KEYWORDS: Sensors, Optical filters, Reconstruction algorithms, Spectrometers, Signal to noise ratio, Quantization, Systems modeling, Optical engineering, Associative arrays, Chemical elements
In recent years, miniature spectrometers have been found to be useful in many applications to resolve spectrum signatures of materials. In this paper, algorithms are proposed to realize a miniature spectrometer using a low-cost filter-array spectrum sensor. Conventionally, the filter-array spectrum sensor can be modeled as an overdetermined problem, and the spectrum can be reconstructed by solving a set of linear equations. In this paper, we instead model the spectrum reconstruction process as an underdetermined problem, and bring up the concept of template-selection by sparse representation. l1 − norm minimization is introduced to achieve a high reconstruction resolution. Both simulation and experimental results show that a superior quality of spectrum reconstruction can be made possible from the presented underdetermined approach.
The visible light communication (VLC) systems using light emitting diodes (LEDs) has been a promising transmission
technology to complement wireless communications. In this work, a spectrum sensor array is proposed to be implemented
on the receiver side. Following the concept of multi-antenna communication systems, signal fusion algorithm is presented.
By proper design of the weighting for each individual spectrum sensor, the effective output signal to interference ratio
(SIR) can be maximized and hence make interference rejection possible.
KEYWORDS: Reconstruction algorithms, Sensors, Optical filters, Spectrometers, Systems modeling, Associative arrays, Linear filtering, Transmittance, Chemical elements, Signal to noise ratio
In recent years, miniature spectrometers have been found useful in many applications to resolve spectrum signature of
objects or materials. In this paper, algorithms for filter-array spectrum sensor to realize miniature spectrometers are
investigated. Conventionally, the filter-array spectrum sensor can be modeled as an over-determined problem, and the
spectrum can be reconstructed by solving a set of linear equations. On the contrary, we model the spectrum
reconstruction process as an under-determined problem, and bring up the concept of template-selection by sparse
representation. L1-minimization algorithm is tested to achieve a high reconstruction resolution. Simulation results
show superior quality of spectrum reconstruction can be made possible from this under-determined approach.
Peak wavelength and full-width-half-maximum (FWHM) are the two important parameters to characterize the spectra of
monochromatic LED lights. In this work, a low-cost miniature filter-array spectrum sensor for accurate LED
measurement is proposed. For mapping the data from the outputs of the filter-array spectrum sensor to the measurement
parameters of peak wavelength and FWHM, Gaussian curves are used as the basis functions to facilitate the estimation.
In addition, particle swarm optimization (PSO) is utilized for searching the optimal center locations and the widths of the
Gaussian basis functions. The resulting measurement accuracy is competitive to a professional optical spectrometer.
Miniature spectrometers with desirable properties such as being small in size, light weight and non-fragile provide
solutions to a variety of promising application. In this work, through the developed algorithms, a low-cost chip-scale
spectrometer is demonstrated in particular for LED spectrum sensing, as the market of LED lights has been booming
and low-cost solution for testing and monitoring LEDs spectral characteristics become essential. The developed
algorithms are in two forms, namely algebraic approach and training approach. For the algebraic approach,
non-negative least square method is reported useful for spectrum reconstruction of narrowband LED spectra. For the
training approach, FWHM measurement and peak wavelength measurement, the two major parameters for specifying
monotonic LED spectra, are reported with accuracy within 1nm.
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