Papers

For over a century, the electric grid has relied on a single statistical assumption: load diversity, the principle that the uncorrelated demands of millions of small consumers produce a smooth, predictable aggregate. AI training data centers break that assumption. A single hyperscale training campus can draw power comparable to a mid-sized city, driven by one tightly synchronized job whose demand swings by hundreds of megawatts in seconds. This paper argues that the resulting entanglement of compute and power infrastructure requires a shift from implicit coexistence to explicit co-development between the historically decoupled data center and electric power industries. We introduce the distinct design principles, operational philosophies, and economic incentives of each sector, and show why their cultural and technical misalignment makes coordination difficult. We identify key research directions, from joint capacity planning, multi-timescale control, a compute–power protocol stack, to market innovation, that must be pursued to power the future of AI sustainably and reliably.

In this paper, we investigate a six dimensional movable antenna (6DMA) enable integrated sensing and communications (ISAC) network in low-altitude economy. The studied 6DMA can move in a three-dimensional space and rotate around its surface center, making full use of spatial freedom to adapt to the different location distributions of uncrewed aerial vehicles (UAVs) adjust channel conditions in time. However, since the rotation and location change of 6DMA requires the assistance of a physical device, it is unreasonable for 6DMA to change locations too frequently. Therefore, we propose a hierarchical deep reinforcement learning algorithm based on twin delayed deep deterministic policy gradient. The first layer optimizes the rotation and location of 6DMA with infrequent updates, and the second layer optimizes the UAV flight direction and base station transmit beamforming matrix in each time slot. Under the condition of satisfying the sensing intensity of the sensing target, the total data transmission rate to the UAVs is maximized. The numerical results show that the proposed 6DMA-enable ISAC algorithm through joint optimization of multiple variables performs significantly better than the partially movable scheme and the fixed antenna position scheme.

Recent advances in emotional voice conversion (EVC) have enabled the generation of expressive synthetic speech, raising new concerns in audio deepfake detection. Existing approaches treat speech as a homogeneous signal and largely overlook its internal phonetic structure, limiting their interpretability in emotionally conditioned settings. In this work, we propose a phoneme-level framework to analyze emotionally manipulated synthetic speech using real and EVC-generated speech under matched emotional conditions with shared transcripts, phoneme-aligned TextGrids, and WavLM-based embeddings. Our results show that phoneme behavior varies across categories, with complex vowels and fricatives exhibiting higher divergence while simpler phonemes remain more stable. Phonemes with larger distributional differences are also found to be more easily detected, consistently across multiple emotions and synthesis systems. These findings demonstrate that phoneme-level analysis is an effective and interpretable approach for detecting emotionally manipulated synthetic speech.

Agentic systems involved in high-stake decision-making under adversarial pressure need formal guarantees not offered by existing approaches. Motivated by the operational needs of security operations centers (SOCs) that must configure endpoint detection and response (EDR) policies under adversarial pressure, we present a tool-mediated architecture: LLM agents use deterministic tools (Stackelberg best-response, Bayesian observer updates, attack-graph primitives) and select from finite action catalogs enforced at the tool-output interface. A composite Lyapunov function machine-checked in Lean 4 with zero sorry certifies controllability, observability from asymmetric sensor data, and Input-to-State Stability (ISS) robustness under intelligent adversarial disturbance, with two corollaries extending the certificate to any controller or adversary from the catalogs. On 282 real enterprise attack graphs, the claims hold with margin. On paired offensive/defensive telemetry, a tool-mediated Claude Sonnet 4 controller reduces the attacker's expected payoff (game value) by 59% relative to a deterministic greedy baseline, with zero variance across 40 runs at four temperatures. A Claude Haiku 4.5 controller converges to suboptimal game values but stays catalog-bounded over an additional 40 runs, demonstrating that architectural stability is not dependent on the controller capability. The LLM agent's non-determinism furthers creative exploration of strategies, while the tool-mediated architecture ensures system stability.

This manuscript contains preprint of a chapter under consideration for inclusion in the forthcoming third edition of Cover and Thomas's Elements of Information Theory, posted with permission from Wiley. The table of contents EIT-3 ToC of the new edition can be found at: https://docs.google.com/document/d/1L-m4oQEJw1PJhoxBeMwrrBD8S_HmvzMEkPbYvS24980/edit?usp=sharing . For feedback, please contact abbas@ee.stanford.edu Learning and information theory intersect in both model training and the characterization of fundamental performance limits. This manuscript provides a concise and accessible treatment of the first intersection, requiring only basic background in information theory and statistics at the senior undergraduate or first-year graduate level. End-of-chapter exercises make the material well suited for classroom use as well as self-study. The chapter focuses on the role of divergence measures in model training, with examples ranging from linear and logistic regression to autoregressive models, variational autoencoders, diffusion models, generative adversarial networks, and score-based models. It introduces the evidence lower bound (ELBO), f-divergences, and the Fisher divergence. In particular, the treatment of the generative diffusion model provides a more systematic and explicit derivation than is typical in the literature.

Speculative decoding accelerates large language model (LLM) inference by using a small draft model to propose candidate tokens that a larger target model verifies. A critical hyperparameter in this process is the speculation length $\gamma$, which determines how many tokens the draft model proposes per step. Nearly all existing systems use a fixed $\gamma$ (typically 4), yet empirical evidence suggests that the optimal value varies across task types and, crucially, depends on the compression level applied to the target model. In this paper, we present SpecKV, a lightweight adaptive controller that selects $\gamma$ per speculation step using signals extracted from the draft model itself. We profile speculative decoding across 4 task categories, 4 speculation lengths, and 3 compression levels (FP16, INT8, NF4), collecting 5,112 step-level records with per-step acceptance rates, draft entropy, and draft confidence. We demonstrate that the optimal $\gamma$ shifts across compression regimes and that draft model confidence and entropy are strong predictors of acceptance rate (correlation $\approx$ 0.56). SpecKV uses a small MLP trained on these signals to maximize expected tokens per speculation step, achieving a 56.0% improvement over the fixed-$\gamma=4$ baseline with only 0.34 ms overhead per decision (<0.5% of step time). The improvement is statistically significant (p < 0.001, paired bootstrap test). We release all profiling data, trained models, and notebooks as open-source artifacts.

Edge machine learning (ML) deployments increasingly rely on per-inference timing measured by software clocks such as Python's perf_counter, but these measurements are not always validated against external hardware references on embedded Linux, and edge ML benchmarking methodologies typically do not isolate platform-dependent instrumentation overhead. This paper reports a preliminary characterization of GPIO call overhead in hardware-validated edge ML inference timing on two embedded platforms running a one-dimensional convolutional neural network (1-D CNN) arrhythmia classifier on electrocardiogram (ECG) data from the MIT-BIH Arrhythmia Database, with five classes per the Association for the Advancement of Medical Instrumentation (AAMI) EC57 standard. Across $n = 10$ trials on each platform at a controlled steady-state baseline, the per-platform constant on the Jetson Orin Nano (TensorRT FP16, Jetson.GPIO) is approximately $-20\,\mu$s, and on the Raspberry Pi 4 (ONNX Runtime CPU, pigpio) approximately $-86\,\mu$s, yielding a cross-platform asymmetry of approximately $66\,\mu$s that is large relative to commonly used uniform validation tolerances. The Jetson constant is well-approximated by direct GPIO call duration (the direct profile accounts for ~88% of the platform constant), while the Pi direct profile over-predicts the platform constant by ~19%, motivating empirical per-platform calibration in the deployed measurement context. The Pi constant is not a single sharp value but exhibits a cross-day range of approximately $6\,\mu$s across the three sessions sampled, while the Jetson constant reproduces to within approximately $0.14\,\mu$s. These preliminary results suggest that cross-platform edge ML timing studies may benefit from platform-aware and potentially session-aware validation gates.

Artificial Intelligence (AI) will play an essential role in 6G. It will fundamentally reshape the network architecture itself and drive major changes in the design of network entities, interfaces, and procedures. The adoption of agentic AI in next-generation networks is expected to enhance network intelligence and autonomy through agents capable of planning, reasoning, and acting, while also opening up new business opportunities. Under this vision, existing network functions are expected to evolve into AI-enabled agents and tools that deliver both connectivity and beyond-connectivity services. As an initial attempt to move toward this vision, this paper presents a tool-based interface design and an experimental prototype that are based on agentic AI for the mobile core network, with the Model Context Protocol (MCP) and the Agent2Agent (A2A) protocol as foundational protocols. MCP is selected to design the interface between the agent and network tools, and the A2A protocol is used for message exchange between AI agents. In such an experimental setup, we analyze packet-level message flows between the agents, tools, and network functions and break down the latency of end-to-end operations, starting from the prompt injection until the completion of the input task. This work demonstrates how an AI agent-based core network combined with network-specific tools can be utilized in next generation mobile systems to execute intent-based tasks.

Speech encodes multiple simultaneous attributes – linguistic content, speaker identity, dialect, gender –that conventional single-vector embeddings conflate. We present a factor-partitioned embedding framework that maps each utterance into a single vector whose subspaces correspond to distinct axes of variation. A shared acoustic encoder feeds per-axis linear projection heads, each trained via distillation from a specialist teacher or a contrastive objective over shared-label pairs. The resulting embeddings support attribute-conditioned retrieval: similarity is computed as a signed weighted sum over per-axis cosine scores, allowing retrieval that jointly considers what was said and how – or explicitly suppresses one attribute to surface another. We evaluate on cross-corpus retrieval over corpora sharing the Harvard sentence prompts, demonstrating that signed axis weighting can suppress same-speaker bias and surface semantically matched utterances across recording conditions. Code is available at: https://github.com/jimregan/spoken-sentence-transformers

Structural damage detection using non-contact sensing remains a challenging problem in structural health monitoring. This study presents a data-driven framework based on Dynamic Mode Decomposition (DMD) for extracting structural dynamics directly from high-speed video recordings of vibrating structures. Within this approach, the underlying dynamics are approximated by a linear operator, whose spectral decomposition yields modal frequencies and corresponding spatial mode shapes, enabling a physically interpretable representation of the system response. The proposed methodology is evaluated through both numerical and experimental investigations. First, a cantilever beam model is simulated in ANSYS under healthy and damaged conditions. DMD is applied to partial observation data to reconstruct and predict the system response, while the extracted modal features are analyzed to characterize damage-induced variations. Second, high-speed video recordings of the beam are processed into spatiotemporal snapshot matrices, allowing DMD to recover full-field dynamic behavior without contact sensors. To enable quantitative assessment, a damage index is formulated based on DMD-derived modal features, capturing deviations in both frequency content and spatial characteristics. The results demonstrate consistent and distinguishable patterns between healthy and damaged states across both simulation and experiments, highlighting the capability of DMD as a robust and interpretable tool for non-contact damage detection using video data.

A number of important modern applications in optimal control can be formulated as open loop control problems in which the underlying dynamical systems are subject to random inputs. These so-called ensemble control problems require the corresponding optimal control to be deterministic, as it must be computed before the realization of uncertainty and the passage of time. Practical applications of ensemble control include quantum control and the training of Neural ODEs. However, the standard approach to ensemble control treats the uncertainty in the objective function via the expectation, which provides optimal controls that only work well on average while ignoring critical outlier phenomena. This study provides a comprehensive mathematical treatment of risk-averse ensemble control. Within this setting, we adopt a control-affine structure that ensures the lower semi-continuity needed for proving the existence of optimal solutions. The central analytical contribution of this paper is a rigorous characterization of the control-to-state mapping in which we establish weak-to-strong continuity, continuous Fréchet differentiability, and weak-to-strong continuity of the derivative operator. Furthermore, this regularity yields primal and dual first-order optimality conditions characterized by an adjoint state of bounded variation, and it fulfills the functional prerequisites required for the convergence of infinite dimensional optimization algorithms. We conclude by validating these theoretical developments through a numerical experiment in quantum control.

Automatic speech recognition (ASR) systems remain brittle on dysarthric and other atypical speech. Recent audio-language models raise the possibility of improving performance by conditioning on additional clinical context at inference time, but it is unclear whether these models can make use of such information. We introduce a benchmark built on the Speech Accessibility Project (SAP) dataset that tests whether diagnosis labels, clinician-derived speech ratings, and progressively richer clinical descriptions improve transcription accuracy for dysarthric speech. Across matched comparisons on nine models, we find that current models do not meaningfully use this context: diagnosis-informed and clinically detailed prompts yield negligible improvements and often degrade word error rate. We complement the prompting analysis with context-dependent fine-tuning, showing that LoRA adaptation with a mixture of clinical prompt formats achieves a WER of 0.066, a 52% relative reduction over the frozen baseline, while preserving performance when context is unavailable. Subgroup analyses reveal significant gains for Down syndrome and mild-severity speakers. These results clarify where current models fall short and provide a testbed for measuring progress toward more inclusive ASR.

Long-term beamforming substantially reduces the channel estimation and inversion overhead of conventional massive MU-MIMO receivers; yet, its construction still hinges on the inversion of a large Hermitian matrix, whose condition number deteriorates with the per-user SNR dynamic range. When this inversion is approximated in hardware via the conjugate gradient (CG) algorithm, the deterioration directly inflates the iteration count and, consequently, the energy and latency budget. We propose a hardware-friendly low-rank preconditioning framework that targets exactly this bottleneck. The preconditioner is constructed from the top eigenpairs of the long-term covariance matrix through a randomized complex eigenvalue decomposition (RC-EVD), whose inner QR factorizations are realized via a Cholesky-based scheme (QRC), confining the dominant cost to generalized matrix multiplication (GEMM) and small triangular solves that map naturally onto systolic arrays. We further show that performing the preconditioned CG inversion in the beamspace domain induces sparsification of the system matrix and provides additional convergence acceleration at negligible transformation cost. Ray-tracing simulations confirm that the joint scheme reduces the required CG iteration count by two to three while matching the post-equalization SINR of the exact inversion.

Overfitted codecs compress an image by learning a decoder tailored to the content during the encoding. As such, they trade increased encoding complexity for strong compression performance and low decoding complexity. This work introduces Cool-chic 5.0, the latest version in the Cool-chic series of overfitted codecs, featuring an updated decoder architecture and an improved optimization process. Cool-chic 5.0 outperforms all overfitted codecs with 10 times less encoding iterations. It offers -11% rate reduction compared to the state-of-the-art conventional codec H.266/VVC. It is also competitive with modern autoencoders such as MLIC++ while featuring a decoding complexity 250 times lower. This work is made open-source at https://github.com/Orange-OpenSource/Cool-Chic.

Self-supervised speech models (S3Ms) achieve strong downstream performance, yet their learned representations remain poorly understood under natural and adversarial perturbations. Prior studies rely on representation similarity or global dimensionality, offering limited visibility into local geometric changes. We ask: how do perturbations deform local geometry, and do these shifts track downstream automatic speech recognition (ASR) degradation? To address this, we present GRIDS, a framework using Local Intrinsic Dimensionality (LID) across layer-wise representations in WavLM and wav2vec 2.0. We find that LID increases for all low signal-to noise ratio (SNR) perturbations and diverges at high SNR: benign noise converges toward the clean profile, while adversarial inputs retain early-layer LID elevation. We show LID elevation co-occurs with increased WER, and that layer-wise LID features enable anomaly detection (AUROC 0.78-1.00), opening the door to transcript-free monitoring in S3Ms.

Vocal hyperfunction (VH) is a prevalent voice disorder whose ambulatory detection remains challenging despite extensive daily voice data. Prior approaches capture week-long neck-surface accelerometer recordings but collapse them into fixed-length subject-level feature vectors, discarding within-day temporal dynamics encoding nuanced voicing feature interactions. We introduce a novel hybrid architecture combining gradient-boosted trees on day-level distributional features with a CNN-based multiple instance learning (MIL) framework that preserves and learns from from temporal dynamics throughout each day. On the held-out test set, our model exceeds the challenge baselines (AUC: 0.82 PVH, 0.77 NPVH), achieving AUCs of 0.879 for PVH (Rank 5) and 0.848 for NPVH (Rank 3), while also providing insights into clinically relevant information about both pathologies.

Agentic artificial intelligence (AI) shows promise for automating O-RAN wireless supervisory control, but translated intents still require an executor-side decision before live network actuation. Existing control flows lack explicit semantics for whether an intent should commit, gate for evidence, or reject under stale telemetry, concurrent policies, deadline and bandwidth limits, and rollback constraints. We propose Progressive Risk-Gated Actuation (PRGA), an executor-side contract for risk-gated wireless intent execution. PRGA structures each intent into executable local triage (C0), on-demand coordination evidence (C1), and post-hoc provenance support (C2), with C2 kept off the online safety path. A deterministic two-stage policy checks expiry, freshness, rollback-handle validity, local conflict, blocking preconditions, and planner-executor risk divergence from C0, then retrieves C1 only for gated intents when deadline and bandwidth budgets allow; evidence-mandatory gates reject when required C1 is unavailable. On two 3GPP-parameterized energy-saving and slice-SLA benchmarks, PRGA reduces time-to-first-safe-action by 23.3-27.4% and per-commit control-plane bytes by 52.7-54.2% against a decision-identical eager full-evidence cost-overlay comparator, thereby isolating retrieval-cost accounting; remains non-inferior within a pre-declared 0.5 percentage-point unsafe-action margin against an invariant-respecting static-threshold comparator; and rejects 100% of injected over-threshold stale inputs in the stale-state fault campaign. On these benchmarks, PRGA improves supervisory responsiveness and control-plane efficiency within the evaluated unsafe-action boundary.

Predicting microsatellite instability (MSI) status from routine hematoxylin and eosin (H&E) whole slide images (WSIs) offers a practical alternative to molecular testing, but models trained at one institution tend to generalize poorly to slides acquired at a different site. Foundation model representations, despite their generality, still encode site-specific texture alongside the conserved biological morphology underlying MSI. We investigate whether tile-level spatial priors derived from known MSI histology can guide these representations toward more site-invariant features. We introduce a biologically motivated spatial prior based on peripheral distance encoding, reflecting the Crohn's-like peripheral lymphocytic reaction at the tumor invasive margin, and evaluate a secondary local immune neighborhood encoding reflecting the lymphocyte-to-tumor ratio in each tile's immediate spatial neighborhood. Both priors are injected into a TransMIL aggregator before self-attention, allowing the transformer to integrate spatial biological context with UNI2-h or Virchow2 features across all attention layers. We evaluate six foundation model and MIL aggregator combinations as a reference, then assess the effect of each spatial prior. Training on TCGA-COAD (137 slides) and evaluating externally on TCGA-READ (50 slides) without retraining, peripheral distance encoding achieves MSI AUC 0.959 +/- 0.012 on COAD and MSS specificity 1.000 on READ, compared to 0.957 and 0.939 for the strongest reference configuration. Local immune neighborhood encoding achieves comparable internal AUC but lower cross-site specificity, suggesting margin proximity encodes a more site-invariant biological signal than local immune density. Results suggest biologically grounded spatial priors act as regularizers that reduce reliance on site-specific imaging patterns.

SLAM is one of the biggest bottlenecks of XR devices, which have strict requirements for latency, power consumption, and user satisfaction. A solution that has been proposed and studied to meet the requirements is to offload SLAM to a remote server, which leverages computational hardware but may suffer due to incurred delays and transmission power. In this work, we propose offloading SLAM using Massive MIMO, which is attractive due to lower latencies, transmission power, and a more reliable link for multiple users. A framework for system-level analysis of latency and localisation error in multi-user offloaded XR with Massive MIMO has been proposed, and a case study with varying system-level parameters has been performed with it. The case study showed that there are important trade-offs between latency, localisation error, and device transmission power. We find that Massive MIMO is a promising technology for XR offloading, but that further evaluations including complete device power consumption are needed to get the full picture.

The lack of analytical models describing diffusion time dependence at intermediate time scales in complex tissue microstructure limits the accurate quantification of extracellular diffusivity and tissue microstructure. We introduce TRACED, a biophysical model that incorporates diffusion time dependence in cell distributions to quantify pathologically-relevant properties in solid tumors. Neural networks were trained on Monte Carlo diffusion simulations using sphere distribution-based geometries to enable the rapid computation of time-dependent diffusion MRI signals in cell populations of variable cell size. Model sensitivity and fit performance were assessed via simulation. Diffusion data from eight mixed-grade glioma patients was fitted using the TRACED model. Data fitting was performed using a novel physics-informed transfer learning pipeline, Sim2PINN. In two patients, cell size measurements were compared directly with image-localized histology. Simulation results indicate improved parameter estimation compared to the simple two-compartment model. TRACED enabled the simultaneous in vivo quantification of intracellular volume fraction, cell size distribution, extracellular intrinsic diffusivity, and tortuosity in glioma patients. Neural network implementations of diffusion time-dependence and tortuosity showed behavior consistent with coarse-graining and effective medium theory, respectively. Future work will explore the clinical utility of TRACED parameters in additional patients.

System identification plays a crucial role in physics and machine learning for discovering governing equations directly from data. A powerful approach is the Sparse Identification of Nonlinear Dynamics (SINDy) method, which assumes that only a few dominant terms drive the essential behavior of a nonlinear dynamical system. While SINDy methods have shown excellent results, they are most often illustrated on synthetic or simulated systems, leaving open the question of how well they perform on complex, noisy, real-world data. Power grid frequency dynamics provide a highly relevant and challenging environment for advancing system identification methods. In this work, we propose PowerSINDy as a framework for empirical power system data. We apply this framework to empirical frequency data from the Continental Europe (CE) and South Korea (SK) synchronous grids, two major power systems with distinct dynamical characteristics. PowerSINDy, which also includes time-dependent terms, can identify the dynamics of these complex real-world systems. Furthermore, we benchmark three sparsity-promoting regression strategies: Sequentially Thresholded Least Squares (STLSQ), Least Absolute Shrinkage and Selection Operator (LASSO), and Sparse Relaxed Regularized Regression (SR3) to evaluate trade-offs between accuracy, sparsity, and robustness. Results show that LASSO consistently achieves the lowest stable RMSEs, reaching 0.0101 for the CE, while STLSQ provides the best balance between accuracy and stability. SR3 exhibits higher variability and sensitivity to regularization, with L0 and L1 producing nearly indistinguishable outcomes.

In this article, we develop an analytical radiation-pattern model for pinching-antenna systems (PASS) based on a two-dimensional dielectric slab waveguide. The model is derived in two steps. First, we employ coupled-mode theory (CMT) to derive a closed-form expression for the field coupled into the pinching antennas (PAs). Second, we use this analytical field profile as a scattering source model and derive the far-field radiation pattern via a two-dimensional radiation integral. We validate the proposed model against full-wave finite-element simulations performed in COMSOL Multiphysics, showing that it accurately reproduces the directional radiation characteristics of PASS. In contrast, most existing works model PAs as omni-directional point radiators, which simplifies system-level analysis but does not accurately capture the underlying electromagnetic radiation mechanism. Because the proposed model is given in closed form, it can be easily integrated into existing system-level PASS models to replace the assumed omni-directional pattern with a physically motivated directional radiation pattern. Finally, numerical simulations quantify the performance degradation that arises when the directional behavior of PAs is neglected in a representative wireless communications scenario.

Rotating-view thick-slice acquisition is highly SNR-efficient for mesoscale diffusion MRI (dMRI) but requires numerous rotating views to satisfy Nyquist sampling, resulting in long scan time. We propose a self-supervised Spatial-Angular Implicit Neural Representation (SA-INR) that reconstructs high-resolution dMRI from a single view per diffusion direction, representing a massive acceleration. Our model, an MLP conditioned on a b=0 structural prior and the b-direction via FiLM, is trained end-to-end on the anisotropic input. The framework not only accurately reconstructs the trained b-directions (spatial SR) but also learns a continuous q-space representation, enabling high-fidelity "zero-shot" synthesis of unseen b-directions (angular SR). On simulated data, our method achieved high fidelity for both trained (34.82 dB) and unseen (33.08 dB) directions. Most importantly, the synthesized angular data also improved the quantitative accuracy of downstream DTI model fitting. Our SA-INR framework breaks the classical sampling limits, paving the way for fast, quantitative high-resolution dMRI.

Frequency diverse arrays (FDA) have attracted sustained interest as a promising architecture for introducing range-dependent responses into array systems. Unlike conventional phased arrays (PA), whose transmit behavior is primarily angle-dependent, FDA employs inter-element frequency offsets to generate time-and range-dependent phase structures, thereby producing a joint time-range-angle array response. Despite extensive research, the physical meaning of FDA-induced degrees of freedom remains debated, particularly in relation to range-angle coupling, the feasibility of time-invariant focusing, and the distinction between frequency-driven and waveform-driven range selectivity. This paper reexamines FDA from a structural and manifold-based perspective. A central contribution is the introduction of an irreducibility criterion, which distinguishes genuine range-domain physical degrees of freedom from effects that can be reproduced by equivalent signal-processing transformations. Based on this perspective, PA, multiple-input multiple-output (MIMO), FDA, and FDA-MIMO are comparatively interpreted according to the physical origin of their effective degrees of freedom, including spatial phase, waveform orthogonality, frequency gradients, and their interaction. The paper further clarifies the role of frequency across different array paradigms, contrasts FDA with time-coding-based architectures, and explains how key FDA properties such as manifold expansion, range–angle coupling, time variation, and multi-frequency diversity translate into system capabilities. Building on these structural insights, the paper connects FDA to a broad range of radar and communication functionalities, including parameter estimation, target detection, imaging, physical-layer security, and integrated sensing and communication.

Atomic norm minimization (ANM) has been extensively applied for gridless angle estimation. However, with the increase of the number of antennas and the communication frequencies in massive MIMO systems, the accompanying beam squint effect significantly degrades angle estimation accuracy. Existing solutions either address this issue only in the one-dimensional (1D) SIMO case, or decouple the two-dimensional (2D) angle estimation into two separate 1D problems, which fails to achieve the optimal solution. In this paper, we employ the multi-frequency model to characterize the beam squint effect in MIMO channels and propose a multi-frequency version of the ANM objective for corresponding 2D angle estimation. To efficiently retrieve the angle parameters, we prove the existence of the equivalent semi-definite program formulation of the ANM objective and develop an algorithm based on the alternating direction method of multipliers for its solutions. Moreover, we derive the certification conditions of this objective to guarantee the existence of a unique optimal solution.

Barrier certificates are scalar functions over the state space of dynamical systems that separate all unsafe states from all reachable states. The existence of a barrier certificate formally verifies the safety of the dynamical system. Recent approaches synthesize barrier certificates by iteratively training a neural network. In each iteration, the candidate is formally verified - if successful, the barrier certificate is found. Instead, we propose a set-based training approach that tightly integrates verification into training via a set-based loss function that soundly encodes all barrier certificate properties. A loss of zero formally proves the validity of the barrier certificate, collapsing the iterative training and verification into a single training procedure. Our experiments demonstrate that our set-based training approach scales well with the system dimension and naturally handles complex nonlinear dynamics.

The quality of an estimated nonlinear model highly depends on the data quality that was used for the system identification. By using a Gaussian Process-based optimal input design approach, a so-called space-filling dataset can be generated in the feature space of the system model. The design method is applicable for a broad type of signals and models and also incorporates information measures through optimality criteria into the signal design. However, the resulting input design can be costly to apply to the real system. The goal of this paper is to propose a space-filling input design that can minimize the experimentation cost in terms of a user defined measure, while still guaranteeing a prescribed level of space-fillingness. Through a Monte Carlo simulation study we demonstrate that the proposed method can appropriately shape the excitation signal to significantly reduce the experimental cost while the identified model performance remains adequate.

Real-time spatial monitoring in underground longwall operations is challenging due to methane-related safety risks, poor visibility, elevated thermal loads, spatial confinement, and bandwidth-limited communications. Currently available camera-based monitoring provides visual context but lacks direct depth information, while standalone underground LiDAR scanners are limited to monochromatic or periodic 3D mapping. This paper presents the design, integration, and experimental validation of a LiDAR-camera monitoring system built around a certified flameproof enclosure that prevents flame propagation into the surrounding atmosphere. The system combines a solid-state LiDAR, an industrial RGB camera, and an onboard processor within a compact hardware assembly, supporting LiDAR-camera fusion, low-light image enhancement, and real-time processing. Laboratory experiments evaluated LiDAR and camera performance through the protective polycarbonate dome and quantified optical and geometric distortions introduced by the enclosure. Thermal testing showed that iterative component placement, heat sinking, and passive conduction reduced peak surface temperature from 106 {\deg}C to 70 {\deg}C, with internal temperature stabilising at 57 {\deg}C. Furthermore, a representative longwall simulation was created to evaluate the complete sensing, fusion, and transmission workflow under controlled geometric and low-light conditions. In the final configuration, more than 97% of LiDAR points fell within the camera field of view, supporting reliable colourisation. Enclosure-aware calibration and correction maintained geometric accuracy, while processed colourised point clouds were transmitted at up to 10 Hz with sustained bandwidth below 25 Mb/s.

In this paper, we present a novel method for synthesising an optimal distributed spatial regret controller using experimentally obtained frequency-response data. Spatial regret provides a measure of the performance gap between a structured distributed controller and an oracle with enhanced communication topology. We relax assumptions on the communication topology, allowing the oracle to adopt any enhanced structure. While this generalisation requires an iterative solution in place of a single convex program, we provide a tractable algorithm that synthesises optimal controllers from frequency-response data while preserving stability and the desired communication structure. Through numerical examples, we illustrate the better performance of the spatial regret controller compared to classical H2/Hinf designs, underscoring the effectiveness of the proposed methodology.

Narrowband interference can severely degrade the performance of wireless links by concentrating significant power on a small portion of the channel. Machine learning detectors trained on baseband I/Q samples can identify the affected subcarriers with high accuracy, surpassing model-based detectors that rely on hand-crafted statistics. The predictive probabilities produced by such detectors are, however, typically poorly calibrated, and downstream mitigation modules generally operate under strict resource budgets that limit the number of candidate interference subcarriers that can be acted upon. Conformal prediction (CP) provides a distribution-free framework for constructing prediction sets that control the miscoverage level, i.e., the probability of excluding the true output, at a prescribed level. However, this target miscoverage level must be fixed in advance, while the resulting prediction-set size remains uncontrolled, which is misaligned with operationally constrained settings. To address this issue, we develop a backward conformal prediction (BCP) framework in which the prediction-set size is fixed by the operational budget and the corresponding per-input miscoverage level is estimated from calibration data with a provable post-hoc reliability guarantee. We instantiate the framework for narrowband interference detection in wireless systems and show through simulations that BCP yields reliable miscoverage estimates with accuracy comparable to that of both the uncalibrated and temperature-scaled baselines.