Analog signal processing, storage, and demultiplexing via in-materia wave-based computing
Abstract
The ability of mechanical systems to perform basic computations has gained traction in recent years, providing a robust supplement to digital computing in off-grid, low-power, and extreme environments where electronics are inaccessible or risk failure. However, prior work has focused on Boolean logic applied via bistable structures, snap-through mechanisms, or origami lattices. Digital signal processing (DSP) underpins a wide range of computational tasks, providing the foundation for information encoding, manipulation, and analysis. In contrast to conventional DSP approaches, we present a new paradigm for analog information processing, where an elastic substrate houses a spatial wave-based platform for mechanical signal manipulation in the temporal domain. The proposed metasurface-based system exploits temperature-dependent phase transitions of shape memory alloys (SMAs) to realize key signal-processing functionalities, including gating, retention, and programmable delay of impinging mechanical loads. In doing so, we address a new set of challenges by manipulating transient mechanical waves through dispersive and lossy media. Building on this capability, we introduce the concept of mechanical wave-based memory, in which a locally resonant metamaterial's dispersion characteristics are actively reconfigured to achieve programmable delays while preserving spatial waveform integrity, and demonstrate its utility in the context of input shaping and frequency demultiplexing of superimposed waves. More broadly, the modular and scalable nature of the demonstrated framework paves the way for advanced mechanical computing architectures, enabling higher-order operations such as signal classification, parallel processing, and reservoir computing through the controlled sequencing and temporal alignment of mechanical signals.
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BibTeX
@misc{behboodi2026analog,
title = {{Analog signal processing, storage, and demultiplexing via in-materia wave-based computing}},
author = {Saeed Behboodi and Mohamed Mousa and Mostafa Nouh},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.05180},
eprint = {2611.05180},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.05180},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Analog signal processing, storage, and demultiplexing via in-materia wave-based computing
AU - Saeed Behboodi
AU - Mohamed Mousa
AU - Mostafa Nouh
PY - 2026
DA - 2026/11/03
PB - arXiv
DO - 10.48550/arxiv.2611.05180
UR - https://arxiv.org/abs/2611.05180
AB - The ability of mechanical systems to perform basic computations has gained traction in recent years, providing a robust supplement to digital computing in off-grid, low-power, and extreme environments where electronics are inaccessible or risk failure. However, prior work has focused on Boolean logic applied via bistable structures, snap-through mechanisms, or origami lattices. Digital signal processing (DSP) underpins a wide range of computational tasks, providing the foundation for information encoding, manipulation, and analysis. In contrast to conventional DSP approaches, we present a new paradigm for analog information processing, where an elastic substrate houses a spatial wave-based platform for mechanical signal manipulation in the temporal domain. The proposed metasurface-based system exploits temperature-dependent phase transitions of shape memory alloys (SMAs) to realize key signal-processing functionalities, including gating, retention, and programmable delay of impinging mechanical loads. In doing so, we address a new set of challenges by manipulating transient mechanical waves through dispersive and lossy media. Building on this capability, we introduce the concept of mechanical wave-based memory, in which a locally resonant metamaterial's dispersion characteristics are actively reconfigured to achieve programmable delays while preserving spatial waveform integrity, and demonstrate its utility in the context of input shaping and frequency demultiplexing of superimposed waves. More broadly, the modular and scalable nature of the demonstrated framework paves the way for advanced mechanical computing architectures, enabling higher-order operations such as signal classification, parallel processing, and reservoir computing through the controlled sequencing and temporal alignment of mechanical signals.
ER -