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US-20260127340-A1 - SPECTROSCOPIC DETECTION OF MATERIALS

US20260127340A1US 20260127340 A1US20260127340 A1US 20260127340A1US-20260127340-A1

Abstract

The present disclosure relates to a system and methodology designed for the spectroscopic detection of materials found on surfaces or dispersed in air. Some embodiments utilize a multi-modal sensor suite that operates through a progressive and multi-level acquisition pipeline, escalating across different sensing modalities such as visible, thermal, and hyperspectral. Data acquired at each level can be fully integrated and fused for efficient and precise material detection. Some embodiments use a physics-constrained computational model that uses latent hypercomplex states and learnable hypercomplex algebra to accurately disentangle and reconstruct spectral signatures, enabling robust substance identification. The system is adaptable to a range of hardware platforms, such as handheld devices, UAVs, and robots.

Inventors

  • Jessica White
  • Karen Panetta
  • Shishir Paramathma Rao
  • Srijith Rajeev
  • Sos Agaian

Assignees

  • Jessica White
  • Karen Panetta
  • Shishir Paramathma Rao
  • Srijith Rajeev
  • Sos Agaian

Dates

Publication Date
20260507
Application Date
20251106

Claims (20)

  1. 1 . A system for spectroscopic analysis of a target area to detect a target substance, comprising: a multi-modal sensor suite configured to acquire sensor data from the target area, wherein the sensor suite includes at least a hyperspectral sensor and a visible-spectrum sensor; and a processor configured to execute a physics-constrained computational model that processes the sensor data to generate a disentangled representation of physical properties of the target area, wherein the disentangled representation is a latent hypercomplex state having a scalar component and one or more imaginary components, and wherein the physics-constrained computational model utilizes a learnable hypercomplex algebra for operations in the latent hypercomplex state, and wherein the learnable hypercomplex algebra is parameterized by trainable structure constants defining multiplication among components of the latent hypercomplex state.
  2. 2 . The system of claim 1 , wherein the multi-modal sensor suite further comprises at least one of a thermal sensor and a near-infrared sensor.
  3. 3 . The system of claim 1 , wherein the processor is further configured to perform multi-level sensing by: conducting a first-level scan using a low-resolution sensor to identify regions of interest; and conducting a second-level scan using the hyperspectral sensor on the identified regions of interest.
  4. 4 . The system of claim 3 , wherein the multi-level sensing further comprises a third-level scan using a high-resolution sensor combined with fluorescent lighting on the regions of interest.
  5. 5 . The system of claim 1 , wherein the physics-constrained computational model comprises a spectral disentanglement module configured to map a real-valued spectral vector to the latent hypercomplex state and a physics-constrained reconstruction module configured to compute (i) a base reflectance spectrum, (ii) a multiplicative absorption profile, and (iii) an additive fluorescence profile from respective components of the latent hypercomplex state and to reconstruct a spectrum therefrom.
  6. 6 . The system of claim 5 , wherein the physics-constrained reconstruction module enforces structural constraints modeling physical interactions, wherein a scalar component of the latent hypercomplex state is processed through a multiplicative pathway representing absorption, and at least one imaginary component of the latent hypercomplex state is processed through an additive pathway representing fluorescence.
  7. 7 . The system of claim 1 , wherein the learnable hypercomplex algebra is defined by trainable structure constants that parameterize multiplication among components of the latent hypercomplex state, the structure constants being learned during training.
  8. 8 . The system of claim 7 , wherein the trainable structure constants are constrained during training by at least one of: bounded ranges, normalization, orthogonality, bounded operator norm, or sparsity.
  9. 9 . The system of claim 1 , wherein the physics-constrained computational model utilizes a hypercomplex activation that applies a phase-difference activation computed between at least one imaginary component and the scalar component of an input hypercomplex state to model interdependencies.
  10. 10 . The system of claim 1 , wherein the processor is further configured to train the physics-constrained computational model using a two-stage parameter-estimation regimen comprising: a masked-wavelength reconstruction subnetwork that predicts omitted contiguous spectral intervals while enforcing non-negativity and spectral smoothness of an attenuation profile and band-limited support of an emission profile; and physics-constrained fine-tuning in which a generative disentanglement module computes a base reflectance spectrum, a multiplicative absorption profile, and an additive fluorescence profile from components of the latent hypercomplex state and reconstructs a spectrum, while concurrently optimizing parameters of a learnable hypercomplex multiplication operator under one or more regularizers including normalization, orthogonality, sparsity, and bounded operator norm.
  11. 11 . The system of claim 1 , wherein the system is embodied in a handheld device configured to display detection results in real time.
  12. 12 . The system of claim 1 , wherein the processor is further configured to perform real-time edge processing of the sensor data to enable on-device analysis without transmitting raw data to an external server.
  13. 13 . The system of claim 1 , wherein the system is further configured to integrate with an external computing device by wireless communication to transmit processed results or receive control inputs.
  14. 14 . A computer-implemented method for spectroscopic detection of a target in a scene, comprising: acquiring scene data at a plurality of progressively escalated scanning levels comprising at least two levels; at each scanning level of the plurality of scanning levels, evaluating a stopping policy or threshold prior to escalation; fusing data the scene data; and outputting a detection decision for the target based on the fused data.
  15. 15 . The method of claim 14 , wherein the progressively escalated scanning levels comprise: a first level using broadband lighting with a visible-spectrum sensor and near-infrared sensor to identify regions of interest; a second level using ultraviolet sensing and shortwave-infrared sensing and selected hyperspectral bands to analyze the regions of interest; and a third level using full hyperspectral sensing and fluorescence sensing.
  16. 16 . The method of claim 14 , wherein fusing comprises feature-level fusion of spectral descriptors, spatial texture metrics, and thermal gradients, followed by a classifier trained to detect a target.
  17. 17 . A computer-implemented method of reconstructing and analyzing spectra, comprising: computing, from a real-valued spectral vector, a latent hypercomplex state; applying a physics-constrained reconstruction that computes, from respective components of the latent hypercomplex state, physically interpretable terms including at least (i) a multiplicative attenuation term constrained to be non-negative and spectrally smooth and (ii) an additive emission term constrained to be band-limited, wherein a spectrum is reconstructed based on the terms; and classifying a target using at least one of the reconstructed spectrum, an absorption profile, or a fluorescence profile.
  18. 18 . The method of claim 17 , wherein reconstructing the spectrum comprises computing a reconstructed spectrum equal to a product of the base spectrum and the absorption profile plus the fluorescence profile and wherein the physics-constrained reconstruction constrains the attenuation profile to be non-negative and smoothly varying across wavelength and constrains the emission profile to be additive and band-limited.
  19. 19 . The method of claim 17 , wherein at least one operation in computing the latent hypercomplex state or in the physics-constrained reconstruction employs hypercomplex convolution over grouped spectral bands using a learnable hypercomplex algebra.
  20. 20 . The method of claim 17 , wherein at least one layer applies a phase-difference hypercomplex activation computed between at least one imaginary component and a scalar component of the latent hypercomplex state.

Description

TECHNICAL FIELD This application relates to the use of spectroscopy to detect adulterants on surfaces or in air or other gases. BRIEF DESCRIPTION OF THE DRAWINGS Detailed descriptions of implementations of the present invention will be described and explained through the use of the accompanying drawings. FIG. 1 is a block diagram that illustrates an example process for applying one or more of the systems and methods herein. FIG. 2A illustrates an example process for on-device processing according to some embodiments. FIG. 2B illustrates an example process for external processing according to some embodiments. FIG. 3 is a block diagram that illustrates various components of a hardware apparatus according to some embodiments. FIG. 4 illustrates a handheld detection device according to some embodiments. FIG. 5 illustrates a device included in a UAV according to some embodiments. FIG. 6 illustrates a movable scanning device according to some embodiments. FIG. 7 is a block diagram that illustrates an example of machine learning model training according to some embodiments. FIG. 8A illustrates a functional block diagram of a progressive, multi-level acquisition pipeline. Each “level” is an acquisition configuration that selects one or more sensing modalities and band sets (e.g., visible, thermal, hyperspectral subsets), illumination conditions, and spatial/spectral resolution. The system escalates through levels until a stopping criterion is met, then fuses data collected up to the stopping level for detection. FIG. 8B depicts a high-level overview of multi-level escalation and fusion, showing the progressive refinement of data acquisition with decision points at each level. FIG. 8C illustrates escalation thresholds and fusion policy for a progressive pipeline with an arbitrary number K≥2 of levels. At level k, the system configures modalities/bands, captures data, computes features, evaluates a policy πk or threshold τk, and either stops and fuses or escalates to level k+1. Fusion may be performed at a feature level and/or decision level. FIG. 9A (Simplified) depicts a high-level block diagram of a generic physics-constrained reconstruction architecture with latent-state encoding and multi-pathway decoding. FIG. 9B illustrates a generic physics-constrained reconstruction module with explicit constraints on base spectrum, multiplicative absorption, and additive fluorescence pathways for generative disentanglement of physical properties. FIG. 9C illustrates a quaternion-specific example implementation using a quaternion latent variable (e.g., a QVAE), where quaternion components (w, x, y, z) route to distinct constrained pathways for spectrum disentanglement according to some embodiments. FIG. 9D depicts a high-level hypercomplex latent-space architecture showing learnable components and their routing through a physics-constrained reconstruction module according to some embodiments. FIG. 9E is a block diagram of a generic hypercomplex architecture implementing learnable structure constants Cijk for hypercomplex multiplication used in latent-space operations according to some embodiments. FIG. 9F depicts a masked-wavelength pretraining policy showing instrument-aware mask selection, application to spectra, reconstruction loss computation, and parameter regularization according to some embodiments. FIG. 10A illustrates a high-level hypercomplex phase-difference activation mechanism that exploits inter-component phase relations in hypercomplex latent representations according to some embodiments. FIG. 10B illustrates a quaternion-specific phase-difference activation according to some embodiments. FIG. 10C illustrates a dimension-agnostic hypercomplex phase-difference activation that generalizes the quaternion case to d-component latent states according to some embodiments. FIG. 11 is a flowchart that illustrates an example target detection process according to some embodiments. FIG. 12 is a flowchart that illustrates an example capture process according to some embodiments. FIG. 13 is a flowchart that illustrates an example AI/ML processing pipeline according to some embodiments. FIG. 14 is a flowchart that illustrates an example calibration and adaptation process according to some embodiments. FIG. 15 is a block diagram depicting an embodiment of a computer hardware system configured to run software for implementing one or more of the systems and methods described herein. The technologies described herein will become more apparent to those skilled in the art from studying the Detailed Description in conjunction with the drawings. Embodiments or implementations describing aspects of the invention are illustrated by way of example, and the same references can indicate similar elements. While the drawings depict various implementations for the purpose of illustration, those skilled in the art will recognize that alternative implementations can be employed without departing from the principles of the present technologie