Papers

In this paper, we study a cooperative game in the cooperative communication network, where each relay makes decisions autonomously and aims to achieve the same optimization objective of maximizing energy efficiency. We consider the non-ideal situation where instantaneous channel state information (CSI) is difficult to obtain and only partially observable outdated CSI is available. To solve this game problem, we define a delayed reward-based state-action value function and propose a multi-agent deep Q network learning framework. Then, we prove analytically that utilities obtained by game-theoretic approaches with the instantaneous CSI serve as upper bounds for those of the proposed method. Simulation results reveal that our approach considerably outperforms its potential alternatives and is only about 5.2% away from the optimal solution.

We propose the concept of noise-aware audio self-supervised learning (SSL), whose goal is to encode audio mixtures while suppressing undesired noise, and present Noise-Aware BEATs (NABEATs) as a BEATs-based realization of this framework. Audio SSL models are designed to handle a wide range of audio signals. Consequently, under noisy conditions, they cannot effectively focus on the target sounds relevant to a downstream task, resulting in degraded performance. To address this issue, NABEATs is trained to estimate clean BEATs representations from a noisy audio signal with an auxiliary reference noise input. This reference noise enables the model to account for specific noise characteristics at inference time, thereby achieving better generalization across operating environments. Our experimental evaluations demonstrate that NABEATs significantly improves performance of various downstream tasks under noisy conditions and also generalizes well to unseen noise types.

Discriminative anomalous sound detection (ASD) methods train a feature extractor through a classification task using machine-information labels. They then detect anomalies in the resulting feature space based on distances to normal samples. The discriminative feature space effectively captures machine characteristics, leading to high ASD performance. However, this approach benefits from detailed labels, which are costly to obtain. An alternative is a self-supervised learning (SSL)-based label-free approach. This approach directly uses SSL features for ASD and has shown competitive performance. However, SSL models are typically large and computationally expensive. To address these problems, we propose a simple pseudo-label distillation framework. The proposed method generates pseudo labels from SSL features and trains a compact discriminative feature extractor using these pseudo labels. To suppress the effect of noise on pseudo-label generation, we also propose lightweight noise-robust feature transformation (NRFT) methods utilizing a small amount of clean machine-sound data or isolated noise data. We conducted comprehensive evaluations and analyses on the DCASE 2020-2025 Task 2 datasets using four SSL models. The results demonstrate that pseudo-label distillation not only transfers the performance of SSL models to a compact model but also further improves performance by leveraging available coarse labels and data augmentation. Also, our NRFT methods provide further gains.

Objective. Automated EEG preprocessing is common in research and clinical work, but few pipelines have been tested systematically. In a recent benchmark, the default EEGLAB pipeline was the only pipeline that significantly outperformed simple high pass filtering. Its MATLAB implementation, however, complicates deployment in Python and cloud workflows. We developed EEGPrep to reproduce this pipeline in Python while supporting BIDS data. The main technical problem was numerical: small floating point differences can accumulate during recursive filtering and ICA. Approach. EEGPrep covers the default EEGLAB workflow: average rereferencing, artifact removal with the clean rawdata plugin, Picard ICA, ICLabel component classification, channel interpolation, and epoching. We compared each stage with MATLAB reference output on ARM arm64, the primary analysis, and Intel x86 64, the supplementary analysis. The test dataset contained 64 channel P300 auditory oddball recordings from 13 participants. We measured maximum absolute difference, RMS error, AMARI distance for ICA, and agreement between ICLabel decisions. Main Results. On ARM arm64, import and rereferencing matched exactly for all 12 analysed subjects. The clean rawdata and Picard ICA stages remained at numerical zero, with maximum RMS equal to 1.5 x 10^-12 microvolts, AMARI distance less than or equal to 0.000001, and mean correlation equal to 1.000. ICLabel neural network inference introduced the only measurable difference, with maximum RMS equal to 2.0 x 10^-5 microvolts. Rejection decisions nevertheless agreed for every subject, and the difference did not increase through interpolation and epoching, with end to end maximum RMS less than or equal to 2.1 x 10^-5 microvolts. The Intel x86 64 analysis matched to the same precision. Significance. EEGPrep reproduces the validated EEGLAB pipeline and can be installed from PyPI or run in Docker.

The Adaptive Chirplet Transform is a flexible framework that can decompose non-stationary signals into sparse chirplets; it has been applied to signals such as electroencephalography, electromyography and radar. However, the practical deployment of this transform has been hindered by two challenges: algorithmic instability in prior implementations, which can lead to divergent decompositions, and the computational cost of searching over a high-dimensional parameter space. This paper addresses both by a sequence of contributions. Firstly, unit normalization and residual-based projection are introduced to align the decomposition with Matching Pursuit Theory, thereby eliminating divergence and substantially reducing residual error across all signal domains, as demonstrated on three representative signal types. A hybrid CPU-GPU architecture offloads chirplet family generation to the CPU while parallelizing the search on the GPU, removing bottlenecks in CPU-only search and GPU-only generation, achieving speedups of 6.6-7.38x on desktop hardware, with consistent gains observed across laptop and embedded platforms. Multichannel batching enabled simultaneous multi-signal processing, amplifying the speedup, which scaled from 3.94x for a single channel to 8.22x at 10 channels. Finally, a hierarchical coarse-to-fine search, inspired by Logon Expectation Maximization, is introduced. This reduced peak memory usage below 1 GB while maintaining similar reconstruction quality, at the cost of longer runtime. Together, these contributions establish a correct, stable and practically deployable foundation for chirplet-based signal decomposition. Index Terms: Chirplet Transform, GPU Computing, Matching Pursuit, Signal Decomposition, Sparse Representation, Time-Frequency Analysis

Dynamic speed limit control has emerged as a promising strategy to improve freeway sustainability in mixed traffic environments with connected automated vehicles (CAVs). However, most existing approaches assume that the CAV penetration rate is deterministic and can be accurately known throughout the control horizon. In reality, the penetration rate has inherent observation errors, leading to uncertainty in mixed traffic composition, which in turn degrades control performance. To overcome this limitation, this study proposes a novel model predictive control (MPC) framework for dynamic CAV speed limit control in freeway networks that explicitly incorporates traffic composition uncertainty into both flow prediction and control optimization. An uncertainty-aware macroscopic mixed traffic model is first developed, where the uncertain penetration rate propagates through the mixed fundamental diagram to the flow dynamics by affecting the mixed free-flow speed, capacity, and capacity drop condition. Then, a traffic composition-aware MPC is formulated to optimize CAV speed limits against multiple admissible penetration rate realizations, thereby improving control robustness under heterogeneous traffic conditions. Simulation experiments are conducted on both a single-bottleneck freeway corridor and a multi-bottleneck freeway network with merge-diverge interactions. The results demonstrate that the proposed controller generates more spatially coordinated speed limits, which effectively reduce travel time spent and provide environmental benefits.

This paper proposes an indoor navigation system for the visually impaired, leveraging Ultra-Wideband (UWB) positioning technology and the D*Lite path planning algorithm. The system utilizes UWB sensors to provide precision localization in GPS-denied environments. The D* Lite algorithm is integrated to optimize travel trajectories and ensure rapid route re-planning in the presence of dynamic obstacles. Experimental results demonstrate that the system operates reliably with low latency, providing safety and flexibility for users in complex indoor spaces.

In this paper, we analyze the performance of a communication-optimized reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system operating in a cluttered environment where multiple scatterers may interfere with the different types of reflected sensing signals. The RIS phases coherently combine the direct and reflected communication paths at the user equipment, whereas the corresponding radar returns remain generally misaligned. In addition, static scatterers near the radar act as environmental clutter that affects only the sensing function. For the communication link over small-scale fading, we derive an exact ergodic-capacity expression for the no-RIS baseline, a moment-matched Gamma approximation for the RIS-assisted link, and a Jensen upper bound, all of which are interpreted as upper bounds on the rate of the underlying binary phase-shift keying waveform. For sensing, our analysis focuses on the average signal-to-clutter-plus-noise ratio (SCNR) at the direct range-Doppler cell. Specifically, we derive the average powers of the direct, RIS-related, and scatterer returns, which scale as constant, linear, linear, quadratic, and constant, respectively, with the number of RIS elements. We then weigh them by the range and slow-time leakage responses to obtain the SCNR, thereby separating RIS-induced clutter from geometry-governed environmental clutter. Range and velocity estimation are evaluated using resolution-normalized metrics. Our Monte Carlo simulation results validate the analysis and show that zero-Doppler clutter leakage dominates the SCNR.

Graph and multimodal extensions to retrieval-augmented generation (RAG) are often evaluated end to end, making it difficult to isolate whether gains arise from retrieval, prompt-side context, visual access, generator capability, or benchmark construction. We present a stage- and evidence-controlled evaluation across five RAG configurations, four multimodal generators, and three document corpora. The same LLM-extracted knowledge graph is used either after retrieval as provenance-constrained triple injection (+KG) or during retrieval as entity-bridged passage expansion (+KGret). Prompt-side graph injection yields no consistent accuracy improvement and generally reduces faithfulness. In contrast, +KGret increases gold-evidence completeness from 0.22 to 0.46 on HotpotQA bridge questions and from 0.50 to 0.72 on SPIQA cross-paper questions, improving accuracy for every generator on both evidence-deficient sets while having little effect on retrieval-complete controls. For visual question answering, matched caption-answerable and verified pixel-only protocols show that apparent multimodal gains are sensitive to textual leakage. Programmatic checks reveal answer recoverability from captions, corpus text, and model responses generated without complete gold evidence. Accuracy on incomplete-evidence questions reaches 0.35–0.71 on widely disseminated corpora, compared with 0 on PubLayNet, indicating that raw accuracy can overstate retrieval-attributable performance. These results show that graph augmentation is most effective when it changes retrieval under evidence deficits, while multimodal evaluation requires explicit verification that answers are unavailable through text.

Singing Quality Assessment (SQA) has become increasingly important for practical multimedia applications and Music AI systems, yet existing studies predominantly focus on short singing clips and remain insufficient for full-length songs. Unlike clip-level assessment, full-length song SQA requires modeling how singing quality varies across different audio segments and how these local variations influence the overall evaluation of vocal performance. Moreover, the scarcity of segment-level annotations makes effective supervision challenging, as directly assigning a single overall score label to every segment tends to treat different segment qualities as equivalent. To address these challenges, we propose SongSQA, a two-stage framework for full-length song SQA. In the first stage, a Segment Score Predictor is trained with pseudo labels generated by a pre-trained teacher model, enabling segment-level singing quality prediction without requiring manual segment annotations. In the second stage, a Song Quality Aggregator integrates segment features and predicted segment scores into unified segment embeddings, and employs a learnable song embedding together with self-attention to capture the connection between segment-level vocal performance and overall song quality. In this way, SongSQA dynamically aggregates critical quality cues across the song to produce a holistic quality prediction, while also generating a temporal segment-level quality curve. Experimental results demonstrate the effectiveness of SongSQA for full-length song SQA, achieving up to a 13.95% relative improvement in KTAU over the strongest baseline, while consistently improving other evaluation metrics across all datasets.

AI data centers running distributed training workloads impose episodic, spatially correlated active-power disturbances on the transmission grid. These synchronized episodes increase cross-substation load correlation and limit the diversification benefit that reserve margin planning assumes. Conventional energy management systems evaluate each substation independently and do not extract the cross-substation statistical structure that defines a synchronized episode. This paper develops a detection method that identifies synchronized AI data-center load episodes from standard active-power telemetry without new instrumentation, trained classifiers, or labeled data. The method computes a Synchronization Index, the dominant eigenvalue fraction of a sliding sample covariance matrix across substations. A cumulative-sum (CUSUM) sequential test converts the index into a delay-bounded episode alarm. The same eigen decomposition yields, at no additional cost, a dominant eigenvector that attributes a detected episode to the substations that drive it. Tests on a real-time digital simulator (RTDS) model of the IEEE 39-bus system with three AI data-center buses show that the method separates episode and normal windows with a wide margin over chance and attributes episode participation at substation granularity.

Witsenhausen's counterexample is a canonical problem in decentralized control: a two-stage linear-quadratic-Gaussian team problem in which nonlinear policies strictly outperform every affine one. This paper develops a variational and computational framework for the scalar counterexample based on optimal transport. Building on the transport formulation of Wu and Verdú, we absorb the first controller into a transport map and recast the problem as $J^\star=\inf_Q\{k^2W_2^2(P,Q)+\mathrm{mmse}(Q)\}$, which trades a quadratic Wasserstein transport cost against a minimum mean-square estimation error. The optimal first controller is the monotone rearrangement that pushes the prior $P$ to a minimizer $Q^\star$. We establish the existence and absolute continuity of minimizers, derive an Euler–Lagrange optimality condition, and obtain a semi-closed-form Gaussian benchmark with an explicit second-variation stability threshold. We then show that restricting to finitely supported laws yields an MMSE-regularized optimal quantizer: its optimal values decrease to $J^\star$, its weak limit points are minimizers of the continuous problem, and its stationarity conditions pair centroid levels with power cells, recovering Lloyd–Max quantization as control becomes expensive. This structure leads to an estimation-aware Lloyd algorithm with signalling-based initialization. As $k\to\infty$, the optimal controllers converge to the classical affine pair; as $k\to0$, $J^\star(k)=\sigma^2k^2+o(k^2)$ and the optimal laws collapse onto $\delta_0$, so signalling constellations are a moderate-$k$ phenomenon. Numerical experiments corroborate the theory, and the framework extends to the vector counterexample, where the finite-level program becomes an MMSE-regularized vector quantizer.

Distribution-network operating tools routinely assume that voltage magnitudes respond monotonically to nodal power injections, so that checking the extreme points of an operating envelope certifies its interior. That the voltage may instead fall under rising active export is documented – measured on feeders and explained through phasor loci and P-V maxima – but its exact onset has not been placed in closed form. We derive the exact sign of the single-branch voltage sensitivity and show that componentwise monotonicity in active power reverses precisely at p* = g v, while the reactive threshold q* = b_a v sits at short-circuit scale and is physically unreachable; the two differ by the branch x/r ratio. A test protocol whose scenario grid, tolerances and acceptance criteria were fixed before the runs, on two independent networks (1594 cells, 26000 ordered injection pairs), corroborated on a second real network and a conductance sweep, places every observed violation at the low-R/X station branch, and a per-cell attribution separates threshold crossings from a second, loss-mediated coupling that reverses sensitivities while every individual branch margin is still positive. Behind a regulated busbar the entire protocol passes, so a network-scale sufficiency condition is stated as an explicit, evidence-supported hypothesis. Because every power-flow model affine in the injections is componentwise monotone by construction, screening built on such models cannot detect this failure. The threshold reads operationally as a per-branch monotonicity margin, giving distribution operators a closed-form flag for where extreme-point screening ceases to be valid.

With the rapid development of 6G, increasingly dense device connectivity imposes more strict requirements on multi-users Physical-Layer Authentication (PLA). Compared with cryptography-based methods, PLA enables lightweight authentication by using the uniqueness of wireless channels. However, existing PLA schemes in dense wireless scenarios often suffer from weak fingerprint discriminability and limited computation and communication resources. To address these challenges, we propose a Semantic Communication-based Distributed PLA (SemDPLA) framework. The framework constructs fused central semantic Channel State Information (CSI) fingerprints by fusing semantic information from a central node and multiple distributed nodes. Specifically, we introduce semantic communication to reduce the impact of low Signal Noise Ratio (SNR) and the consumption of communication resource during the transmission from distributed nodes to central node. Furthermore, we propose an ArcFace-based classification method and a semantic fingerprint-oriented distributed voting consistency mechanism to enhance device classification accuracy. Simulation results demonstrate that the proposed SemDPLA scheme performs better than single-node authentication, decision fusion, raw-CSI transmission, and feature fusion baselines. It achieves equal error rates (EERs) of 8.6% at 0 dB and 3.4% at 20 dB. It also achieves classification accuracies of 91.4% at 0 dB and above 95.8% from 5 to 20 dB. Moreover, SemDPLA is robust in low SNR environment and under attacks of abnormal nodes.

Adaptive loop filter (ALF) for video coding, which is designed to minimize the mean square error between original and reconstructed samples by using Wiener-based filter, has attracted increasing attention for its significant capability in improving coding efficiency. In the second and third Audio Video Coding Standard, i.e., AVS2 and AVS3, ALF is adopted as one of the in-loop filters. In current design, each frame is divided into 16 regions at most and corresponding filter coefficients are then derived and utilized to reconstruct each region. In this paper, a flexible-region based ALF (FRALF) scheme is proposed to improve the adaptability of existing ALF in AVS3, which introduces multiple region partition templates, such as $2\times4$, $4\times4$, $4\times8$ and $8\times8$. We subsequently propose the filter coefficients merging algorithm to further improve coding efficiency by estimating the distortion level of different partition regions. The proposed FRALF can fully consider the local texture characteristics as well as non-local similarities synthetically. The experimental results show that FRALF outperforms the existing region-based ALF in AVS3 with relatively low complexity increasing.

Wafer fabrication exhibits unique characteristics, including reentrant process flows, variable bottlenecks, and highly variable process conditions. In order to identify the most severe bottleneck at each moment in time for semiconductor wafer fabrication, this research presents the Dynamic Multi-Criteria Bottleneck Severity Index (DMBSI), a new, data-driven methodology for analysing multiple diagnostic signals of cycle time to changes in process parameters, and the impact of reworks on cycle time in order to generate an interpretable, unified measure of bottleneck severity. The experimental validation of DMBSI was conducted using Manufacturing Execution System (MES) logs collected from 22 wafer production lots at a commercial 200 mm wafer fabrication line operated by Seagate Technology. Using 5-fold cross-validation, the GA-optimised DMBSI achieves a Pearson correlation of r = 0.80 with observed cycle-time contributions, representing an 8.1% improvement over the expert heuristic baseline (r = 0.74) and substantially outperforming the Theory of Constraints (TOC; r = 0.60) and Value Stream Mapping (VSM; r = -0.30). Furthermore, the unique time-windowed component of DMBSI enabled the identification of temporal bottleneck migration patterns, which shifted from the dominant constraints associated with dielectric deposition steps in the early production windows to those associated with over- and under-inspection in the later production windows. The integrated what-if counterfactual analysis demonstrated that a 50% reduction in waiting time at the top-ranked bottleneck step would reduce the mean cycle time by 7.2%, with the top five bottleneck steps offering a combined potential reduction of approximately 19%.

We present TTSYoruba, a rule-based concatenative diphone speech synthesizer for Yoruba, deployed at online as part of the YorubaName.com open dictionary of Yoruba personal names. The system takes tone-marked Yoruba text as input and produces audio output by applying a hand-crafted phonological rule system to a recorded inventory of 651 diphone units spanning five tonal variants of every consonant-vowel combination in the language. We describe the phonological architecture of the system in detail, including our complete tonal file-selection logic, our treatment of the three-way nasal disambiguation problem (oral /n/, nasalized vowel, and syllabic nasal), and the derivation of contextual rising and falling tones from level-tone input. We also present, as an orthographic contribution, the adoption of the caron and circumflex, which are symbols with prior standing in Yoruba phonological transcription, as standard single-vowel contour tone markers, integrated into the TTS normalization pipeline and the WriteYoruba keyboard input tool. The system's performance was evaluated through a listener study (N=50), with detailed results on Mean Opinion Scores (MOS) presented in Section 6. Keywords: Yoruba, text-to-speech, low-resource languages, diphone synthesis, contour tones, African language NLP, rule-based synthesis

Identifying which string produces a given pitch in monophonic electric guitar audio is a classification challenge: a single pitch can often be produced on multiple strings, with timbral differences largely imperceptible to untrained humans. We present Fretiq, a preliminary single-instrument, single-player browser-based string classification system using a 26-dimensional feature representation of frequency band energies, spectral statistics, and 13 Mel-Frequency Cepstral Coefficients. Across five seeds, a shuffled frame-level validation split yields 97.25 +/- 0.32 percent accuracy, with an ablation study identifying MFCCs as the primary accuracy driver (92.09 +/- 0.50 percent without MFCCs). We introduce Comparison Training, a data collection method recording same-pitch pairs on adjacent strings in deliberate alternation. An initial shuffled-split comparison found no net benefit but was confounded by non-comparable validation sets across conditions. A corrected matched evaluation, using an identical recording session held out from training and model selection, shows including comparison-session data improves accuracy by 25.78 +/- 1.46 percentage points; a size-matched control shows this is not explained by training-set size alone. A recording-session-held-out evaluation yields 86.53 +/- 1.23 percent accuracy, closely matching an independently collected free-play evaluation (87.8 percent), both well below the shuffled-split figure, showing shuffled validation substantially overestimates real generalization here. We describe the feature extraction pipeline in Python and TypeScript for training-inference parity and document two implementation failure modes. The system runs entirely in-browser with no specialized hardware required.

In X-lingual automatic speaker verification (ASV), fixed front-end scores vary in reliability with language match, duration, and score source. We propose AMECxSV, an adaptive metadata-driven embedding-fusion calibration backend for metadata-available settings. AMECxSV fuses trial scores with metadata to produce calibrated target posteriors, with optional posterior-confidence abstention; metadata serve as calibration context, not speaker evidence. On a development-derived speaker-disjoint held-out split, score+metadata heads reduce equal error rate (EER) from 3.15% to 2.42% for the official TidyVoice score source and from 0.64% to 0.43% for LI-MSV; the dual-score head reaches 0.43% full-coverage EER. At 0.79 coverage, abstention yields 0.03% accepted-trial EER, not a full-coverage metric. Matched score-only, metadata-permutation, and metadata-only controls support a calibration-context interpretation and limit claims to metadata-available scoring.

Linepack is a critical buffer in gas networks, providing short-term storage and operational flexibility. This paper presents a comparative dynamic analysis of the linepack impact in hydrogen (H2) and natural gas (CH4) networks under compressor contingency conditions. A three-day dynamic simulation is conducted for a CH4 network and a H2 network integrated with a power system. Two cases are investigated: Case 1 with identical and Case 2 with different pipe inner diameters in CH4 and H2 networks. The results in both cases show that H2 exhibits faster transient recovery after the compressor contingency, although its linepack is less than that of CH4. On the other hand, depending on the selected pipe diameter, pressure losses in the two networks can differ significantly. With identical pipe diameters, H2 exhibits lower pressure drop than CH4, and its demand therefore experiences less curtailment. However, when the H2 pipe diameter is reduced, the pressure drop across the H2 pipes increases and the curtailed load becomes higher than in the CH4 network. These results can be used by gas TSOs in designing and operating their grid more efficiently.

Batteries are becoming a central part of modern energy systems, especially as electricity, transport and heat are decarbonised. In Great Britain, batteries already play an important role by providing flexibility and acting as a buffer for the system, and their importance will continue to grow as the system moves towards net zero by 2050. This perspective reviews how the role of batteries in Great Britain is evolving, their current value, and the growing contribution of batteries in the transport sector.It argues that the full value of electric vehicle batteries will only be realised through coordinated dispatch and better integration into system operation. Evidence from future scenarios developed by the National Energy System Operator (NESO) shows that while electric vehicles could provide most of the storage capacity, unmanaged charging could create challenges for the system rather than benefits. Unlocking this value will require improvements in market design, optimisation, and control strategies.

In this work, a stochastic time series-based framework is introduced for multicopter propeller damage diagnosis using functionally pooled autoregressive (FP-AR) models. The framework addresses damage detection, motor-level identification, and damage magnitude estimation using only standard inertial measurement unit data, without requiring additional sensors. Functional pooling provides a compact representation of system dynamics across varying operating conditions and supports reliable model estimation from short data records. Damage detection is performed through statistical testing of prediction residuals, damage identification through model selection, and damage quantification through a Bayesian inference scheme that provides posterior estimates and uncertainty bounds. The framework is experimentally evaluated through outdoor flight tests of a custom-built hexacopter following figure-eight trajectories under ambient wind disturbances. Six IMU channels, including three-axis acceleration and angular velocity, are analyzed across multiple motors and propeller damage levels. The results demonstrate consistent cross-flight performance without case-specific retuning. Compared with conventional batch-based quantification, the Bayesian approach provides more stable estimates and explicit uncertainty characterization. Overall, the proposed framework offers a data-efficient, interpretable, and statistically rigorous solution for multicopter structural health monitoring.

This paper studies distributed adaptive state estimation for discrete-time nonlinear systems with unknown source dynamics over directed communication networks. Each sensing agent estimates the source state using only local measurements and information exchanged with neighboring agents, enabling a fully distributed implementation without requiring shared excitation or control inputs. A normalized adaptive estimation scheme is proposed to identify unknown linear and nonlinear dynamics while ensuring robust discrete-time adaptation. A Lyapunov-based analysis establishes input-to-state stability (ISS) of the estimation error dynamics, guaranteeing bounded adaptive parameters under bounded disturbances and asymptotic convergence of the estimation errors in the disturbance-free case under suitable conditions. To characterize the network-induced coupling, explicit norm-based and LMI-based Schur stability conditions are developed for the coupling operator, including a robust formulation accounting for bounded model uncertainty. Numerical simulations on star, cyclic, and path communication topologies demonstrate accurate distributed state estimation and validate the proposed stability conditions. Computational results further show that the proposed estimator scales efficiently with the network size.

Fish-like swimming has inspired the design of several dozens if not hundreds of bioinspired robots in the last few decades. But the control and motion planning of such robots has been challenging due to the poorly modeled fluid-structure interaction and the nonlinear underactuated dynamics of such robots. While reinforcement learning has allowed significant advances in the context of ground and aerial robots, the lack of a suitable simulation environment with appropriate computational speed and accuracy have prevented similar progress for fish-like robots. We address this two-fold problem by developing a simulation platform that approximates the motion of our fish-like robot with computational efficiency. Then the motion control and path tracking by the robot is performed using PID control where the (variable) gains are learned using back propagation through time and training on a curriculum. The policy learned in the simulation is then applied on the physical platform, demonstrating an excellent match.

Integrated sensing and communication (ISAC) and AI-and-communication (AIAC) are identified as separate usage scenarios in the ITU IMT-2030 vision for sixth-generation (6G) networks. In practice, however, these two directions are already beginning to merge. ISAC gives the network a way to observe the physical world, while AI gives the network a way to learn from those observations and act on them. This article introduces AI-integrated sensing and communication (AISAC) as a closed-loop framework for this merger. In AISAC, AI is not only a tool used to optimize an ISAC system. ISAC is also the physical substrate through which AI receives data, context, and connectivity. The key technical message is that AISAC requires a new physical-layer design principle, in which the ISAC waveform, beam, power, bandwidth, and sensing mode should be configured for learning alignment, not for sensing distortion or communication rate alone. In particular, the sensing configuration that is most accurate from a classical estimation viewpoint need not be the one that is most useful for training or inference. We present the AISAC landscape, explain why imperfect sensing changes the learning problem, develop the closed-loop architecture and its three-way sensing-communication-learning tension, and outline a vehicular edge-intelligence use case together with open problems for theory, implementation, and standardization.

This paper presents a sub-milliwatt K$_u$-band cryogenic low-noise amplifier (LNA) implemented in GlobalFoundries (GF) 130CBIC SiGe BiCMOS, intended for a sub-Kelvin qubit readout chain integrating a JPA and SiGe LNA on the Still stage. The four-stage cascode LNA features balun-coupled interstage matching with a Q-enhanced resonance tank controlled by 2-bit switches tank for frequency tuning and a voltage-controlled cross-coupled negative-$g_m$ cell. The operating point of the Q-cell is optimized at each temperature stage to achieve the best in-band performance under a stable condition. Characterized at 2.5 K for the initial demonstration, the LNA achieves an average noise temperature of $\sim$14 K across 12-18 GHz and 28 dB peak gain around 16.5 GHz, while consuming only 0.74 mW. To the authors' knowledge, this is the lowest reported DC power for a K$_u$-band cryogenic SiGe LNA.

In Dynamic Data Driven Applications Systems (DDDAS), non-linear continuous-discrete (CD) tracking algorithms have been proposed to recursively estimate stochastic processes which follow continuous-time stochastic differential equations (SDE) using non-linear discrete-time measurement sequences. In this paper, a new filter in this class which is based on the polynomial chaos expansion (PCE) is proposed as an alternative solution to these tracking problems. Using the orthogonality properties of the PCE basis, PCE coefficient-wise prediction and update steps are derived via the Galerkin projection. This differs from previous PCE based filters where collocation points are independently propagated through the motion model and the PCE is recomputed at each time step. For this reason, we call the proposed filter the CD-PCE coefficient filter (CD-PCE-CF). A case study is provided where the CD-PCE-CF and the CD-Extended Kalman Filter (CD-EKF) are used to track a ballistic object undergoing process noise using radar measurements. It is shown that the proposed method significantly outperforms the CD-EKF in terms of estimation accuracy and stability.

Attention-based neural estimators achieve strong channel-estimation accuracy, but the computational cost of global attention over the time-frequency resource grid grows quadratically with the number of subcarriers, and these estimators are typically tied to a single resource allocation. This paper proposes Channel Estimation Attention (CHEA), a low-complexity channel estimator for 5G New Radio (5G NR) multi-user multiple-input multiple-output (MU-MIMO). CHEA replaces global attention with a multi-resolution windowed design: a high-resolution encoder preserves local pilot detail, a low-resolution encoder captures wider frequency-domain context, and a local cross-attention decoder transfers this coarse context back to the high-resolution pilot tokens. A per-Physical Resource Block (PRB) upsampling module then reconstructs the channel over the full slot. Because every attention operation is confined to a fixed-size window and reconstruction is performed per PRB, the cost of CHEA scales linearly with the number of subcarriers, and a single trained model supports different PRB allocations without retraining. On a standard-compliant Physical Uplink Shared Channel (PUSCH), CHEA achieves the lowest Mean Squared Error (MSE) among conventional and state-of-the-art neural estimators, while requiring 2.8\(\times\) to 22.0\(\times\) lower operations than existing attention-based estimators.

The integration of distributed energy resources (DERs) into the power grid has introduced new challenges to AC optimal power flow (AC-OPF) problems. Traditional OPF optimize consider transmission systems, treating distribution networks as static loads. However, the growing presence of DERs makes accurate distribution system modeling crucial for grid operations. Consequently, efficiently solving the resultant large-scale, nonconvex transmission and distribution (T&D) AC-OPF problem remains a significant challenge. This paper proposes a Smoothed Two-Stage Decomposition Optimizer (StsDOpt) to address these complexities by decomposing the T&D AC-OPF problem into a master-subproblem(s) structure, enabling parallel solving. Unlike traditional methods, StsDOpt does not rely on approximations or relaxations. It uses a smoothing technique to render the subproblems responses differentiable with respect to the master problem, leveraging the barrier problem properties inherent in primal-dual interior point methods. This approach is crucial for accurately modeling and solving distribution systems, which are multiphase, unbalanced, and nonlinear, distinguishing StsDOpt apart from other methods. Integrated into the PowerModelsITD framework, StsDOpt has been validated through numerical experiments, demonstrating reduced wall-clock solve time and increased scalability. Results highlight its efficacy as a robust, scalable solution for large-scale T&D AC-OPF problems, facilitating the reliable integration of DERs into complex T&D systems.

Wind farm control (WFC) plays a crucial role in mitigating the wake effect, the negative aerodynamic interactions among wind turbines. Recent advances in data-driven control and artificial intelligence offer new opportunities to design more intelligent WFC systems, motivating the need for a tool that supports interactive design and validation in simulation. To address this, we present ffconnect, a general, open-source Python-based interface for FAST.Farm, a mid-fidelity wind farm simulator. Compared to prior work, ffconnect introduces a restructured Application Programming Interface (API) with enriched state access and supports integrating FAST.Farm with modern scientific computing and machine learning ecosystems by building entirely on Python. In experiments, ffconnect shows negligible runtime overhead compared to the original FAST.Farm across a range of simulation lengths and farm sizes, and demonstrates its effectiveness through a yaw-tracking case study. Finally, we provide the source code of ffconnect to keep it accessible for general users.