Papers

In this work, we present a simple method to engineer a small neural network to control power converters. Learning-based methods have emerged as a promising approach for power converter control over wide operating point variations. However, many of these approaches do not exploit the analytical structure of converter dynamics and, therefore, search over unnecessarily broad policy classes. We address this gap with a systematic framework that combines model-based trajectory generation with policy learning. First, we use differential flatness of averaged converter models to generate feasible state and constrained duty cycle trajectories where duty acts as a control effort signal. Then for policy learning, we use these model-generated trajectories to construct labeled state-to-duty data and train a multilayer perceptron to approximate the control law with a static map. We apply this framework across buck, boost, and buck-boost converters through topology-dependent flat output selection and a common learning pipeline. Simulations show close agreement between learned and model-generated duty trajectories and stable regulation under large synchronous input and load disturbances.

To address parameter sensitivity commonly in interior permanent magnet synchronous motor (IPMSM) sensorless control, this paper proposes a parameter-robust control framework by extending the adaptive flux observer from surface-mounted PMSM to salient-pole machines. First, parameter mismatches are interpreted as an equivalent flux vector with $d$-axis and $q$-axis components. Permanent magnet flux, $d$-axis inductance, and resistance mismatches are mapped to the $d$-axis flux component, whereas $q$-axis inductance mismatch is mapped to the $q$-axis component. Then, a scalar adaptive flux update is designed to compensate the $d$-axis flux variation by tracking the equivalent flux magnitude. Since the $q$-axis component rotates the observed flux direction and cannot be eliminated by scalar adaptation, a high-frequency $q$-axis voltage injection method is introduced to identify the $q$-axis inductance. Meanwhile, stability analysis proves bounded equivalent flux magnitude tracking and identifies the residual tangential flux responsible for position error. Finally, experimental results show that the proposed method does not require precise resistance, $d$-axis inductance, or flux, while the $q$-axis inductance is provided by online identification.

With the rapid development of the low-altitude economy, low-altitude operations are booming, where complex missions require collaborative efforts among multiple heterogeneous low-altitude aircrafts (LAAs). Specifically, different LAAs assume distinct roles: some for sensing, some for communication, some for computing, and others for mission execution, together forming a sensing-communication-computing-control (SC3) closed loop, akin to a reflex arc. To enable efficient coordination in such multi-LAA swarms, we introduce the concept of operational-capability entropy (OCE) to quantify the effective work capability of operational LAAs. Accordingly, by jointly considering heterogeneous OCE and channel conditions among LAAs, we formulate the power allocation problem with the goal of minimizing the linear quadratic regulator (LQR) cost, which serves as a metric for mission efficiency. The resulting complex optimization problem is decomposed into two convex subproblems that are solved iteratively, with closed-form solutions derived for each. Simulation results demonstrate that the proposed mission-oriented adaptive power allocation scheme significantly outperforms traditional ones.

High-altitude platform stations (HAPSs) are expected to coexist with terrestrial networks (TNs) to provide seamless wide-area wireless connectivity in future sixth-generation (6G) systems. However, the strong line-of-sight (LoS) propagation of HAPS links introduces severe cross-tier interference under spectrum sharing. This paper proposes a reverse time division duplexing (RTDD)-based transmission framework for integrated HAPS–TN systems, where the HAPS and terrestrial base station (BS) operate in opposite transmission directions to mitigate cross-tier interference while preserving TDD channel reciprocity. Standardized 3GPP channel models are adopted for both HAPS and terrestrial links. To enable tractable performance analysis, an analytical Kronecker channel model is developed to approximate the empirical 3GPP terrestrial BS channel, based on which a random matrix theory (RMT)-based large-system analysis is established to derive deterministic approximations for the uplink and downlink signal-to-interference-plus-noise ratios (SINRs). Numerical results demonstrate an excellent agreement between the proposed analysis and Monte Carlo simulations. Moreover, the proposed RTDD framework effectively suppresses cross-tier interference, achieves near interference-free performance.

User-generated content has become one of the most-consumed content types. However, capturing spatial audio with consumer hardware is still challenging. Given the widespread success of smart earbuds, binaural audio could be a promising option to capture spatial audio on consumer devices, but its inherent signal characteristic limits its usability as a recording format. In this paper, we propose and define a new task, Binaural to Ambisonics conversion (Bin2Ambi). In our proposed system, we exploit simultaneously captured head-tracking data provided from the motion sensors in smart earbuds. We show that this motion data help resolve the inherent directional uncertainty of two-channel binaural audio due to front-back localization ambiguities and lateral errors in the cone of confusion. Our results show that our system learns directional and diffuse-field information and that head-tracking especially reduces extreme localization errors. Objective metrics and a subjective listening test suggest that the converted Ambisonics soundfield achieves an average directional error of up to $11.8^\circ$ and a perceived spatial quality similar to a DirAC ground-truth model. The proposed algorithm can serve as a baseline for future improvements to this novel Bin2Ambi task.

Grid-forming (GFM) inverters are gaining attention as a promising alternative for conventional synchronous generators in the modern power systems. Unlike conventional synchronous generators, GFM inverters have limited overcurrent capability that makes them vulnerable during large disturbances. During disturbances i.e., voltage and frequency dips, GFM inverters are pushed into current-limited operation to protect the semiconductor switches. This causes the internal angle of the GFM inverters to accelerate and lose synchronism with the rest of the grid. To address this limitation, this article proposes a transient triggering grid-forming (TTGFM) synchronization control to enhance the synchronization stability performance under voltage and frequency dips. This method uses two feedback signals; terminal voltage and the difference between unsaturated and saturated power to adjust the internal angle which is generated by power synchronization loop (PSL) of the GFM inverters. These two signals manipulates the internal reference angle generation that act as a virtual braking mechanism. This mechanism limits the angle acceleration during voltage and frequency dips without requiring an extra supervisory signal or parameters to tune. The proposed method is benchmarked against two state-of-the-art synchronization enhancement schemes and validated through high-fidelity electromagnetic transient (EMT) simulations with a grid dynamic equivalent (GDE) model in MATLAB/Simulink\textsuperscript{\textregistered}. An analytical framework is developed to derive the synchronization instability mechanism and the critical limits of the stability margins. Generalization of the GDE model further shows that the TTGFM control is not restricted to a single configuration but is applicable to any standard benchmark system.

This paper develops a cooperative integrated sensing and communication (ISAC) framework in which a multi-antenna base station (BS) simultaneously localizes a target and serves multiple communication users, a subset of which is equipped with reconfigurable intelligent surfaces (RISs). The Fisher information matrix (FIM) for target positioning is derived, explicitly characterizing its dependence on the BS beamforming coefficients, RIS phase profiles, and bistatic sensing geometry. Under the stated scaling assumptions, coherent RIS phase alignment yields a Fisher-information gain that scales quadratically with the number of RIS elements, whereas independent random phases provide a linear gain in expectation. We formulate a sensing-centric joint active and passive beamforming problem that minimizes the position error bound (PEB) subject to per-user signal-to-interference-plus-noise ratio (SINR) and transmit-power constraints. The resulting non-convex problem is addressed through an iterative successive convex approximation (SCA) procedure that solves a sequence of convex subproblems. We further develop a target localization estimator that fuses one direct time-of-arrival (ToA) measurement, one angle-of-arrival (AoA) measurement, and multiple RIS-assisted indirect ToA measurements. Under small measurement errors and asymptotically efficient first-stage ToA/AoA estimation, the estimator covariance approaches the Cramér–Rao bound (CRB) to first order. Numerical results validate the analytical scaling laws and demonstrate the localization gains enabled by cooperative RIS-equipped users.

Emerging non-matrix ML kernels, such as LayerNorm, GeLu, FFT or circular convolutions, demand low-latency, energy-efficient spatial accelerators beyond MatMul-centric arrays. STELLA presents a spatio-temporal elastic 16 nm coarse-grained reconfigurable array (CGRA) with a rapid configuration path, per-PE hardware loop control, and a low-latency, deeply pipelined elastic fabric with spatio-temporal data reuse. STELLA reaches up to 110 GOPS/mm2 at 850 MHz, and improves effective kernel throughput by 4.84-7.14x over baseline CGRAs.

Single-image HDR reconstruction requires inferring missing detail while preserving the visible content of an LDR image. Differences in sensor dynamic range and exposure cause LDR images to lose varying amounts of information in shadows and highlights. We present ExpandDiff, a conditional diffusion pipeline that jointly reconstructs clipped shadows and highlights. To account for this variation, we introduce Dynamic Clipping Synthesis (DCS), which randomly samples shadow and highlight clipping percentiles when constructing training inputs from HDR targets. A pixel-space diffusion model guided by spatially-adaptive normalization then predicts perceptually encoded HDR through a bounded output head, reconstructing both clipping directions in one sampling trajectory. On the SI-HDR benchmark, ExpandDiff variants improve HDR reconstruction accuracy by 3.43 dB in PU21-PSNR over the strongest evaluated competing method, and by 7.34 dB under two-sided clipping. The code and supplementary material are available at https://memreandiran.github.io/expanddiff/.

In this paper, the dispersion behavior of periodic cross-shaped unit cells is investigated with emphasis on Dirac-type dispersion using an equivalent circuit modeling approach. Two unit-cell geometries, including a simple cross structure and a modified configuration with a central patch, are analyzed using full-wave eigenmode simulations and scattering-parameter-based dispersion extraction. An equivalent circuit model is developed to capture the dominant coupling mechanisms and accurately reproduce the dispersion characteristics near the $\Gamma$-point. The results demonstrate that the proposed circuit model exhibits good agreement with full-wave simulations and provides a physically intuitive framework for analyzing stopband behavior and mode degeneracy. Furthermore, the limitations of S-parameter-based dispersion extraction in the presence of low-Q radiative modes are highlighted.

We present an Integrated Sensing And Communication (ISAC) framework for the joint estimation of the Doppler frequency of a passive mobile target in a bistatic scenario with clock-asynchronous nodes and where the Receiver (RX) is static but the Transmitter (TX) is mobile. In such a setup, previous geometric solutions are integrated with an Inertial Measurement Unit (IMU) device at the TX, coming up with a truly joint data fusion and estimation algorithm based on extended Kalman filtering. The approach jointly estimates the target Doppler frequency, along with the speed and direction of motion of the TX and it is robust to the unavailability of static paths between the TX and RX pair, a condition that makes previous solutions ineffective. The developed extended Kalman filter strikes a balance between the accuracy of ISAC and the IMU reliability. The proposed solution is validated via numerical simulations, obtaining a median Doppler error of 1.2% with a smartphone-grade IMU under realistic operating conditions.

Accurate human torque estimation is crucial for enabling task-agnostic control in robotic exoskeleton systems. However, estimation errors may cause mismatches between the robot assistance and the human intention, degrading controllability and task performance. In this paper, we address this issue by formally defining matched assistance as scenarios in which the robot positively contributes to human movement. Based on this definition, we develop a theoretical framework to design the robot's desired interaction torque that guarantees a lower bound on the matched assistance probability. Importantly, the proposed guarantee holds over the entire torque distribution, including unseen data beyond the training tasks. This provides our method with strong reliability and generalization, both of which are critical for effective exoskeleton control. The proposed strategy is implemented on the ABLE upper-limb exoskeleton and evaluated in a multi-task setup. Experimental results validate the theoretical guarantees and demonstrate that the proposed strategy achieves effective general performance across several tasks, guaranteeing movement smoothness while reducing human physical effort.

This work investigates the spatially wideband (SWB) array factor (AF) of linear thinned antenna arrays. The SWB AF is formulated as a matched filter and expressed as a spatially variant convolution of a spatially narrowband (SNB) AF and a SWB kernel. Originally derived for uniform arrays, the SWB AF approximation is revisited and extended to thinned arrays. The approximation is shown to remain accurate across a wide range of parameters. Representing the SWB AF as a convolution offers an intuitive interpretation of how the bandwidth-aperture product modifies the SNB AF. Specifically, the SWB kernel acts as an averaging window for the SNB AF, suppressing the sidelobe levels (SL) of the SNB AF. In thinned arrays, the sidelobes away from the mainlobe can be treated as random, noise-like, making averaging and suppression particularly effective. Next, the analytical formulas for the expected SL and peak-to-sidelobe level (PSL) in SNB thinned arrays are first introduced and subsequently extended to the SWB regime. Finally, simple closed-form expressions for the expected SL and PSL levels of the uniformly thinned arrays with a uniform spectrum are provided. The expected SWB SL and PSL are formulated as functions of the bandwidth-aperture product and are shown to steadily decrease with distance from the mainlobe.

Speech emotion recognition (SER) is the task of assigning emotion labels to utterances. Early systems relied on acoustic features, whereas recent approaches combine multiple modalities, most commonly speech and text. Still, performance remains poor on many datasets. Large language models (LLMs) have therefore attracted interest for SER, as they can process diverse inputs jointly with instructions. However, direct audio input raises questions of explainability. To address similar questions in image classification, concept bottleneck models were introduced. This work adapts concept bottlenecks to SER to examine how individual predictions depend on transcripts, acoustic descriptions and speaker attributes. Experiments test three LLMs on CREMA-D, IEMOCAP and MELD, with concepts extracted by separate tools. On scripted corpora, LLMs are strongly biased towards the transcript in the zero-shot setting, which lowers Macro-F1 from 27.8 to 5.8 on CREMA-D. Fine-tuning removes this bias, and the transcript raises Macro-F1 from 41.8 to 45.1. Removing speech rate changes 48% of Neutral predictions to Disgust on CREMA-D; removing intensity level on MELD changes predictions despite little change in Macro-F1. These findings show that aggregate performance changes alone do not capture the effects of concept removal on individual predictions.

In integrated sensing and communication (ISAC) systems involving multiple operators, unmanned aerial vehicle (UAV) tracking can benefit from sensing at multiple base stations, while raw sensing records, channel information, and transmit decisions remain local to each operator. The resulting problem is to obtain a reliable UAV prediction from different track estimates and use it to guide transmission without centralizing their data or control variables. To resolve this, we propose a two-timescale framework comprising slow-timescale motion-uncertainty calibration and online track fusion with predictive beamforming. Local radar innovations calibrate the uncertainty assigned by each tracker to unmodeled UAV motion, with only the resulting parameters shared across operators. During online tracking, covariance intersection (CI) combines local estimates without requiring knowledge of correlations between their estimation errors. The fused prediction then guides each operator's transmission design subject to communication QoS, power, and tracking constraints. We further establish exact rank one recovery for the communication covariances and finite interval bounds on local tracking uncertainty. Simulations show improved consistency of tracking uncertainty under substantial motion model mismatch. CI avoids overconfidence under correlated errors, while using the current fused prediction reduces RMSE by about 31% relative to transmission designed for communication alone, with less than 2% additional power. Ablation further shows that the power difference between current and outdated predictions is driven mainly by predictive covariance rather than predictive mean.

This paper proposes a communication-free multi-robot task allocation framework based solely on local observations. In this study, tasks are defined as reaching target locations. Each robot estimates the positions of neighboring robots using a Labeled Multi-Bernoulli (LMB) filter and independently assigns tasks through a greedy auction-based strategy. By continuously updating state estimates and reallocating tasks during execution, the proposed method enables decentralized coordination without explicit communication. Monte Carlo simulations demonstrate that the proposed method enables effective cooperative task allocation without inter-robot communication while remaining robust to measurement clutter and observation uncertainty.

A facilitator is an intermediary that offers renewable producers and consumers fixed-price contracts and, acting as their balance responsible party, manages the residual imbalances in the market. Pooling imperfectly correlated residuals nets consumers' imbalances and reduces the portfolio's total imbalance cost, but raises an allocation question: how should these savings be divided among heterogeneous consumers? We formulate this as a cooperative game, the imbalance netting game, and compare six allocation mechanisms under a two-price imbalance settlement in terms of computational requirements, budget balance, group rationality, and additivity. We establish three analytical results: a necessary and sufficient condition for budget balance of the marginal cost contribution mechanism, group rationality of the marginal cost contribution and Vickrey-Clarke-Groves mechanisms, and a necessary and sufficient condition for when the Gately point is well-defined. On Danish 2024 data for 19 consumers, aggregation reduces imbalance cost by 10%, but how these savings are shared depends strongly on the allocation rule. A flat-rate allocation proportional to consumption, used as a benchmark for socialized imbalance pricing, charges some consumers more than twice their standalone cost, whereas all the game-theoretic mechanisms studied produce stable allocations in practice.

Unit Commitment is a computationally demanding mixed-integer linear optimisation problem requiring many binary commitment decisions across a scheduling horizon. To reduce this computational burden, machine learning approaches can predict a subset of these decisions, thereby shrinking the search space explored by the optimisation solver. Existing confidence-based approaches determine which variables to fix based on user-defined probability thresholds, which are agnostic to the downstream optimisation problem. We address this limitation by introducing an optimisation-aware framework that yields generator-specific confidence thresholds based on the impact of fixing errors on Unit Commitment solution quality. This approach won first place in the 2025 EPRI AI-ccelerating Unit Commitment competition. The proposed framework achieves a mean optimality gap below 0.5% while also delivering an average speed-up of more than 20x. It therefore provides a general mechanism for translating probabilistic predictions into controlled reductions of mixed-integer search spaces, directly linking learning decisions to their downstream computational and economic consequences.

We introduce the Dual-state Sparsifying Recurrent Unit (DuSpaR) as a computationally efficient building block for speech processing models on resource-constrained edge devices. It employs dual-state recurrence to modulate its input vectors in a stateful feedback loop. Its recurrent cells sparsify the input vector operand involved in matrix-vector multiplication using ReLU activation. By skipping the zero entries dynamically, inference-time savings in multiply-accumulate operations and weight memory fetches can be achieved. We evaluate DuSpaR on three speech tasks: keyword spotting (KWS) on the Google Speech Commands dataset, spoken language understanding (SLU) on the Fluent Speech Commands dataset, and speech enhancement (SE) on the Voice Bank + Demand (VBD) dataset. At similar parameter counts, DuSpaR requires 51.0% and 68.4% less computation than Gated Recurrent Unit (GRU) on KWS and SLU, respectively, while achieving higher accuracy, and 50.1% less computation on SE while maintaining similar quality. At comparable computational cost and across a range of model sizes, DuSpaR also achieves higher KWS/SLU accuracy and better SE quality than other sparsity-aware recurrent models. Ablation studies show that compared with the single-state recurrence baseline, dual-state recurrence reduces the effective compute by factors of 3.1 to 11.3 at similar task performance.

Deep learning methods like neural networks have greatly simplified the computation of safety certificates for complex nonlinear systems with unknown dynamics. However, due to the data-driven nature of these certificates and the complex architecture of neural networks, computation time as well as robustness guarantees across unseen data remain a challenge. This work aims to formally verify safety properties of discrete-time unknown systems by synthesizing extreme learning machine (ELM)-based barrier certificates. Compared to neural network counterparts, this approach greatly improves convergence guarantees and computational time due to its architectural simplicity and the convex nature of the underlying optimization problem. By minimizing the Lipschitz constant of the candidate barrier, we present a grid-based sampling technique to formally verify its validity using the minimum number of samples required. We demonstrate through numerical examples the effectiveness of our approach, and compare with traditional deep-learning based certificate synthesis to highlight its benefits.

Sound event localization and detection (SELD) aims to jointly recognize sound events and estimate their directions of arrival from multichannel audio. Although recent deep learning approaches have achieved strong performance, their ability to generalize across acoustic environments remains limited, as room reverberation introduces environment-specific characteristics into the learned representations. In this work, we address this challenge by leveraging a physical free-field model as a room-independent reference. Specifically, we propose PG-SELD, a training framework that combines free-field with physics-guided knowledge distillation. Our approach aligns intermediate representations extracted from reverberant signals with those produced by a free-field teacher for matched acoustic scenes. This guidance encourages the model to preserve event- and localization-relevant information while reducing sensitivity to room-specific characteristics. Experimental results on the STARSS23 benchmark show that PG-SELD consistently improves the generalization performance of multiple baseline SELD architectures.

In knee arthroscopy surgery, accurate registration between preoperative and intraoperative anatomy is a critical step for patient-specific navigation. Achieving an accurate registration requires a reliable 3D reconstruction of the joint during surgery. Preoperative 3D models can be obtained from patient imaging through segmentation and reconstruction, but generating an intraoperative 3D representation remains particularly challenging. Arthroscopic imaging suffers from a limited field of view, low surface texture, and strong specular reflections, which make conventional feature-based 3D reconstruction methods unreliable. In this work, we investigate the application of MIS-NeRF (Minimally-Invasive Surgery Neural Radiance Fields) for reconstructing intraoperative knee 3D models from monocular arthroscopic images. The approach is evaluated on six simulated arthroscopic acquisitions representing six patient-specific knee 3D models. Both qualitative and quantitative results are presented to assess the reconstruction quality. The reconstructed knee 3D models were evaluated through their rendered images, achieving PSNR (Peak Signal-to-Noise Ratio) of 31.88 $\pm$ 2.82, SSIM (Structural Similarity Index) of 0.98 $\pm$ 0.004 and LPIPS (Learned Perceptual Image Patch Similarity) of 0.017 $\pm$ 0.006. These preliminary results suggest the feasibility of NeRF-based reconstruction in the challenging context of arthroscopy and may represent a promising step toward accurate in-silico preoperative-to-intraoperative 3D registration for computer-assisted orthopedic surgery. Further, validation on real arthroscopic data will be necessary to assess clinical applicability.

Wireless split learning (SL) reduces on-device computation by offloading upper layers to a server, yet transmitting high-dimensional intermediate features at each iteration remains a major communication bottleneck. Existing methods select features at the client side using task-agnostic criteria such as magnitude, statistics, or clustering, which increases client-side processing and often degrades accuracy under non-independent and identically distributed (non-i.i.d.) client data. We propose importance-aware class-balanced sparsification (ICS), a lightweight approach in which the server ranks feature channels using Grad-CAM-based scores obtained from the true-class logit during backpropagation. The per-class scores are aggregated into a class-balanced, label-agnostic importance vector that mitigates head-class bias under label skew, and each client reuses this vector in the next round to retain the top-$N$ feature channels, incurring no additional client-side forward or backward passes. We further derive a non-asymptotic convergence bound that isolates the sparsification-induced error and characterizes how the sparsification ratio and mini-batch size jointly affect convergence under a fixed communication budget, and we analyze the communication and computational overhead of ICS against representative baselines. Beyond sequential CNN-based SL, we extend ICS to parallel split learning and to transformer-based models. Experiments show that ICS consistently outperforms the baselines, with larger gains under severe non-i.i.d. partitions.

A deep classifier is defined not only by the decision it produces, but also by the sequence of transformations through which that decision is formed. Treating this evolution as a dynamical system across layers provides a natural framework for asking how decision geometry emerges through depth and how far back we can trace a boundary's dynamical signature. We model a feed-forward classifier as a finite, nonautonomous discrete dynamical system, with layers playing the role of discrete time steps. We study the Finite-Time Maximum Lyapunov Exponent (FTMLE) of the data samples' dynamical trajectory through depths of the classifier. The FTMLE measures the rate of convergence/divergence of nearby trajectories. We move the observation endpoint backward from probabilities to logits and then to hidden representations. For Gaussian classes, we prove that probability-level FTMLE carries a clear geometric signature of the decision boundary, with its dominant direction aligned with the boundary normal. Moving one step backward to the logits, we prove this relationship is no longer universal but depends critically on how the classifier is trained, particularly on the choice of loss function. Moving further backward to the hidden representation, the connection becomes more conditional: boundary-related FTMLE can persist, but only under identifiable structural conditions. We propose geometry-aware fine-tuning for restructuring the classifier's hidden FTMLE, and propose conditions for guaranteed concentration of high hidden FTMLE near the decision boundary. Through our numerical results, we show the generality and validity of our theoretical results. Understanding the evolution of data samples as traveling through the layers of classifier provides a principled foundation for identifying where boundary-relevant sensitivity emerges and for developing layer-aware regularization strategies.

Over-the-air (OTA) radar target emulation is an important method for evaluating the sensing capabilities of integrated sensing and communication (ISAC) and radar devices. Conventional single-feed compact antenna test ranges (CATRs) are generally limited to a single target direction, while multi-feed angular-synthesis methods commonly used in radar testing still require far-field illumination over the device-under-test (DUT) aperture. To overcome these limitations, this paper proposes a radar target OTA emulation framework based on multi-feed plane wave generator (PWG). The key capability is the simultaneous emulation of spatially close targets from different directions with joint angle–range–Doppler control. Target range is emulated through the corresponding delay, while Doppler and the complex echo coefficient are controlled by a channel emulator. Meanwhile, a multi-input amplitude and phase matrix (APM) maps each independent target-signal path to a dedicated PWG excitation vector that synthesizes the corresponding incident plane wave over the same test zone. This enables multiple target directions to be reproduced as local plane waves at a test distance substantially shorter than the conventional far-field requirement. The framework is experimentally validated at 3~GHz using a real $1\times11$ phased-array DUT. The PWG–DUT separation is 1.6~m, substantially shorter than the 7.2~m Fraunhofer distance of the DUT. At this separation, the synthesized $0^{\circ}$ and $10^{\circ}$ incident waves each exhibit an amplitude peak-to-peak error of 1.0~dB. Across two four-target angle–delay cases and one two-target angle–velocity case, the estimated target parameters show angular deviations within $1^{\circ}$, interpolated delay errors within 2.4~ns, and velocity errors within 0.15~m/s.

Electromagnetic (EM)-reconfigurable antennas provide multiple candidate radiation patterns per element, thereby introducing an additional EM-domain degree of freedom. Integrating radiation-pattern reconfigurability, realized as EM-domain precoding, with conventional hybrid analog-digital precoding yields tri-hybrid multiple-input multiple-output (MIMO) precoding, which can substantially improve the spectral efficiency of wideband multi-user MIMO orthogonal frequency-division multiplexing (OFDM) systems. However, the joint design of EM, analog, and digital precoding remains challenging. To address this challenge, we propose a tri-hybrid precoding network (Tri-PNet) based on Conformer, an emerging neural architecture that combines the local modeling strength of convolutional neural networks with the global dependency modeling of Transformers. Furthermore, two representative radiation-pattern modes, i.e., the non-regular mode and the 3rd Generation Partnership Project (3GPP) Technical Report (TR) 38.901 mode, are investigated. Tri-PNet is trained in an unsupervised manner to jointly learn EM, analog, and digital precoding by maximizing the average sum spectral efficiency. Its radiation-pattern selection network (RPSNet) employs a Conformer encoder to capture both local and global frequency-domain correlations, whereas its hybrid analog-digital precoding network (HPNet) combines cross-attention and dual-path processing with singular-value-decomposition (SVD) and zero-forcing (ZF) priors. Simulation results under both radiation-pattern modes demonstrate that Tri-PNet outperforms random EM precoding and conventional hybrid MIMO without EM precoding, approaches the greedy EM precoding search scheme with substantially lower online complexity, and remains robust to imperfect channel state information (CSI).

We investigate whether predicted Mean Opinion Scores (MOS) can reliably support system-level comparisons of speech enhancement (SE) methods by introducing system-level preference accuracy (SPA). Although MOS prediction models are widely used to evaluate SE systems, their performance is typically assessed by correlation with human-rated MOS, which does not guarantee agreement on which system is better. SPA addresses this gap by directly evaluating whether predicted and human-rated MOS yield the same system preferences. Using SPA, we systematically evaluate three settings: single prediction models, ensembling, and domain adaptation. Through experiments, SPA varies substantially across single prediction models, from 9.4% to 76.8%. Even the best model disagrees with human judgments in approximately 23% of system comparisons. Ensembling yields only limited improvement, while domain adaptation tends to substantially improve SPA in the closed condition but brings only modest gains in the more practical open condition, where neither the target systems nor the speakers are known. These results suggest that SPA can reveal errors correlation-based evaluation alone does not expose, and that predicted MOS alone can lead to unreliable conclusions in practical SE system comparison.

Audio and speech models are released rapidly, but reported scores often conflate model capability with deployment and evaluation choices. The same checkpoint can produce different predictions under different runtimes, hardware, decoding settings, or fallback policies. Even fixed predictions can receive different scores under different normalization and metric implementations. Existing speech benchmarks standardize selected datasets or scoring procedures, but rarely connect heterogeneous model onboarding, controlled inference, and versioned scoring in one executable workflow. We introduce SURE-EVAL, a Systematic and Unified Agentic framework for Reproducible Evaluation of audio and speech systems. A Tool Agent Workflow converts model releases into isolated, verified callable tools. A Main Agent Workflow commits task-specific inference and scoring protocols, then delegates all score-bearing operations to versioned deterministic programs. Each result retains its runtime, protocol, pipeline nodes, predictions, and audit artifacts. Across 18 public releases covering automatic speech recognition, text-to-speech, voice conversion, speaker diarization, speaker-attributed recognition, and multi-task audio understanding, a Codex-only baseline completes 12 models in one shot, while the same agent with the SURE-EVAL Tool Agent Workflow completes all 18. We also conduct unified evaluations over seven ASR test conditions and two TTS subsets. A protocol analysis of three TTS systems finds absolute differences of 0.02-0.52 points between paper-reported and unified results, with the direction varying by model and language. These results show that reproducible evaluation requires controlling both model execution and output scoring.

Non-intrusive MOS predictors are widely used instead of subjective listening tests to evaluate and rank speech enhancement (SE) systems. If they accurately reflect perceived quality, raising their scores should lead to higher-quality speech. We present the first comprehensive analysis of test-time optimization for the SE task, which directly modifies the enhanced signal to raise the average of multiple MOS predictor scores. On seven systems from the URGENT 2026 challenge, we find that 1)~all the optimized predicted scores increase while reference-based metrics remain nearly unchanged, 2)~a non-optimized predicted score does not increase, and 3)~a MUSHRA listening test shows no improvement in perceived quality. These findings reveal a risk that such optimization can distort evaluations, e.g., biasing comparisons of SE systems regardless of their perceived quality. We believe these findings can inform future evaluation practices: they suggest that predictors used for optimization should not be used for evaluation, and that challenges should keep the predictors used for ranking undisclosed.

Radar hardware faults threaten automated perception, motivating accurate, compact diagnosis and understandable maintenance guidance. We introduce SCORE-LM, which couples a small scatterer-conditioned operator-response encoder (SCORE) to an adapted local language model. SCORE combines self-referenced complex trajectories, physical descriptors, and a selective state-space branch, with source-only self-supervision and directional fault inference. On eight capture-excluded Rad-R fault recordings, it achieves state-of-the-art performance within the evaluated nine-model comparison: 88.39% mean capture recall and 88.20% four-fault macro-F1 at ten frames. Its 39,520 radar inference coefficients are 119.7 times fewer than RadrNet-DS-CI's, while recall is 15.56 percentage points higher than this strongest competitor. In a separate low-label protocol, SCORE reaches 71.58% recall with one labeled source window per class. A nonlinear projector converts four frozen fault similarities into five soft tokens, linking compact diagnosis to class-conditioned maintenance guidance. On 75 development questions covering 24 radar windows, language adaptation raises correct-fault answers from 45 to 62 (60.0% to 82.7%) relative to removing the co-trained adapters, while retaining the same projector. SCORE-LM thus combines a compact radar specialist with a language interface for communicating fault-specific inspection guidance.