Papers

Automatic accent identification (AID) remains a challenging task due to the complex variability of accents, the entanglement of accent cues with speaker traits, and the scarcity of reliable accentlabelled data. To address these challenges, we propose a speaker augmentation strategy using voice conversion (VC), with which we generate additional training data by converting original training utterances into different speaker voices while preserving accentual cues. For this purpose, we select two recent VC systems and evaluate their capability to preserve accent. Alternatively, we also explore the use of non-timbral embeddings in AID, for their ability to convey accent information among other non timbral cues. The effectiveness of both methods is demonstrated on the GenAID benchmark, achieving a new state-of-the-art F1-score of 0.66, compared to the previous score of 0.55. Beyond AID, we show that non-timbral embeddings enable accent-controlled Text-to-Speech, producing high-fidelity speech with accurate accent transfer.

The integration of HAPS, RISs, and MIMO technologies is emerging as a promising paradigm for extending the coverage and reliability of future wireless communication networks. However, in a HAPS-mounted RIS-assisted MIMO (HAPS-RIS-MIMO) system, the received SNR statistics become difficult to characterize due to the cascaded Rician small-scale fading and log-normal large-scale shadowing effects. To address this challenge, this paper develops a tractable analytical framework for the SNR characterization of HAPS-RIS-MIMO systems under LoS-aligned precoding. Specifically, saddlepoint approximation is employed to characterize the distribution of the small-scale effective channel power, while Gauss-Hermite quadrature is used to incorporate the composite log-normal large-scale fading effect. Based on the resulting cumulative distribution function, the outage probability expression is derived and validated through Monte Carlo simulations. The numerical results provide both theoretical validation and practical design insights by analyzing the effects of transmit power, HAPS altitude, transmit antenna number, RIS size, RIS amplitude response, and RIS phase resolution. It is shown that optimizing the RIS phases to enhance the LoS power contribution provides substantial transmit-power savings compared with random RIS phase configurations. Moreover, LoS-aligned precoding achieves a performance close to eigenmode precoding when the RIS phases are properly optimized, indicating a promising low-complexity alternative for practical HAPS-RIS-MIMO deployments. Furthermore, sufficiently large RIS deployments with LoS-aware phase optimization can mitigate the degradation caused by increased HAPS altitude and limited transmit-power budgets, while practical RIS hardware improvements in amplitude response and phase resolution provide additional transmit-power gains of approximately 3-5 dB.

Real-time immersive video communications, particularly high-fidelity 3D telepresence, necessitates a synergistic balance between instantaneous dynamic scene reconstruction and high-efficiency data transmission. While recent advancements in feed-forward 3D Gaussian Splatting (3DGS) have enabled real-time rendering, performing multi-view video coding and 3D reconstruction in a decoupled manner leads to suboptimal compression efficiency and high computational complexity. To address this, we propose GS-SCNet, the first unified end-to-end framework that seamlessly integrates generalizable 3DGS reconstruction with a dedicated deep Semantic Coding pipeline. Our architecture is underpinned by two core technical contributions: (i) we introduce a Disparity-Guided Parallel Semantic Codec that exploits epipolar geometric priors to facilitate cross-view contextual interaction via disparity compensation and semantic fusion, thereby enabling real-time parallel processing of stereo streams while significantly enhancing rate-distortion performance, and (ii) we develop a Lightweight Gaussian Parameter Predictor which directly projects decoded semantic latents into 3DGS attributes, obviating the need for intermediate pixel-domain reconstruction. By coupling the codec with the task-specific predictor, our framework extracts geometric correlations only once, effectively eliminating the redundant computational bottleneck inherent in conventional decoupled paradigms. Extensive evaluations on both synthetic and real-world human datasets demonstrate that GS-SCNet achieves a superior trade-off across compression efficiency, rendering quality, and real-time performance. Notably, our framework exhibits strong cross-domain generalization and robustness against compression artifacts when applied to out-of-domain real-world data, significantly outperforming conventional decoupled transmission paradigms.

This work addresses the critical problem of tracking fast-moving objects through strongly scattering media in a low-light environment. Different from existing approaches that use frame-based cameras with fixed exposure times, which trade off signal-to-noise ratio for temporal resolution, we introduce computational neuromorphic tracking (CNT), a physics-informed framework that combines asynchronous event sensing with task-driven speckle analysis for robust motion estimation. We formulate the neuromorphic speckle aggregation as a spatiotemporal speckle representation, jointly optimizing the temporal and spatial parameters to maximize tracking stability under extreme conditions. Extensive experiments demonstrate that our method enables robust motion tracking of 10x faster motion and under 10x dimmer illumination compared to conventional systems. These improvements significantly broaden the operational regime for tracking through scattering media, providing an efficient and scalable solution for demanding scenarios involving rapid motion and low-light conditions.

Under-resourced languages remain underrepresented in quantitative rhythm research,particularly in systematic intra-branch analysis of acoustic differentiation within closely related linguistic groups.This study investigates acoustic differentiation within the Tani language subgroup by examining speech rhythm in Nyishi and Adi,two under-resourced Tani languages spoken in Arunachal Pradesh,North-East India,using a frequency domain framework based on amplitude modulation(AM) low-frequency(LF) spectrum analysis,commonly referred to as rhythm formant analysis(RFA).The analysis is designed to identify whether intra-branch differentiation follows a hierarchical pattern across rhythmic and spectral domains.From the LF modulation spectrum,three rhythm formant features were derived:Number of Dominant peaks(NDP),Mean Frequency of Dominant Peaks(MFDP),and Variance of Dominant Frequencies(VFDP).In addition,Discrete Cosine Transform (DCT)coefficients and Mel Frequency Cepstral Coefficient(MFCC) were extracted to characterise the spectral modulation structure and broad spectral organisation of the speech signal.Statistical modelling reveals a hierarchical pattern of differentiation,where rhythmic features show consistent but moderate separation,with Nyishi exhibiting higher dominant modulation frequencies as well as greater dispersion than Adi.Classification experiments further support this hierarchy,with rhythm-only features achieved approximately 84-85% classification accuracy.Fusion using MFCC representations improved performance to 90.9% classification accuracy using support vector machine (SVM) and 93.96% using multilayer perceptron (MLP).These findings demonstrate that rhythmic and spectral features encode complementary levels of linguistic variations,with low frequency modulation capturing constrained macro temporal structure and spectral features reflecting finer phonological differentiation.

Deploying high-resolution tiny-object perception on edge platforms requires not only accurate localization, but also selecting a small set of informative patches under compute, transport, and latency constraints. We study budgeted tiny-object selection, where a frontend ranks patch centers from a lightweight proxy and a downstream detector processes only the selected regions. DenseScout is a 1.01M-parameter deployment-oriented dense-response selector that removes detector-style box regression and directly optimizes ranked patch-center prioritization. Its contribution lies in the task-specific selector formulation, the alignment among output representation, supervision, and decoding, and its joint design with transport-aware execution and QoS-oriented evaluation. Under unified protocols on VisDrone and DOTA, DenseScout provides stronger low-budget recall than the evaluated detector-derived selectors; controlled fixed-K inspection experiments further demonstrate advantages over selection-style proxy baselines. Cross-platform profiling on Jetson Orin NX and RK3588 shows that deployable utility depends jointly on selector quality, memory movement, and heterogeneous runtime realization. These results support treating edge tiny-object perception as a selection-and-deployment co-design problem rather than evaluating model accuracy and runtime in isolation.

This paper addresses the problem of traffic congestion management in fixed-wing unmanned aerial vehicle (UAV) corridors by further developing a recently introduced loiter-lane framework. A semi-cooperative guidance strategy is developed for inserting fixed-wing UAVs into a loiter lane with minimal disruption to the UAVs already operating within it, while enabling a more compact fixed-wing UAV corridor. Building on the concepts of cooperative and non-disruptive loiter-lane insertion, the proposed strategy makes the incoming UAV first attempt, within its speed bounds, to rendezvous with an existing empty loiter slot. If direct insertion is infeasible, a minimal number of loitering UAVs perform coordinated slot hopping to create a suitably positioned empty slot. The feasibility and performance of the method are demonstrated through numerical simulations.

As a pivotal usage scenario for 6G networks, integrated sensing and communication (ISAC) has emerged as a focal point of both academic and industrial research. To accommodate the heterogeneous connectivity requirements of future networks while jointly enhancing both the sensing and communication performance, this paper integrates the multi-static ISAC architecture with double-side fluid antenna system (DS-FAS) to fully exploit the available spatial degrees-of-freedom. Specifically, we establish a joint optimization framework for FA positions and transmit beamforming to maximize the target detection probability while satisfying the communication quality-of-service requirements. Recognizing the intricate coupling between the double-side FA positions and transmit beamforming, instead of trying to obtain an initial feasible point, we resort to the penalty-based mechanism to ensure the robustness against initial feasibility without introducing additional non-convexity. An alternating optimization-based algorithm is proposed to solve the decoupled subproblems. Specifically, the transmit beamforming is globally optimized via the semidefinite relaxation technique, while the transmit FA positions are determined using the majorization-minimization method. Finally, leveraging the analyzed FA mechanism, the feasibility subproblem for receive FA positions is transformed into a signal-to-interference-plus-noise ratio maximization one, solved efficiently via a gradient ascent-based approach, which yields superior performance over the feasibility-based benchmark with reduced complexity. Numerical results demonstrate the superiority of the considered DS-FAS-assisted multi-static ISAC systems in both noise-limited and interference-limited scenarios, while key insights for practical deployment are further extracted from the simulation analysis.

The development of intelligent and diversified ser vices in urban rail transit (URT) has resulted in an increasing de mand for high-rate communication between vehicles and ground equipment. However, existing URT communication systems strug gle to handle the massive data exchange required for vehicle-to ground (V2G) communication. To address this issue, we propose a distributed dual-polarized MIMO architecture suitable for URT tunnel scenarios. Specifically, the channel model is based on spatial three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM), which takes into account the geometric distribution of URT tunnels and the cross-polarization effects between dual-polarized antennas. For dual-polarized MIMO systems, the polarized-aware sparse channel estimation (PASCE) method is proposed for effective channel estimation. Additionally, we derive closed-form expressions for the MMSE and MR precoding schemes. The polarized-aware dynamic interference cancellation (PADIC) algorithm is developed to eliminate in terference between different polarization modes and multiple users. The simulation results demonstrate that the proposed dual-polarized precoding algorithm can withstand high cross polarization correlation (XPC) and improve the efficiency of V2G communication to achieve high rates.

Reliable and secure communication is essential for mission-critical aerospace and defence operations involving autonomous platforms such as Unmanned Aerial Vehicles (UAVs), satellites, and ground control systems. In contested or dynamic environments, communication links are frequently exposed to jamming, interference, and cyberattacks, making network resilience a key operational requirement. This paper presents a trust-aware Software-Defined Networking (SDN) framework that enables secure, low-latency failover between heterogeneous communication channels. The proposed architecture integrates a high-bandwidth primary link (e.g., satellite or tactical LTE) with a low-power fallback channel (e.g., RF or mesh), managed by an SDN controller that enforces zero-trust routing policies. A real-time Intrusion Detection System (IDS) continuously updates node trust scores; when trust or link reliability degrades, the controller autonomously switches traffic to the secondary channel, ensuring uninterrupted connectivity. Simulation results in a Mininet-based test environment demonstrate sub-5 ms failover latency, efficient flow installation, and significant reduction in packet loss compared with conventional single-channel or static routing systems. The proposed framework provides a scalable and resilient communication backbone for next-generation aerospace networks, enhancing mission reliability, cyber defence, and autonomous coordination across distributed aerial and space assets.

In renewable power-to-ammonia (ReP2A) systems, the intermittency of wind and solar generation propagates through electrolytic hydrogen production and induces thermal instability in the ammonia synthesis reactor (ASR). The resulting temperature cycling accelerates fatigue and shortens service life, while reactor thermal inertia limits flexible start-up, shutdown, and load adjustment. To address this issue, this study integrates molten-salt thermal energy storage (MS-TES) into the Haber-Bosch synthesis loop and develops a coordinated electro-hydrogen-thermal scheduling framework. MS-TES decouples hydrogen supply fluctuations from reactor thermal dynamics by enabling hot standby operation and sustained thermal support during start-up and low-load conditions. A state-space model is established to capture the thermal dynamics of the ASR and MS-TES. Based on this model, an optimal scheduling program coordinates ammonia synthesis operation with hydrogen production, battery energy storage (BES), and hydrogen storage (HS). The problem is formulated as a mixed-integer linear program (MILP) and extended with information gap decision theory (IGDT) to address renewable uncertainty. Case studies based on an industrial-scale project in northern China show that MS-TES enhances reactor thermal stability and system-level flexibility, while diminishing the marginal benefit of large BES capacity. As a result, a configuration combining small BES, HS, and MS-TES achieves near-equivalent performance to large-BES systems, with lower investment and improved economic returns. Year-round simulations further show that MS-TES avoids ASR start-up and shutdown and delivers consistently higher net revenue under variable renewable conditions.

We study the problem of distributed control of large-scale robotic swarms which can be modeled as continuum densities evolving under the continuity equation. We propose a formalization of distributed controllers as (generally nonlinear) spatial differential operators, in which control inputs depend only on local information about the state and environment. This perspective yields a fully local, PDE-based framework for analysis and design. We apply this framework to the problem of stabilizing a swarm density around an arbitrary target density, and investigate fundamental limitations of low-spatial-order distributed controllers in achieving this goal. In particular, we show that controllers which act in a purely pointwise manner are incompatible with natural system symmetries and strong forms of stability, and must rely on mixing-type behavior to achieve stabilization. In contrast, we present a simple first-order control law which achieves stabilization and enjoys substantially stronger properties.

Low Earth orbit (LEO) satellite relays will significantly extend the coverage of mobile networks, enabling users in remote areas to transmit data of real-time events. Nevertheless, the limited power of user devices and the long distance to satellites lead to low signal-to-noise ratio (SNR), which results in high error rates and frequent retransmissions, severely hindering the transmissions of high-dimensional data such as videos. In this paper, we propose a novel method to achieve high error tolerance in satellite-relay video transmissions using generative semantic communications (GSC). For the transmitter, we design and optimize a semantic encoder integrating a pre-trained video encoder with a low-density parity-check (LDPC) encoder, efficiently achieving generalizability and enabling forward error correction. For the receiver, we fine-tune a generative video model using an efficient in-context adaptation algorithm, enabling it to reconstruct videos from error-corrupted semantic information. Simulation results show that our method achieves 2.5 dB higher video peak SNR than conventional semantic communications at an error rate of 45%, and remains robust when the error rate exceeds 80%.

Data assimilation (DA) integrates observational information with model predictions to improve state estimation in complex systems. While filtering provides the basis for online forecasts by using only past and present observations, it can exhibit delays and biases when the underlying dynamics evolve rapidly or undergo regime transitions. Smoothing, which additionally incorporates future observations, provides a natural pipeline for hindcasting and reanalysis that yields an uncertainty reduction beyond the filter. This paper introduces an ensemble Kalman–Bucy smoother (EnKBS) for continuous-time DA of nonlinear dynamical systems, where the smoother's conditional distributions are reconstructed using ensemble moments. The result is a derivative-free framework that does not require explicit computation of tangent-linear or adjoint models, which recovers the exact smoothing mean and covariance equations in the infinite-ensemble limit for linear–Gaussian systems. Incorporating standard regularization techniques for high-dimensional systems, such as covariance localization and inflation, the skill of the EnKBS is demonstrated in various important scientific problems. By integrating future observations, which reveal the underlying causal mechanisms for retrospective state updates, the EnKBS is used for Bayesian-based inference of causal relationships and their temporal influence range in a dyadic trigger-feedback model and the development of a causality-driven iterative learning algorithm that identifies the structure and recovers the hidden parameters of a nonlinear reduced-order model mimicking midlatitude atmospheric circulation. Notably, both tasks remain effective with an ensemble size of $O(10)$ under partial observations, suggesting that EnKBS can support the instantaneous discovery of high-dimensional complex systems over time.

Mathematical analysis has long underpinned wireless communication theory, yet the growing complexity of next-generation systems demands increasingly sophisticated reasoning from domain experts. Recent advances in AI mathematical reasoning, from formal theorem proving to large language model (LLM)-based derivation, offer a promising but largely unexplored path forward. Here we argue that wireless communications is a uniquely structured domain for formal AI reasoning, and propose a three-layer framework of verification, derivation, and discovery to rethink how wireless mathematical knowledge is established.

Korean aegyo is a socially recognized childlike speaking style used predominantly in romantic interactions among adults. This study examined vowel space modification in aegyo by analyzing formant frequencies from twelve Seoul Korean speakers who produced identical scripts in aegyo and non-aegyo styles. Results show that aegyo speech features a significant increase in F1 values across vowels and selective fronting of front vowels, leading to vowel space expansion but mainly a shift to higher F1. These findings suggest that adult speakers stylize childlike speech by imitating the shorter vocal tract of children, mainly through global vowel lowering and partial fronting.

Weak constitutive fluctuations in dispersive subsurface media can induce distributed clutter that reshapes the observation structure of ground-penetrating radar (GPR). This paper analyzes this effect for single-snapshot frequency-diverse array multiple-input multiple-output GPR. Focusing on medium-induced clutter, rather than on general target–clutter joint modeling, it establishes a statistical propagation chain from Cole–Cole parameter perturbations to electromagnetic contrast, first-order Born channel snapshots, clutter covariance, and subspace descriptors. A medium-aware snapshot model and a covariance propagation framework are then derived to characterize how constitutive uncertainty alters observation-domain spectral structure under a local weak-fluctuation regime. Numerical experiments verify the consistency of the proposed propagation relation under the adopted first-order Born and constitutive-linearization approximations. Within the tested setting, medium-induced clutter reshapes the eigenspectrum and changes target–clutter overlap metrics. Spatial correlation length and background-scene variation act as consistently strong structural drivers, while the FDA frequency increment also produces measurable changes in the normalized covariance geometry.

Integrated sensing and communication (ISAC) combines sensing and communication within a shared system framework by using the same transmitted signal for both objectives. ISAC can improve the efficiency of spectrum and hardware use but also gives rise to new security challenges, as users associated with one function may need to be prevented from inferring information related to the other. This paper surveys information-theoretic approaches to secure ISAC with emphasis on formulations, performance metrics, and fundamental limits. We first review the information-theoretic ISAC models that underlie secure formulations. We then organize the secure ISAC literature according to the protected functionality and the adversary model, covering secure communication, sensing security, and active-adversary settings such as jamming. We also discuss formulations in which communication security and sensing security interact more directly, as well as their connections to privacy and covert communication. Throughout, we highlight the main modeling assumptions and the insights they provide on the tradeoffs among communication reliability, sensing performance, and security.

Wearable sensor-based human gait analysis holds great promise in healthcare, rehabilitation, clinical diagnosis and monitoring, and sports activities. Specifically, ground reaction force (GRF) provides essential insights into the body's interaction with the ground during movement and is typically measured using instrumented treadmills equipped with force plates. However, such equipment is expensive and restricted to laboratory environments. To enable a more portable solution, wearable insole sensors have been used to measure GRF. These sensors, however, are prone to noise and external interference, which reduces measurement accuracy. Deep learning methodologies could be adopted to address these issues, but they often require significant computing resources to achieve high accuracy, limiting their applicability for real-time analysis on portable devices. To overcome these limitations, we propose Selective Correlation Based Knowledge Distillation (SCKD) for estimating GRF from data collected by insole sensors. Our proposed method utilizes selected features considering temporal characteristics in the process of extracting correlation maps for knowledge transfer, enhancing interpretability and mitigating issues in high dimensional data processing. We demonstrate the effectiveness of the compact models generated by our distillation framework through comparison with existing methods. Various configurations of teacher-student architectures and training approaches are examined based on multiple evaluation criteria, utilizing data collected at different walking speeds and with different window sizes. Experimental results confirm that our approach outperforms existing methods in estimating GRF from wearable insole sensor data. Therefore, our approach offers a reliable and resource-efficient solution for human gait analysis.

Remote and webcam-based eye tracking in multi-line reading suffers from various noise factors and layout ambiguity, precisely where real-time reading support needs reliable, per-fixation line assignment. Prior work largely addresses this challenge post hoc or by restricting behavior (e.g., disallowing re-reading), undermining interactive use. We propose CONF-LA (Confidence-score-based Online Fixation-to-Line Assignment), a principled, low-latency approach that integrates knowledge about reading behavior and Gaussian line likelihoods over fixations to compute a posterior-line-score and defers assignments when uncertainty is high. Evaluated on existing open-source data, CONF-LA demonstrates stable performance in post hoc analysis and closes the online-offline gap (1-2 %) with a mean per-fixation latency of 0.348 ms. Our approach exhibits particular invariance toward regressions, yielding significant improvement in ad hoc median accuracies on children data (approx. 95 %) over all tested algorithms. We encourage further research in this direction and discuss possibilities for future development.

In this work, we introduce the Deceptive Resource Allocation Game (DRAG), which studies purposeful deception within a Bayesian game framework. In DRAG, a Defender allocates resources across the true asset and several decoys to influence an Attacker's beliefs and actions, with the goal of diverting the Attacker away from the true asset. We seek to characterize purposeful deception, whereby the Defender deceives only when doing so improves its performance. To this end, we solve for the Perfect Bayesian Nash Equilibrium (PBNE) of the corresponding game. We show that, despite the coupled belief-policy interdependence, the problem admits an efficient, non-iterative linear programming formulation. Numerical results demonstrate that the resulting policies naturally balance effective allocation and belief manipulation, giving rise to purposeful and emergent deceptive behaviors.

Custom policy-learning pipelines in Spark fail for two coupled systems reasons: rowwise Python execution makes inference impractical, and driver-side candidate materialization makes split search fragile at feature scale. We present Spark Policy Toolkit, a semantics-governed systems toolkit for scalable policy learning in Spark. The toolkit provides two Spark-native primitives: partition-initialized vectorized inference through mapInPandas and mapInArrow, and collect-less split search that scores candidates on executors. Both primitives are governed by one fixed-input semantic contract: the same rows, feature order, treatment vocabulary, preprocessing manifest, and split boundaries must preserve per-row score vectors, best-split decisions, and end-to-end learned policy outputs. The evaluation combines practical baseline ladders, backend parity checks, measured split-search scale results, synthetic and Hillstrom end-to-end policy preservation, missingness stress, partition and order perturbation tests, quantile-boundary sensitivity, and a concrete adversarial failure catalog. On a 40-worker Databricks cluster, mapInArrow reaches 4.72M rows/s at 10M matched rows and 7.23M rows/s at 50M rows, while collect-less split search remains valid from F = 10 through F = 1000 with 124000 candidate rows, where the driver-collect baseline is intentionally skipped. Across 24 backend-ablation settings, mapInArrow wins 18 while mapInPandas wins 6, so the paper treats backend choice as workload-dependent rather than universal. Once the fixed-input lock is enforced, all six tested repartition/coalesce/shuffle perturbations preserve identical signatures; before lock, all six drift. The central result is not speed alone: throughput and collect-less execution are the mechanisms that let policy semantics survive at Spark scale.

The assessment of reactive power demand plays an instrumental role in power system planning. This paper presents a methodology for calculating reactive power demand based on a two-step approach. Unlike existing methodologies in the literature that focus primarily on optimization of reactive power compensation equipment placement and sizing through single-simulation approaches, this methodology directly calculates the actual reactive power demand through a comprehensive back-to-back simulation framework. While existing methods address either long-term or short-term voltage stability using either steady-state analysis or individual dynamic simulations, the proposed approach integrates both stability assessments sequentially through iterative Quasi-Dynamic Simulation, Q-V analysis and dynamic simulation. Furthermore, this methodology employs comprehensive time-series analysis over a full annual period (8760 hours) with multi-criteria violation assessment (number, severity and duration of voltage violations). In the final section of this paper, a case study was conducted to demonstrate the application of the proposed methodology. Simulations were performed to validate the effectiveness of the methodology, with the results showing that all buses with voltage issues were successfully addressed and finally the total reactive power demand across the network was calculated.

Ultra-reliable and low-latency communication (URLLC) will play a key role in fifth-generation (5G) and beyond networks, enabling mission-critical applications. Meeting the stringent URLLC requirements, characterized by extremely low packet error rates and minimal latency, calls for advanced statistical modeling to accurately capture rare events in wireless channels. Traditional methods, such as those that rely on large datasets and computationally intensive estimation techniques, often fail in real-time scenarios. In this paper, a novel framework is proposed to meet URLLC requirements through a synergistic integration of extreme value theory (EVT) with generative artificial intelligence (AI). EVT is used to model channel tail distributions, providing an accurate characterization of rare events. Concurrently, generative AI enables data augmentation and channel parameter estimation from limited samples. The integration of EVT with generative AI can thus help overcome the limitations of generative models in capturing extreme events during channel characterization. Using an experimental dataset collected from an automotive environment, it is demonstrated that this integration enhances data augmentation for extreme quantiles, while requiring fewer samples than traditional analytical EVT methods and generative baselines in online estimation of channel distribution.

Pinching-antenna systems are a promising architecture for flexible wireless communications, but energy efficiency (EE) maximization remains largely unexplored, as limited existing studies mainly focus on transmit power minimization. This paper investigates EE maximization in a downlink non-orthogonal multiple access (NOMA)-assisted PASS by explicitly modeling the pinching antenna (PA) activation power and jointly optimizing discrete PA activation and power allocation under both quality-of-service and transmit power constraints. To tackle the resulting mixed-integer nonlinear programming problem, a two-layer iterative algorithm is proposed with an EE-oriented matching-based PA activation and a Dinkelbach-based power allocation with closed-form updates. Numerical results demonstrate that the proposed solution achieves substantial EE gains over the considered benchmark schemes, while exhibiting fast convergence. The impact of activation power has been analyzed and the significance of accounting it in EE maximization problem is also demonstrated.

Energy harvesting promises maintenance-free operation of wireless sensor nodes but introduces strong dependencies on stochastic and deployment-specific environmental conditions. In particular, solar-powered systems are highly sensitive to variations in irradiance and spectral composition, which complicates system-level design, parameter tuning, and reliable verification. This work presents a solar testbed in which active control via Hardware-in-the-Loop (HIL) enables stable and repeatable illumination conditions for evaluating ultra-low-power energy harvesting systems. The proposed LED-based solar testbed provides spectrally configurable illumination over a wide dynamic range, from 5.7 mW/m2 to 908 kW/m2. It achieves Class AAA performance according to IEC 60904-9, with a spectral match below 1.3% and a spatial non-uniformity below 1.28% over a 16.5 cm x 16.5 cm test area. The long-term irradiance instability remains below 0.6%. Closed-loop control using integrated illuminance and spectral sensors ensures high temporal stability, while a temperature-controlled DUT stage supports long-term experiments. Experimental results demonstrate high repeatability and suitability for systematic laboratory characterization of solar energy harvesting systems.

We provide a systematic interval observer design method for detectable linear time-invariant (LTI) systems, where a part of the state is observable from the measured output. An observability-based invertible LTI transformation decomposes the state into two parts. The first part is decoupled from the other and observable from the output, while the second is affected by the first, does not appear in the output, but is detectable. A Sylvester-based LTI interval observer is designed for the first part. For the second part, a Jordan-based linear time-varying interval observer is built, treating the interaction from the first part as inputs with known bounds. The intervals in the original coordinates are constructed either by inverting the decomposition online for the intervals in the transformed coordinates or by directly implementing the observer written in the original coordinates. Academic examples illustrate the interest of our approach.

Intelligent Transportation Systems (ITS) increasingly rely on vision-based perception and learning-based control, necessitating experimental platforms that support realistic hardware-in-the-loop validation. Small-scale platforms for autonomous racing offer a practical path to hardware validation, but often suffer from limited modularity, high integration complexity, or restricted extensibility. This paper presents TEACAR, a 1/14- to 1/16-scale autonomous driving platform designed with modular mechanical architecture, hardware abstraction, and ROS 2-based software. The system adopts a four-layer deck structure that physically decouples sensing, computation, actuation, and power subsystems, improving structural rigidity while simplifying reconfiguration. We constructed and comprehensively evaluated the prototype of TEACAR. Its mechanical stability, structural characteristics, and software performance were quantified based on three CNN-based steering controllers. Inference latency, power consumption, and system operating time were measured to evaluate computational capability and robustness. Our experiments demonstrated that TEACAR offers a scalable, modular, and cost-effective testbed for ITS research, education, and development. Our project repository is available on GitHub.

A Fourier-curve constellation places $M$ points on a closed curve through $k$ complex slots. A Gaussian perturbation along the curve's tangent, whether injected as artificial noise or arising from first-order jitter of the curve parameter, gives every symbol an observation with a symbol-dependent rank-one covariance, and the maximum-likelihood symbol metric differs from the Euclidean rule by one rank-one correction per candidate. We realize this metric as a max-log soft demapper beside a Euclidean correlator bank at $2kM$ additional multiply–accumulate operations per symbol. On a regular $(3,6)$ LDPC-coded link at $(k,M){=}(20,64)$ it recovers $5.1$\,dB of the Euclidean mismatch at BLER${=}10^{-1}$ under natural labeling and $1.1$\,dB under Gray labeling, of which an average-covariance receiver recovers $0.7$\,dB and nothing measurable, respectively, and it makes the tangential perturbation $0.2$ to $1.0$\,dB cheaper than white noise of the same power on the same codebook. The per-tone phase orientation of the curve acts as an orthogonal rotation, so these results hold at every orientation, and the demapper decodes at the level of an exactly oriented receiver up to $0.2$\,rad of orientation error per component. A bit-interleaved coded-modulation achievable rate corroborates the ordering, a Woodbury extension keeps the rank-one structure under per-tone Rician fading, and $6$-bit lookup-table quantization costs no measurable degradation.

Robotic fruit harvesting often fails to reliably detect whether a fruit has been successfully picked, limiting efficiency and increasing crop damage. This problem is difficult due to compliant fruit and grippers, variable stem attachment, and occlusions in orchard environments. Prior work has explored vision-based perception and multi-sensor learning approaches for pick state estimation. However, minimal sensor sets and phase-dependent sensing strategies for accurate pick and slip detection remain largely unexplored. In this work, we design and evaluate a multimodal sensing suite integrated into a compliant suction-based apple gripper. Our approach is unique because it identifies which sensors are most informative at different phases of the pick, enabling predictive detection of failures before they occur. The contributions of this paper are a phase-dependent evaluation of multimodal sensors and the identification of minimal sensor sets for reliable pick state classification. Experiments in a real apple orchard show that Random Forest and Multilayer Perceptron classifiers detect successful picks and impending failures with over 90% accuracy, and Random Forest predicts pick/slip events within 0.09 s of human-annotated ground truth.