Papers

Commercial millimetre-wave (mmWave) Wi-Fi platforms based on IEEE 802.11ad/ay have demonstrated sensing capabilities, but they provide limited visibility into the packet-level measurements and synchronisation mechanisms that determine temporal coherence. We present a programmable 60~GHz IEEE 802.11ad testbed that exposes the complete complex 128-tap channel impulse response (CIR) for every detected packet. This access exposes packet-to-packet delay shifts and common phase drift that commercial-transceiver studies generally do not characterise. We develop a delay-profile alignment method and define a static-reference relative CIR phase that cancels common drift while preserving target-induced variation. We use 30~s respiration sensing to evaluate long-term phase stability for sub-wavelength motion tracking. Across 39 recordings, median absolute waveform correlation increases from 0.41 (timestamp regularised) to 0.52 (delay aligned) and 0.61 (relative-phase calibrated); breathing-rate mean absolute error decreases from 5.1 to 4.1 and 3.6~breaths/min. The results demonstrate controlled characterisation and stabilisation of communication-native WiGig CIR measurements on a fully programmable physical layer.

Lossless audio coding is a highly mature field of research, with limited potential for significant improvements in pure compression performance. However, emerging real-time wireless applications increasingly require dynamic transitions between lossy and lossless coding to adapt to fluctuating network capacities. Existing standalone lossless codecs cannot achieve this seamless switching without discontinuity. In this paper, we propose a lossless codec based on time-domain aliasing cancellation (TDAC) that can match the overlap in the CELT mode of the Opus codec. This allows the transition between lossy and lossless coding to be achieved without discontinuity or the transmission of redundant information. We show that the proposed codec still achieves state-of-the-art lossless compression without being penalized by its use of TDAC.

Low-earth orbits (LEO) satellites have recently gained wide attention for their potential to provide high-data-rate services in desert, sea, and rural areas. Currently such systems operate in the Ku band on the down-link. However, inter-operator LEO satellites produce frequent In-Line-Events (ILE) creating co-channel interference between their waveforms. This situation will be exacerbated with the launch of more constellations being launched. To address this challenge, we propose a novel interference mitigation system based on power control. Specifically, satellites from one operator will adjust its transmit power to create a sufficient received-power gap for co-frequency beams, enabling successive interference cancellation (SIC) or AI-assisted algorithms on the user-side for interference mitigation, where AI requires a smaller power gap. We use the waveforms from Starlink and EutelsatOneweb to evaluate the designed system. Results show the designed system can significantly mitigate interference and increase the overall spectrum efficiency.

Estimation of the derivatives of a function with bounded high-order derivative in the presence of bounded measurement noise is considered, in a deterministic setting. Theoretical fundamental limitations of causal differentiators in terms of the lowest achievable worst-case differentiation error and desired properties such as exactness—the zero-error estimation of derivatives in the absence of noise—and robustness—the small sensitivity of the estimate under small perturbations—are established for the first time for arbitrary differentiation order. Differentiators that achieve these theoretically lowest bounds on their differentiation error are formally defined, fully characterized, and their properties are studied. In particular, deterministically optimal differentiators—those featuring optimal differentiation error bounds among the class of exact differentiators—are shown to exist by means of a novel construction exhibiting, in addition to exactness from the beginning, a very strong form of robustness.

This work presents a delayed safe Newton-based extremum seeking (SANES) framework for minimizing an unknown static map subject to an unknown safety constraint. The objective and safety measurements are assumed to be affected by the same constant time delay. To compensate for delayed measurements, a model-free predictor is developed to construct the quantities required for optimization, including the nominal Newton-based extremum-seeking control input and the gradient information used to formulate control Lyapunov function (CLF) and control barrier function (CBF) conditions. Robust CLF–CBF quadratic programs (QPs), subject to parameter-update constraints, are then formulated to account explicitly for derivative-estimation and prediction errors. For the delay-free case, robustness margins are derived from bounds on the extremum-seeking estimation errors, whereas for the delayed case, the margins incorporate both estimation and prediction errors. Practical stability of the nominal Newton-based extremum-seeking dynamics is established directly through a Lyapunov analysis, thereby providing convergence guarantees over the admissible parameter set without relying exclusively on local averaging arguments. Robust CLF and CBF conditions are subsequently derived to establish practical convergence and forward invariance of a robust subset of the prescribed safe set. A numerical case study demonstrates the effectiveness of the proposed SANES framework in achieving constrained optimization despite unknown objective and safety maps and delayed measurements.

BraTS-GoAT evaluates tumor segmentation across heterogeneous populations. We trained a conventional 3D nnU-Net on 1,351 labeled cases using five-fold cross-validation and 1,000 epochs per fold. The final predictor averaged all folds and applied test-time mirroring. On pooled official validation, global DSC values were 0.7805, 0.8288, and 0.8854 for enhancing tumor (ET), tumor core (TC), and whole tumor (WT). Under matched fold-0 inference, mean regional Dice decreased from 0.9058 on source out-of-fold (OOF) cases to 0.8310 on pooled validation (difference–0.0747). Mirroring gave small single-fold gains but no clear ensemble benefit; a residual-encoder alternative reached 0.8282 mean Dice. In labeled OOF predictions, failure cases had substantially smaller reference ET volumes; after adjustment for ET and WT volume, lower Dice remained associated with more disconnected ET components and a smaller fraction of ET contained in the largest component.

Medium-term forecasting of Power Quality (PQ) parameters, on horizons of weeks to about one year, supports proactive maintenance and the early detection of limit exceedances in transmission network monitoring. Its practical value, however, depends on knowing in advance which time series can be forecast reliably at all. This article addresses that question in two stages, based on 2,807 weekly time series of PQ parameters from a long-term measurement campaign at 66 sites in the German transmission system, covering the 110 kV, 220 kV, and 380 kV levels. First, eight forecasting models are benchmarked. The STL-ARIMA hybrid achieves the highest accuracy, with an average sMAPE of 17.63% and a lower error than the seasonal naive benchmark for 72% of the time series, while accuracy varies substantially across PQ parameters and measurement sites. Second, model-free features computed from the training set alone are evaluated as measures of intrinsic predictability. SVD entropy and the Crest Factor correlate strongly with the realized forecast accuracy of all eight models and are used in a logistic regression to estimate the probability of a poor forecast before any model is applied. The resulting measures allow operators to separate time series suitable for automated forecasting from those requiring manual review.

A buoyless acoustic-to-LEO gateway with predictable contacts is analyzed. Advanceable cross-medium preparation competes with acoustic collection, while residual RF acquisition consumes contact time. Exact fluid and packet service, a fluid-optimal fixed preparation lead, tandem-queue stability, and one-contact reliability are derived. An explicit service-bias identity shows why treating acquisition as advanceable can select an insufficient lead. For lognormal preparation, conditions are characterized under which variability improves mean service while degrading reliability. Monte Carlo and packet-queue simulations corroborate the analysis with synthetic parameters; in a diagnostic case, accounting for residual RF acquisition raises the actual sustainable rate from 8.776 to 9.079 kbit/s.

Optimizing the sensitivity of the unbalance voltage in Wheatstone bridges with respect to changes in the bridge parameters remains a fundamental objective in circuit design and instrumentation. When finite source and detector resistances are taken into account, determining the optimal bridge configuration becomes increasingly complex, and a closed-form analytical representation of the optimal solution has not yet been established. This paper derives an analytical solution for the optimal configuration of a Wheatstone bridge with finite source and detector resistances. Furthermore, the proposed optimal solution is compared with the conventional equal-arm configuration.

Selective auditory attention decoding (sAAD) infers from electroencephalography (EEG) which speaker a listener attends to in multi-speaker scenarios. A widely used approach reconstructs the attended speech envelope from EEG, correlates it with candidate envelopes, and selects the speaker with the highest Pearson correlation. However, correlations in each decision window have high intrinsic variance, making per-window decisions unreliable, especially for short windows. Recent work introduced a hidden Markov model (HMM) that integrates correlation evidence over time and requires state-dependent emission distributions. Since labeled attention states are typically unavailable in practice, these distributions can be inferred from unlabeled observations by fitting a Gaussian mixture model (GMM). However, when the underlying Gaussians are close, unsupervised fitting becomes less reliable with limited samples. We therefore propose a multiscale GMM whose parameters are jointly estimated across multiple window lengths. The model exploits the window-length dependence of Fisher-transformed correlation estimates, whose means are approximately stable while their variances decrease with the number of samples per window. Experiments show that the multiscale GMM estimates emission parameters more accurately than single-scale fitting, particularly at short windows. Its HMM-post-processed accuracy advantage is largest for short recordings and narrows as more data become available, motivating multiscale fitting in data-constrained settings.

This paper investigates hybrid beamforming for multi-user large multiple-input multiple-output millimeter-wave unmanned aerial vehicle (UAV) downlink systems under mobility-induced channel aging and delayed beam-training feedback. Analog beam selection from compact delayed reports is a partial-observation decision, while additional candidate evaluations consume processing time and reduce the useful payload interval. We propose a mobility- and feedback-aware multi-level refinement strategy, termed MLR-TG, to improve robustness without always-on candidate search. Candidate subsets are ranked by a predicted net utility constructed from quantized complex coefficients of the reported codewords and the UAV mobility state, while the transmission regularized zero-forcing precoder is computed once from pilot-estimated effective channel state information (CSI) after analog selection. The refinement level is adaptively selected according to conflict severity and aging sensitivity. The selection rule is a two-statistic approximation of predicted-utility maximization, employs a system-size-invariant conflict score, and is calibrated offline on training data disjoint from evaluation. Simulations on a three-dimensional air-to-ground model with UAV attitude dynamics and common channel trajectories show that MLR-TG reduces system outage probability by 26.7% and improves the 5th-percentile user rate by 53.9% relative to greedy sector beamforming, while net spectral efficiency remains within 0.96%. Compared with always-on global top-3 refinement, MLR-TG improves net spectral efficiency by 5.5% while evaluating 77.9% fewer candidates, and remains within 3.4% of a noncausal-CSI level oracle in net spectral efficiency while requiring 86.9% fewer feedback bits than full-CSI reporting.

Steel wire ropes (SWRs) are critical load-bearing components whose local flaws (LFs) pose serious safety risks. Magnetic flux leakage (MFL) inspection commonly detects LFs from temporal or axial morphology, which can change with the sensing axis and operating condition. We show instead that LF responses remain localized over neighboring channels of a circular array. This observation motivates a channel-feature-oriented (CFO) detector that removes smooth channel backgrounds, applies circular matched filtering, and fuses spatially co-located tri-axis responses. Experiments performed on real-world equipment show that CFO attains the highest localization performance among three representative baselines, reaching F1@0.5/F1@0.7 scores of 73.9%/59.5%. It attains the best temporal localization performance under all four conditions and achieves at least 19.4 times the throughput of the evaluated signal-processing baselines. These results demonstrate that circular channel locality provides a robust LF representation across the evaluated operating conditions.

An orthogonal affine-precoded superimposed training sequences (AP-STS)-based architecture is developed for the cyclic prefix (CP)-aided multiple input multiple output (MIMO) direct-current-biased orthogonal time frequency space (DCO-OTFS) visible light communication (VLC) systems relying on arbitrary transmitter-receiver pulse shaping. The data and pilot symbol matrices are affine-precoded (AP) and superimposed in the delay-Doppler (DD)-domain for each transmit light-emitting diode (LED), followed by the development of an end-to-end DD-domain relationship for the input-output symbols. At the receiver for each receiver photodiode (PD), the decoupled pilot and data symbol are extracted by employing orthogonal precoder matrices, which eliminates the mutual interference. Furthermore, a novel pilot-aided (PA) variational Bayesian inference (PA-VBI) technique is conceived for the channel state information (CSI) estimation of MIMO DCO-OTFS VLC systems based on the expectation-maximization (EM) technique. Subsequently, a data-aided (DA) variational Bayesian inference (DA-VBI)-based joint CSI estimation and data detection technique is proposed, which beneficially harnesses the estimated data symbols for improved CSI estimation. Moreover, the Bayesian Cramer-Rao lower bounds (BCRLBs) are also derived for MIMO DCO-OTFS VLC systems. Finally, simulation results demonstrate that the proposed method yields superior performance in terms of normalized mean-square-error (NMSE), pilot overhead, and symbol error-rate (SER).

Laser diode (LD)-based luminaires are gaining increasing attention in automotive applications and are expected to extend to residential and commercial environments, creating opportunities for high-bandwidth visible light communication (VLC) systems. However, practical LD-based VLC links are impaired by input-signal-dependent shot noise (ISDSN), relative intensity noise (RIN), and thermal noise, which affect reliable channel estimation (CE). This work investigates their joint impact on receiver-side CE in a single-input single-output (SISO) optical orthogonal frequency division multiplexing (OOFDM) VLC system under a statistically random channel model. A statistical inference framework is developed in which the receiver exploits observed signal variations to estimate the channel under optical impairments. Closed-form expressions are derived for least squares (LS), maximum likelihood (ML), maximum a posteriori probability (MAP), minimum mean square error (MMSE), and linear MMSE (LMMSE) estimators. In addition, the Bayesian Cramer-Rao lower bound (BCRLB) is derived to benchmark mean square error (MSE) performance. Monte Carlo simulations for direct current-biased O-OFDM (DCO-OFDM) and asymmetrically clipped O-OFDM (ACO-OFDM) validate the analysis. Results show substantial CE degradation under the joint presence of ISDSN and RIN, while the MMSE estimator consistently achieves the lowest MSE, demonstrating strong potential for robust and adaptive receiver operation in practical VLC systems.

The proliferation of distributed energy resources is increasing the prevalence of both overvoltages and undervoltages in low-voltage (LV) distribution networks. Smart inverter functionalities, such as Volt-VAr and Volt-Watt control, can regulate voltage at the consumer level but are challenging to capture in optimization models due to their nondifferentiability. This paper proposes three nonlinear models for four-wire unbalanced optimal power flow that incorporate these nonsmooth functions without binary or integer variables. The first model encodes these nonsmooth functions directly as user-defined functions using control flow, a feat enabled by many state-of-the-art algebraic modeling languages. The second and third introduce bespoke smooth approximations with tunable approximation errors to address potential numerical issues arising from nondifferentiability, improving reliability while maintaining accuracy. All three methods are evaluated on real four-wire unbalanced LV networks with varying rooftop solar adoption levels, including a 539-bus system with 302 smart inverters. This paper is not only the first to demonstrate accurate, reliable, and tractable Volt-Var-Watt optimization (VVWO) on real four-wire unbalanced LV network models, but it also establishes that optimization-based, non-incremental methods offer superior reliability compared to commonly used incremental (quasi-steady-state) approaches.

A multipath CIR estimator is proposed, followed by sparse frequency-domain (FD) signal detection, which harnesses the simultaneous-sparsity innate in the multipath CIR and FD signals across measurement vectors, conceived for optical OFDM (O-OFDM) utilized for visible light communication (VLC) systems having reduced peak-to-average power ratio. At the outset, we derive the input-output relationships for the asymmetrically clipped O-OFDM (ACO-OFDM) as well as for the direct current-biased O-OFDM (DCO-OFDM) systems. Next, traditional benchmarking methods are introduced for channel estimation (CE), including both the LMMSE and LS methods, followed by an orthogonal matching pursuit (OMP)-based technique capable of exploiting sparsity in the multipath CIR of the VLC system. Subsequently, an CE technique based on a novel group sparse OMP (GOMP) concept is proposed, which capitalizes on the group-sparsity in the delay domain of the CIR across measurement vectors, attributed to the multipath characteristics inherent in the NLoS components of the VLC channel model. Additionally, an advanced group-sparse CE technique is put forth based on the group-sparse Bayesian learning (GBL) approach, which considerably reduces the pilot overhead. A low complexity version of GBL, termed LCGBL, is also developed that reduces the computational cost of GBL significantly. Consequently, the GOMP and GBL frameworks are also extended to the data detection of the sparse FD O-OFDM symbols, which utilizes the group-sparsity of the FD O-OFDM symbols across measurement vectors. The Bayesian Cramer-Rao lower bound (BCRLB) is computed to assess the estimation performance of the proposed CE techniques. Our simulations demonstrate that, despite its reduced pilot overhead, the proposed GBL technique outperforms the other CE schemes in terms of its BER, outage probability, and NMSE, quantitatively reinforcing its superiority.

As networked dynamical systems grow in scale, distributed controller architecture design has become a critical issue. To address this, prior studies have incorporated the regularization for design (RFD) framework into system-level synthesis (SLS), focusing primarily on actuator and sensor placement. However, communication architectures also fundamentally impact the performance of distributed control, and scalable explicit co-design of the underlying communication topology within SLS remains challenging. This paper proposes a scalable convex framework for the co-design of communication architectures and distributed controllers within the SLS paradigm. We explicitly characterize a communication topology-induced feasible subspace of system responses, parameterized by the communication adjacency matrix and separate from the physical plant topology. By deriving a communication link norm from this characterized subspace, we formulate a convex SLS problem for communication architecture co-design. A proximal ADMM scheme is employed to appropriately decompose the optimization problem in a manner that preserves scalability to large networks. Numerical experiments validate the efficacy of the proposed framework in optimizing the trade-off between closed-loop performance and communication architecture complexity, while also confirming its scalability.

Autoregressive generation of interleaved text and acoustic tokens is a common approach to spoken-response generation in speech large language models. Although this design enables streaming generation with explicit textual guidance, generated acoustic tokens become part of the context for subsequent text predictions. Given identical speech inputs, we observe markedly lower answer accuracy for the internal text generated in speech-to-text-and-speech (S2TS) mode than for speech-to-text (S2T) responses. We term this discrepancy the output-mode gap (OMG). To reduce OMG, we propose Joint-Output On-Policy Distillation (JO-OPD), which distills the model's stronger S2T policy into joint generation using student-generated S2TS trajectories. At each text position, the S2T teacher provides soft targets from a text-only projection of the student's preceding outputs, while the student predicts from the corresponding full interleaved history. A preservation objective further regularizes native non-text predictions. Experiments on Step-Audio-2-mini and Baichuan-Audio-Instruct reveal OMG across two interleaved generation architectures. On Step-Audio-2-mini, JO-OPD reduces OMG from 42.87 to 16.26 percentage points on Spoken-MQA and from 29.72 to 13.04 points on speech-rendered GSM8K, with little change in S2T accuracy and substantially larger reductions than matched SFT baselines. ASR-based evaluation further shows a 7.49-point improvement in spoken-answer accuracy on Spoken-MQA.

Vital Signs Sensing, enabled by the evolution of wireless communications, is inherently challenging as the chest's micro-Doppler signatures can be corrupted by multipath, body movement and noise. Additionally, the prospects of the Frequency Range (FR) 3 band, spanning 7-24 GHz, are compelling due to centimeter-scale wavelength improving phase sensitivity to chest motion, while its abundant bandwidth and rich multipath enhance temporal and spatial diversity. In this paper, we validate the feasibility of using cellular communications in the FR3 band to monitor a human's breathing and heartbeat in an indoor environment. Using Blender to model physiologically accurate vital-signs signals as ground truth and Sionna Ray Tracing simulations, we find that it is feasible to extract breathing and heartbeat waveforms at FR3 frequencies. Furthermore, we perform a comparative analysis of ISAC performance against representative sub-6 GHz (FR1) and mmWave (FR2) frequencies, indicating the utility of the study across the frequency bands.

We investigate the $k$-variation bounding property of the discrete-time Hankel operator, i.e., its invariance under the set of signals with a variation (number of sign changes) of at most $k$. Building on existing sign-consistency criteria, it is shown that this property is equivalent to the external positivity of $k+1$ explicitly realized linear discrete-time systems. Thus, making the property tractable via numerical and analytic certificates. We also derive dominant-pole restrictions for the case of $k=1$. A three-node thermal example illustrates the results and distinguishes variation bounding from variation diminishing.

Advanced artificial intelligence models think in tokens; contemporary communication systems carry bits. The direct way to bridge this gap is to transmit tokens, but that makes a model-specific representation part of the air interface, coupling the endpoints through a shared tokenizer, codebook, and often a neural transceiver. We take a different route: keep bits in the payload and let tokens control how those bits are generated and protected. The resulting token-bit interface transition aligns task-side tokens with source- and channel-coding units, translates token relevance into codec controls, and preserves the induced priority order across the coding chain. We instantiate it for image classification, where a vision transformer scores the task relevance of each image region from its attention maps: those scores steer block-wise JPEG rate allocation, then group the compressed bits for protection at different polar-code rates. The payload remains an explicit, reconstructable bitstream recovered by a correspondingly configured decoder. Over-the-air experiments on a software-defined radio testbed show improved accuracy–latency tradeoffs over separate source-channel coding, performance competitive with far more memory-intensive neural joint source-channel coding, and graceful degradation under channel mismatch. Token communication, then, need not transmit tokens explicitly; what it needs is an interface through which tokens determine how bits are communicated.

Technical systems are increasingly monitored using sensor and operational data to detect degradation and performance deterioration at an early stage. However, observed trends in measurement data do not necessarily correspond to physical aging, since noise, outliers, unstable initial regions, or changing operating conditions may produce similar patterns. This paper proposes Trend-based Reference Evaluation for Degradation Detection (TREDD), an interpretable method for detecting degradation as a persistent, trend-based deviation from an early reference state. TREDD combines rolling-window smoothing, baseline estimation, a direction-dependent degradation index, long-term trend extraction, and persistent drift detection. In addition, the method separates computational drift detection from the interpretation of drift as plausible physical aging by incorporating data-quality assessment, context checking, and robust auxiliary analysis. The approach is evaluated on the NASA Lithium-Ion Battery Aging Dataset using discharge-cycle capacity as the degradation-relevant condition variable. The representative case study illustrates that TREDD can identify clear degradation trajectories while assigning reduced confidence to weak, gradual, or atypical trends. The method therefore supports transparent and confidence-based degradation interpretation rather than purely predictive battery health estimation.

Large language model (LLM) inference in AI datacenters creates a coupled control problem between GPU serving and facility cooling. Raising ambient temperature setpoints can reduce cooling energy and carbon, but also shrinks thermal headroom, induces GPU throttling, and leads to Service-Level-Objective (SLO) violations. In this paper, we study joint cooling–computing control for LLM inference: minimizing per-job GPU-plus-cooling energy while satisfying thermal safety and latency SLO constraints. We present ETCInfer, an energy-efficient, thermal-aware scheduler that selects a pre-job Computer Room Air Conditioner (CRAC) setpoint and adapts per-GPU frequency and micro-batch size during execution. ETCInfer builds compact physics-informed control models by calibrating GPU heat generation, chassis heat dissipation, CRAC power, and prefill/decode latency relations from telemetry. These models estimate hidden thermal states and time-to-throttle, enabling the scheduler to evaluate energy, temperature, and latency before applying an action. We formulate this joint setpoint–frequency–micro-batch control problem as a partially observable Markov decision process and design ETCAdapter, a learning-based controller that minimizes per-job energy under thermal safety and SLO constraints. We implement ETCInfer as a coordination layer over typical inference and cluster management stacks. Evaluation across real-trace simulation and validation experiments shows that ETCInfer reduces total job energy by up to 33.1%, thermal throttle exposure by up to 92.9%, and keeps SLO violation rates below 0.7% even at ambient temperatures up to $48^{\circ}\mathrm{C}$.

Timestamps and speaker attribution are useful additions to speech recognition, creating a rich text transcript. This information can either be extracted during transcription or aligned to a given transcript. In this paper we present models that add timestamps and speaker information to a given transcript using a non-autoregressive LLM-based architecture. Compared to an autoregressive model built from similar components, the models are more accurate and annotate a given transcript one to two orders of magnitude faster. Compared to other models, our models achieve state-of-the-art timestamp accuracy and the best cpWER for speaker attribution.

Speaker recognition neural networks recognise speaker identities from input utterances by learning latent representations (i.e. speaker embeddings). However, these networks' internal mechanisms remain largely opaque, motivating research in explainable artificial intelligence (XAI) to understand them. Existing studies have analysed how speaker embeddings are organised, but rarely frame these analyses within XAI. This work proposes to explain and interpret the organisation of speaker embeddings from an XAI perspective. To this end, we apply a hierarchical clustering algorithm, Single-Linkage Clustering (SLINK), to analyse whether our prepared speaker embeddings naturally form clusters with hierarchical relationships. The resulting hierarchical organisation (i.e. hierarchical clusters) is evaluated using the Cluster-Class Matching (CCM) method. Moreover, we propose a new method, termed Hierarchical Cluster-Class Matching (HCCM), to identify which hierarchical clusters best match individual semantic classes like male and conjunctive semantic classes like UK & male, thereby interpreting the clusters using their matched classes. We quantify the matching degree with a new metric called the L-score, which makes imperfect matches diagnosable. HCCM's results reveal that the hierarchical clusters analysed by SLINK are well interpreted using classes related to speaker identity, gender, and nationality, showing the semantics inside the hierarchical organisation of the examined speaker embeddings.

This letter proposes a formal synthesis of Robust Koopman-Model Predictive Control (RK-MPC), a novel data-driven approach to formal synthesis of systems with nonlinear dynamics. We formulate a novel optimization problem for RK-MPC by incorporating specifications described by Signal Temporal Logic and prove its closed-loop performance. Effectiveness of the proposed RK-MPC is evaluated by applying it to the reliable design of an AC-DC power converter.

Practical reconfigurable intelligent surfaces (RISs) can exhibit deterministic parasitic scattering that is not captured by idealized element-wise models. As a result, such models may overestimate the gain of the intended RIS-assisted path and bias multipath prediction. This paper develops a full-wave-calibrated element-wise model using three Bragg-order basis functions to represent the intended and dominant parasitic scattering components. Environmental multipath is incorporated by identifying RIS–Rx reflection sequences with ray tracing (RT) and unfolding them by image theory into path-dependent image points. This allows direct and reflected RIS-assisted paths to be evaluated using the same per-element kernel and coherently combined with Tx–Rx bypass paths. To reduce the full-wave calibration burden for large RISs under a prescribed focusing configuration, only the three Bragg-order coefficients are transferred from a 25-by-25 calibration aperture, while the target-aperture basis functions and geometry are recomputed. At 154 GHz, the transferred coefficients keep the intended-order errors within 0.8 dB for the 50-by-50 and 100-by-100 RISs, while reducing the full-wave calibration time for the prescribed configuration from 7.56 to 1.08 h relative to direct 100-by-100 calibration. For multipath validation with the 50-by-50 RIS, the calibrated model using the same transferred coefficients keeps the nominal-Rx gain error within 0.88 dB across four PEC reflector configurations, compared with 1.75–5.51 dB for the uncalibrated general model, and reduces the error from 6.49 to 0.23 dB in the scaled indoor environment.

Integrated sensing and communication (ISAC) base stations (BSs) are emerging as critical infrastructure for 6G networks, requiring advanced over-the-air (OTA) testing methodologies that can emulate dynamic, multi-target scenarios under far field (FF) conditions. However, existing testing solutions often suffer from limited angular flexibility, insufficient bandwidth, and prohibitive costs. {To address these challenges, this paper proposes an OTA testing system that integrates a radar target simulator (RTS), a multi-port amplitude and phase modulation (APM) network, and a multi-feed linear array with a single parabolic cylindrical compact range (SPCCR).} The proposed system is capable of emulating closely spaced spatial targets with varying incident azimuth angles. This setup meets the essential conditions for ISAC BS testing, including FF characteristics, wideband and multi-band compatibility, and the capability for sensing multiple dynamic closed-spaced targets. The feasibility of the proposed method is validated through both FEKO simulation and measurement. Experimental results demonstrate that the system successfully emulates multiple closely spaced targets within an azimuth range of $\pm 10^\circ$ with high emulation accuracy while maintaining excellent quiet zone (QZ) performance.

Deep learning has led to numerous proposed architectures for Automated Emotion Recognition (AER) from electrocardiogram (ECG) data, but inconsistencies in preprocessing, training, and evaluation make direct comparisons difficult. Most studies train and validate models on individual datasets collected under homogeneous conditions, limiting variability and raising concerns about generalizability. Cross-dataset validation is sometimes used but primarily assesses model adaptability rather than true generalization. This study presents a comparative analysis of prominent deep learning architectures in AER, emphasizing model generalization over dataset adaptability. To enable this benchmark, we introduce two open-source frameworks: Affective Research on Representations and Classifications (ARRC), a standardized benchmarking toolkit, and Affective Research Dataset Toolkit (ARDT), a framework for inter-dataset training and validation. Using ARDT, we consolidate three publicly available AER datasets, CUADS, ASCERTAIN, and DREAMER, into a single dataset, increasing variability in sensor types, recording conditions, and participant demographics. We then use ARRC to evaluate three widely studied deep learning models and two CNN baselines through hyperparameter optimization and 10-fold cross-validation. Our findings provide insights into the trade-offs between classification accuracy and model complexity, establishing a reproducible benchmark for AER research. All source code for ARRC, ARDT, and model evaluation is publicly available to ensure transparency and facilitate further research.

Open Thai automatic speech recognition (ASR) is dominated by offline, Whisper-based models that read the whole utterance before transcribing, ruling out low-latency uses such as live captioning and voice agents. We present a deployable system for streaming Thai ASR that lets a user steer its vocabulary at decode time, without retraining. A widely used open Thai model, trained with full context, collapses when run as a true stream; we restore streaming with a cache-aware encoder, by converting it or adapting a natively streaming one, and add a shallow-fusion layer that re-ranks candidates inside the streaming decoder with a GPU n-gram language model and phrase boosting. Across two Thai benchmarks and two model sizes, the streaming models stay usable where the full-context model fails, cutting character error rate 4.3-4.5x at a one-second look-ahead while running faster than real time. Decode-time steering then lifts keyword recall from 16.6% to 20.7% at no accuracy cost and negligible overhead; most of the gain comes from an n-gram over ordinary training transcripts, which resolves the written form of code-switched words the model hears but spells inconsistently, with phrase boosting adding targeted control over rare domain terms.