Papers

Networked control systems (NCSs) are vulnerable to faults and hidden malfunctions in communication channels that can degrade performance or even destabilize the closed loop. Classical metrics in robust control and fault detection typically treat impact and detectability separately, whereas the output-to-output gain (OOG) provides a unified measure of both. While existing results have been limited to linear systems, this paper extends the OOG framework to nonlinear NCSs with quadratically constrained nonlinearities, considering false-injection attacks that can also manipulate sensor measurements through nonlinear transformations. Specifically, we provide computationally efficient linear matrix inequality conditions and complementary frequency-domain tests that yield explicit upper bounds on the OOG of this class of nonlinear systems. Furthermore, we derive frequency-domain conditions for absolute stability of closed-loop systems, generalizing the Yakubovich quadratic criterion.

Digital twin (DT) enables smart manufacturing by leveraging real-time data, AI models, and intelligent control systems. This paper presents a state-of-the-art analysis on the emerging field of DTs in the context of milling. The critical aspects of DT are explored through the lens of virtual models of physical milling, data flow from physical milling to virtual model, and feedback from virtual model to physical milling. Live data streaming protocols and virtual modeling methods are highlighted. A case study showcases the transformative capability of a real-time machine learning-driven live DT of tool-work contact in a milling process. Future research directions are outlined to achieve the goals of Industry 4.0 and beyond.

To address the issues that arise due to the manual navigation of guidewires in endovascular interventions, research in medical robotics has taken a strong interest in developing robotically steerable guidewires, which offer the possibility of enhanced maneuverability and navigation, as the tip of the guidewire can be actively steered. The COaxially Aligned STeerable (COAST) guidewire robot has the ability to generate a wide variety of motions including bending motion with different bending lengths, follow-the-leader motion, and feedforward motion. In our past studies, we have explored different designs of the COAST guidewire robot and developed modeling, control, and sensing strategies for the COAST guidewire robot. In this study, the performance of a modified COAST guidewire robot is evaluated by conducting navigation experiments in an anatomical phantom model with pulsatile flow. The modified COAST guidewire robot is a simplified version of the COAST guidewire robot and consists of two tubes as opposed to three tubes. Through this study, we demonstrate the effectiveness of the modified COAST guidewire robot in navigating the tortuous phantom vasculature.

Electroencephalographic (EEG) signals have long been applied in the field of affective brain-computer interfaces (aBCIs). Cross-subject EEG-based emotion recognition has demonstrated significant potential in practical applications due to its suitability across diverse people. However, most studies on cross-subject EEG-based emotion recognition neglect the presence of inter-individual variability and negative transfer phenomena during model training. To address this issue, a cross-subject EEG-based emotion recognition through source selection with adversarial strategy is introduced in this paper. The proposed method comprises two modules: the source selection network (SS) and the adversarial strategies network (AS). The SS uses domain labels to reverse-engineer the training process of domain adaptation. Its key idea is to disrupt class separability and magnify inter-domain differences, thereby raising the classification difficulty and forcing the model to learn domain-invariant yet emotion-relevant representations. The AS gets the source domain selection results and the pretrained domain discriminators from SS. The pretrained domain discriminators compute a novel loss aimed at enhancing the performance of domain classification during adversarial training, ensuring the balance of adversarial strategies. This paper provides theoretical insights into the proposed method and achieves outstanding performance on two EEG-based emotion datasets, SEED and SEED-IV. The code can be found at https://github.com/liuyici/SSAS.

We study the trade-off between Age of Information (AoI) and maximal leakage (MaxL) in discrete-time status updating systems. A source generates time-stamped update packets that are processed by a server that delivers them to a monitor. An adversary, who eavesdrops on the server-monitor link, wishes to infer the timing of the underlying source update sequence. The server must balance the timeliness of the status information at the monitor against the timing information leaked to the adversary. We consider a model with Bernoulli source updates under two classes of Last-Come-First-Served (LCFS) service policies: (1) Coupled policies that tie the server's deliveries to the update arrival process in a preemptive queue; (2) Decoupled (dumping) policies in which the server transmits its freshest update according to a schedule that is independent of the update arrivals. For each class, we characterize the structure of the optimal policy that minimizes AoI for a given MaxL rate. Our analysis reveals that decoupled dumping policies offer a superior age-leakage trade-off to coupled policies. When subject to a MaxL constraint, we prove that the optimal dumping strategy is achieved by dithering between two adjacent deterministic dump periods.

Prenatal ultrasound is the cornerstone for detecting congenital anomalies of the kidneys and urinary tract, but diagnosis is limited by operator dependence and suboptimal imaging conditions. We sought to assess the performance of a self-supervised ultrasound foundation model for automated fetal renal anomaly classification using a curated dataset of 969 two-dimensional ultrasound images. A pretrained Ultrasound Self-Supervised Foundation Model with Masked Autoencoding (USF-MAE) was fine-tuned for binary and multi-class classification of normal kidneys, urinary tract dilation, and multicystic dysplastic kidney. Models were compared with a DenseNet-169 convolutional baseline using cross-validation and an independent test set. USF-MAE consistently improved upon the baseline across all evaluation metrics in both binary and multi-class settings. USF-MAE achieved an improvement of about 1.87% (AUC) and 7.8% (F1-score) on the validation set, 2.32% (AUC) and 4.33% (F1-score) on the independent holdout test set. The largest gains were observed in the multi-class setting, where the improvement in AUC was 16.28% and 46.15% in F1-score. To facilitate model interpretability, Score-CAM visualizations were adapted for a transformer architecture and show that model predictions were informed by known, clinically relevant renal structures, including the renal pelvis in urinary tract dilation and cystic regions in multicystic dysplastic kidney. These results show that ultrasound-specific self-supervised learning can generate a useful representation as a foundation for downstream diagnostic tasks. The proposed framework offers a robust, interpretable approach to support the prenatal detection of renal anomalies and demonstrates the promise of foundation models in obstetric imaging.

Traditional graph signal processing (GSP) methods applied to brain networks focus on signals defined on the nodes. Thus, they are unable to capture potentially important dynamics occurring on the edges. In this work, we adopt an edge-centric GSP approach to analyze edge signals constructed from 100 unrelated subjects of the Human Connectome Project. Specifically, we describe structural connectivity through the lens of the 1-dimensional Hodge Laplacian, processing signals defined on edges to capture co-fluctuation information between brain regions. We demonstrate that edge-based approaches achieve superior task decoding accuracy in static and dynamic scenarios compared to conventional node-based techniques, thereby unveiling unique aspects of brain functional organization. These findings underscore the promise of edge-focused GSP strategies for deepening our understanding of brain connectivity and functional dynamics.

Accurately predicting topologically correct masks remains a difficult task for general segmentation models, which often produce fragmented or disconnected outputs. Fixing these artifacts typically requires hand-crafted refinement rules or architectures specialized to a particular task. Here, we show that Neural Cellular Automata (NCA) can be directly re-purposed as an effective refinement mechanism, using local, iterative updates guided by image context to repair segmentation masks. By training on imperfect masks and ground truths, the automaton learns the structural properties of the target shape while relying solely on local information. When applied to coarse, globally predicted masks, the learned dynamics progressively reconnect broken regions, prune loose fragments and converge towards stable, topologically consistent results. We show how refinement NCA (rNCA) can be easily applied to repair common topological errors produced by different base segmentation models and tasks: for fragmented retinal vessels, it yields 2-3% gains in Dice/clDice and improves Betti errors, reducing $\beta_0$ errors by 60% and $\beta_1$ by 20%; for myocardium, it repairs 61.5% of broken cases in a zero-shot setting while lowering ASSD and HD by 19% and 16%, respectively. This showcases NCA as effective and broadly applicable refiners.

Unlicensed spectrum supports diverse traffic with stringent Quality-of-Service (QoS) requirements. In NR-U/Wi-Fi coexistence,the values of MAC parameters critically influence delay, collision behavior, and airtime fairness and efficiency. In this paper, we investigate the impact of (i) cost scaling and violation modeling, (ii) choice of MAC parameters, and (iii) an enhanced collision resolution scheme for the Listen-Before-Talk (LBT) mechanism on the performance of a state-augmented constrained reinforcement learning controller for NR-U/Wi-Fi coexistence. Coexistence control is formulated as a constrained Markov decision process with an explicit delay constraint for high-priority traffic and fairness as the optimization goal. Our simulation results show three key findings: (1) signed, threshold-invariant cost scaling with temporal smoothing stabilizes learning and strengthens long-term constraint adherence; (2) use of the contention window parameter for control provides smoother adaptation and better delay compliance than other MAC parameters; and (3) enhanced LBT significantly reduces collisions and improves airtime efficiency. These findings provide practical insights for achieving robust, QoS-aware coexistence control.

Signal Temporal Logic (STL) provides a powerful framework to describe complex tasks involving temporal and logical behavior in dynamical systems. This work addresses controller synthesis for continuous-time systems subject to STL specifications and input constraints. We propose a neural network-based framework for synthesizing time-varying control barrier functions (TVCBF) and their corresponding controllers for systems to fulfill a fragment of STL specifications while respecting input constraints. We formulate barrier conditions incorporating the spatial and temporal logic of the given STL specification. We also incorporate a method to refine the time-varying set that satisfies the STL specification for the given input constraints. Additionally, we introduce a validity condition to provide formal safety guarantees across the entire state space. Finally, we demonstrate the effectiveness of the proposed approach through several simulation studies considering different STL tasks for various dynamical systems (including affine and non-affine systems).

This work addresses the Assembly Line Rebalancing Problem driven by cycle-time changes in manual assembly systems where multiple workers operate in parallel within the same station. A multi-objective optimization model is proposed that incorporates task reassignment, worker allocation, ergonomic evaluation, and explicit spatial feasibility through work-area constraints. The formulation minimizes deviations from the current configuration while promoting balanced workload and ergonomic conditions among workers. The main contribution is the extension of assembly line rebalancing to multi-worker settings with explicit spatial constraints. Computational experiments on synthetic instances demonstrate that the model consistently generates feasible reconfigurations, highlighting its potential as a decision-support tool for industrial rebalancing in flexible production environments.

This paper proposes a parallel-in-time method for computing continuous-time maximum-a-posteriori (MAP) trajectory estimates of the states of partially observed stochastic differential equations (SDEs), with the goal of improving computational speed on parallel architectures. The MAP estimation problem is reformulated as a continuous-time optimal control problem based on the Onsager-Machlup functional. This reformulation enables the use of a previously proposed parallel-in-time solution for optimal control problems, which we adapt to the current problem. The structure of the resulting optimal control problem admits a parallel solution based on parallel associative scan algorithms. In the linear Gaussian special case, it yields a parallel Kalman-Bucy filter and a parallel continuous-time Rauch-Tung-Striebel smoother. These linear computational methods are further extended to nonlinear continuous-time state-space models through Taylor expansions. We also present the corresponding parallel two-filter smoother. The graphics processing unit (GPU) experiments on linear and nonlinear models demonstrate that the proposed framework achieves a significant speedup in computations while maintaining the accuracy of sequential algorithms.

The evolution toward sixth-generation (6G) wireless networks demands high-performance transceiver architectures capable of handling complex and dynamic environments. Conventional orthogonal frequency-division multiplexing (OFDM) receivers rely on cascaded discrete Fourier transform (DFT) and demodulation blocks, which are prone to inter-stage error propagation and suboptimal global performance. In this work, we propose two neural network (NN) models DFT-Net and Demodulation-Net (Demod-Net) to jointly replace the IDFT/DFT and demodulation modules in an OFDM transceiver. The models are trained end-to-end (E2E) to minimize bit error rate (BER) while preserving operator equivalence for hybrid deployment. A customized DFT-Demodulation Net Accelerator (DDNA) is further developed to efficiently map the proposed networks onto field-programmable gate array (FPGA) platforms. Leveraging fine-grained pipelining and block matrix operations, DDNA achieves high throughput and flexibility under stringent latency constraints. Experimental results show that the DL-based transceiver consistently outperforms the conventional OFDM system across multiple modulation schemes. With only a modest increase in hardware resource usage, it achieves approximately 1.5 dB BER gain and up to 66% lower execution time.

In this paper, a reconfigurable intelligent surface (RIS) assisted cell free massive MIMO (CFmMIMO) framework is designed to enhance physical layer security (PLS) and mitigate multi user (MU) interference in next generation wireless networks. A channel state information (CSI) based precoder is designed at the access point (AP) to suppress MU interference, enabling interference free reception for the legitimate users. To further enhance secrecy performance, we formulate a joint optimization problem that maximizes the secrecy sum rate using an alternating optimization (AO) framework, which iteratively updates the active beamforming at the AP, user power allocation, and the RIS phase shift matrix. The highly nonconvex problem is addressed under the Riemannian manifold optimization (RMO) framework and solved using a Riemannian Conjugate Gradient (RCG) algorithm for RIS phase shift design. Simulation results verify that the proposed framework effectively enhances the secrecy sum rate and eliminates interference, demonstrating its potential for secure and scalable CFmMIMO networks in dense wireless environments.

A non-destructive, real-time method for estimating the volume fraction of a dielectric mixture inside a resonant cavity is presented. A convolutional neural network (CNN)-based approach is used to estimate the fractional composition of two-phase dielectric mixtures inside a resonant cavity using scattering parameter (S-parameter) measurements. A rectangular cavity sensor with a strip feed structure is characterized using a vector network analyzer (VNA) from 0.01–20~GHz. The CNN is trained using both simulated and experimentally measured S-parameters and achieves high predictive accuracy even without de-embedding or filtering, demonstrating robustness to measurement imperfections. The simulation results achieve a coefficient of determination ($R^2$)=0.99 using $k$-fold cross-validation, while the experimental model using raw data achieves an $R^2=0.94$ with a mean absolute error (MAE) below 6%. Data augmentation further improves the accuracy of the experimental prediction to above $R^2=0.998$ (MAE$<$0.72%). The proposed method enables rapid, non-destructive, accurate, low-cost, and real-time estimation of material fractions, illustrating strong potential for sensing applications in microwave material characterization.

As cyber-physical systems (CPSs) become more dependent on data and communication networks, their vulnerability to false data injection (FDI) attacks has raised significant concerns. Among these, stealthy attacks, those that evade conventional detection mechanisms, pose a critical threat to closed-loop performance. This paper introduces a controller-oriented method to enhance CPS resiliency against such attacks without compromising nominal closed-loop behavior. Specifically, we propose the concept of plant equivalent controller (PEC) realizations, representing a class of dynamic output-feedback controllers that preserve the input-output behavior of a given base controller while exhibiting distinct robustness properties in the presence of disturbances and sensor attacks. To quantify and improve robustness, we employ reachable set analysis to assess the impact of stealthy attacks on the closed-loop dynamics. Building on this analysis, we provide mathematical tools (in terms of linear matrix inequalities) to synthesize the optimal PEC realization that minimizes the reachable set under peak-bounded disturbances. The proposed framework thus provides systematic analysis and synthesis tools to enhance the attack resilience of CPSs while maintaining the desired nominal performance. The effectiveness of the approach is demonstrated on the quadruple-tank process subject to stealthy sensor attacks.

Over the past decade, several studies have explored the potential of magnetic resonance fingerprinting (MRF) for the quantification of brain hemodynamics, oxygenation, and perfusion. Recent advances in simulation models and reconstruction frameworks have also significantly enhanced the accuracy of vascular parameter estimation. This review provides an overview of key vascular MRF studies, emphasizing advancements in geometrical models for vascular simulations, novel sequences, and state-of-the-art reconstruction techniques incorporating machine learning and deep learning algorithms. Both pre-clinical and clinical applications are discussed. Based on these findings, we outline future directions and development areas that need to be addressed to facilitate their clinical translation. Evidence Level N/A. Technical Efficacy Stage 1.

Controlling the deformation of flexible objects is challenging due to their non-linear dynamics and high-dimensional configuration space. This work presents a differentiable Material Point Method (MPM) simulator targeted at control applications. We exploit the differentiability of the simulator to optimize a control trajectory in an active damping problem for a hyperelastic rope. The simulator effectively minimizes the kinetic energy of the rope around 2$\times$ faster than a baseline MPPI method and to a 20% lower energy level, while using about 3% of the computation time.

Path generation, the process of converting high-level mission specifications, such as sequences of waypoints from a path planner, into smooth, executable paths, is a fundamental challenge in mobile robotics. Most path following and trajectory tracking algorithms require the desired path to be defined by at least twice continuously differentiable functions to guarantee key properties such as global convergence, especially for nonholonomic robots like unicycles with speed constraints. Consequently, path generation methods must bridge the gap between convenient but non-differentiable planning outputs, such as piecewise linear segments, and the differentiability requirements imposed by downstream control algorithms. While techniques such as spline interpolation or optimization-based methods are commonly used to smooth non-differentiable paths or create feasible ones from sequences of waypoints, they either produce unnecessarily complex trajectories or are computationally expensive. In this work, we present a method to regularize non-differentiable functions and generate feasible paths through mollification. Specifically, we approximate an arbitrary path with a differentiable function that can converge to it with arbitrary precision. Additionally, we provide a systematic method for bounding the curvature of generated paths, which we demonstrate by applying it to paths resulting from linking a sequence of waypoints with segments. The proposed approach is analytically shown to be computationally more efficient than standard interpolation methods, enabling real-time implementation on microcontrollers, while remaining compatible with standard trajectory tracking and path following algorithms.

Manufacturing processes are often perturbed by drifts in the environment and wear in the system, requiring control re-tuning even in the presence of repetitive operations. This paper presents an iterative learning framework for automatic tuning of Nonlinear Model Predictive Control (NMPC) weighting matrices based on task-level performance feedback. Inspired by norm-optimal Iterative Learning Control (ILC), the proposed method adaptively adjusts NMPC weights Q and R across task repetitions to minimize key performance indicators (KPIs) related to tracking accuracy, control effort, and saturation. Unlike gradient-based approaches that require differentiating through the NMPC solver, we construct an empirical sensitivity matrix, enabling structured weight updates without analytic derivatives. The framework is validated through simulation on a UR10e robot performing carbon fiber winding on a tetrahedral core. Results demonstrate that the proposed approach converges to near-optimal tracking performance (RMSE within 0.3% of offline Bayesian Optimization (BO)) in just 4 online repetitions, compared to 100 offline evaluations required by BO algorithm. The method offers a practical solution for adaptive NMPC tuning in repetitive robotic tasks, combining the precision of carefully optimized controllers with the flexibility of online adaptation.

Deep learning models in medical imaging are susceptible to shortcut learning, relying on confounding metadata (e.g., scanner model) that is often encoded in image embeddings. The crucial question is whether the model actively utilizes this encoded information for its final prediction. We introduce Weight Space Correlation Analysis, an interpretable methodology that quantifies feature utilization by measuring the alignment between the classification heads of a primary clinical task and auxiliary metadata tasks. We first validate our method by successfully detecting artificially induced shortcut learning. We then apply it to probe the feature utilization of an SA-SonoNet model trained for Spontaneous Preterm Birth (sPTB) prediction. Our analysis confirmed that while the embeddings contain substantial metadata, the sPTB classifier's weight vectors were highly correlated with clinically relevant factors (e.g., birth weight) but decoupled from clinically irrelevant acquisition factors (e.g. scanner). Our methodology provides a tool to verify model trustworthiness, demonstrating that, in the absence of induced bias, the clinical model selectively utilizes features related to the genuine clinical signal.

Driven by the ongoing energy transition, shared mobility providers are emerging actors in power systems aiming to shift combustion-based vehicles towards electric ones. Meanwhile Energy Communities are deployed to promote investment in distributed renewable production and enhance the local usage of it. The complementarity in their electrical demand, enhanced by a coordinated operational planning, can help both actors reduce the electricity supply cost. Considering this original collaboration, this paper presents a Mixed-Integer Quadratic Programming problem which jointly optimizes the EC members and EVs flexibility usage to take advantage of the local production. Besides economic benefits comparison, authors analyses the impact of grid tariffs and bi-directional charging on the distribution network. Results from a Belgian mobility case study show that coordination can help reducing the yearly cost up to 15.6% compared to their stand-alone situation and that it may reduce by 30.8% the stress on the substation transformer when subject to peak penalties from the grid operator.

Orthogonal time-frequency space (OTFS) modulation has emerged as a powerful wireless communication technology that is specifically designed to address the challenges of high-mobility scenarios and significant Doppler effects. Unlike conventional modulation schemes that operate in the time-frequency (TF) domain, OTFS projects signals to the delay-Doppler (DD) domain, where wireless channels exhibit sparse and quasi-static characteristics. This fundamental transformation enables superior channel estimation (CE) performance in challenging propagation environments characterized by high-mobility, severe multipath effects, and rapidly time-varying channel conditions. This article provides a systematic examination of CE techniques for OTFS systems, covering the extensive research landscape from foundational methods to cutting-edge approaches. We present a detailed analysis of DD and TF domain CE techniques presented in the literature, including separate pilot, embedded pilot, and superimposed pilot approaches. The article encompasses various algorithmic frameworks including Bayesian learning, matching pursuit-based techniques, message passing algorithms, deep learning (DL)-based methods, and recent CE approaches. Additionally, we explore joint CE and signal detection (SD) strategies, the integration of OTFS with next-generation wireless systems including massive multiple-input multiple-output (MIMO), millimeter wave (mmWave) communications, reconfigurable intelligent surfaces (RISs), and integrated sensing and communication (ISAC) systems. Critical implementation challenges are presented, including leakage suppression, inter-Doppler interference mitigation, impulsive noise handling, signaling overhead reduction, guard space requirements, peak-to-average power ratio (PAPR) management, beam squint effects, and hardware impairments.

In many multi-agent systems, communication is limited by bandwidth, latency, and energy constraints. Designing controllers that achieve coordination and safety with minimal communication is critical for scalable and reliable deployment. This paper presents a method for designing controllers that minimize inter-agent communication in multi-agent systems while satisfying safety and coordination requirements, while conforming to communication delay constraints. The control synthesis problem is cast as a rank minimization problem, where a convex relaxation is obtained via system level synthesis. Simulation results on various tasks, including trajectory tracking with relative and heterogeneous sensing, demonstrate that the proposed method significantly reduces inter-agent transmission compared to baseline approaches.

This comprehensive literature review examines the emerging applications of Large Language Models (LLMs) in power system engineering. Through a systematic analysis of recent research published between 2020 and 2025, we explore how LLMs are being integrated into various aspects of power system operations, planning, and management. The review covers key application areas including fault diagnosis, load forecasting, cybersecurity, control and optimization, system planning, simulation, and knowledge management. Our findings indicate that while LLMs show promising potential in enhancing power system operations through their advanced natural language processing and reasoning capabilities, significant challenges remain in their practical implementation. These challenges include limited domain-specific training data, concerns about reliability and safety in critical infrastructure, and the need for enhanced explainability. The review also highlights emerging trends such as the development of power system-specific LLMs and hybrid approaches combining LLMs with traditional power engineering methods. We identify crucial research directions for advancing the field, including the development of specialized architectures, improved security frameworks, and enhanced integration with existing power system tools. This survey provides power system researchers and practitioners with a comprehensive overview of the current state of LLM applications in the field and outlines future pathways for research and development.

To address system frequency challenges driven by the integration of renewable generation, advanced control strategies are designed at the device level to provide effective frequency support following disturbances. However, typically relying on energy-based performance metrics, these methods cannot guarantee the system frequency constraints such as frequency nadir and maximum Rate-of-Change-of-Frequency (RoCoF). Moreover, locally-designed frequency support cannot minimize the overall system cost to maintain frequency stability. On the other hand, the concept of frequency-constrained system scheduling is introduced, which incorporates frequency dynamic constraints into the system economic optimization, so that frequency requirements can be maintained with minimum cost. However, these works rely on analytical approximations of the frequency dynamic metrics, which are mathematically complicated and tend to be over-conservative for the approximation of IBR headroom requirements.

Over the past few years, per-title and per-shot video encoding techniques have demonstrated significant gains as compared to conventional techniques such as constant CRF encoding and the fixed bitrate ladder. These techniques have demonstrated that constructing content-gnostic per-shot bitrate ladders can provide significant bitrate gains and improved Quality of Experience (QoE) for viewers under various network conditions. However, constructing a convex hull for every video incurs a significant computational overhead. Recently, machine learning-based bitrate ladder construction techniques have emerged as a substitute for convex hull construction. These methods operate by extracting features from source videos to train machine learning (ML) models to construct content-adaptive bitrate ladders. Here, we present a new ML-based bitrate ladder construction technique that accurately predicts the VMAF scores of compressed videos, by analyzing the compression procedure and by making perceptually relevant measurements on the source videos prior to compression. We evaluate the performance of our proposed framework against leading prior methods on a large corpus of videos. Since training ML models on every encoder setting is time-consuming, we also investigate how per-shot bitrate ladders perform under different encoding settings. We evaluate the performance of all models against the fixed bitrate ladder and the best possible convex hull constructed using exhaustive encoding with Bjontegaard-delta metrics.

The efficacy of unmanned aerial vehicle (UAV) swarm cooperative perception fundamentally depends on three-dimensional (3D) formation geometry, which governs target observability and sensor complementarity. In the literature, the exploitation of formation geometry and its impact on UAV sensing have rarely been studied, which can significantly degrade multimodal cooperative perception in scenarios where heterogeneous payloads (vision cameras and LiDAR) should be geometrically arranged to exploit their complementary strengths while managing communication interference and hardware budgets. To bridge this critical gap, targeting the active tracking phase where a prior target estimate is available, we propose an information-optimal optimization framework that optimizes the allocation of UAVs and multimodal sensors, configures formation geometries, and executes flight control. The UAV-sensor allocation is optimized by the Fisher Information Matrix (FIM) determinant maximization. Under this framework, we introduce an equivalent formation transition strategy that enhances field-of-view (FOV) coverage and reduces dynamic communication interference without compromising perception accuracy. Furthermore, we design a novel Lyapunov-stable flight control scheme with logarithmic potential fields to generate energy-efficient trajectories for formation transitions. Extensive simulations demonstrate that our formation-aware design achieves 25.0% improvement in FOV coverage, 104.2% enhancement in communication signal strength, and 84.7% reduction in energy consumption compared to conventional benchmarks. A high-fidelity Gazebo validation further shows up to 20.2% gain in perception accuracy over hemispherically sampled formations. These results suggest that task-driven geometric allocation is an important rather than incidental component in next-generation UAV swarm perception systems.

Restoring speech communication from neural signals is a central goal of brain-computer interface research, yet EEG-based speech reconstruction remains challenging due to limited spatial resolution, susceptibility to noise, and the absence of temporally aligned acoustic targets in imagined speech. In this study, we propose an EEG-to-Voice paradigm that directly reconstructs speech from non-invasive EEG signals without dynamic time warping (DTW) or explicit temporal alignment. The proposed pipeline generates mel-spectrograms from EEG in an open-loop manner using a subject-specific generator, followed by pretrained vocoder and automatic speech recognition (ASR) modules to synthesize speech waveforms and decode text. Separate generators were trained for spoken speech and imagined speech, and transfer learning-based domain adaptation was applied by pretraining on spoken speech and adapting to imagined speech. A minimal language model-based correction module was optionally applied to correct limited ASR errors while preserving semantic structure. The framework was evaluated under 2 s and 4 s speech conditions using acoustic-level metrics (PCC, RMSE, MCD) and linguistic-level metrics (CER, WER). Stable acoustic reconstruction and comparable linguistic accuracy were observed for both spoken speech and imagined speech. While acoustic similarity decreased for longer utterances, text-level decoding performance was largely preserved, and word-position analysis revealed a mild increase in decoding errors toward later parts of sentences. The language model-based correction consistently reduced CER and WER without introducing semantic distortion. These results demonstrate the feasibility of direct, open-loop EEG-to-Voice reconstruction for spoken speech and imagined speech without explicit temporal alignment.

Existing Wi-Fi sensing systems rely on injecting high-rate probing packets to extract channel state information (CSI), leading to communication degradation and limited deployment flexibility. Although Integrated Sensing and Communication (ISAC) is a promising direction, existing solutions still rely on auxiliary packet injection because they exploit only uniform CSI from a single frame type, discarding approximately 70% of naturally available packets. We present FuseFi, a novel Wi-Fi-based ISAC framework that directly exploits irregularly sampled CSI from diverse communication packets across multiple frequency bands, eliminating intrusive packet injection and introducing no sensing-specific communication overhead. FuseFi integrates a CSI sanitization pipeline to harmonize heterogeneous packets and remove burst-induced redundancy, together with a time-aware attention model that learns directly from non-uniform CSI sequences without resampling. We further introduce CommCSI-HAR, a new dataset with irregularly sampled CSI from real-world dual-band communication traffic. Extensive evaluations on this dataset and, separately, on four public sensing tasks across three benchmark datasets show that FuseFi achieves state-of-the-art accuracy with a compact model size, while fully preserving communication throughput.