Papers

Severe signal blockage and fast-varying channels in vehicular environments pose critical challenges to reliable semantic communication. To address these, this paper proposes a novel Row-Movable Active Reconfigurable Intelligent Surface (RM-A-RIS) assisted vehicular semantic communication system. This architecture uniquely combines active signal amplification with element mobility to compensate for multiplicative fading and reconstruct channel geometry, thereby enhancing spatial diversity. We formulate a joint optimization problem to maximize Semantic Spectral Efficiency (SSE) by coordinating RIS element positions, active reflection coefficients, and semantic symbol length. An efficient Alternating Optimization (AO) algorithm is developed to tackle the coupled non-convexity. Simulation results demonstrate that the proposed scheme substantially outperforms existing benchmarks, achieving up to 132.9%, 9.2%, and 35.2% improvements in Sum-Semantic Spectral Efficiency (Sum-SSE) compared to the passive RIS, fixed-position active RIS, and QPSO baselines, respectively.

We propose an extended Perron–Frobenius Operator Filter (PFOF) for nonlinear state estimation. The method learns the Perron–Frobenius operator, an infinite-dimensional linear operator fully preserving properties of a nonlinear dynamical system, using the extended Dynamic Mode Decomposition (eDMD). This enables us to explicitly account for non-Gaussian distributions exhibited by the nonlinear system within a linear-operator representation, while retaining the freedom to choose basis functions in eDMD. Through two numerical examples, we show that the extended PFOF achieves high computational efficiency and high estimation accuracy by exploiting the flexibility in the choice of basis functions in eDMD.

Mask-based beamforming is a popular geometry-agnostic approach for speech enhancement, typically applying a single mask across all microphones to estimate the required covariance matrices. While effective for compact arrays, this strategy may be suboptimal for spatially distributed microphones, where signal characteristics may vary strongly across microphones. To effectively capture the spatial diversity across microphones, we extend the mask-based beamformer to a multi-channel formulation, where each microphone is pre-filtered by a separate mask before covariance estimation. To address time-varying acoustic scenes, caused by spectro-temporal nonstationarity, we adopt a frame-causal online implementation with a sliding window. Experiments with simulated compact arrays and distributed microphones show that multi-channel masking yields a benefit over using a single mask when microphone signals differ substantially, while retaining similar performance in compact arrays. We further demonstrate the robustness of the multi-channel masking approach by comparing oracle ideal ratio masks to blind DNN-based mask estimation.

This paper investigates multi-user localization in uplink wireless systems assisted by electromagnetically reconfigurable antennas (ERAs). Unlike traditional localization schemes, we formulate an active sensing problem where a base station (BS) exploits historical pilot observations accumulated over previous sensing stages to adapt the shared ERA configuration and progressively refine position estimates. To capture both theoretical flexibility and practical hardware constraints, we establish a unified wideband geometric signal model accommodating two complementary ERA paradigms: a synthesis-based model utilizing spherical-harmonic basis functions, and a finite-state model based on measured radiation codebooks. Because analytically solving the resulting joint design problem is highly intractable due to the high-dimensional observation and the shared-aperture coupling among multiple users, we develop a learning-based active sensing framework. Specifically, pilot-matched wideband observations are compressed into compact user-wise features and sequentially accumulated by a long short-term memory (LSTM) module. These temporal features are then processed by a graph neural network (GNN) to capture multi-user shared-aperture coupling. Model-specific output heads generate either continuous synthesis coefficients or finite-state ERA selections, while a localization head produces stage-wise position estimates. Numerical results under a specific channel distribution show that the proposed ERA-assisted active sensing framework achieves progressive localization refinement across sensing stages and obtains better performance than conventional non-reconfigurable arrays and representative ablation baselines.

PID tuning for chemical processes commonly relies on identified process models, whereas plant engineers often retune loops iteratively by observing responses, diagnosing deficiencies, adjusting gains, and validating the result. This work formalizes this engineer-like workflow in a language-model-assisted PID tuning framework applicable to both large and small language models (LLMs/SLMs). Hosted LLMs receive closed-loop response features, control-engineering diagnoses, tuning preferences, and internal model control (IMC)-based demonstrations to generate and iteratively correct PID gains under common acceptance criteria. For local deployment, Qwen3-0.6B is adapted through supervised fine-tuning (SFT) with simulation-verified IMC targets and physics-informed group relative policy optimization (PI-GRPO) with non-compensable stability and performance rewards. On 100 first-order plus dead time (FOPDT) and 100 second-order plus dead time (SOPDT) test cases, hosted LLMs (DeepSeek-V4-Flash and Qwen3.7-Plus) achieve final success rates of 75-89% and 77-79%, respectively. As for Qwen3-0.6B, supervised fine-tuning raises first-recommendation success to 86.5%, and PI-GRPO further increases it to 94.0%, primarily improving first-attempt reliability and stability margins.

We propose a deep unfolded REM network for robust tracking of a single moving speaker in mild reverberant environments. Unlike classical REM algorithms, which rely on fixed-step-size decay schedules, the proposed architecture learns an adaptive update policy by unfolding the iterative procedure into differentiable layers. We introduce a Step Size Network that leverages FiLM and PE to dynamically adjust the recursion weights based on temporal context and convergence state. Experimental results for tracking a single speaker under reverberant conditions demonstrate that the proposed unfolded network outperforms the classical CREM baseline, which employs a spatial grid search to map the estimated centroids to physical positions. In the single-speaker tracking task, the proposed method achieves a lower RMSE than the CREM baseline, highlighting its potential for dynamic acoustic scenarios.

Current spacecraft collision-avoidance operations rely on intermittent ground-station contacts, requiring operators to plan with delayed and asynchronously updated information. Consequently, maneuvers must be planned with only intermittent information sharing between operators, raising the question of how much coordination is needed to achieve collision-avoidance performance comparable to centralized planning. Although decision-theoretic approaches such as partially observable Markov decision processes (POMDPs) capture the sequential and uncertain nature of collision avoidance, existing multiagent extensions typically assume either continuous information sharing or communication models that do not reflect operational ground-station constraints. To explicitly model this intermittent information availability, we formulate the spacecraft-to-spacecraft collision avoidance problem as a semi-decentralized POMDP (SDec-POMDP), where we govern information propagation directly by realistic ground-station visibility windows. Joint maneuver policies are computed using approximate Recursive Small-Step Semi-Decentralized A* (RS-SDA*), following the state-of-the-art A*-based lineage for decentralized multiagent planning. Across a representative suite of conjunction scenarios, semi-decentralized planning recovers near-centralized maneuver quality while requiring 28.5% fewer synchronization events than continuous coordination. Comparisons with representative rule-based operator heuristics further show that communication-aware planning more consistently achieves the desired operational miss-distance band while minimizing unnecessary trajectory deviation. Together, these results establish a practical planning framework for autonomous collision avoidance under realistic intermittent communication, bridging the gap between idealized centralized coordination and fully decentralized planning execution.

As a major contributor to carbon emissions, the decarbonization of power systems has garnered significant societal attention. Nodal carbon intensity (NCI), a critical factor in carbon-oriented demand response, has traditionally been determined through ex-post calculations. However, this ex-post approach introduces latency in low-carbon dispatch. To address this, this paper presents a proactive ex-ante spatial-temporal carbon response framework. At its core, we develop a novel deep learning-based hierarchical design, enhanced by a dual-stage attention mechanism and a large language model (LLM)-based multi-agent cooperation system, to accurately forecast day-ahead NCI. This design effectively mitigates the impact of renewable energy uncertainty and enhances predictive resilience. On the demand side, the framework proposes a spatial-temporal carbon scheduling model that integrates geographically dispatchable loads (GDLs), including mobile energy storage systems (MESSs) and distributed data centers (DDCs). Leveraging high-accuracy day-ahead NCI predictions, the framework can effectively reduce system emissions by quickly responding to carbon intensity fluctuations. The proposed framework is tested on the modified IEEE 33-bus system. According to the simulation results, the impacts of proposed framework on dispatching latency and emission outcomes are analyzed. The results demonstrate that under a one-hour reduction in carbon scheduling latency, the proposed model and methodology can achieve over 30% emission reduction. This research breaks through the limitations of passive carbon accounting, advancing toward proactive carbon management. It offers an intelligent solution that accelerates the transition to cleaner power systems while directly supporting sustainable production goals.

Fluid antenna systems (FAS) introduce additional spatial degrees of freedom to enable integrated sensing and communication (ISAC) in air-ground networks. However, conventional studies often overlook or simplify the physical overheads and switching costs of FAS. In practice, port switching incurs non-negligible time, during which communication and sensing may continue but with potentially degraded slot-level performance. This leads to two key challenges: (1) the characterization of a slot-level, cost-aware ISAC metric is difficult, and (2) the large port space and accompanying abrupt environmental variations demand more reliable online decision-making. To address these challenges, a cost-aware multi-objective FAS switching problem is formulated, jointly considering slot-level ISAC performance and switching energy. The online conformal Bayesian optimization (OCBO) algorithm is then proposed to learn the unknown gray-box ISAC objectives and calibrate surrogate uncertainty for robust stay-or-switch decisions. Simulation results demonstrate that the proposed cost-aware optimization framework achieves substantially improved long-term ISAC performance compared to existing baselines.

In this paper, a relay network with hybrid fixed-position and rotatable antenna arrays is proposed. The deployment of rotatable arrays in conventional relay networks is considered to provide more secure communications for low-altitude economy applications. Specifically, both the base station and the relay station are equipped with fixed-position antenna arrays and rotatable arrays to serve ground users and aerial users, respectively. To address the challenge of multi-user interference, a low-cost reconfigurable intelligent surface is exploited as a candidate path. Accordingly, under constraints on transmit power, user quality of service, rotatable range, and path selection, the objective is to maximize the worst-case secrecy rate (SR) through joint beamforming, power allocation, and rotatable antenna orientation design. First, the SR performance in the \textcolor{black}{single ground-aerial user pair scenario} is investigated, and a step-by-step leakage-based scheme is proposed. Then, the general multi-user scenario is studied, and a Distributional Soft Actor-Critic with Three refinements (DSAC-T)-based learning scheme, which supports hybrid discrete and continuous actions, is proposed to maximize the worst-case SR. Simulation results validate the effectiveness of the proposed schemes. The proposed schemes achieve approximately a twofold improvement in SR performance compared to isotropic antennas. \textcolor{black}{The proposed system can achieve approximately 99.7% power saving, 55% antenna saving, and can serve more users.}

This paper considers the problem of online state estimation and predictive risk assessment for non-cooperative unmanned aerial vehicles (UAVs) in the presence of asynchronous heterogeneous sensing and uncertain motion modes. To address this problem, a unified estimation and safety-assessment framework is developed by integrating an interacting multiple-model multi-rate Kalman filter with signal temporal logic (STL). The proposed framework enables simultaneous low-level state tracking and high-level safety reasoning within a common recursive architecture. Its main contribution is an STL-aware time-varying mode transition mechanism that updates model probabilities online using robustness measures induced by formal safety specifications. By embedding safety semantics directly into the mode inference and estimation process, the method improves responsiveness to maneuver variations, sensing asynchrony, and evolving threat patterns. Based on the estimated state distributions, the framework further generates multi-step state predictions and probabilistic reachable sets, which are used for finite-horizon safety evaluation and risk-triggered warning generation. Consequently, the proposed method provides not only estimates of the current target state, but also early indication of unsafe behaviors before they become fully observable. Finally, experimental results obtained from a real-time UAV monitoring platform show that the proposed approach improves estimation accuracy and produces earlier and more informative safety warnings, demonstrating its effectiveness for real-time UAV surveillance and safety monitoring applications.

The reliability of the internal micro-motors is crucial for the performance and lifespan of electric toothbrushes. In this paper, a vibration-based fault detection method is proposed to identify micro-motor defects in electric toothbrushes. A dedicated signal acquisition device was designed and developed to capture the vibration signals of micro-motors using a high-precision accelerometer. To effectively characterize the micro-motor conditions, comprehensive features were extracted from the raw vibration data in both the time and frequency domains. A random forest (RF) algorithm was then employed to evaluate the importance of all extracted features. To better interpret the extracted features based on fault mechanisms, and to reduce dimensionality and computational overhead while avoiding overfitting, the top three features with the highest importance scores were selected to form the optimal feature subset. Finally, a support vector machine (SVM) model was utilized to classify the motor states based on the selected features. Experimental results demonstrate that the proposed method, combining RF-based feature selection and SVM classification, achieves outstanding diagnostic performance. Specifically, the model yields a balanced accuracy of 94.44%, a defect recall of 88.89%, a defect F1-score of 94.12%, a Matthews correlation coefficient of 93.74%, a geometric mean of 94.28%, and an area under the receiver operating characteristic curve of 100.00%. These robust metrics confirm that the proposed approach can accurately and efficiently detect micro-motor faults in electric toothbrushes, providing a practical and reliable solution for quality control and condition monitoring in manufacturing.

Digital twins (DTs) can reduce over-the-air validation cost in industrial wireless networks, but their utility depends on the fidelity of the underlying channel twin (CT). At W-band, site-specific ray tracing captures deterministic propagation geometry, yet its channel frequency responses (CFRs) do not reproduce the small-scale impairments and capture-to-capture variability observed in laboratory orthogonal frequency-division multiplexing (OFDM) measurements above 90 GHz. This paper proposes Statistics-Consistent Sim-to-Lab Adaptation (SC-SLA), a calibration framework that improves the fidelity of a 95 GHz Sionna ray-traced CT toward that of the testbed by aligning the mean power delay profile (PDP), the distribution of root-mean-square delay spread ($\tau_{\mathrm{rms}}$), and per-subcarrier statistics at 50 MHz sampling bandwidth. SC-SLA uses a generative adversarial network (GAN)-inspired, cycle-consistent architecture with ResNet generators and batch-level channel-statistics losses on the PDP, sub-band PDP, $\tau_{\mathrm{rms}}$ moments and quantiles, and normalized mean-square error (NMSE) of the mean CFR-magnitude profile. The framework is non-adversarial and requires neither paired simulated/measured samples nor discriminators. On held-out 95 GHz data, SC-SLA reduces the $\tau_{\mathrm{rms}}$ Kolmogorov-Smirnov (KS) statistic from 0.86 to 0.050 relative to the impairment-augmented ray-traced input, and by 44% (from 0.089 to 0.050) relative to the strongest of four supervised baselines (FCNN, CNN1D, BiLSTM, and UNet1D). Without retraining, the same checkpoint also generalizes to 92-94 GHz carriers, where it achieves the lowest PDP and CFR-magnitude errors among all baselines while reducing the $\tau_{\mathrm{rms}}$-distribution mismatch relative to the uncalibrated twin.

This paper introduces a novel approach to the General Assembly Line Balancing Problem (GALBP) by utilizing a receding horizon optimal control framework. The proposed discrete model for the assembly line offers a flexible representation, avoiding assumptions about specific line configurations. The control actions aim to optimize the industrial assembly line by minimizing the completion time while adhering to constraints such as task precedence, workstation capacity, and resource requirements. Control actions are represented through task assignment and resource allocation matrices, assigning tasks to specific workstations and assigning resources to workstations, respectively. The optimization problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem. The inherent robustness of the receding horizon approach ensures optimal solutions for the assembly line, effectively adapting to sudden changes. Numerical experiments demonstrate the robustness and effectiveness of the proposed control synthesis in efficiently distributing tasks and resources, minimizing the overall completion time.

Detecting when the statistical behavior of an engineered system changes, and identifying which component is responsible, are core problems in the monitoring of telecommunication networks, robotic platforms, security infrastructure, and multi-agent systems. In safety- and mission-critical deployments, such decisions must be accompanied by statistical reliability guarantees rather than by point estimates alone. Conformal changepoint localization (CONCH) and conformal root cause analysis (CROC) meet this need by returning confidence sets that contain the true changepoint, or the true root-cause stream, with a user-specified probability, without parametric assumptions on the data-generating process. In practice, however, observations are frequently corrupted, e.g., by outliers, sensor faults, or adversarial perturbations. While the finite-sample coverage of these procedures is preserved under contamination, the resulting confidence sets can become uninformatively large. Adopting a Huber-type contamination model, this paper proposes weighted CONCH (W-CONCH) and weighted CROC (W-CROC), which downweight observations that are likely to be corrupted with the goal of reducing confidence set size when data may be corrupted. The weighting mechanism, derived from a formal bound on the unknown corrupted data densities, leverages pre-existing second-order classifier-based uncertainty signals, such as those produced by evidential deep learning or Bayesian learning. W-CONCH and W-CROC are further generalized by introducing a meta-learning procedure for the weights that optimizes a differentiable surrogate of the confidence set size. Experiments on image-based and real-world changepoint and root-cause benchmarks show that uncertainty-based weighting substantially reduces confidence set size while maintaining the target coverage.

Cell-free massive multiple-input multiple-output (MIMO) is a novel beyond 5G (B5G) and 6G paradigm that, through the use of a common central processing unit (CPU), coordinates a large number of distributed access points (APs) to coherently serve mobile stations (MSs) on the same time/frequency resource. By exploiting the characteristics of new less-congested millimeter wave (mmWave) frequency bands, these networks can improve the overall system spectral and energy efficiencies by using low-complexity hybrid precoders/decoders. For this purpose, the system must be correctly dimensioned to provide the required quality of service (QoS) to MSs under different traffic load conditions. However, only heavy traffic load conditions are usually taken into account when analysing these networks and, thus, many APs might be underutilized during low traffic load periods, leading to an inefficient use of resources and waste of energy. Aiming at the implementation of energy-efficient AP switch on/off strategies, several approaches have been proposed in the literature that only consider rather unrealistic uniform spatial traffic distribution in the whole coverage area. Unlike prior works, this paper proposes energy efficient AP sleep-mode techniques for cell-free mmWave massive MIMO networks that are able to capture the inhomogeneous nature of spatial traffic distribution in realistic wireless networks. The proposed framework considers, analyzes and compares different AP switch ON-OFF (ASO) strategies that, based on the use of goodness-of-fit (GoF) tests, are specifically designed to dynamically turn on/off APs to adapt to both the number and the statistical distribution of MSs in the network. Numerical results show that the use of properly designed GoF-based ASO strategies under a non-uniform spatial traffic distribution can serve to considerably improve the achievable energy efficiency.

At parking speeds, the kinematic bicycle is the prevailing model for car-like vehicles. Yet, despite its wide use, stabilizing feedback laws for this system are scarce in the literature, and existing designs often do not reproduce realistic parking maneuvers. This limitation is inherent to the Cartesian coordinates, where Brockett's condition rules out smooth static feedback stabilization. We bypass this obstruction by transforming the system into polar coordinates together with additional range-normalized coordinates that encode the geometry of human-like parking maneuvers. In the transformed coordinates, the dynamics take a strict-feedback form, enabling a nonconventional backstepping design. We exploit the particular structure to develop smooth feedback laws that achieve global exponential stabilization in the transformed coordinates which in turn generates parking trajectories resembling the one performed by human drivers through feedback alone.

Object contours serve as compact structural priors for many receiver-side vision tasks such as image super-resolution, edge-conditioned generation, and machine vision. When such tasks are deployed over a bandwidth-limited channel, the sender transmits the high-quality object contour as structural side information to guide reconstruction at the receiver, while-to save bandwidth-only a low-quality reference such as a downsampled image or base-layer reconstruction is delivered. As a result, the decoder can already extract a coarse contour from this reference at no transmission cost, creating an encoder-decoder asymmetry: the fine contour must be coded and sent, yet a free coarse version is available at the decoder. This asymmetry is ignored by existing contour codecs such as JBIG2 and chain coding, which are lossless, symmetric, and offer no rate-distortion control, leading to high bitrates. In this paper, we propose a coarse-to-fine contour coding framework that models a high-quality contour as a structured geometric refinement of the decoder-available coarse contour. The encoder extracts ordered anchors along the fine contour and performs adaptive anchor skipping under a distortion constraint. The decoder then reconstructs the contour by using the coarse prior to guide anchor connectivity. This formulation enables lossy contour compression with an explicit rate-distortion trade-off. Experiments show 54.5%-66.9% bitrate reduction over methods without decoder-side guidance, and up to 5 times savings over JBIG2, while preserving high geometric accuracy.

Building upon the well-established distributed biased min-consensus protocol, which serves as an efficient approach to address the shortest path problem in a distributed fashion, the continuous-time generalized adaptive Bellman-Ford algorithm (GABF) introduces flexibility by accommodating various forms of distance metrics. This adaptability makes GABF suitable for more complex scenarios, such as time-dependent shortest path problem and robotic path planning. However, existing research on this protocol primarily focuses on asymptotic stability, providing no insights into convergence speed, which limits its practical applications. To address this gap, this paper proposes two control strategies that achieve prescribed-time stabilization of GABF by ensuring its convergence to the stationary value within a user-defined time, thereby broadening its applicability. Simulation scenarios, including robotic manipulator path planning with real-world data and learning-based path planning, are provided to validate the effectiveness of the proposed approaches.

Integrated sensing and communication (ISAC) enables wireless systems to reuse communication signals for environmental sensing, where reconstructing the geometry of surrounding objects is a representative sensing task. However, many conventional methods rely on coherent processing and require accurate phase information, which is often hard to guarantee in practical communication systems, particularly at high carrier frequencies. To address this problem, this paper proposes a Multi-View Likelihood Accumulation Geometry Reconstruction (MVLA-GR) method based on channel impulse response (CIR) measurements, which uses only delay and power observations without requiring phase information. The method extracts dominant multipath components from each observation, and for each candidate spatial location, accumulates components across views whose propagation distances match the location as supporting evidence. A soft distance-matching kernel is introduced to tolerate range estimation errors and viewpoint-dependent scattering migration, and the received power of each component is used as a reliability weight. A joint thresholding strategy combining response magnitude and angular support continuity then converts the continuous support map into a binary geometry estimate. Ray-tracing simulations on canonical and complex targets, as well as real-world vehicle measurements at 36 GHz, demonstrate that MVLA-GR can effectively recover target geometry, providing a low-complexity phase-free solution for ISAC.

We present Voice Memory, a inference-only scheme for agentic speech recognition: at stream time, a frozen corrector reads a single per-domain memory.md and decides per utterance whether to act on the hypothesis or abstain and keep the 1-best. Asynchronously, a score-gated optimizer revises that file through bounded edits, accepting an edit only when it strictly improves a held-out score. Extended from classical ASR-LM framework, we refer this split the listener-thinker architecture; the two roles are coupled only through the memory, so no weights change and the learned skill stays auditable and portable. Restraint turns out to be the operative skill this loop discovers: unconstrained generative error correction (GER) over-corrects, breaking correct tokens on up to 64% of its edits on financial news, and Voice Memory, reduces this rate to 35%. Across ten HyPoradise domains with an open corrector, Voice Memory, lowers weighted word error rate from 8.36% to 7.52% (7.47% with three added in-context examples) without regressing any dataset below its 1-best baseline; gains concentrate where recoverable headroom is largest, including air-travel commands (8.40% to 3.40%) and noisy far-field speech (CHiME-4, 12.69% to 10.46%). The memory transfers across corrector families and adds zero parameters to the inference path. A demo and example code are provided for future studies.

Sonomyography (SMG) enables continuous device control via ultrasound-measured muscle deformation signals, but existing SMG interfaces generally require substantial user- and sensor-location-specific training data and provide only one proportional signal or task-specific classification. We present a real-time, sensor-placement-agnostic SMG control system based on sparse optical flow tracking that enables continuous 1-DOF control after minimal calibration (3 pose definitions). We also present a preliminary expansion of this method that augments this algorithm with a short computer-aided calibration to enable 2-DOF control. We evaluate both 1- and 2-DOF systems' performance for a preliminary cohort of 3 cervical spinal cord injury survivors and 6 uninjured individuals across 6 sensor placements spanning the arm, neck, and upper torso. As assessed by a cursor trajectory tracking task, all participants achieved continuous 1-DOF control at all tested sensor locations (even those that relied on passive tissue motions), with all participants achieving <5.5% tracking error using at least one placement (and many <4% across many). All participants were also able to modulate 2D cursor position via the 2-DOF system, with varying levels of control authority, and several were able to complete a 2D drawing task, constituting the first (to our knowledge) demonstration of location-agnostic multi-DOF continuous SMG-based control. These results highlight the promise of SMG to enable rapidly calibratable, high-dimensional, sensor-placement-agnostic device control by users with tetraplegia, and also illuminate key challenges in both signal processing and practical system deployment. To enable further development by scientific and user communities, developed algorithms have been open-sourced as part of the OpenMyoControl project on SimTK (simtk.org/projects/openmyocontrol).

Cable-driven manipulators exhibit strong nonlinearities and low structural stiffness, which make precise control challenging under time-varying uncertainties and external disturbances. This paper presents a time-delay-estimation (TDE)-based adaptive fractional-order nonsingular terminal sliding mode (AFONTSM) control strategy for cable-driven robots. A robust controller is constructed within a TDE-based model-free framework by combining fractional-order nonsingular terminal sliding mode error dynamics with a fast terminal sliding mode reaching law. The main contribution is a new adaptive law that introduces an adaptive exponential term into the update gain to form a nonlinear adaptive mechanism. This design improves adaptive regulation under different operating conditions by suppressing noise-induced chattering during smooth tracking while preserving or enhancing the adaptive gain during trajectory reversal. Lyapunov analysis proves the ultimate uniform boundedness of the tracking error. Experimental results show that, compared with the baseline method, the proposed controller reduces RMSE by 34.52% and 31.11%, ITAE by 33.79% and 32.97%, and ISCT by 6.69% and 17.77% for the two joints, respectively. Further comparisons with recently reported adaptive laws demonstrate that the proposed law provides faster adaptive response, more stable gain evolution, and improved chattering suppression. Additional payload tests further verify the robustness and repeatability of the proposed method.

Modern power systems rely on real-time substation communication protocols, such as the Routable Generic Object-Oriented Substation Event (R-GOOSE), for critical control and protection functions. However, these protocols often lack built-in security features and prioritize availability over confidentiality and integrity, making them susceptible to false data injection and denial-of-service attacks. This vulnerability is exacerbated when communications are transmitted over wide-area or public networks. Addressing these cyber threats is essential to comply with current security mandates, including the DOE's defense-in-depth and zero-trust guidelines. This paper introduces QUIC-TRIP, a transparent security methodology for low-latency IP-based industrial communications. By operating at the Open Systems Interconnection (OSI) Transport Layer (Layer 4), the solution encapsulates and protects data flows without affecting the operation of existing protocol endpoints. Baseline echo Round-Trip Time (RTT) results over a Frankfurt-Amsterdam communication path show that the underlying transport-layer proxy used by QUIC-TRIP achieves a lower average RTT than OpenVPN and only 2.88% higher average RTT than integrated DTLS 1.2, even with DTLS session reuse. We evaluate the resilience of QUIC-TRIP multipath communication under DoS flooding by securing R-GOOSE communications. In these tests, traffic is transparently delivered through three different paths, and QUIC-TRIP forwards the earliest-arriving duplicate while discarding later copies. The framework provides a triple-redundant defense scheme with a measured communication overhead of 32.18% per enabled proxied path, offering a bounded trade-off between resilience and bandwidth cost for time-critical grid operations.

We propose a self-adaptive online learning for control method for tracking unknown target dynamics. The target dynamics can exhibit switching behavior, particularly, a mixture of structured, random, and/or adversarial motion. Such challenging target tracking scenarios arise in applications of dynamic mapping, traffic control, and pursuit evasion, where robots need to track, pursue, or avoid collision with moving landmarks, objects, humans, etc., whose dynamics are unknown. Our method simultaneously learns multiple predictors from scratch, via self-supervised, one-shot, and computationally efficient learning, and adaptively selects the best one to match the observed target behavior. The method enjoys finite-time near-optimality guarantees in expectation, characterized as a function of the learning error of the target dynamics and the frequency that the target dynamics switch. In the absence of both error and switching, the method asymptotically matches the optimal non-causal control policy that knows a priori the target dynamics, i.e., the method enjoys no regret in expectation. In the presence of learning errors and switching, the method degrades gracefully, \eg when there are errors and no switching, the average regret is proportional to the average learning error and switching times. To prove these guarantees, a novel technical approach is required compared to the existing works that employ RFF-based online learning. We validate our method in Crazyflie simulations and hardware experiments, across target trajectories that vary from structured to random to adversarial, in comparison to non-stochastic, kernel-based, and neural-network-based methods for online learning.

Multimodal large language models (MLLMs) are typically built through a multi-stage pipeline consisting of cross-modal alignment, supervised fine-tuning (SFT), and preference optimization. This pipeline assumes that adapting an LLM to a new modality requires extensive task-specific supervision. However, pretrained LLMs already possess strong reasoning and instruction-following abilities. As LLMs evolve rapidly, an important question remains: can we efficiently transfer these capabilities to a new modality with minimal intervention, and is alignment alone sufficient for building a multimodal model? We introduce an Instruction-Free Alignment-Only large audio-language model (LALM) that keeps both the audio encoder and the LLM fully frozen, learning only a lightweight projector. Borrowing insights from AzeroS [1], we train on (audio, response) pairs from Self-Generated Data Construction, where an LLM expands captions into free-form responses without explicit task instructions. Across MMAU, MMAR, MMSU, and MMAU-Pro, our approach matches or surpasses heavily post-trained baselines using substantially less data. By keeping the LLM frozen, our model preserves its native instruction-following competence and can port seamlessly across model generations. Our results suggest that competitive MLLM can emerge from alignment alone, reducing multimodal extension to a lightweight projector-training problem that generalizes across modalities and adapts rapidly to each new LLM release.

The rapid growth of the low-altitude economy calls for integrated sensing and communication (ISAC) networks capable of robust flight monitoring. This article advocates wireless imaging as a unifying sensing paradigm that enables ISAC networks to function as comprehensive low-altitude guardians. We present a hierarchical imaging framework that enhances sensing capability from wide-area snapshot imaging to dynamic trajectory-aware imaging and target-centric fine-grained characterization. At the wide-area level, low-altitude sensing is reformulated as a spatial imaging problem, where distributed base stations and communication users collaboratively construct a holistic view of the aerial space. Building on this foundation, multi-frame dynamic imaging exploits temporal correlations to support robust trajectory tracking and prediction under mobility induced challenges such as occlusions. For security-critical scenarios, the framework further enables fine-grained imaging of flying and hovering uncrewed aerial vehicles, providing detailed characterization beyond conventional point-target abstractions. Additionally, we propose a novel evaluation metric named imaging coverage to examine the sensing fidelity of the proposed framework. Illustrative case studies demonstrate the potential of imaging in ISAC networks to support wide-area monitoring, motion-aware tracking, and fine-grained target analysis.

Uncrewed aerial vehicle~(UAV) swarms provide a flexible platform for Internet of Things~(IoT) data collection by gathering delay-sensitive measurements from distributed sensor clusters and relaying them to a fusion center~(FC). Between successive updates, the FC estimates the current value of each monitored quantity; however, the estimation accuracy degrades as the last update ages. Such missions require jointly optimizing estimation accuracy, short-packet communication reliability, and swarm connectivity, yet existing approaches typically prioritize freshness while neglecting its impact on connectivity. This paper proposes a topology-coupled urgency~(TCU) scheduler that minimizes the FC's accumulated estimation error while incorporating the swarm's expected algebraic connectivity into the scheduling objective. The resulting connectivity reward encourages idle UAVs to reposition so as to preserve swarm connectivity. For each time slot, the resulting problem is a mixed-integer nonconvex program, which we solve by successive convex approximation. Specifically, the binary assignments are relaxed, while the connectivity reward is represented through a linear matrix inequality on the expected Laplacian. The finite-blocklength rate and the inter-UAV link reliability are linearized in the same convex subproblem, thereby preserving their coupling rather than separating them into alternating blocks. The integer assignments are subsequently recovered by Hungarian matching and verified against the original nonconvex constraints, while a deficit mechanism ensures long-term cluster coverage without requiring explicit long-horizon planning. In simulations comprising more than $19{,}000$ transmission attempts, all delivered updates satisfy the reliability target...

Modeling and forecasting nonlinear dynamics under distribution shifts is essential for robust decision-making in real-world systems. In this work, we propose MetaKoopman, a Bayesian meta-learning framework for modeling nonlinear dynamics through linear latent representations. MetaKoopman learns a Matrix Normal-Inverse Wishart (MNIW) prior over the Koopman operator, enabling closed-form Bayesian updates conditioned on recent trajectory segments. Moreover, it provides a closed-form posterior predictive distribution over future state trajectories, capturing both epistemic and aleatoric uncertainty in the learned dynamics. We evaluate MetaKoopman on a full-scale autonomous truck and trailer system across a wide range of adverse winter scenarios, including snow, ice, and mixed-friction conditions, as well as in simulated control tasks with diverse distribution shifts. MetaKoopman consistently outperforms prior approaches in multi-step prediction accuracy, uncertainty calibration, and robustness to distributional shifts. Field experiments further demonstrate its effectiveness in dynamically feasible motion planning, particularly during evasive maneuvers and operation at the limits of traction. Project website: https://mahmoud-selim.github.io/MetaKoopman/

Suture-needle pickup is necessary for autonomous suturing, as a needle can be unexpectedly dropped or strategically released to adjust the grasping configuration. Current methods for autonomous needle pickup typically guide a robot to move straight toward the needle and grasp it, limited to conditions where the needle is observable and directly approachable. In addition, grasping the needle lying on tissue can lead to the robot pinching nearby tissue or the needle jumping around due to its slippery surface, posing potential safety issues. This work proposes an autonomous framework that uses a suture thread as an assistive tool for indirect needle pickup, avoiding unnecessary tool-tissue contact and enabling pickup even when the needle is occluded or inaccessible. The framework spans the entire workflow, including thread and tissue reconstruction, safe grasp-point selection, stable thread lifting, and bimanual thread-following until securing needle grasping. The robot policies account for visual uncertainty to maximize robustness in real-world environments. We evaluate the proposed framework on a da Vinci Research Kit under various real-world conditions. The results demonstrate robust performance even with a challenging thread configuration or a non-approachable needle, closing the gap in applying autonomous robot policies to unstructured suturing environments.