Searcharxiv⌕ Search

arXiv · 2611.08401

Spectral Regimes of Truncated sinc-Power and Pulse-Train Kernels: Edge Exponents, Per-Edge Plunges, and the Landau-Widom Limit

Abstract

A signal band-limited to one rectangular band and observed through a finite window is the classical concentration problem of Slepian, Landau, Pollak, and Widom. The eigenvalues of the operator cluster near one and near zero, separated by a transition called the plunge. The plunge width grows like the logarithm of the window length, with a universal coefficient of one over pi squared set by the band-edge jump. This paper follows the clusters and the plunge as the band is deformed. An elementwise power of the kernel smooths the band edge, and the plunge gives way to a power-law edge with exponent one less than the power. Disjoint copies of the band widen the plunge in proportion to the number of band edges, as the multiple-interval theorem of Landau and Widom implies, so the coefficient is a per-edge constant. Shrinking each band to a line gives the classical finite-rank operator. Overlapping bands give a multilevel staircase, and a self-similar band gives a cluster hierarchy governed by the Cantor dimension. At the smooth edge, rescaled eigenvalues do not collapse onto one curve. The edge descriptor passes from a constant to an exponent to an integer to a fractal dimension. For the single band, classical results place all eigenfilter zeros on the unit circle, with more of them inside the band as the order rises, so the dominant eigenvectors act as lowpass filters.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Unnikrishna Pillai. 2026-11-05. Spectral Regimes of Truncated sinc-Power and Pulse-Train Kernels: Edge Exponents, Per-Edge Plunges, and the Landau-Widom Limit. https://arxiv.org/abs/2611.08401

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Learning Latent Fields in Multiobject Tracking

Multiobject tracking using measurements from arrays of wireless sensors is a key task in applications ranging from autonomous navigation to public safety. Many existing methods compress raw complex-valued signals into detections or intensity maps during preprocessing, thereby discarding potentially relevant information about the objects and the propagation environment. We propose a Bayesian framework for multiobject tracking that relates object states to raw complex-valued signals via a physics-based superpositional model. The model combines a physics-based array manifold, such as radar steering vectors or the replica vectors of matched-field processing, with a latent field. This latent field is a family of context-dependent signal-power distributions that captures the effects of the objects and the propagation environment and is shared across objects and time. It is parameterized by a conditional normalizing flow, which is trained during runtime without labeled data by maximizing an evidence lower bound (ELBO) derived from the Bayesian model. Learning and inference are thus coupled in a sequential framework. We evaluate the method in simulation and in real-world radio and acoustic experiments, with spatial scales ranging from meters to kilometers, and show that it outperforms detection-based methods.

eess.SP↗

Energy-Aware Vertically Layered Pinching-Antenna System Design with Probabilistic LoS Availability

This paper investigates a vertically layered pinching antenna (PA) system, in which dielectric waveguides deployed at different heights provide additional vertical spatial degrees of freedom for line-of-sight (LoS)-favorable PA placement. We consider a practical energy model that captures in-waveguide attenuation, base station-to-feed-point optical-fiber loss, and PA repositioning energy, along with a statistical LoS-availability model that depends on elevation and distance. We then jointly optimize the transmit beamforming, PA positions, time allocation, and transmission rates to maximize the expected successfully delivered frame-effective sum rate under per-user outage and quality-of-service constraints. To avoid enumerating the exponentially many LoS states, we derive a certified conditional-moment outage envelope for outage-guaranteed rate selection and a differentiable safe approximation of the chance constraints to facilitate tractable optimization. Building on these results, a block coordinate descent algorithm with a penalty-aided majorization-minimization procedure attains a high-quality suboptimal solution. Simulation results confirm the gains of vertical layering and reveal a fundamental energy-LoS tradeoff: LoS-favorable PA placement generally enhances both performance and reliability, although it incurs higher attenuation, path loss, and energy costs.

eess.SP↗

An Ultra-Low-Noise Tunable TIA in 45nm Silicon Photonics for Electro-Optical Ultrasound Data Acquisition

This paper presents the design and experimental characterization of a 32-channel, WDM-compatible electronic-photonic receiver array fabricated in the GlobalFoundries 45nm monolithic silicon photonics process. Optimized for miniaturized, millimeter-scale electro-optical ultrasound data acquisition probes for broadband photo-acoustic imaging (PAI), the chip utilizes integrated micro-ring resonator (MRR) demultiplexers and photodiode (PD) arrays to decode multi-wavelength acoustic signals outside the body. This work focuses specifically on the comprehensive characterization of the receiver front-end, evaluating both the optical WDM filter bank and the transimpedance amplifier (TIA) performance. Monolithic co-integration minimizes critical photodiode-to-TIA interface parasitics. The TIA incorporates a DC-offset cancellation loop that compensates for photodiode-induced offset currents while providing tunable low-frequency cutoff control over a range of 30 kHz to 1.5 MHz. This tunability enables effective suppression of flicker noise and improves overall signal integrity. The front-end achieves the peak transimpedance gain of 144 dB$Ω$ while maintaining an ultra-low input-referred noise current density of $1\text{ pA}/\sqrt{\text{Hz}}$, demonstrating a highly scalable architecture for high-sensitivity intravascular and endobronchial imaging.

eess.SP↗