Papers

Efficient Channel State Information (CSI) feedback is indispensable for frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. Existing compressed sensing (CS) algorithms exploit delay-domain sparsity but suffer from prohibitive iterative latency and discrete grid mismatch. Conversely, deep learning (DL) approaches achieve rapid inference but lack spatial scalability and domain adaptability, failing to generalize to unseen propagation environments, and demand computationally heavy encoders and decoder. In this paper, we propose TAP, a Tap-Assisted Parametric CSI Compression. TAP is a one-shot neural framework that unifies the speed of DL with the mathematical interpretability of CS. TAP replaces iterative pursuit with a lightweight 1D neural network that extracts dominant continuous propagation delays from temporal channel sequences via a differentiable sub-grid interpolation operator. TAP achieves true architecture independence, enabling zero-shot generalization across diverse array geometries and unseen propagation environments. Furthermore, TAP yields a completely decoder-free payload, allowing the BS to reconstruct the channel via a simple inverse fast Fourier transform (IFFT). Extensive evaluations across five 3GPP environments demonstrate that TAP achieves a 3.13 to 12.22 dB channel frequency response normalized mean square error (CFR-NMSE) improvement over CsiNet while shrinking the model footprint by 660 times to under 1 MB. Operating with sub-millisecond latencies, TAP accelerates inference by 2700 times over classical iterative OMP, providing a scalable and deployment-ready solution for next-generation networks.

In this paper we study feedback interconnections of positive nonlinear SISO systems and introduce two complementary properties: persistent-input/persistent-output (PIPO) for the plant and persistent-input/transient-output (PITO) for the controller. Assuming forward completeness of the closed loop and a one-sided affine growth bound on the controller output, we show that PIPO and PITO jointly imply boundedness of the control signal. The result is input-output in nature and does not require linearity or monotonicity of the interconnected subsystems, although it does not in general guarantee boundedness of the full controller state. We provide a structural PIPO condition for positive monotone plants with a class-\(\mathcal K_\infty\) steady-state characteristic, establish PITO for the antithetic integral controller with explicit gains, and illustrate the framework on a nonlinear integral-feedback motif with multiplicative controller growth.

This paper addresses the sim-to-real gap in dynamics arising when a parallel-link mechanism is represented by a serial-tree surrogate in simulation. Conventional Jacobian-based state and torque mappings preserve consistency with the kinematic and virtual-work relations but do not account for the coordinate-induced redistribution of actuator inertia and damping and the linkage inertia omitted during serial-tree reduction. To address this gap, Simulator-Side System Normalization (S3N) is proposed to normalize the serial-tree simulator's effective dynamics while preserving its tree topology. S3N-Act incorporates actuator inertia and damping into the serial-coordinate dynamics through coordinate transformation, whereas S3N-Full restores residual linkage inertia by separately identifying actuator- and leg-level frequency responses. In the 2-DoF validation, S3N-Full reduced the joint-position and torque RMSEs by 80.9% and 82.1%, respectively, relative to the Jacobian-mapping baseline. During pitch-in-place motion, S3N-Act and S3N-Full reduced the RMSE of the ground reaction force norm by 65.1% and 62.4%, respectively. During circular locomotion, S3N-Full reduced the phase-averaged, command-normalized sim-to-real gap from 17.3% to 9.9%. These results show that simulator-side normalization improves motion- and force-level sim-to-real consistency. It enables policy training in a serial-tree framework with hardware-consistent dynamics that better represent the physical parallel-link mechanism.

In this article, we consider received-power maximization in RIS-assisted wireless links using only limited received signal strength (RSS) feedback, avoiding explicit estimation of the high-dimensional cascaded channel. We propose stochastic and deterministic algorithms for passive and active beamforming. In the deterministic method, each RIS element requires only three fixed-phase RSS measurements to recover the phase, followed by tree-structured quantization. We prove a non-asymptotic received-power guarantee for the fixed-beamformer single-user RIS sweep under cascaded Rayleigh fading and extend the bound to finite-resolution RIS phase shifters. Simulations over Rayleigh and temporally correlated channels show that the proposed methods approach full-CSI alternating optimization. We extend the channel-estimation-free framework to multi-user RIS-assisted transmission by proposing a unified single-bit consensus method for both RIS phase adaptation and transmit beamforming. In this mechanism, all users vote on each shared perturbation using only local SINR comparisons, allowing the update to account for both desired-signal improvement and interference generated to other users. The weighted consensus allows for fairness among users based on their priority.

Uniform INT8 quantization is attractive for deploying learned image coding (LIC), but its rate–distortion (R–D) performance is often limited by heavy-tailed tensors and large inter-channel variations. Existing methods mainly adapt the quantizer through mixed precision or non-uniform codebooks. We propose Hadamard-Transform-domain Quantization (HaTQ), which uses orthogonal Hadamard reparameterization before quantization to redistribute weight and activation responses in the original domain across channels. The reparameterization preserves the original function mapping of each linear operator, while making its weights and activations more amenable to uniform INT8 quantization. HaTQ provides two complementary forms. Double-Hadamard quantization transforms both the input activations and weights, whereas weight-only Hadamard quantization transforms only the weights. This distinction is important because the constant Hadamard basis can coherently accumulate a nonzero channel mean and enlarge the activation range in sensitive layers. We identify these sensitive layers through offline profiling and assign the appropriate form to each layer without input-dependent branching. HaTQ supports both post-training quantization (PTQ) and quantization-aware training (QAT), uses uniform INT8 quantizers, and is compatible with integer-only execution. Experiments on representative LIC architectures and datasets demonstrate consistent improvements across different quantization settings. The resulting QAT models further outperform competing mixed-precision and non-uniform quantization methods. TensorRT deployment results demonstrate practical INT8 inference efficiency. The source code will be publicly released.

EEG-guided target speaker extraction is challenging under in-trial auditory attention switching, where neural noise and intrinsic latency can delay or destabilize attention tracking. Conventional methods struggle with dynamic switches and often cause discontinuities at switching points. Therefore, we propose SAGE, a switch-aware EEG-guided soft gating framework that treats in-trial switching as dynamic selection. SAGE generates two candidate speech streams with a robust separator and uses an EEG-guided switch-aware gating module to produce smooth fusion weights and suppress transition artifacts. We further integrate latency-compensated alignment and an uncertainty-driven conservative strategy to handle latency discrepancies and fluctuating EEG reliability. SAGE outperforms baselines, achieving 8.67 dB SI-SDR and 88.24% STOI while reducing average switching latency to 2.04 s. By coupling neural decoding with speech separation, it enables robust target extraction in dynamic scenarios.

Digital biomarkers of cardiovascular aging, often termed heart or vascular age, have been widely studied, but most rely on resting electrocardiography (ECG), imaging, or specialized vascular assessments. Evidence linking wearable photoplethysmography (PPG) to arterial stiffness and hypertension remains limited. We developed an ECG-guided cross-modal framework that uses synchronized smartwatch ECG to enhance PPG representation learning during pretraining while requiring only PPG at inference. The study included three OPPO cohorts across China, comprising 581,804 participants and 7,452,131 recordings. The Vascular Health Study cohort supported ECG-PPG self-supervised pretraining, fine-tuning, and internal validation, while two external cohorts assessed associations with pulse wave velocity (PWV) and prevalent hypertension. Combining subject-aware learning with ECG-PPG contrastive alignment, the PPG-only model achieved subject-level mean absolute errors of 5.895 years (Pearson r=0.819) in the PWV cohort and 4.344 years (r=0.800) in the home blood pressure monitoring cohort. Aggregating repeated recordings further improved short-term stability. After adjustment for chronological age, heart age gap was associated with PWV (partial r=0.2627, P<0.001); each 1-year increase corresponded to 0.062 m/s higher PWV, and accelerated versus decelerated heart aging was associated with 0.91 m/s higher adjusted PWV. Each 1-SD increase in adjusted heart age gap was associated with greater odds of prevalent hypertension (OR 1.72, 95% CI 1.49-1.99), while the highest versus lowest quartile had an OR of 4.25. These findings support smartwatch PPG-derived heart age gap as a scalable digital biomarker of arterial stiffness and prevalent hypertension.

Achieving robust EEG-based auditory attention switch decoding (AASD) is crucial for intelligent hearing aids. However, its application is limited as EEG non-stationarity complicates sequential decision-making, and insufficient control of potential confounding factors may overestimate performance. Therefore, we propose a state-guided adaptive decision (SGAD) framework that infers attention transition states via causal state detection and dynamically modulates temporal smoothing through state-guided adaptive gating. We further introduce six hierarchical evaluation protocols to assess generalization across audio, speaker, and subject dimensions. Experimental results show that SGAD improves decoding accuracy and stability while maintaining low response latency across evaluation scenarios. Performance variations across protocols further suggest data partition-related biases. Together, these findings advance robust AASD for neuro-steered hearing applications.

The typical common-bus structure and its inherent bus stabilization requirement can become a bottleneck for flexibility in power supplies for battery-powered autonomous systems, limiting scalability and plug-and-play capability. Power packetization overcomes this by eliminating the common bus through time-division multiplexing of physically isolated power flows. However, conventional power packet routers face two physical-layer limitations: uncontrollable transmission direction and limited controllability of the amount of energy per packet. This paper proposes a fully controllable bidirectional power packet transmission to address these limitations. We introduce an inter-router circuit featuring a parallel inductor as a temporary energy buffer, enabling power transmission in any direction with full control over the transferred energy. We derive the switching algorithm for the routers to perform this operation and verify its feasibility through experiments using prototype hardware.

Wearable AI/ML research needs raw, synchronized, and reconfigurable multimodal data, but consumer devices are closed and many research platforms remain tied to one embodiment or sensor set. This paper presents SensWear, an open, modular, and AI-ready wearable platform that decouples embodiment, sensing, data interfaces, and learning. A compact flexible-Printed Circuit Board (PCB) main board and programmable 1.2 V to 5.5 V daughter-board interface support plug-and-play Photo- PlethysmoGraphy (PPG), touch, temperature, haptic, and LED modules across wearable form factors. Zephyr firmware pro- vides drivers, timestamping, raw streaming/logging, and sensor- presence metadata. Case studies show arterial PPG waveform capture and competitive heart-rate accuracy while preserving inspectable raw data for reproducible closed-loop experiments.

The safety and reliability of intelligent-transportation-systems (ITS) can be greatly enhanced through adding redundancy in the information-sharing network of the vehicles. In this paper, we first model the mixed traffic of human-driven and autonomous vehicles as a distributed system observability problem over a network of communicating vehicles. We clearly show that a strongly-connected network with minimum n links (with n as network size) is sufficient for the observability of mixed traffic network. Then, we present graph-theoretic results on adding redundancy to the changing network of vehicles to make it resilient to the failure of certain number of vehicles/sensors or their data-sharing links. Finally, we employ distributed observer design to validate our results over a simple mixed traffic example.

Speech editing for content creation requires precise control over both what an edit should do and where it should apply. Free-form natural language provides a flexible interface for expressing edit requests, but its ambiguity may leave the intended operation, parameters, or target region underspecified. We study a precise and explicit interface for speech editing: a transcript-grounded structural edit instruction with XML-style tags explicitly specifies typed operations and localizes them to transcript spans or boundaries. This semantic timeline avoids explicit timestamp alignment and provides an externally inspectable contract for compositional edits. We instantiate the interface in dots$.$tts$.$edit, an editor adapted from the continuous autoregressive dots$.$tts foundation model. Four representative speech-creation controls cover lexical content, affective expression, pitch and speaking-rate delivery, and temporal phrasing through text, emotion, prosody, and pause editing. Task-specific data pipelines construct operation- and scope-controlled pairs while retaining source-derived context outside each target region. We further introduce doteBench, a bilingual evaluation suite that measures precise instruction following, local preservation, and audio quality across the four controls and their composition. Experiments show leading overall instruction following and local preservation across its five editing categories, while audio quality remains comparable to existing open-source systems. Across three Seed-TTS-Eval shards, the model shows negligible differences from the base model in zero-shot TTS recognition error rate and speaker similarity.

Since the inception of modern communication networks, the quest for operations automation has never ceased. Yet the evolution of network automation is difficult to characterize with a single maturity ladder. Throughout this history, network control systems have expanded their capabilities for observation, decision support, routine execution, and operator interaction, but these capabilities have not advanced uniformly. Such uneven progress makes the degree of automation an unreliable proxy for trustworthy network-side actuation. The unresolved question is not simply how much automation a system provides, but under what conditions it can be entrusted to change the network state. This paper examines that question through Network Control Intelligence (NCI), a five-axis framework spanning Decision Logic, Adaptability, Knowledge, Control Delegation, and Interface. We use NCI to organize the evolution of network-control systems into three eras: rule-based and scripted automation, programmable and data-driven control, and Large Language Model (LLM)-enabled network operations. Viewed through this framework, the three eras reveal a persistent asymmetry. None of these gains, however, automatically determines when network control should be trusted to change the network state. We frame trustworthy autonomy as a governed alignment between what a system can infer, what it can verify, and what it is authorized to execute. On that basis, the paper develops a reference architecture that separates proposal generation from governed execution, identifies recurring integration patterns for LLM-enabled operations, and derives a research agenda for higher network autonomy under explicit assurance, safety, and governance constraints.

Prolonged sitting with poor posture results in musculoskeletal injury. Early intervention and prevention methods to monitor posture rely on cameras, wearable devices, and dense pressure arrays. Although effective, these approaches introduce privacy concerns, calibration needs, higher cost, etc. In this paper, we explore the spatial pattern of body contact as a binary posture feature vector for a distinct contact-based sitting posture recognition system without the need for analog signal conditioning and calibration. Our sensing principle uses 10 mechanical contact switches arranged in a 5 x 2 array on the backrest. Each switch encodes local body contact into 10-bit binary posture signatures for four postures: normal sitting, leaning back, leaning left, and leaning right. Decision tree and logistic regression classifiers achieved highest accuracy of 96%. Low correlation amongst the switches indicates that every switch captures complimentary information for successful posture classification. SHAP analysis identified the central contacts as the most informative and significant region for posture discrimination. Our results show that binary contact patterns can capture sufficient spatial information for reliable posture recognition. This sensing strategy offers a simple & low-cost alternative to pressure-based systems to monitor posture without the need for mapping the biomechanical pressure distribution.

We present an extension of the publicly available Spinal-Multiple-Myeloma-SEG dataset, a dual-energy CT imaging resource for multiple myeloma research. The purpose of this dataset is to enable voxel-wise analysis of vertebral bone microstructure by adding expert-validated segmentation of the trabecular compartment of lumbar vertebrae. The dataset consists of 72 dual-energy CT examinations from 67 adult patients (mean age 66 years, range 48–85; 36% female), acquired retrospectively using a dual-layer dual-energy CT system. It includes conventional CT, virtual monoenergetic images, and calcium-suppressed reconstructions, along with structured clinical metadata. The data are provided in DICOM format, while segmentation masks are available in both NIfTI and DICOM-SEG formats. The primary intended applications include trabecular bone segmentation, quantitative bone mineral density-related analysis, and development of deep learning models for vertebral anatomy and disease-affected bone structures in multiple myeloma. The dataset supports both segmentation and multimodal learning tasks in pathological and non-pathological bone. Initial trabecular segmentation masks were generated using a pretrained nnU-Net model and subsequently refined through manual expert correction and radiological quality control, ensuring anatomical consistency. The original dataset is publicly available via TCIA (\href{https://doi.org/10.7937/k4qv-hh78}{https://doi.org/10.7937/k4qv-hh78}), while the trabecular segmentation extension (Version 2) is released through Zenodo (\href{https://doi.org/10.5281/zenodo.21628232}{https://doi.org/10.5281/zenodo.21628232}) under the CC BY 4.0 license. The Zenodo release provides immediate public access to the segmentation masks and will be additionally incorporated into the official TCIA collection after completion of the curation process.

Earth-observation satellites capture more imagery than intermittent ground contacts can transmit. Onboard systems threshold a cloud detector, discard frames or tiles, and compress the survivors with a fixed codec. On expert-labeled imagery, these rules remove more than one-fifth of clear pixels, primarily through detector false positives. We train a neural codec with a clear-probability-weighted reconstruction loss, reallocating coded bytes from clouds to clear ground without requiring or transmitting a cloud map onboard. Each capture is encoded into a resumable base layer and a dependent refinement layer, while clear content is estimated from features produced by the encoder. At each contact, we causally rank arrived layers using estimated clear content, unfinished bytes, deadline slack, and aggregate deadline pressure. The scheduler serves base and computational deadlines, bounds stored residual bytes, and resumes interrupted packets. We evaluate the onboard-to-downlink pipeline using real entropy-coded bytes, orbit-derived interruptible contact capacities, and measured service time and energy on resource-constrained embedded accelerators. Clear-weighted codecs require up to 47.8% fewer bytes than learned-compression baselines at matched clear-region quality. The optimized encoder consumes less time and energy than one pass of the cloud detector used by the frame-discard rules. Relative to fixed two-stage service on the same streams, our scheduler more than doubles deadline-full clear-content delivery for the interrupted combined cohort, reaches 83.6% of a certified clairvoyant upper bound, and exceeds replayed reference orders in deadline-usable delivery.

P-Time Event Graphs (P-TEGs) are discrete event systems able to model synchronization and delay phenomena. They extend the modeling power of Timed Event Graphs (TEGs) by including not only lower-bound, but also upper-bound constraints on the sojourn times of tokens in places. In this work, we consider the problem of estimating the firing time of transitions in P-TEGs, assuming that only a subset of transitions can be directly observed. Building on the (now classical) Luenberger observer for TEGs, we design an algorithm that takes into account the additional restrictions posed by the upper-bound constraints of P-TEGs to obtain a more accurate firing time estimation.

Modern sensing, communication, and learning systems generate heterogeneous network signals, with local data differing in dimension, modality, and geometric structure. Processing such data requires a mathematical framework capable of simultaneously modeling heterogeneous local signal spaces and the transformations relating them. Network sheaves provide such a framework by associating local vector spaces with network entities and linear restriction maps with their interactions. This is the first paper to develop a unified sheaf signal processing (SSP) framework on network sheaves, extending the fundamental operations of signal processing, namely spectral analysis, filtering, and sampling, to heterogeneous local spaces. Unlike graph and topological signal processing, where signals are modeled over a common vector space, SSP jointly models heterogeneous local signal spaces and the linear transformations relating neighboring spaces through restriction maps. We define the Sheaf Fourier Transform (SFT), whose frequencies quantify signal inconsistency induced by the network topology, the restriction maps, and the local geometry. Building on this representation, we develop polynomial sheaf filters and formulate sampling as the joint selection of network nodes and intra-node components. We derive perfect recovery conditions for bandlimited sheaf signals and propose a greedy sampling-set design algorithm. To incorporate application-dependent signal models, including different bases, dictionaries, and learned embeddings, we introduce representation sheaves and characterize the natural transformations that preserve spectral properties and guarantee interoperability across representations. Experiments on synthetic, motion-capture, and financial datasets validate the proposed framework and demonstrate consistent improvements over canonical graph signal processing baselines.

Automated process control systems, widely used in industry, have a hierarchical multi-level tree-based architecture and are built using specialized hardware and software. Constructing such a control system from off-the-shelf industrial components with predetermined characteristics is a complex combinatorial optimization problem with multiple constraints. A hybrid method for constructing a tree-based structure is considered, combining a deterministic construction algorithm and an ant colony metaheuristic for device selection. The effectiveness of the metaheuristic algorithm depends on its hyperparameters, which are proposed to be optimized. This paper presents a general problem formulation, defines optimization criteria, describes the solution method, and provides several numerical examples.

Phoneme-based multilingual automatic speech recognition (ASR) can share acoustic evidence across languages more directly than language-specific subword modeling. When tonal and non-tonal languages are jointly trained, however, their supervision granularity does not match: tonal languages annotate tone-marked vowels, whereas non-tonal languages typically provide only base-vowel labels. A standard softmax either treats the two as unrelated classes, weakening cross-lingual sharing, or collapses tones, losing distinctions required by tonal languages. We propose Latent Softmax, a connectionist temporal classification (CTC)-compatible output layer that models tone-marked vowels as subclasses and base vowels as major classes, while consonants and the CTC blank remain singleton labels. When only a base-vowel major-class label is observed, the tone-marked vowel subclass is treated as latent and marginalized out. Multilingual experiments on AISHELL-1 Mandarin and LibriSpeech English show that Latent Softmax reduces speech-to-phoneme (S2P) phoneme error rates over a standard softmax multilingual baseline by 8.4% on AISHELL-1, 17.5% on LibriSpeech test-clean, and 12.6% on test-other. The improved speech-to-phoneme encoders also yield consistent word error rate gains for both large-language-model phoneme-to-grapheme conversion and projector-based interfaces. After code-switching adaptation, Latent Softmax further reduces projector-based mixed error rate by 2.6% on ASRU2019 and 9.5% on CS-Dialogue datasets.

Unlimited sensing and its extension via modulo hysteresis provide an efficient encoding scheme for high dynamic range signals by folding the signal's amplitude into the range of the analog-to-digital converter prior to sampling. Motivated by shortcomings of the modulo hysteresis model, in this work we introduce generalized modulo encoders that flexibly design the folding by incorporating a general transition function and allowing the folding times to depend on the nonideality of the folding transition. We rigorously study mathematical properties of the respective operators and derive conditions under which a bandlimited signal is uniquely determined by its generalized modulo samples. Finally, we propose a recovery algorithm backed by mathematical guarantees and illustrate our theoretical results through numerical experiments.

Large language model (LLM) inference serving is a fast-growing electricity load whose power dynamics remain uncharacterized from a grid-planning perspective. Using real, measured GPU power traces, we show that chunked prefill scheduling, a latency-motivated technique already deployed by default in production LLM serving, is a controllable knob that regulates power ramp rate without touching peak power. Contrary to the intuitive hypothesis that splitting a long prompt's computation into smaller steps should flatten its power spike, peak power stays relatively the same while mean ramp rate falls substantially. Critically, this ramp-rate benefit is not a fixed property of the policy: it grows monotonically with system saturation, and we confirm this along two independent axes: concurrency (7.0% at light load to 34.6% at heavy load, mean-ramp reduction) and long-prompt ("whale") request load (from statistically flat at low whale incidence to 42.6% at high whale fraction/size). We translate this single-GPU mechanism into an operational grid quantity, regulation-reserve procurement, posed and solved as a chance-constrained problem using a model-free bootstrap directly resampling real measured power traces. At a representative operating point, this translates to an estimated 20.3-22.7% reduction in the fast-ramping reserve capacity a grid operator would need to provision, across reliability levels from 95% to 99.9%. Together, these results give grid operators a no-cost demand-shaping tool available today, whose benefit is largest precisely when data centers run hottest and grid stress is most salient.

Earthquake-induced structural damage assessment remains a key challenge for population-based Structural Health Monitoring (PBSHM), where damage representations must generalize across heterogeneous buildings. Existing vibration-based methods often rely on structure-specific damage-sensitive features (DSFs) and uniform drift thresholds, limiting transferability. This study proposes a physics-informed feature fusion and structural metadata integration framework for post-earthquake damage classification and resilience-oriented assessment. A nonlinear simulation dataset is generated for building populations with varied geometrical and material properties under multiple earthquake scenarios. To obtain consistent labels, damage states are defined through nonlinear pushover analysis and capacity-based thresholds rather than fixed drift limits. Using sparse ground and roof acceleration measurements, physics-informed DSFs are compared with Catch22 and MiniRocket representations under group-wise cross-structure validation with multiple machine-learning classifiers. Results show that physics-informed DSFs outperform generic time-series representations under sparse sensing. Structural metadata further improves robustness by providing context for interpreting response-based DSFs. Modal information offers limited gains in simulations but substantially improves out-of-distribution shaking-table validation, helping bridge simulation-experiment discrepancies.

Conventional transepithelial electrical resistance (TEER) technique provides only a low-content analysis of cell-layer conditions, necessitating repeated microscopic assessments of morphology and cell-cell contacts outside the incubator for barrier-on-chip systems. This work presents a novel high-content TEER device in the form of a novel nanoporous membrane that facilitates continuous electrical measurement of cell-substrate impedance sensing (ECIS). The ultrathin (700 nm) membrane, composed of ultra-low-stress SixNy, is monolithically integrated into wafer-level fabricated chips sealed with glass lids. Coplanar ECIS electrodes were connected to custom electronics to record impedance under sinusoidal excitation. Human umbilical vein endothelial cells (HUVECs) were seeded and continuously recorded impedance spectra were compared with bright-field and fluorescence microscopy, revealing distinct phases of monolayer formation. With one-dimensional convolutional neural network (Conv1d) and Kolmogorov-Arnold Network (KAN) trained with a small amount of Nyquist-diagrams, phases of (I) adherence, (II) outspreading, (III) confluence and (IV) barrier maturity with tight junction formation could be recognized with 95% confidence. As further proof of concept, reversible and irreversible barrier weakening using modulators PN159 and BAC was identified in this way. Our studies have demonstrated that an immediate and automatable non-invasive detection of in-vitro barrier dynamics within barrier-on-chip systems, eliminating the need for microscopy and endpoint staining. We expect this ECIS technique will find broad applications in organ-on-chip systems for in situ monitoring physiological or pathological states of tissue barrier.

Integrating inverter-based resources (IBRs) from multiple vendors into power systems is challenging because their controllers are typically designed independently, with little coordination. To address this problem, this paper proposes a decentralized control synthesis framework for IBRs based on block-diagonal dominance (BDD) theory, which ensures the small-signal stability of multi-IBR power systems. By leveraging the grid frequency response, this approach enables a decentralized multi-input multi-output (MIMO) control design. Furthermore, the BDD-constrained design integrates a guaranteed minimum decay rate and defines a novel numerical metric to quantify the conservatism inherent in the decentralized stability certificate. The proposed control design with BDD constraints and minimum decay rate implementation is validated through a case study of the IEEE 9-bus test system.

Distributed target tracking in multi-agent networks plays a critical role in cooperative sensing and autonomous navigation. However, it faces significant challenges in highly dynamic and adversarial setups. This study aims to enhance the resilience of decentralized target tracking algorithms against measurement faults and cyber-physical threats, especially false data injection attacks. We propose a consensus-based estimation algorithm that integrates a nearly-constant-velocity model with saturation-based filtering to suppress impulsive measurement variations and promote robust, distributed state estimation. To counteract adversarial conditions, we incorporate a dynamic false data injection detection and isolation mechanism that uses innovation thresholds to identify and disregard suspicious measurements before they can degrade the global estimate. The effectiveness of the proposed algorithms is demonstrated through a series of simulation-based case studies under both benign and adversarial conditions. The results show that increased network connectivity and higher consensus iteration rates improve estimation accuracy and convergence speed, while properly tuned saturation filters achieve a practical balance between fault suppression and accurate estimation. Furthermore, under localized, coordinated, and transient false data injection attacks, the detection mechanism successfully identifies compromised agents and prevents their data from corrupting the distributed global estimate. Overall, this study illustrates that the proposed algorithm provides a simplified fault-tolerant solution that significantly enhances the accuracy and resilience of distributed target tracking without imposing excessive communication or computational burdens.

In recent years, the prevalence of large-scale data-sets and the demand for sophisti-cated learning models have necessitated the development of efficient distributed ma-chine learning (ML) solutions. Convergence speed is a critical factor influencing the practicality and effectiveness of these distributed frameworks. Recently, non-Lipschitz continuous optimization algorithms have been proposed to improve the slow conver-gence rate of the existing linear solutions. The use of signum-based functions is previ-ously considered in consensus and control literature to reach fast convergence in the prescribed time and also to provide robust algorithms to noisy/outlier data. However, as shown in this work, these algorithms lead to an optimality gap and steady-state re-sidual of the objective function in discrete-time setup. This motivates us to investigate the distributed optimization and ML algorithms in terms of trade-off between conver-gence rate and optimality gap. In this direction, we specifically consider the distributed regression problem and check its convergence rate by applying both linear and non-Lipschitz signum-based functions. We check our distributed regression approach by extensive simulations. Our results show that although adopting signum-based func-tions may give faster convergence, it results in large optimality gaps. The findings pre-sented in this paper may contribute to and advance the ongoing discourse of similar distributed algorithms, e.g., for distributed constrained optimization and distributed estimation.

Reconfigurable intelligent surfaces (RISs) can improve high-frequency wireless links by shaping the propagation environment, but their passive architecture makes channel acquisition costly. Conventional estimate-then-optimize methods usually require pilot overhead that scales with the number of reflecting elements, which is undesirable under short coherence times and hybrid near-/far-field propagation. This paper proposes the Bayesian bandit framework for RIS phase-shift configuration with implicit channel learning. The method updates a Gaussian posterior of the cascaded channel from one scalar pilot observation per slot and uses Thompson sampling to balance channel estimation and beamforming gain. We derive a Bayesian regret decomposition that connects Bayesian received-power regret to posterior uncertainty contraction, and further establish a conditional sublinear Bayesian-regret guarantee. To exploit sparse hybrid-field propagation, we develop an energy-focusing angle-distance dictionary and a sparse Bayesian learning (SBL)-based Thompson-sampling algorithm with warm-started hyperparameter refinement. Simulations show that the proposed policies approach the perfect-channel state information (CSI) benchmark in the line-of-sight (LOS)-dominant setting within 10 time block and improve transmission efficiency over the considered baselines in multipath and Rayleigh fading scenarios.

In this work, an end-to-end generative adversarial framework for computational microwave imaging (CMI) is proposed to reconstruct the targets of interest directly from the measurements of targets obstructed by undesired objects. It integrates a conditional generative adversarial network (cGAN) with a learnable soft-threshold module (STM) to adaptively suppress non-target related information. The proposed framework is evaluated on a diverse dataset comprising MNIST digits obstructed by E-MNIST letters for training and testing, as well as on measurements acquired with an experimental CMI system. In addition, further studies involving objects with different geometries are conducted, demonstrating that the proposed approach can be adapted to other types of objects. Numerical experiments show that the proposed cGAN-STM achieves a normalized mean square error (NMSE) of 0.066 and a structural similarity index (SSIM) of 0.876 under ideal conditions. Comprehensive analyses, including benchmarking, analysis of the STM mechanism, and evaluation under different obstruction sizes, are also conducted. The performance of the model under different signal-to-noise ratio (SNR) scenarios is also evaluated, achieving reasonable reconstruction quality at 15 dB SNR with an NMSE of 0.168 and an SSIM of 0.700. Even at low SNR levels, recognizable target outlines are preserved. These results highlight the effectiveness and adaptability of the proposed method.

In this paper, we consider stochastic optimal control problems with infinite-horizon joint chance constraints. By means of an appropriate state augmentation, we reformulate the original problem as a constrained Markov decision process, in which both the cost and the constraint function exhibit an additive structure. We then prove that this formulation enjoys strong duality, thereby enabling us to reformulate the problem as an equivalent unconstrained one in the Lagrange dual framework. We propose a dual-ascent algorithm to solve the resulting problem and show that it converges to a deterministic Markov policy defined over the augmented state space that is both optimal and feasible. To accommodate continuous state-input spaces, we propose a dedicated learning algorithm to approximate the value function in an offline training setting, thereby significantly reducing the computational complexity of the online control phase. We then test our approach on a numerical example and demonstrate its effectiveness compared to online predictive control methods in terms of performance and computational complexity.