piles into the cheapest hours, and the resulting synchronization can stress the feeder more than uncoordinated operation does. This work quantifies that effect on a rural low-voltage feeder (97 buses, 250 kVA transformer) across 160 simulation runs spanning seven scenarios, four seasons, and electric-vehicle, heat-pump, and photovoltaic penetration from 0 to 90 %. At 30 % penetration of each device, this decentralized cost minimization drives the transformer to 253 % of rated capacity, more than twice the level of uncoordinated charging, and up to 478 % at 90 % electric-vehicle penetration, with the first undervoltage violations at the far end of the longest feeder arm. The ranking is device-mix dependent: each 30 pp of heat-pump penetration adds about twelve times more to the price-based peak than to any fixed schedule, so price-based scheduling becomes the worst strategy once heat pumps are present. Only a central schedule that explicitly caps the transformer removes these demand-driven violations, while at high photovoltaic penetration a summer floor from midday reverse power flow bounds what any demand-side strategy can achieve. A regulated time-variable grid tariff added to the spot price provides no relief for heat-pump-driven synchronization and only marginal relief for electric-vehicle-driven synchronization in autumn. The simulation and optimization code is available at https://github.com/lukas-wagner/SynchronizationEffectsAnalysis.

The flat voltage plateau of lithium iron phosphate (LFP)/graphite cells makes electrochemical-state errors and voltage-measurement abnormalities produce similar innovations, complicating state-of-charge (SOC) estimation. This work proposes an electrochemically constrained residual-bias compensation dual extended Kalman filter (RBC-DEKF) with uncertain-initialization commissioning. A thermal control-oriented parameter-grouped single-particle model (CPG-SPMT) provides paired-electrode dynamics and terminal-voltage prediction, while a separately configurable voltage-discrepancy state accommodates systematic residuals. When the initial SOC is uncertain, residual adaptation is suspended over a verified-healthy startup window. Buffered data support electrochemically constrained trajectory matching, followed by calibrated state replay and residual-channel reactivation. Frozen cross-cycle healthy-residual calibration supports commissioning and remains in the voltage prediction after handover. Evaluation covers 216 additive-bias and 168 multiplicative-gain profiles over 24 A123 temperature-drive-cycle trajectories. Relative to Single-EKF, the original RBC-DEKF reduces mean SOC RMSE from 7.646 to 0.170 percentage points for additive bias and from 22.646 to 0.356 percentage points for gain faults. Separately, 18 nonzero-initial-error profiles on three representative 50%-SOC-anchored segments give a full-record mean SOC RMSE of 1.747 percentage points, including startup, for the commissioned realization, versus 11.192 and 11.400 for the always-on RBC-DEKF and conventional joint EKF. Representative delayed bias, 10-s ramp, and gain tests show that the corrected SOC trajectory remains stable after fault introduction. The central contribution is electrochemically constrained temporal coordination of uncertain-state recovery and subsequent voltage-discrepancy accommodation.

Deploying large antenna arrays transitions wireless communication into the radiative near-field (NF), where spherical wavefronts enable beamfocusing in the joint angle-distance domain. However, wideband beamforming with large antenna arrays suffers from beam split across both spatial dimensions. Although fully digital arrays can eliminate this impairment, their prohibitive hardware cost and power consumption motivate the adoption of hybrid array architectures equipped with a limited number of radio-frequency (RF) chains. This paper investigates the minimum number of RF chains required to mitigate the beam split. We derive closed-form expressions for the required number of RF chains in fully-connected (FC) architectures and propose an algorithm for sub-connected (SC) architectures, considering both uniform linear arrays (ULAs) and uniform circular arrays (UCAs). Our analysis reveals that beyond a focal distance of 10L, where L denotes the aperture length, the NF RF chain count converges to its far-field counterpart and becomes a function of the angle alone. Moreover, the FC hybrid architecture mitigates the beam split by using fewer RF chains than the SC architecture, albeit at the cost of increased complexity in the phase shifter network.

A bipedal robot cannot deviate from its path to avoid an obstacle without disturbing its balance, and this coupling is most severe on underactuated platforms such as the biped considered here, which has four actuated joints per leg and no hip or ankle roll. This paper presents a Hierarchical Reinforcement Learning (HRL) framework in which a High-Level (HL) policy observes the robot pose, 36 raycast proximity measurements, moving-obstacle states, and a receding-horizon local goal, and outputs a body-velocity command $(v_x, v_y, \omega_{yaw})$ every ten control steps, while a velocity-conditioned Low-Level (LL) policy tracks each command through PD-controlled joint targets. Both policies are trained jointly with Soft Actor-Critic (SAC). Because the converged gait is task-agnostic, it is frozen and driven by classical planners over the same command interface, yielding three controlled baselines: SAC+A*, SAC+RRT*, and SAC+APF. Across 100 evaluation trials per method in randomized PyBullet environments, the proposed method reaches the goal in 98.0% of static and 88.0% of dynamic trials, against at most 78.0% and 68.0% for the planner hybrids, with path lengths within 4% of the A* reference, and ablations confirm that each observation channel and reward term contributes materially to this performance.

Robotic ultrasound couples a high-rate contact-force loop with slower, delayed image feedback, so image-guided ultrasound probe rotation can perturb contact force before the resulting image response is observed. We derive a closed-form orientation-rate limit that bounds the modeled rotation-induced estimated-force excursion over a finite horizon while accounting for disturbance rejection by the fast force loop. The limit depends on local contact stiffness, force-loop gains, a conservative rotation-to-force gain bound, the excursion budget, and the prediction horizon. We implement this model in a dual-rate controller with timestamp-based delay reconstruction and joint-torque-based force estimation, and evaluate it on a curved gelatin phantom using paired controller comparisons and component ablations. Relative to unconstrained image guidance, the proposed rate-limited controller reduced first-second root-mean-square (RMS) estimated-force error by 0.40 N while increasing cue-convergence time by 0.94 s. A fixed rate cap near the analytically predicted ceiling produced no resolvable difference in force error and converged 0.32 s faster, indicating that the principal practical value of the model is the rate-design rule rather than online prediction. Delay reconstruction had no resolvable effect at the tested latency. A single-subject popliteal scan demonstrated feasibility, although the image cue was noise-limited on heterogeneous tissue.

Battery systems and algorithms are developed and evaluated primarily on laboratory cycling data, but fielded battery systems are subjected to duty cycles, temperatures, and measurement conditions that cannot be reproduced in a lab. Field data from batteries in service has begun to appear in public releases attached to individual papers, but each release has its own format, units, labels, and other gaps that make it difficult to compare against others. We present a curated collection of 23 public field-data releases from 17 research groups, covering applications that span passenger EVs, buses, light electric mobility, grid-scale storage, distributed storage, consumer electronics and industrial robotics. Where the releases are complete enough to measure, this heterogeneous collection contains 226 GB of data from at least 131,096 lithium-ion cells, 3,025 battery systems, 44.4 MWh of energy storage capacity, and 2,918 released unit-years of observation. It also contains 784 labeled or reported faults, which provide greater visibility into what real battery performance and safety issues look like outside the lab. We release a public repository of metadata and code for each of the field datasets. For each dataset it provides a registry entry with sources, license and access links, checksums to confirm a download is complete, a metadata table, and code for how to load it. We also report where released datasets differ from their papers. We hope the collection can help serve researchers (human or otherwise) as a useful platform for improving battery field diagnostics, fault detection and health estimation. Better field evidence can also feed back to improve manufacturing quality, inform second-life screening and residual-value estimates, and broadly accelerate electrification.

In this paper we provide an extension of the partial-state Deadzone-Adapted Disturbance Suppression (DADS) adaptive control scheme in the case of strict-feedback systems. We study two cases: the cases of strict-feedback systems with and without integrators. The DADS controller achieves attenuation of the output to an assignable small level, despite the presence of (time-varying) disturbances, unknown (time-varying) parameters and unknown (time-varying) input coefficients; all of them with arbitrary and unknown bounds. The mechanism that makes DADS work is the same in all previously studied cases where DADS has been applied: the update law "pumps up" the nonlinear controller gain to a sufficient size, adapting the gain to the unknown uncertainties, and shuts off further pumping up of the gain once the output is in the residual set. The results are directly extended to the infinite-dimensional case. Two simple examples are provided: one finite-dimensional example and one infinite-dimensional example. The finite-dimensional example is used for a detailed comparison with existing results in the literature.

Optimisation-based parking planners usually impose collision constraints only at the time samples, so a vehicle corner can cut an obstacle between samples, and no executable trajectory exists until the solver converges. We present a planner for car-like vehicles with reverse gear in which every iterate of the optimisation phase satisfies the discretised dynamics exactly and keeps the whole vehicle rectangle clear of obstacles between the samples. Each time interval receives one convex corridor that holds all vehicle corners at both ends and is shrunk by a sweep margin bounding how far the corner paths leave their chords. Separating half-planes give the corridors a direction out of obstacles when the initial guess is in collision; later, heading-aligned boxes are grown from the speed, curvature and step of the current iterate and rebuilt after accepted steps. A feasibility-perturbed trust-region sequential quadratic programming method projects each step onto the dynamics by feedback and verifies it exactly; the cost decreases monotonically, and once the corridors stop changing, limit points are Karush-Kuhn-Tucker points of the corridor-constrained problem or violate a constraint qualification. On 820 benchmark cases the planner succeeds in 818 without penetration (797 from the first initial guess), none of the 7103 evaluated optimisation-phase iterates is unusable, and it succeeds in 96.5% of the cases when 99% of the initial guesses intersect an obstacle. Its maneuvers take 0.7% longer in the median than those of a similarly certified exact-collision baseline. The guarantees hold for the planning model, not for a physical vehicle.

Adaptive control is attractive for nuclear reactors expected to operate predictably with extensive automation or bounded autonomy under dynamic electric and nonelectric demands, even as reactor and actuator characteristics depart from their nominal values. Standard adaptive control results, however, assume fixed matched dynamics and an input applied as computed. In this work we evaluate a recent model reference adaptive control with linear quadratic regulation (MRAC-LQR) construction on the nonlinear point-kinetics model of Purdue University Reactor Number One (PUR-1), with a moving physical reference, nonlinear rod worth, rate and travel limits, quantization, deadband, and optional power-only state reconstruction. An exact sampled decomposition separates matched adaptation from observer, command processing, actuator realization, independent reactivity, and unmatched-dynamics residuals. Numerical results emphasize compatible reference trajectories, plant parameter variation, response to unexpected rod motion, and actuator duty. Across sampled plants, one unchanged nominal design yields closely aligned responses through adaptively calculated, plant-dependent actuation histories, illustrating both the premise and the theory limits of adaptation at a pool-reactor scale that can support follow-on experimental verification and progression toward higher-consequence nuclear applications.