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Laurent Jacques

@laurentjacques.gitlab.io.web.brid.gy

FNRS Senior Research Associate and Professor 🌉 bridged from https://laurentjacques.gitlab.io/ on the web: https://fed.brid.gy/web/laurentjacques.gitlab.io

1 Followers  |  0 Following  |  2 Posts  |  Joined: 10.08.2025  |  0.9046

Latest posts by laurentjacques.gitlab.io.web.brid.gy on Bluesky

Herglotz-NET: Implicit Neural Representation of Spherical Data with Harmonic Positional Encoding **Abstract** : Representing and processing data in spherical domains presents unique challenges, primarily due to the curvature of the domain, which complicates the application of classical Euclidean techniques. Implicit neural representations (INRs) have emerged as a promising alternative for high-fidelity data representation; however, to effectively handle spherical domains, these methods must be adapted to the inherent geometry of the sphere to maintain both accuracy and stability. In this context, we propose Herglotz-NET (HNET), a novel INR architecture that employs a harmonic positional encoding based on complex Herglotz mappings. This encoding yields a well-posed representation on the sphere with interpretable and robust spectral properties. Moreover, we present a unified expressivity analysis showing that any spherical-based INR satisfying a mild condition exhibits a predictable spectral expansion that scales with network depth. Our results establish HNET as a scalable and flexible framework for accurate modeling of spherical data.
04.11.2025 00:00 — 👍 0    🔁 0    💬 0    📌 0
A Novel Multiplicative Phase Dithering Scheme for 1-bit Compressive Radar **Abstract** : In this paper, we tackle the issue of implementing a dithering procedure for the 1-bit quantization of radar signals that is able to generate high-quality estimates while remaining a low-complexity and cost-efficient solution. Classic Dithering techniques that add a signal before the Analog to Digital Converter (ADC) have been used in many acquisition chain designs and have been studied as a way to shape the quantization noise more favourably. Here, we stray away from this additive dithering, which, as will be made clear later, induces a complex and high-cost implementation. Instead, we propose the use of a multiplicative phase dithering. This process can leverage already existing radar architectures of Frequency Modulated Continuous Wave (FMCW) radars and can thus be efficiently implemented. The efficiency of this multiplicative dithering is first studied theoretically, and its link to another coarse quantization scheme, namely the Phase-Only acquisition, is highlighted. The performances of this novel dithering scheme are then extensively tested using Monte Carlo simulations and are thoroughly compared to their additive counterparts. A hardware-relaxed version of the random phase dithering is also introduced and compared to the other 1-bit schemes. The observations made in simulations are then validated using actual radar measurements at 24 GHz. Combined with the simulations, these measurements show that the multiplicative dithering is an appealing alternative to the additive random dithering in a low number of measurement setting. Specifically, we show that this procedure is a good trade-off between strong theoretical guarantees and reconstruction quality for low-complexity hardware.
04.09.2025 00:00 — 👍 0    🔁 0    💬 0    📌 0