Papers

It has been well known that inverter-based resources (IBRs) can lead to converter-driven stability issues under weak grid connections. However, as the number of IBRs increases, instabilities can also occur even under strong grid connections. A practical case is presented to demonstrate this conclusion, using large-scale energy storage systems (ESSs) as an example. In this study, the ESSs induce oscillations with a frequency of 150 Hz in the d-q coordinates while providing both capacitive and inductive reactive power support (achieved by ESS functional control loops) to the connected power system. Theoretical analysis reveals that under strong grid connections, the dynamic interactions among power conversion systems (PCSs) of ESSs can be superimposed and intensified as the ESS scale extends, which reduces oscillation damping and leads to system instability. This indicates that ESS functional control loops also have potential instability risks when providing supports to power systems, which should be carefully examined. Finally, major impact factors are identified to mitigate the oscillations, and the conclusions are validated based on the SIMULINK platform. This paper provides valuable practical insights into system instabilities even under strong grid conditions, emphasizing the importance of functional control design and careful planning of the scale for IBR-dominated systems.

Massive controlled DC resources (CDCRs), such as battery energy storage systems, are connected to AC power systems through bidirectional inverters for power balance requirements. This study investigates converter-driven stability (CDS) issues in the sub-synchronous frequency range caused by large-scale bidirectional inverter-based stations (IBSs). The impacts of the AC and DC connections of IBSs on subsynchronous oscillations (SSOs) are compared by examining three factors: the number of CDCRs, power flow direction, and control parameters of the inverters. For AC connections, IBSs may induce instability as the number of CDCRs increases, regardless of the power flow direction. To maintain stability, the maximum power amplitude of the IBS is calculated. It is found that switching to DC connections can reduce these instability risks if the DC line resistance is much less than the AC line reactance. Moreover, the method of tuning control parameters is demonstrated to be more effective in improving power-related critical stability under DC connections. Therefore, The DC-IBS is preferred for high-voltage transmission. Finally, the conclusions are validated in power systems connected with both AC- and DC-IBSs under various network topologies and system scales.

LiDAR sensors are widely deployed in autonomous systems for safety-critical 3D perception. We identify a previously unexplored attack surface in which dormant malware, embedded in the LiDAR sensing pipeline, remains inactive during normal operation and is triggered externally after deployment, without access to sensor hardware or networking at attack time. This malware was developed in a closed research test environment with vendor technical support, rather than by exploiting an inherent production supply-chain vulnerability. To trigger the malware, we repurpose the sensor's own native dual-return signal processing as a covert channel for a modulated optical signal delivered into the environment. Once triggered, the malware performs false data injection and suppression on static and mobile platforms. Unlike optical spoofing, where the transmitted signal is itself the attack, here it carries only a small key that the sensor decodes with existing signal processing. We establish feasibility with static triggering at 300~ft and drive-by runs reaching 35 mph, and verify that injected artifacts are taken up as detections by a fine-tuned 3D object detector. On a deployed tactical autonomous vehicle running an unmodified production autonomy stack, we demonstrate both fail-safe and fail-dangerous impact: emergency stop, mission abort, and collision. These results motivate stronger integrity guarantees across LiDAR development and deployment pipelines.

Movable antennas (MAs) are a promising technology to improve wireless data rates by dynamically adjusting their positions to avoid deep fading. However, finding the optimal MA positions requires full channel state information (CSI) for all possible locations within the movement region, creating massive channel estimation overhead. This paper proposes a deep neural network (DNN)-based learning framework to predict the optimal positions of multiple transmit MAs in a multi-user multiple-input single-output (MISO) system, entirely bypassing explicit channel estimation.First, we analyze a single-user MISO case, revealing a complex, highly nonlinear mapping between the optimal MA positions and the channel power gains from a specific subset of locations in the transmit region to the user. Because this mapping cannot be mathematically characterized for practical channel models, we train a DNN via supervised learning to capture it. The pre-trained DNN can then determine optimized MA positions in real-time relying only on partial power measurements from the transmit region.Extending this to multi-user scenarios is challenging due to complex rate expressions and the lack of globally optimal position solutions to use as training labels. To overcome this, we develop an unsupervised training framework that directly maximizes the multi-user sum-rate. This framework utilizes an attention-based architecture to extract latent features from the partial channel measurements and effectively manage inter-user interference. Simulation results show that our proposed approach achieves near-optimal performance in single-user systems and surpasses conventional CSI-based alternating optimization algorithms in multi-user environments.

Non-autoregressive (NAR) decoding generates output tokens in parallel, making speech recognition faster than autoregressive decoding, which generates them sequentially from left to right. However, the recognition performance is degraded because NAR decoding cannot resolve uncertainty by conditioning on previously generated tokens. To address this issue, we propose a novel NAR decoding framework based on minimum Bayes' risk (MBR) decoding, termed NAR-MBR decoding, that maximizes the expected utility calculated from samples drawn from the output probability of an NAR model rather than maximizing the output probability. Notably, by leveraging the nature of NAR models, multiple samples are obtained efficiently with a single forward computation. Our experiments across LibriSpeech, Switchboard, AMI, and web presentation corpus demonstrated that our NAR-MBR decoding outperformed previous NAR decoding and ran faster than AR decoding.

Despite rapid advances in UAV technologies, current deployments remain limited due to several gaps in UAV systems research. To address these challenges, we propose OmniDroneX, a unified Drone-as-a-Service ecosystem, in which drones are transitioned from fixed function platforms into dynamically composable entities that can be integrated with external infrastructures to offer omni-capabilities. OmniDroneX bridges low-level physical primitives with high-level mission intent through a unified vendor-agnostic interface (libUAV) and a formal physical-service abstraction model (PT-SOA). A core innovation is the diverse application of large language models (LLMs) across multiple layers of the OmniDroneX architecture. LLMs are used to assist in identifying and formalizing primitive device functions and abstract service definitions, supporting automated service composition and workflow generation, and enabling interactive, natural-language mission specification and refinement. OmniDroneX also incorporates important categories of composition techniques that are essential in dynamic UAV systems, including physical layer composition for drone capability augmentation, as well as spatiotemporal, functional, collaborative, exception-aware, and QoS-based service compositions. Collectively, these features allow OmniDroneX to serve as a foundation for scalable, resilient, and self-evolving UAV ecosystems operating in complex and dynamic environments.

We propose a direct data-driven method for controller synthesis of infinite networks composed of unknown linear time-invariant subsystems. Using a single set of noise-corrupted input-state trajectories collected from each subsystem, and provided that certain linear matrix inequalities hold, each subsystem is rendered exponentially input-to-state stable (eISS) by locally constructing an eISS control Lyapunov function together with an exponentially input-to-state stabilizing feedback controller. We then compose these local components under a compositional small-gain condition in infinite-dimensional spaces to obtain a global control Lyapunov function and an associated stabilizing controller, ensuring uniform global exponential stability of the infinite network. The approach is validated on a physical case study with unknown dynamics.

This paper presents a unified framework for exploiting the boundaries of reconfigurable intelligent surfaces (RIS) joint optimization in multi-user wireless systems, where a single RIS accommodates diverse objectives.We first propose an adaptive gradient-scaling mechanism that accelerates the convergence of the underlying optimization algorithm while maintaining stable performance across varying channel and system parameters. The proposed mechanism enables the solver to reach a reasonably good solution rapidly without requiring manual tuning of step sizes or algorithmic hyperparameters when system inputs change. We then propose a low-complexity beamformer recovery method tailored for single-user scenarios, which circumvents the full matrix decomposition required by traditional approaches, thereby significantly reducing computational overhead. Building on these foundations, we develop an element allocation strategy that enables user-specific prioritization through assignment of RIS subsets. This is further extended by a modular add-drop mechanism that supports partial-panel optimization in general multi-user settings. The framework is evaluated across three representative scenarios: (i) signal amplification for all users, (ii) signal suppression for all users, and (iii) selective amplification and suppression. To characterize performance limits, we derive power trade-off boundaries using scalarized joint optimization, which closely align with Monte Carlo simulations. Our unified joint optimization method consistently yield solutions near these boundaries, confirming its near-optimality. Extensive simulations under realistic channel models demonstrate that the proposed approach outperforms conventional semidefinite relaxation techniques, offering a scalable and effective RIS control strategy for cooperative and competitive multi-user environments.

A compact dynamic omnidirectional array is proposed for antenna-level physical-layer security through directional modulation. Unlike conventional directional-modulation transmitters based on phased-array beam synthesis or multiple RF chains, the proposed architecture uses a single RF input and a switching-controlled four-element printed meander-line monopole array operating at 5.05 GHz. The state-dependent excitation introduces controllable magnitude and phase perturbations in the radiated field, producing angle-dependent constellation distortion and bit error rate behavior. Reliable information recovery is confined to a narrow broadside region in the E-plane, whereas the H-plane remains quasi-static and omnidirectional, providing a full 360-degree information-recoverable region. The antenna is implemented on a single-layer Rogers RO4350B substrate with a compact footprint of 0.57 x 1.11 lambda_0^2. A four-path switching network based on commercial RF components is used for experimental validation. Communication measurements using 16-QAM at 5.05 GHz demonstrate BER-defined E-plane information beamwidths of 30 to 36 degrees for calibrated switching modes under a BER <= 10^-3 criterion, while no bit errors are observed in the measured H-plane and the SNR remains above approximately 33 dB. Feed-phase offsets are also used to steer the BER-defined information-recoverable sector, demonstrating information-beam steering with the same antenna-level switching mechanism. These results show that compact antenna-level directional modulation can provide angularly selective information recovery in one principal plane while preserving omnidirectional coverage in the orthogonal plane.

Traditional CPU, GPU, and NPU architectures are increasingly limited by the von Neumann bottleneck. While In-Memory Computing (IMC) using ReRAM crossbar arrays offers a high-density, energy-efficient alternative, its practical deployment is constrained through their non-idealities. Existing hardware-aware training frameworks often require training from scratch, which is computationally prohibitive for modern large-scale models. In this work, we propose a finetuning-based hardware-aware training algorithm that enables robust DNN deployment on ReRAM with minimal training overhead. Our approach mitigates I-V non-linearity by applying a range-shrunk sinh transformation and incorporates retention errors directly into a regularization loss during the finetuning process. We evaluate our framework across models and tasks such as image classification and question-answering (QA). Experimental results demonstrate that our method achieves similar accuracy on large-scale models like ResNet18 and DeiT-Tiny as the base model. In-case of ImageNet for MobileNetV3 families the technique has only less than 2% accuracy degradation. Further, applying the technique on the SQuAD v2 dataset results in only 1 point degradation of F-1 score.

We introduce Perron–Frobenius Operator Matching (PFOM), a generative framework that matches density evolution via the integral PF operator, subsuming flow, diffusion, and jump models. We prove that among Bregman divergences, only Kullback–Leibler divergence preserves equality between density-level and sample-conditioned objectives, yielding a practical loss equivalent to Koopman path matching. We further develop Nesterov-accelerated training and sampling that stabilize discretization and accelerate convergence. %On Gaussian mixtures and two-moons, PFOM achieves faster KL/$W_2$/MMD decrease and improved wall-clock efficiency with empirical validation. PFOM unifies operator-theoretic identification with modern generative modeling and opens paths to adaptive dictionaries and high-dimensional applications.

Affine frequency division multiplexing (AFDM) is an emerging chirp-based multicarrier waveform with strong diversity in doubly selective channels, but practical systems suffer from transmitter and receiver IQ imbalance, causing image interference and performance degradation. This paper proposes a two-stage IQ imbalance estimation and compensation method for AFDM systems. First, a preamble-assisted iterative algorithm estimates the time-invariant IQ imbalance parameters by exploiting their slowly time-varying nature. Then, a joint channel estimation and data detection scheme combines basis expansion model (BEM)-based channel estimation with an improved LMMSE detector for interference suppression. Simulations show rapid convergence and near-ideal BER performance.

Tau positron emission tomography (PET) is widely used for the in vivo characterization of disease stage and progression in Alzheimer's disease (AD). With the adoption of multiple tau PET tracers including AV-1451, PI-2620, MK-6240 with different binding behaviors in various large-scale studies, there is a great need of effective harmonization methods to enable the cross-tracer integration of tau PET datasets. While previous methods such as CenTauR were proposed to standardize scalar tau PET measures, they are limited in accounting for the heterogeneity of tau pathology. In this work, we propose Feynman-Kac Reweighted Schrödinger Bridge Matching (FKRSBM), a surface-based framework for cross-tracer tau PET harmonization. FKRSBM learns a direct stochastic transport between tracer domains using Schrödinger Bridge matching, avoiding the Gaussian-prior routing used in diffusion-based translation. To promote biologically consistent transport, FKRSBM introduces an endpoint penalty favoring bridge pairings with matched tau-pathology status and implements it through a Feynman-Kac reweighted endpoint proposal. To preserve cortical organization, FKRSBM uses a spherical convolutional network for vertex-level harmonization on cortical surface meshes. In our experiments, we demonstrate our method by harmonizing Tau PET images acquired with the AV-1451 (n=1480) and PI-2620 (n=2458) tracers from two large-scale datasets. Compared to previous methods including ComBat, CycleGAN, Diffusion Model(DF), and unregularized Schrödinger Bridge Model(DSBM), the proposed FKRSBM method outperforms these baselines in subgroup-level alignment, tau-positivity consistency, and diagnostic classification while preserving subject-specific cortical topography of tau pathology. The code is available at: https://github.com/jianweizhang17/FKRSBM.

Text-to-audio (TTA) generation, synthesizing audio from natural language, has been widely studied for its ability to capture precise user intent. To effectively advance TTA models, it is essential to reliably evaluate generated audio without relying on costly human subjective ratings, motivating the development of automatic evaluation metrics that correlate well with human judgments. While recent CLAP-based metrics provide practical reference-free solutions, their coarse-grained text-audio similarity matching often correlates poorly with human ratings. To address this, we propose ELSA, a reference-free evaluation metric for fine-grained text-audio alignment. ELSA decomposes generated audio guided by distinct acoustic events derived from the text query and assesses event-level alignment. Experiments across four TTA benchmarks show that ELSA reveals a higher correlation with human subjective ratings than prior metrics, highlighting its effectiveness for reliable TTA evaluation.

In this paper, we generalize the concept of Voronoi regions, define agent utility as the integral of a utility density over the corresponding Voronoi region, derive gradients of the utility, and illustrate the approach in a two-team example from soccer. The generalization of Voronoi regions is in the form of so-called Cost-Induced Voronoi (CIV) regions, where the agent state space may differ from the space being partitioned. One example of such regions is when the cost is given by the optimal solution of an LQR control problem. Then the agent states include position as well as velocity, while the partitioned space only includes positions. The agent utility is defined by integrating some utility density over the CIV region of the agent. This utility density might be the probability density of some beneficial event, such as receiving a pass in soccer. The utility is then the overall probability of receiving a pass and the gradient represents a way to improve that probability. We show how this utility gradient can be computed using the Reynolds Transport Theorem from fluid mechanics, and that this approach achieves similar accuracy while reducing computation time by about an order of magnitude compared to a baseline finite-difference approximation.

Electrochemical Impedance Spectroscopy (EIS) is a widely used, non-destructive technique for characterizing electrochemical systems, and its analysis typically relies on fitting the measured spectra to an Equivalent Circuit Model (ECM). Selecting an appropriate ECM, however, remains a major bottleneck: knowledge-based selection requires expert judgment and is difficult to reproduce, while existing automated approaches either choose from a fixed set of candidate circuits or, in the case of Gene Expression Programming, require repeated equivalent-circuit fitting and a predetermined circuit scale. Here, we propose a machine learning method that estimates an ECM directly from an impedance spectrum by representing the circuit as a serialized string of symbols and generating this string with a Long Short-Term Memory (LSTM) network coupled to a convolutional feature extractor. Because the LSTM inherently handles variable-length sequences, the method produces the circuit topology directly, without any fitting during estimation nor prior assumption for the number of elements. A fourth-root transformation of the impedance is introduced to emphasize the mid-frequency features essential for distinguishing circuits, and an adaptive beam search yields multiple ranked candidates. Evaluated on 100,000 synthetic datasets generated from 119 circuit topologies with 1% added noise on impedances, the method identified the correct topology as the most probable ECM in 77.8% of cases and among the top five candidates in 98.8% of cases, with an average estimation time of 17.8 milliseconds per dataset - several orders of magnitude faster than reported fitting-based approaches. These results indicate that direct topology generation with a neural network is a promising route toward fully automated, expert-independent ECM estimation.

Accurate intraoperative liver registration is challenging due to substantial soft-tissue deformation yet sparse intraoperative measurements. Biomechanical models regularize this ill-posedness with prior knowledge but exhibit persistent prediction bias due to simplifying assumptions, while data-driven learning solutions struggle with data efficiency, generalization, and physical plausibility. We propose a hybrid registration framework that adapts a biomechanical prior using sparse intraoperative correspondences. Rather than learning a full deformation field, we learn a residual deformation function that corrects linear biomechanical predictions, modeled as a graph neural diffusion function with geometry-aware attention over the 3D liver mesh. To enable long-range information transfer of sparse observations, we take a novel perspective of sparse intraoperative measurements as context samples where input-output pairs of the residual deformation function are fully observed, casting the problem into learning-to-learn this residual function from intraoperative context samples with feedforward meta-learners. Experiments on a deformable liver phantom dataset demonstrate improved registration accuracy and generalization compared to rigid, biomechanical, and data-driven baselines, particularly for out-of-distribution geometries and deformations.

We study performance-driven environment abstraction for decision-making in large Markov decision processes. Rather than preserving geometric or topological structure, we seek abstractions that directly optimize decision quality. We model abstraction as a controlled approximation obtained by aggregating the state space and enforcing a shared action distribution within each aggregated state. For a fixed partition, we establish a performance guarantee that separates value-function approximation error from the loss introduced by action sharing. Guided by this analysis, we develop a multi-timescale reinforcement learning framework that jointly adapts the policy and a tree-structured environment abstraction. The resulting algorithm refines and coarsens regions of the state space based on Q-value discrepancies, balancing performance against abstraction size and complexity. Empirical results demonstrate substantial state compression, improved sample efficiency, and faster replanning compared to actor-critic baselines.

Data centers are critical to today's digital economy, but are also among the largest industrial consumers of freshwater. Beyond the sheer volume of water use, the environmental impact of data center water consumption varies significantly across locations and seasons, depending on local and regional water stress. However, prior research has largely focused on reducing total water use, overlooking that the same unit of water can have drastically different environmental consequences depending on when and where it is consumed. In this paper, we introduce a stress-adjusted water framework that quantifies the true sustainability impact of data center water consumption by incorporating both spatial and temporal water stress. Using the AWARE-US model, we capture county-level monthly variations in water availability and extend this framework to account for the off-site water footprint of electricity generation. Based on this stress-aware accounting, we analyze stress-adjusted water-computing strategies spanning both the software and infrastructure layers. Specifically, we study workload scheduling policies that jointly optimize water and carbon efficiency, evaluate the potential of rainwater harvesting as a supplemental water source, and investigate the feasibility of dry cooling as a water-free alternative to evaporative cooling.

Ubiquitous perception through RF signals is a pivotal opportunity for future technology: it enables personalized services such as smart living, remote healthcare, automated logistics or interaction through free-space gestures. The ubiquity of Wi-Fi and cellular networks presents a promising platform for the development of innovative sensing tools. Future standards will also introduce dedicated sensing features which, for example, will allow routers to work as frequency modulated continuous wave radios targeting radar applications. Most of the current chip designs support ad-hoc firmware for CSI extraction with MIMO arrangements of the transmitter (TX) and receiver (RX) antennas and OFDM subcarriers. The CSI describes the phase shift and amplitude attenuation of multiple propagation paths on each subcarrier. The latest IEEE 802.11be standard (Wi-Fi 7) offers a wider subcarrier bandwidth of 160MHz (up to 320MHz), providing at least 120 usable pilot subcarriers for CSI or CIR estimation. Additionally, Wi-Fi signals have been recently exploited to track daily human movements and behaviors, while Wi-Fi signal variations have been shown to differ between different people and can consequently be used for their re-identification.

Integrated sensing and communication (ISAC) in cell-free (CF) massive multi-user multiple-input multiple-output (MU-MIMO) system is a promising architecture for high-rate communications and high-accuracy multi-target sensing. However, centralized coordination among distributed access points (APs) incurs substantial fronthaul overhead and computation complexity. This paper proposes a low-complexity hybrid precoding framework for CF massive MU-MIMO ISAC systems with partially-connected architectures at the APs. By applying hybrid architecture at the APs, the proposed framework converts the original high-dimensional channel information into a low-dimensional effective channel, enabling digital precoding over the compressed channel domain and thereby substantially reducing both fronthaul overhead and baseband computational complexity. We formulate the joint hybrid precoding design as an ergodic sum-rate (ESR) maximization problem with position error bound (PEB) constraints to ensure multi-target sensing accuracy. An efficient alternating optimization (AO)-based solver is then developed, where the PEB constraint is reformulated into tractable convex constraints, while the digital-domain optimization is carried out over the reduced-dimensional effective channel and the analog precoding is refined on the constant-modulus manifold. For dynamic user topology, we further propose multi-branch (MB) rate-splitting (RS) minimum mean-square-error Tomlinson-Harashima precoding (MMSE-THP) update algorithm that combines multi-branch ordering with recursive MMSE-THP matrix updates, enabling common and private digital precodings to be refreshed without repeated full matrix recomputation. Simulation results demonstrate that the proposed scheme achieves high ESR and accurate multi-target sensing while reducing computational complexity by 87.02% compared with conventional baselines.

Decoding inner speech from non-invasive brain signals remains a fundamental challenge due to the absence of overt linguistic output, limited training data, and large inter-subject variability. Existing brain-to-text approaches often rely on task-specific decoder fine-tuning, which restricts scalability and complicates adaptation to new participants. We propose MindAlign, a decoupled two-stage brain-to-language framework that enables open-ended text generation from fMRI signals without modifying the underlying language model. The first stage learns a subject-specific neural-semantic alignment that maps fMRI activity into a shared multimodal semantic space, extracting a latent semantic sketch of the internally generated sentence. The second stage integrates this sketch with visual context to prompt a frozen multimodal language model for free-form generation. Experiments on fMRI data collected during silent image description demonstrate that the proposed approach consistently outperforms fMRI-only and random baselines. We further show that the learned semantic-to-language projection can generalize across subjects, enabling effective decoding when paired with subject-specific neural alignment. These results indicate that neural signals modulate semantic content beyond image-driven priors, supporting a scalable and modular direction for brain-to-text decoding.

This article proposes a novel methodology for the classification of transient and stationary regimes in dynamic systems. Several sensor-based solutions for regime classification in the literature require the setting of several parameters, or are not suitable for scenarios involving multivariate systems that may contain periodic signals. The proposed method introduces a spatial curve representation of the considered system based on its sample mathematical moments. Then, by connecting concepts of stability theory, geometrical properties of spatial curves and stationary stochastic processes, two regime classifiers are designed using the arc length and the curvatures of the proposed curve. Both classifiers are capable of describing and detecting transient regimes, considering behaviors such as: multivariate asymptotically, marginally stability, and cyclostationarity. Furthermore, a quantitative comparison in performance and computation resources of the proposed classifiers against existing classifiers in the literature illustrates that the proposed regime classifier based on the arc length outperforms other techniques in classifying transient regimes for simulated linear, non-linear, and discontinuous multivariate systems under the specified studied conditions.

In this study, we focus on cough-based tuberculosis screening (CBTS) and hypothesize that fusing speech/audio foundation representations with spectral descriptors will yield stronger screening performance. We expect this fusion to reveal complementary strengths: spectral features preserve fine-grained short-time acoustic detail in cough signals, while foundation embeddings capture higher-level temporal and event-level patterns learned from large-scale pretraining. To this end, we propose COBALT, a novel fusion framework based on codebook-aligned hyperbolic prototypes and bandit-style reliability weighting to integrate heterogeneous representations effectively. Using the CODA TB DREAM Challenge benchmark, COBALT consistently outperforms individual representations and a concatenation baseline, achieving the best overall performance when fusing MFCC with PaSST thereby establishing a new state-of-the-art on the benchmark.

Abscisic acid (ABA) is a central plant hormone for coordinating responses to drought, salinity, cold stress, pathogen attack, wounding, and developmental aging. This paper reviews the biological stimuli that increase ABA biosynthesis, the main production sites and pathways, and the long-distance movement of ABA through plant vascular tissues. It then discusses experimental quantification approaches, including gas-liquid chromatography with electron-capture detection and high-performance liquid chromatography with ultraviolet detection. Finally, the paper presents a molecular-communication-inspired model of ABA transport in which root-side ABA release is represented as a transmitter, the xylem pathway as a bounded channel, and soybean tissue as a receiver. MATLAB Brownian-motion simulations are used to evaluate the effects of released molecule quantity and receiver radius on the detected ABA signal. The results show that higher release quantities produce smoother and stronger reception trends, while larger receivers increase molecule-capture probability.

This paper studies indoor tracking from wall-mounted backscatter fiducials in corridor segments outside direct transmitter illumination. In the measured setup, the transmitter-to-fiducial links are NLOS, whereas the fiducial-to-receiver links along the corridor are largely LOS. The main challenge is that the effective fiducial response is deployment-dependent, so a fixed calibrated link budget is not reliable. We therefore use a grid-based penalized-likelihood tracker that profiles the receiver path, a fitted log-distance slope parameter, and fiducial-specific offsets directly from received powers. The resulting paths can then be reused as surrogate calibration coordinates for residual-map correction, while the same correction with measured calibration coordinates is reported only as a reference. On a short four-fiducial corridor segment, the profiled dual-band tracker gives a 0.52 m median error without measured calibration coordinates, and surrogate residual correction improves this to 0.46 m. With measured calibration coordinates, the same correction and a RADAR-style fingerprint reference both reach 0.31 m. The main remaining limitation is therefore the quality of the surrogate calibration paths rather than the structured observation model itself.

Passive backscatter devices (BDs) can enable indoor non-line-of-sight (NLOS) positioning by serving as virtual anchors whose Doppler-separated signatures are observable in standard channel estimates. This paper studies continuous user-equipment (UE) tracking in corridor environments using a noncoherent power-domain formulation that avoids BD phase synchronization and remains robust to residual carrier offsets and strong multipath. The BD-dependent measurements are modeled by a log-distance law with unknown BD-specific offsets, which allows passive asynchronous devices to be used as anchors without transmit-power calibration. Based on this model, we develop a corridor-constrained maximum a posteriori (MAP) tracker with motion regularization and Huber-robust estimation. In ray-tracing-inspired simulations, the method achieves median positioning errors of 0.23–0.27 m with 90th-percentile errors below 0.45 m. In office-corridor measurements with four passive BDs at 866 MHz, it attains an aggregated median error of 0.505 m and outperforms a simple weighted-average baseline. The results show that passive asynchronous BDs can provide practical sub-meter indoor NLOS tracking while remaining compatible with existing channel-estimation pipelines and energy-autonomous BD deployments.

Transmission failures can lead to cascading failures and system blackout affecting millions of customers if not handled in time, and choosing the best locations to monitor the condition of the transmission system is crucial for power system reliability. In this paper, we propose a new sensitivity factor, the line outage impact factor (LOIF), which is especially useful for power system monitoring and can reveal the impacts of a transmission outage on the power flow of other lines more effectively than existing sensitivity factors, such as the line outage distribution factors (LODF). In this study, we apply the LOIF in transmission line outage detection in three test systems and compare it with LODF using a number of observed transmission line (OTL) selection methods based on these two sensitivity factors. Then we apply a machine learning algorithm to detect the outages of other lines by monitoring the selected OTLs, and the detection accuracy is evaluated using the F1-score. The results show that, in general, with the same number of OTLs, detection using the OTLs selected using LOIF achieved higher F1-scores. The pattern was especially consistent in large-scale systems, showing its potential in real-world applications.

This paper presents a pilot-aided study of multiple-input multiple-output (MIMO) channel identification and linear deconvolution under spatially correlated Gaussian noise. A real-valued $4\times4$ baseband model is analyzed for both memoryless and finite-impulse-response channels. The noise process is generated from a Toeplitz covariance matrix, the channel is estimated from pilot symbols through maximum-likelihood/least-squares formulations, and the empirical mean-square error is compared with the Cramer–Rao bound. The estimated channel is then used for data-symbol recovery through maximum-likelihood zero-forcing and linear minimum-mean-square-error deconvolution. The results show that sufficiently long and well-conditioned pilot blocks allow the channel estimator to approach the theoretical lower bound, whereas short training intervals cause rank and conditioning limitations, especially for the four-tap model. The deconvolution experiments further show that MMSE regularization provides a more stable inverse than unregularized zero forcing at low signal-to-noise ratios and for inaccurate channel estimates.

In this paper, we investigate secure uplink \ac{PD-NOMA} in an integrated sensing and communication (ISAC) system where a full-duplex \ac{BS} simultaneously receives uplink transmissions and transmits a sensing waveform to estimate and jam an eavesdropping target (Eve) whose location is imperfectly known. To explicitly account for sensing-derived uncertainty, we use the angle–delay submatrix of the Cramér–Rao bound as an error-covariance proxy and propagate the BS–Eve estimation errors to the user–Eve link parameters through geometry-dependent Jacobians. This approach enables tractable uncertainty-aware approximations of the sensing and information-leakage metrics. We then jointly optimize the BS receive combiners, user transmit beamformers, and sensing power to maximize a weighted communication–sensing utility subject to transmit-power constraints and a statistical leakage constraint based on uncertainty-averaged received powers. The resulting nonconvex problem is addressed through alternating optimization: the receive combiners have closed-form generalized-eigenvector updates, while the transmit beamformers and sensing power are optimized using a quadratic transform and \ac{SDR}. Numerical results show that the proposed design maintains Eve's statistical SINR below the required threshold under location uncertainty while achieving strong sensing performance and communication rate, with only a small secrecy-rate loss relative to an ideal secure design with perfect Eve-location knowledge.