Papers

Deploying drone swarms to track a dynamic target in cluttered environments presents severe computational and safety challenges. We propose MROPE, a hierarchical strategy that decouples the cooperative monitoring mission from strict local safety requirements. To overcome the computational bottlenecks typical of dense spaces, our approach dynamically aggregates complex obstacle geometries into a single safe bounding ellipse for each drone. Methodologically, this architecture is realized by combining distributed aggregative optimization for high-level swarm coordination, a decentralized consensus scheme for the safe area computation, and local Predictive Safety Filters (PSF) for real-time collision avoidance. Virtual and real-world experiments validate the framework, demonstrating superior real-time efficiency and scalability compared to centralized approaches.

Interturn short circuits are among the most critical faults in permanent magnet synchronous motor drives, as they combine localized heating in the shorted stator phase with electrical asymmetry that distorts the current feedback used for torque-producing control. This article proposes a control-based mitigation method enabling the post-fault operation of standard three-phase motor drives without additional dedicated hardware. Using the diagnostic features inferred from standard control-loop signals, the method augments the field-oriented control structure with two mechanisms: resistive-loss-limited current-reference generation and reconstruction of the torque-producing current components in the feedback path. The reference generator is derived from a discrete-time post-fault model and minimizes resistive losses, whereas the feedback reconstruction provides fault-free torque-producing current components as controlled variables of the current loop. The experimental validation has demonstrated reductions of up to 23-36% in the fault-induced increase in the input power and 18-27% in the local segment-loss increase, while confirming real-time adaptation to progressive fault aggravation emulated by stepped changes in the short circuit resistance.

Wireless power transfer is a method for energizing future implantable medical electronics. In this study, a metasurface-based near-field magnetic wireless power transfer system for deep implants is presented, and electromagnetic safety parameters, including field distributions, specific absorption rate (SAR), and temperature variations, are evaluated. The power transfer is modeled for a receiver implant at a distance of 8.5 cm from the designed metasurface. Based on the results, a maximum localized SAR of 0.072 mW/kg is achieved when the efficiency is 1.62%. Moreover, continuous power transfer shows that the local tissue temperature rises by less than 1.1 degrees Celsius.

Magnetoelectric (ME) coreshell devices have interesting applications in biomedical technologies including biosensing and communication, due to their strong inter-coupling between the magnetostrictive core and the piezoelectric shell. This property could be utilized for specific applications in localized bio-stimulation of cells. This paper provides a conceptual proof of using the non-linear property of ME coreshell devices for frequency demodulation and stimulation. We use the Multiphysics simulation approach, wherein the ME coreshell was biased with a DC magnetic field and perturbed through dual coil alternating magnetic fields. The combined effect of the ME non-linear magnetostriction and the dual coil perturbation resulted in the demodulation of the interference frequency component, that induced equivalent electrical hotspots on the piezoelectric shell. We provide a cross model verification, where the generated electrical current density on the piezo shell was provided as an input to a Hodgkin-Huxley (HH) neural cell model to actively induce membrane potentials on the cell. Future applications as a standalone, battery- and electronics-free, controllable, multi-functional and localized coreshells for targeted drug delivery, backscatter communication and bio-stimulation, is envisioned.

Transcranial focused ultrasound (tFUS) is a non-invasive technique that delivers focused acoustic energy through the skull for neuromodulation and therapeutic applications. However, the heterogeneous structure of the skull induces complex, patient-specific phase and amplitude aberrations that distort the acoustic focus and deviate it from the intended target, compromising therapeutic efficacy and safety. Conventional time-reversal (TR) simulations can correct these aberrations but rely on computationally expensive full-wave solvers, making them impractical for real-time use and iterative treatment planning. We propose a few-shot deep surrogate framework that predicts per-element phase and amplitude corrections for a 96-element 3D phased-array transducer from patient CT images. A geometry-aware encoder extracts skull-path features shared across dedicated phase classification and amplitude regression branches, where phase periodicity is handled via circular expectation decoding. The framework is pretrained on diverse skull geometries and fine-tuned with only ten target points, enabling rapid adaptation to unseen patients without full patient-specific simulation. Evaluated via leave-one-out cross-validation across 12 skulls, it achieves a mean phase CMAE of 0.155 rad and amplitude rMAE of 9.089%, a focal centroid error of 0.467 mm, Dice score of 94.422%, and peak pressure ratio of 92.332%, with an approximately 2,535 times speedup over TR simulation. The code is available at https://github.com/Minju-Seol/fewshot-tfus-correction.

The coordination of a large number of flexibility-providing units across various grid layers requires innovative control concepts. This is needed, e.g., to allow active distribution systems to provide ancillary services for the transmission system. A cascaded control structure based on Online Feedback Optimization (OFO) can be used to meet flexibility requests at the point of common coupling by tracking an active power set point at the point of common coupling. This paper investigates the practical influence of the parameterization of the individual controllers on the performance of the hierarchical flexibility provision in three case studies. One case study includes a two-level controller cascade acting on one medium and two low voltage grids, and the other one includes a three-level cascade acting on low to high voltage levels. The results show that the behavior of one controller is highly dependent on the choice of control parameters of the other controllers in the cascade. Additionally, the choice of parametrization has a significant impact on the accuracy and speed of flexibility provision. A third case study investigates the effects of model mismatch and measurement noise on the appropriate selection of parameters. Overall, careful tuning enables the efficient vertical coordination of flexibility-providing units with a cascaded structure based on OFO.

Implantable wireless brain-machine interfaces (BMI) encounter significant challenges in miniaturization, power consumption, and high data volume. While systems utilizing high resolution microelectrode arrays offer precision brain readout and/or stimulation, achieving high-rate wireless connectivity (32-128 Mbps) consumes excessive power, unsuitable for long-term use with implant batteries. This paper addresses wireless connectivity and power challenges by employing radio frequency backscatter and near-field wireless charging. This approach eliminates transceiver electronics in the implantable, reducing implant power consumption by offloading complexity to off-body reader electronics. It enables wireless powering of implantable neural recording and stimulation chips through magnetic coupling, enabling a fully implantable brain-machine interface. We present preliminary test results for this design scenario, demonstrating the feasibility of our approach.

Recent advances in diffusion models have enabled high-fidelity Foley sound generation directly in the waveform space. Existing waveform diffusion models primarily rely on time-domain architectures, such as CNN-based U-Nets and DiffWave-style models, or frequency-domain Transformers modeling temporal dependencies. However, these systems are typically built with large model capacities and substantial computational costs, leaving compact and efficient waveform diffusion architectures largely underexplored. In this work, we introduce a Dual-Path (DP) architecture for waveform diffusion that performs dimension-wise self-attention along both subband and frame axes in the time-frequency domain. This DP design enables fine-grained temporal-spectral modeling while maintaining high efficiency. Based on the proposed DP backbone, we develop two variants: DP-DiT and DP-U-Net. Experiments on the DCASE and FSD-Kaggle2018 datasets demonstrate their superior performance. Notably, the 3M parameter variant achieves performance comparable to models with more than 50M parameters. Audio samples are available at https://samplesdemo.github.io/DP-Foley/.

This paper addresses the robust control of uncertain discrete-time switched affine systems subject to a single unitary input delay. The unique feature of this class of systems lies in the fact that the control input is the switching signal, which belongs to a finite set of values. Consequently, the closed-loop trajectories do not converge to an equilibrium point but rather to a limit cycle. To mitigate the impact of the input delay, we propose a min-switching predictive control approach, which is based on the known nominal dynamical characteristics of each mode. The objective of this approach is to ensure the robust stabilization of the uncertain system using this nominal predictor, employing a Lyapunov argument. Our main result provides tractable robust stabilization conditions that guarantee the convergence to a robust limit cycle under system uncertainties and delayed switching. Additionally, an optimization procedure has been incorporated to minimize the size of the attractor, which represents the region where the trajectories asymptotically converge. A numerical example validates the effectiveness of the proposed approach.

We propose a novel doubly-dispersive (DD) multiple-input multiple-output (MIMO) channel model incorporating flexible intelligent metasurfaces (FIMs), suitable for integrated sensing and communications (ISAC) in high-mobility scenarios. We show how the proposed FIM-parameterized DD (FPDD) channel model extends to multicarrier waveforms known to perform well in DD environments, namely, orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), and affine frequency division multiplexing (AFDM). Leveraging this model, we formulate an achievable rate maxi-mization problem with a sensing constraint for all waveforms and solve it via gradient ascent with closed-form gradients. Numerical results indicate that FIM technology significantly impacts the achievable rate, with careful parametrization essential for strong ISAC performance across all waveforms.

Distributed multichannel active noise control (DMCANC) has emerged as a scalable framework for large-area noise reduction, where multiple nodes operate local single-channel ANC controllers and exchange essential information to achieve global control. A key limitation of existing DMCANC implementations lies in their reliance on zero or random initialization, which leads to slow convergence of adaptive filters and restricts the efficiency of internode collaboration. To address this issue, this paper introduces a model-agnostic meta-learning (MAML) based initialization strategy for DMCANC. By aggregating heterogeneous acoustic characteristics across nodes-ncluding primary and secondary paths-a MAML framework is trained to learn an initialization that generalizes effectively across distributed ANC systems. The MAML initialization is then deployed to all nodes to improve convergence speed under both stationary and time-varying noise conditions. Numerical simulations applied on broadband and real-world noise demonstrate that the proposed algorithms achieves substantially faster convergence and improved noise reduction performance compared with conventional DMCANC, highlighting the potential of MAML initialization as an effective method for large-scale ANC.

Internet of Things (IoT) devices equipped with Bluetooth Low Energy (BLE) radios provide received signal strength indicator (RSSI) measurements for link management; however, these readily available measurements can also capture signal variations caused by materials obstructing the propagation path. This paper investigates material identification using two commodity Nordic nRF5340 development kits without an additional RF sensing front end. First, the distance-dependent RSSI baseline is characterized for the transmitter-receiver through experiments and demonstrates the need for distance-aware calibration. Next, RSSI traces are collected for five well-known materials as obstructions: namely, wood, ceramic, an empty plastic bottle, glass, and a human body. The traces are characterized using their mean, standard deviation, and transient deviation from a local baseline, and then the path-loss exponent is estimated. A lightweight threshold-based classifier is designed using these estimates, and thus the designed classifier is shown to correctly identify the human body, glass, and wood in the reported unknown trials, while materials like paper and brass are mapped to the most similar trained classes, namely a plastic bottle and ceramic. The results demonstrate the feasibility of coarse material identification using commodity BLE RSSI.

Accurately reconstructing satellite transmit-antenna patterns on orbit is difficult because only sparse directional measurements are available during normal mission operations. This paper develops a cooperative on-orbit pattern-reconstruction framework that converts received calibration power into normalized directional samples and represents the antenna power pattern using a truncated discrete cosine transform (DCT) basis. The resulting low-dimensional model transforms high-dimensional pattern recovery into coefficient estimation, for which a closed-form maximum-likelihood estimator and error characterization are derived. The analysis shows how DCT truncation error, measurement noise, and sampled-basis conditioning jointly affect reconstruction accuracy. For regularly accessible angular sectors, midpoint-uniform sampling provides an information-balanced baseline for the retained DCT modes. For constrained feasible opportunities, D-optimal sampling is used to select informative measurement directions. Simulations verify the accuracy of the angular discretization, the sample efficiency of the truncated-DCT model, and the reconstruction gain of D-optimal sampling under irregular orbit-generated opportunities.

Root cause analysis (RCA) for contextual anomalies in industrial time series is challenging because responses depend jointly on control commands, operating states, and coupled physical variables. A response can appear marginally normal yet violate its operating context. Events may involve multiple roots and alarms, with each root assigned an observation-only effect confined to its recorded trajectory or a physical-propagation effect on descendants. We propose Mode-Aware Trajectory-Level Energy-Based Root-Set Optimization for Root Cause Analysis (MATERO-RCA), which jointly optimizes a root set, root-effect modes, and auxiliary counterfactual trajectories. Its graph-wide objective combines alarm resolution with temporal compatibility across local causal relations. A Temporal Compatibility Network(CompatNet) maps parent-conditioned trajectory likelihoods to calibrated compatibility energies. A Counterfactual Repair Network (RepairNet) initializes mode-aware counterfactual trajectories for objective-directed gradient refinement. An exact mixed-integer linear program minimizes a residual-cover lower bound, enabling certified best-bound search over the finite admissible root–mode space under the fixed inner solver. Experiments on simulated and real industrial datasets demonstrate superior RCA performance over representative baselines.

The multiport autonomous reconfigurable solar power plant (MARS) is a promising solution to integrate renewable energy resources and energy storage systems into the ac power grid and HVdc links. In the MARS system, various input power sources are connected to the individual submodules (SMs) through dc-dc converters. However, the presence of external power sources can result in unbalanced capacitor voltages of SMs, thereby violating stability constraints under multiple/diverse operating conditions. This paper aims to address the research gap by accurately determining the stability boundary for the MARS system. A novel machine learning (ML)-assisted energy balancing control (EBC) criterion is proposed. In conjunction with a refined EBC, this approach ensures balanced capacitor voltages across various types of SMs, significantly enhancing the overall system efficiency. The proposed EBC criterion effectively controls EBC activation and deactivation, achieving remarkable accuracy. Both PSCAD/EMTDC simulations and control hardware-in-the-loop (cHIL) tests are conducted to validate the feasibility and efficiency of the proposed method. By combining the EBC and ML-assisted EBC criteria, efficient energy management becomes achievable for systems featuring multiple input power sources, such as MARS. This approach enables the system to fully exploit its potential across an expanded operational range while upholding high efficiency standards.

Network-level integrated sensing and communication (ISAC) is recognized as a transformative technology for next-generation mobile radio systems. By enabling collaboration among multiple transceivers, network-level ISAC can significantly enhance both communication and sensing performance through spatial diversity. However, existing resource allocation strategies typically overlook the impact of spatial geometry, where identical time-frequency resources contribute differently to sensing accuracy depending on the transceiver's location. This leaves the fundamental coupling between spatial topology and resource efficacy unclear, rendering optimal resource allocation a critical challenge for unlocking the full potential of network-level ISAC.To address this challenge, this paper investigates the optimal distribution of time-frequency resources across spatially distributed transceivers through a theoretically grounded two-stage framework. First, we analytically derive the optimal time and frequency aperture distributions for sensing, defined as the variances of the allocated symbol and subcarrier indices, respectively, under both two-transmitter and multi-transmitter scenarios. By exploiting the mathematical isomorphism between delay and Doppler estimation, we prove that the optimal resource allocation strategy follows the gradient direction of the Cramer-Rao Lower Bound (CRLB) with respect to the apertures. Second, to bridge the gap between theoretical aperture values and practical OFDMA constraints, such as the minimized communication rate of each user equipment (UE), we formulate the resource allocation as a combinatorial integer partitioning problem. To tackle the NP-hard nature of the formulated problem, a low-complexity Variance-Guided Partitioning Algorithm (VGPA) is proposed to jointly optimize the subcarrier and symbol patterns for communication and sensing.

This paper studies a low-rank Kalman-Bucy filtering framework for linear time-varying systems through the tracking analysis of Oja's principal component flow. Under structured assumptions on the eigenspaces and their time variation, we show that the Oja flow can remain in a neighborhood of the time-varying dominant subspace by tuning a parameter of the flow, rather than tracking it exactly. These restrictive assumptions identify a tractable class of linear time-varying systems and provide a theoretical basis for low-rank filtering, as illustrated by a numerical experiment.

Advanced Air Mobility (AAM) represents a transformative shift in urban transportation; however, successful implementation depends strongly on public acceptance, with noise emerging as a major concern for low-altitude electric vertical takeoff and landing (eVTOL) operations. Existing studies commonly describe eVTOL noise using acoustic metrics such as A-weighted sound level and day-night average sound level. This study develops a Geographic Information System (GIS)-based framework that translates eVTOL acoustic outputs into maps representing the percentage of the population that is highly annoyed (%HA) for a representative medical delivery route in Northwest Arkansas. The results show that noise and annoyance generally decrease with distance from the route, while the highest annoyance occurs during descent, followed by climb and cruise. Census population data are integrated to estimate the number of highly annoyed individuals and identify spatial impact hotspots. Noise-annoyance results are then combined with route distance and airspace factors to evaluate alternative routes and identify balanced routing strategies. The proposed framework connects acoustic assessment with human response and supports the identification of noise-sensitive areas, comparison of route alternatives, and socially sustainable AAM infrastructure planning.

We propose a tensor-based subspace method for the joint estimation of pitch and direction of arrival (DOA) of multiple harmonic sources. Unlike conventional matrix-based approaches that unfold the spatio-temporal data into a single matrix, the proposed method preserves the multidimensional Hankel tensor structure and exploits a Kronecker product constraint satisfied by the true signal subspace. The matrix-based subspace estimate is refined by projecting it onto an empirically estimated Kronecker-structured signal cage obtained from mode-unfolded tensor subspaces. Beyond the estimator itself, we provide a non-asymptotic theory explaining when this tensor refinement improves the matrix baseline. We show that the oracle refinement using the true Kronecker projector is non-worsening, decompose the overall tensor gain into oracle gain and empirical loss, and derive deterministic and high-probability sufficient conditions for positive overall tensor gain under complex Gaussian noise. The analysis reveals a sweet-spot behavior: the largest certified gain occurs when the matrix estimate is neither nearly perfect nor too inaccurate. Simulations over closely spaced, harmonically related, and coherent-source scenarios show that the proposed method improves subspace accuracy and downstream pitch/DOA estimation, and also outperforms representative matrix and tensor-train-based baselines.

The upper-mid band, particularly the 7-8 GHz range within frequency range 3 (FR3), has emerged as a leading spectrum candidate for wide-area sixth-generation (6G) cellular networks. Its shorter wavelength enables hundreds of antenna elements to be integrated within the physical aperture of an existing 5G base-station panel. In principle, the resulting aperture gain can compensate for the increased path loss and enable extreme MIMO (E-MIMO) with 256 or more antenna ports while reusing current cell sites. In practice, however, simply scaling the 5G New Radio (NR) architecture from tens to hundreds of ports encounters fundamental system-level limitations. This paper identifies where 5G-style MIMO scaling breaks and develops a research roadmap for practical upper-mid-band E-MIMO. We first review the evolution of FR3 spectrum, its propagation and channel characteristics, and the emerging 6G system requirements. We then organize the principal challenges into four coupled areas: maintaining effective coverage across all physical channels and protocol states; implementing wideband, energy-efficient RF devices and radio units; developing new low-power array and beamforming architectures; and acquiring sufficiently refined channel state information with manageable sounding and feedback overhead. Representative system studies illustrate the coverage asymmetry between user-specific data transmission and common or channel-acquisition signals, as well as the spectral- and energy-efficiency tradeoffs among fully digital, hybrid, tri-hybrid, dynamic-metasurface, and fluid-antenna architectures. Finally, we discuss how distributed apertures, integrated sensing, AI-assisted channel acquisition, and environment-aware operation can transform fixed-aperture scaling into a deployable 6G E-MIMO architecture.

Cooling demand constitutes a significant and flexible component of AI data center electricity consumption, but time-synchronized measurements are scarce and constant coefficient-of-performance models cannot represent thermal dynamics. This letter proposes a closed-loop simulation model which couples a linear thermal dynamic model with deadband-based control to capture the nonlinear cooling dynamics. The model is validated using operational telemetry from the Marconi100 supercomputer. Compared with the baseline, the proposed model reduces the mean absolute error from 95.80 to 20.88~kW and the root-mean-square error from 109.79 to 27.27~kW. Evaluation over approximately 520 daily profiles further shows improved reproduction of daily peak demand and intraday variability. The proposed model provides a computationally tractable means of generating physically interpretable cooling load profiles for power system studies.

This work studies the problem of mesh stability in connected and automated vehicles. Mesh stability, also known as 2D string stability, refers to studying how disturbances propagate in vehicular platoons in both longitudinal and lateral direction. As opposed to available decentralized results only relying on on-board sensing, the distinguishing feature of this work is a cooperative version of mesh stability, where onboard sensing is augmented by vehicle-to-vehicle communication. This cooperative version dramatically improves the state-of-the-art decentralized performance: for longitudinal control, a new cooperative non-identical protocol (i.e. with non-identical control gains) is proposed that improves the state-of-the-art decentralized non-identical protocol in terms of scalability of the control gains and strong notion of string stability. For lateral control, after showing that the non-identical approach is not necessary, a cooperative non-identical protocol is proposed to achieve another strong notion of string stability. Robustness of the proposed implementation against vehicle-to-vehicle communication delays and actuation time lags is considered. Numerical experiments, also performed with the vehicle simulator CarSim, validate the robustness and effectiveness of the proposed protocol.

Control with unstructured uncertainties refers to controlling systems where not only the parameters are unknown, but also the way the parameters appear in the dynamics. This problem becomes pivotal in autopilots, where a unified control architecture is sought for aerial/ground/marine vehicles with different structures. By only making use of basic Euler-Lagrange properties valid in most mechanical systems independently of their specific structure, this brief proposes an adaptive design that does not rely on structural knowledge of the uncertainties. The proposed adaptive method, here validated in the ArduPlane module of ArduPilot, applies also to other modules like ArduCopter, ArduRover, ArduSub. Enhanced performance with respect to state-of-the-art methods addressing unstructured and state-dependent uncertainties is verified.

Inspired by a classical algorithm for online prediction, we propose a novel denoising scheme which is universal for families of probability distributions both in terms of the source that generates the noiseless signal and in terms of the channel that generates the noisy signal. The new denoising scheme does not rely on assumptions of exact channel knowledge or finite signal alphabets. Our analysis provides an upper bound for the performance gap compared with the Bayes envelope. For the special case in which the signal is an i.i.d. sequence and the family is countable, we characterize the consistency condition under which our scheme approaches the Bayes envelope as the length of the sequence tends to infinity. We also show that in general, approaching the Bayes envelope is not possible for a universal denoising scheme when this consistency condition is not satisfied. Furthermore, we show that, as in the online prediction case, the so-called plug-in approach (which relies on a maximum likelihood estimation of the underlying distribution parameter) does not approach the Bayes envelope in general. We also include numerical evaluations of our scheme for denoising of a Poisson signal.

Student engagement (SE) is a critical predictor of academic performance and retention in STEM education, yet existing measurement approaches are often intrusive, manually intensive, or unsuitable for real-time classroom use. This thesis proposes a novel $\textit{Biometric Sensor Network}$ (BSN) designed to enable real-time measurement and continuous tracking of individual student engagement in STEM classroom environments. The system enables capturing of behavioral, emotional, and cognitive indicators through camera-based sensing while preserving ethical and privacy constraints. To measure these indicators unobtrusively and ethically, we propose a BSN composed of $\textit{Student Processing Units}$ (SPUs) that function as distributed sensing nodes. The network is explicitly designed to satisfy five objectives: it must be $\textbf{non-intrusive}, \textbf{non-invasive}, \textbf{non-stigmatizing}, \textbf{real-time}$, and $\textbf{automatic}$, while ensuring rigorous protection of student data security and privacy. Each SPU supports two operational modes: (i) a $\textit{dataset-collection mode}$, in which raw student video is temporarily recorded to construct a private SE dataset for model training and validation, and (ii) an $\textit{analysis mode}$, in which the SPU performs real-time inference on 10-second video segments without storing or transmitting raw frames. In this analysis role, each SPU enables fully on-device processing—including face detection, gaze estimation, and affective analysis—ensuring that no identifiable video data leaves the device. A secure backend infrastructure manages device authentication, session orchestration, and encrypted data ingestion. The full system integrates hardware design, computer-vision pipelines, wireless networking, security protocols, and session-level data management.

In this article, a flexible and lightweight filtering wearable antenna without extra circuits is presented. The proposed antenna starts from a flexible directional antenna with lightweight structure, which includes a layer of ultra-thin flexible substrate, a metal ground layer, and a flexible foam layer sandwiched between them. Then, by introducing two pairs of vertical slots to the radiation patch printed on the flexible substrate, two radiation nulls are realized at both band edges without extra circuits. Moreover, to mitigate the deterioration of in-band radiation under different curvature, a pair of inverted Fshaped slots are loaded on the radiation patch. The coupling of Fshaped slots suppresses non-radiated lateral current components along the curvature direction, maintaining stable performance after bending. In addition, deformation analysis of the proposed antenna with a three-layer human tissue model under different bending radii is carefully carried out, showing stable bandwidth, effective out-of-band radiation suppression, and low specific absorption rate (SAR) value. To verify this method, a prototype is fabricated. Measurements are conducted both in free space and conformal on the curved body tissue. The results show that the proposed antenna achieves a bandwidth from 2.7 GHz to 3 GHz, an out-of-band radiation suppression more than 11 dB with maxmium suppression of 23 dB, and an average gain of 8.5 dBi. As a flexible wearable antenna with stable performance and integrated reliable filtering features, it has several advantages including flexible wearable structure, stable filtering properties, lightweight characteristic, and low SAR. This makes it an excellent candidate for wearable IoT applications.

Neural operators such as the deep operator network (DeepONet) and the Laplace neural operator (LNO) are effective surrogates, but they are almost exclusively trained to reproduce the forward response of a system rather than its intrinsic structure, such as the modal properties of a structural system. We introduce ModalONet, which puts operator learning to a different use, such as recovering the modal basis, namely: mode shapes, natural frequencies, and damping ratios of a dynamical system directly from its response field, with no eigensolver and no labeled modes. Our key observation is that the DeepONet branch-trunk factorization is itself a learnable form of modal superposition: the trunk supplies continuous, mesh-free mode shapes, while an LNO branch supplies the modal coordinates in pole-residue form, so that each learned pole yields a natural frequency and a damping ratio. Training uses the response field alone, under a composite loss of reconstruction, orthonormality, and temporal projection consistency. The degenerate (equal-frequency) modes are resolved by a separable trunk and a shared frequency parameter, with post-hoc log-envelope regression improves the damping ratio estimates. Across four structural systems, namely: simply supported and cantilever Euler-Bernoulli beams and rectangular and (degenerate) square Kirchhoff plates, the ModalONet recovers the analytical modal basis with modal assurance criterion (MAC) values of at least 0.998 for every mode shape, natural frequency errors within 5%, and damping ratio errors within 7%, demonstrating the potential of neural operators as accurate and interpretable tools for modal identification.

Light curve inversion enables the estimation of orbit, attitude, optical properties, and shape of space objects. Because a light curve is the temporal evolution of a scalar apparent magnitude, the estimation problem can be ill-posed owing to the non-uniqueness of the attitude that reproduces a given light curve. The initial estimate in Kalman filtering can therefore be sensitive, and, depending on the object properties, observation geometry, and number of estimated parameters, an inaccurate initial estimate may lead to divergence of the filter. A previous study uses a sudden change of light curves, called glint, to constrain the range of attitude estimate. The current paper extends the attitude estimation method using glint for multiple-surface objects. Such objects have multiple attitudes to yield glint, and the attitude estimate is not uniquely determined. To address this issue, this paper employs the interacting multiple model (IMM) algorithm that runs multiple parallel filters with model interaction in the estimation sequence. Each filter assumes that the glint occurs on the corresponding surface. The mode probability is updated by the likelihood of each filter, determining the correctness of the hypotheses. Furthermore, the mixing step in the IMM allows interaction among the filters through a transition probability matrix, enabling adaptation to the time-varying glint source. Numerical simulations are conducted for a box satellite in a geosynchronous orbit. Monte Carlo trials with initial attitude errors of up to 80~deg show that the proposed method improves the convergence rate from 10% for a single surface filter to 73%, and the mixing step is shown to be essential, since the convergence rate drops to 40% when it is removed.

Deep joint source–channel coding (Deep JSCC) enables visual semantic transmission by mapping inputs directly to channel symbols and task outputs, but its performance can deteriorate under distribution shifts between training and deployment domains. We study single-source domain adaptation for task-oriented Deep JSCC and formulate a classification-capacity-invariance (CCI) function to characterize how the available channel capacity and class-conditional cross-domain invariance affect target domain classification accuracy. A scalar linear analysis of source-domain-optimal solutions and a controlled shallow nonlinear validation show that target domain classification accuracy can vary non-monotonically with the invariance constraint and with available capacity along separate control paths obtained by varying the transmitted dimension or CSNR. We then propose a domain-adaptive Deep JSCC framework that combines pseudo-label-based class-level adversarial alignment with supervised contrastive learning on confidence-filtered target samples. Experiments on digit and PACS datasets over AWGN and Rayleigh fading channels demonstrate improved target domain generalization without introducing additional inference-time networks. On SVHN $\rightarrow$ MNIST, the proposed method achieves 98.15% target-domain accuracy at a CSNR of 10 dB.

At candidate 6G sub-terahertz (sub-THz) carriers near 140 GHz and 300 GHz, half-power beamwidths of 1{\deg}-6{\deg} create a scheduling regime that 5G beam management was never designed for: a base station must simultaneously hold high-SNR beams on active user equipments (UEs), monitor UEs drifting toward the edge of resolvability, and spend scarce beam time scanning for new arrivals-all within a thermally limited duty cycle. This is structurally the track-while-scan (TWS) problem of electronically scanned array (ESA) radar. Building on a companion paper that defined per-UE resolving-window metrics (normalized range separation W_R, SNR-gated angular separation W_{\theta}^eff, LoS-convergence urgency C_k, and beam time-to-exit T_exit), this paper converts those metrics into an implementable control plane. We (i) formulate the joint tracking-scheduling problem as a constrained multi-objective optimization over outage probability, beam mis-association (ambiguity) probability, and discovery latency, subject to an RF-chain budget B_max and a thermal duty factor {\eta}; (ii) define a three-state UE classification-stable, boundary, ambiguous-driven by the resolving-window state with radar-consistent polarity (short time-to-exit and low separability escalate a UE's state, never relax it); and (iii) propose a low-complexity, priority-driven TWS scheduler that reserves scan capacity first, stabilizes boundary UEs preventively, and serves stable UEs at relaxed revisit rates bounded by their individual T_exit.