VIS-NIR Detector Tunable Filter Hyperspectral Imaging
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Solution Overview
Problem
Current spectroscopic imaging technologies face challenges in efficiently identifying target materials across various environments and scales, particularly in inaccessible locations, due to limitations in sample size handling and the need for complex data analysis of hyperspectral images.
Innovation Solution
A portable system utilizing a VIS-NIR detector with a tunable filter and processor to generate and analyze hyperspectral images, allowing for simultaneous RGB and VIS-NIR imaging, enabling the identification of target materials by comparing collected photon data to a database of known images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional spectroscopic imaging systems use separate detectors for different wavelength ranges (Si CCD for visible, InGaAs for near-infrared), then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent combines visible and near-infrared detection capabilities into a single InGaAs detector that can operate across both wavelength ranges. This merging of functions eliminates the need for multiple separate detectors (Si CCD for visible, InGaAs for NIR) and their associated switching mechanisms, thereby reducing device complexity while maintaining detection accuracy across the full VIS-NIR spectrum
Solution Approach 2:
The InGaAs detector is configured to perform multiple functions: detecting both visible and near-infrared wavelengths, operating in both linear and logarithmic response modes, and functioning as the sole imaging detector across the entire VIS-NIR range. This multi-functionality replaces what previously required multiple specialized detectors, simplifying the overall system architecture
2Adaptability or versatility
If hyperspectral imaging collects data at numerous wavelengths simultaneously, then material identification capability is improved, but data analysis complexity increases
Solution Approach 1:
The system uses a tunable filter that periodically scans through different wavelength bands to build the hyperspectral datacube. This periodic wavelength scanning, combined with the detector's ability to switch between linear and logarithmic response modes during the scan, enables comprehensive material identification while managing data acquisition complexity through structured, sequential measurement
Solution Approach 2:
The detector dynamically changes its response parameter between linear and logarithmic modes during the hyperspectral acquisition process. This parameter switching allows the system to optimize detection sensitivity for different wavelength regions and material types, enhancing material identification capability while the structured parameter changes provide a framework for managing the complexity of the resulting hyperspectral data
3Device complexity
If a single detector is used for both visible and near-infrared ranges, then device simplicity is improved, but detection accuracy may worsen
Solution Approach 1:
The InGaAs detector operates in a dynamic manner, switching between linear and logarithmic response modes depending on the wavelength being detected and the intensity of the signal. This dynamic adaptability allows the single detector to maintain high detection accuracy across the entire VIS-NIR spectrum, compensating for the potential limitations of using a single detector type for such a broad wavelength range
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient and portable identification of target materials in real-time across different environments, providing effective color discrimination and material classification, suitable for applications in pathology, forensic analysis, and threat detection.
Implementation Method 1
a tunable filter configured to filter a first plurality of interacted photons collected from the first collection optic. The tunable filter is configured to filter the first plurality of interacted photons into a plurality of wavelengths to generate filtered interacted photons
Implementation Method 2
a VIS-NIR detector configured to detect the filtered interacted photons and to generate a VIS-NIR hyperspectral image representation of the filtered interacted photons
Implementation Method 3
a first collection optic configured to collect a plurality of interacted photons. Interacted photons are those photons that have interacted with the sample
Data Source
AI summary
The present disclosure provides systems and methods for determining the presence of a target material in a sample. In general terms, the system and method disclosed herein provide collecting interacted photons from a sample having a target material. The interacted photons are passed through a tunable filter to a VIS-NIR detector where the VIS-NIR detector generates a VIS-NIR hyperspectral image representative of the filtered interacted photons. The hyperspectral image of the filtered interacted photons is analyzed by comparing the hyperspectral image of the filtered interacted photons to known hyperspectral images to identify the presence of a target material in a sample. The systems and methods disclosed herein provide easy identification of the presence of a target material in a sample.