arXiv preprints from January 1, 2026 through September 23, 2026 — 00:50:55 EST
This paper presents a fully reconfigurable RF vector modulator (RFVM)-based phase shifter for wideband beamforming phased array of 6G/NextG satellite communication (SATCOM). It covers the frequency range from S-band up to Ku-band. Specifically, the proposed RFVM features a novel vector modulation approach that relaxes the frequency...
This paper describes a fuel blending algorithm based on artificial intelligence and model predictive control. A gas-fired power plant was modeled using physical laws and on-site measurements. A neural network is used to calculate the methane number of the fuel and determine the fuel blending ratio limits so that the methane number is...
This paper addresses the fundamental design problem of plug-and-play distributed energy resource (DER) systems, which are emerging as a scalable solution for integrating distributed generation through user-driven connection of modular units. In such systems, the number of connected units is not fixed but dynamically varies due to user...
Dual-Orthogonality waveforms are multi-antenna signaling schemes that enforce mutual orthogonality across transmit channels and over a prescribed set of delay shifts. By relaxing strict time orthogonality to the physically admissible propagation region, they preserve full-band operation per transmit antenna while embedding...
We consider a multistatic radio-frequency imaging problem with anisotropy, in which the reflection from a point depends on the positions of the transmit (Tx) and receive (Rx) arrays. The goal is to label the voxels of a field of view by a finite set of semantic classes and to group them into object instances. For the image formation...
Nonlinear Dynamic Inversion (NDI) has become a standard methodology in flight control law design. It offers an intuitive approach to decouple commanded variable responses, handle system nonlinearities, and adapt to operating conditions. NDI also comes with a well-structured architecture that reduces design effort by addressing various...
While recent advancements in TinyML have significantly reduced the computational complexity of on-device vision pipelines, image acquisition remains a dominant contributor to system-level energy consumption and memory footprint. In vision-enabled IoT platforms, the image sensor consumes energy comparable to the inference engine,...
Direct policy optimization is widely used in reinforcement learning and control, but generally leads to nonconvex optimization problems. For state-feedback $H_\infty$ control, the policy objective is also nonsmooth, despite possessing a benign landscape whose hidden convexity can be revealed by the recently developed extended convex...
The increasing UAV traffic in urban areas has prompted the creation of U-space, an EASA framework for safe and efficient unmanned aerial vehicle (UAV) operations. Within this context, this work presents a flight authorization framework that leverages autonomous UAVs, using their motion models and control characteristics to improve...
This paper proposes a framework for treating the inverse obstacle problem for nonlinear elliptic equations with nonlinear materials. The problem is challenging because nonlinear materials exhibit a rich diversity of scenarios to consider, since nonlinearity can take different forms. In this article, after categorizing the...
Source seeking arises in applications such as gas leak localization, radiation monitoring, and environmental surveillance, where the origin of an unknown signal field must be estimated from spatial measurements. In practice, the source location is not directly observable and must be inferred from noisy scalar measurements collected...
Existing studies on the pinching-antenna system (PAS) assume that waveguides are deployed straight, which fails to serve communication regions with curved boundaries. To address this limitation, this paper proposes a curved waveguide-enabled pinching-antenna system (C-PAS), where the waveguide is placed along the building ceiling in...
In recent years, low-power wide-area network (LPWAN) technologies have gained significant traction as a connectivity option for Internet of Things (IoT) applications. While these networks have been successful in providing long-range, low-power, and low-cost connectivity, they currently face scalability, reliability, and efficiency...
Seamless indoor--outdoor coverage conventionally relies on coordinated outdoor macro cells and indoor small cells, incurring high deployment and backhaul costs. Intelligent omni-surfaces (IOSs) provide a promising alternative by enabling simultaneous reflection and transmission across building boundaries. Enabled by recent advances in...
Dynamic spectrum access must coordinate secondary users under bursty primary-user activity while preserving packet reliability and delay. We present TACAN, a centralized policy that represents each channel as a token containing occupancy history and automatic-modulation-classification entropy; a context token supplies queue class,...
Robot manipulators are commonly engineered around a decoupled motion-generation stack: a planner computes a collision-free path and a lower-level controller tracks the resulting reference. This separation is computationally convenient, but it can produce references that are difficult to execute under actuator limits, tracking error,...
Mixture-of-experts (MoE) architectures enable efficient large language model (LLM) inference at the wireless edge by reducing per-token computation through sparse expert activation. The wireless distributed MoE (WIDE) architecture addresses edge-device resource constraints by distributing computation-intensive experts across devices...
Probability density functions are commonly estimated through preliminary smoothing or aggregation procedures, e.g., histograms or kernel density estimation, before subsequent functional representation and functional data analyses. Such a two-stage approach can lead to additional approximation bias and weaken the direct connection...
Timely risk classification is essential in many clinical monitoring settings, where decisions must balance the benefit of classifying patients early for subsequent intervention against the value of observing additional data. Yet most existing statistical and machine-learning methods are designed for fully observed trajectories and...
Policies that require organizations to use new systems, such as prescription drug monitoring programs (PDMPs), are often implemented in phases, with an initial period of voluntary access followed by mandated compliance. This allows the policy intervention to be adopted before compliance is required (early adoption), causing outcomes...
This study presents a reproducible framework combining Fourier and wavelet analysis to examine the seasonality of daily inflows at three sites on the Senegal River (Bafing Makana, Felou, Gouina), based on 65,631 daily observations spanning nearly 60 years (1961-2020). Using harmonic regression, Welch spectral analysis, stationary...
We establish a strong averaging principle for fast-slow stochastic differential equations with a time-dependent scale-separation parameter $(\varepsilon_t)_{t \geq 0}$ satisfying $\varepsilon_t \to 0$ as $t \to \infty$. In contrast to approaches based on noise-induced smoothing or elliptic regularity, our approach relies on...
Interpretability is critical for machine learning models deployed in scientific space missions such as ESA's Ariel, where ground truth is unavailable during operations and physical plausibility must be assessed. While most explainable AI methods focus on feature attribution, this work investigates training data attribution through...
Randomized trials support causal inference, but differences between trial and target populations can limit the transportability of treatment effects to real-world settings. Many existing approaches model the propensity of trial participation and can therefore be sensitive to model misspecification and weak overlap of the covariate...
Forecasting long-range influenza-like illness (ILI) matters for public health readiness. Publicly available surveillance datasets typically pair numeric epidemiological signals with textual information that is noisy, loosely structured, only indirectly related to near-term trends, and often lagged relative to the numeric signal....