Multi-channel low-cost light spectrum measurement using a multilayer perceptron

Botero-Valencia, Juan S.; Valencia-Aguirre, Juliana; Durmus, Dorukalp; Davis, Wendy

doi:10.1016/j.enbuild.2019.07.026

Cited by 30 publications

(18 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Looking at Figure 2 , one may also question why we would bother forming the reconstruction problem as Equation ( 4 ) in lieu of training a neural network on K , thereby learning a map between the measured and target dimensions. We refrain from taking such an approach for two reasons: first, it is well known that “black box” models do not generalize well and this has, in fact, been demonstrated to be the case in the application of recovering visible spectral distributions from encoder-array spectrometers [ 36 , 37 ]; second, we lose any interpretability or contextual reference of the scene. While Equation ( 4 ) may seem trivially simple, enforcing sparsity via the

-norm has demonstrated incredible success in not only reconstructing low-dimensional measurements, but also in reconstructing them in a way that mimics the underlying physical system [ 38 ].…”

Section: What Is Reconstruction?mentioning

confidence: 99%

Towards ‘Fourth Paradigm’ Spectral Sensing

Webler

Spitschan

Andersen

2022

Sensors

View full text Add to dashboard Cite

Reconstruction algorithms are at the forefront of accessible and compact data collection. In this paper, we present a novel reconstruction algorithm, SpecRA, that adapts based on the relative rarity of a signal compared to previous observations. We leverage a data-driven approach to learn optimal encoder-array sensitivities for a novel filter-array spectrometer. By taking advantage of the regularities mined from diverse online repositories, we are able to exploit low-dimensional patterns for improved spectral reconstruction from as few as p=2 channels. Furthermore, the performance of SpecRA is largely independent of signal complexity. Our results illustrate the superiority of our method over conventional approaches and provide a framework towards "fourth paradigm" spectral sensing. We hope that this work can help reduce the size, weight and cost constraints of future spectrometers for specific spectral monitoring tasks in applied contexts such as in remote sensing, healthcare, and quality control.

show abstract

-norm has demonstrated incredible success in not only reconstructing low-dimensional measurements, but also in reconstructing them in a way that mimics the underlying physical system [ 38 ].…”

Section: What Is Reconstruction?mentioning

confidence: 99%

Towards ‘Fourth Paradigm’ Spectral Sensing

Webler

Spitschan

Andersen

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The material used for this purpose was polylactic acid (PLA), and the final result is shown in Section 5 . As described in [17] , multi-spectral sensors are located on the upper face of the device, but to reach the sensor the light must go through 1/16 ”film of polytetrafluoroethylene (PTFE), and a transparent glass below that protects the sensors from dust.…”

Section: Hardware Descriptionmentioning

confidence: 99%

“…The proposed device can be used, among other things, to reconstruct the spectrum from the measured wavelengths using machine learning techniques. For example in [17] , this device is used to recover a light source spectrum using only the 10 specific wavelengths that are obtained by the AS7262 and AS7263 sensors. The reconstructed SPD has an error lower than 2% and allows the estimation of colour and light quality measurements, such as the International Commission on Illumination (CIE) color rendering index (CRI) R a and R i (1–14) [18] , the color quality scale (CQS) Q a , Q f , and Q g [19] , and the Illuminating Engineering Society’s TM-30-15 R f and R g [20] .…”

Section: Validation and Characterizationmentioning

confidence: 99%

A low-cost IoT multi-spectral acquisition device

2021

Self Cite

View full text Add to dashboard Cite

“…The limitations are highly relevant for integrative lighting systems that are based on the modeling of the spatial and spectral sensitivity of the visual system. For example, it is possible to conceptualize a real-time integrative system that detects the physical characteristics of the built environment using sensors (CCD or CMOS for spectral, spatial, and luminance imaging of the built environment [76], [77]) to optimize the light output for energy efficiency, visual comfort, and visual performance. Such a system can estimate the perceived spatial quality and complexity of the built environment and enable making predictions through computational models based on machine learning and optimization algorithms.…”

Section: Implications For Immersive Viewing Conditionsmentioning

confidence: 99%

Spatial Frequency and the Performance of Image-Based Visual Complexity Metrics

Durmus

2020

IEEE Access

View full text Add to dashboard Cite

There is a wide range of visual and spatial complexity measurement methods that aim to quantify perceived image complexity. While image-based calculation methods (edge detection, image compression, contrast) characterize a digital image, visual perception studies focus on fundamental visual mechanisms, such as contrast sensitivity and visual task performance. Despite the evidence from several vision studies, spatial frequency information has not been widely utilized to assess image complexity. Previous studies suggest that image-based performance metrics are limited in explaining perceived complexity due to confounding factors, such as context, memory, familiarity, and expectation. Here, a visual experiment is conducted to assess the performance of image-based metrics and spatial frequency information using 16 abstract and natural images. A new image complexity metric (R spt), based on detectability suprathreshold, was proposed to benchmark the performance of existing measures. Forty-four naïve participants used a 5-point Likert-type scale to judge the visual complexity of the images displayed on a tablet. Results indicate that root-mean-square error (RMSE) and R spt correlate statistically significantly with subjective evaluations. Biological sex did not affect perceived spatial complexity. While RMSE and R spt can potentially be used to estimate the spatial complexity of display images, the performance of spatial frequency information and image assessment measures in immersive viewing conditions require further research.

show abstract

Multi-channel low-cost light spectrum measurement using a multilayer perceptron

Cited by 30 publications

References 30 publications

Towards ‘Fourth Paradigm’ Spectral Sensing

Towards ‘Fourth Paradigm’ Spectral Sensing

A low-cost IoT multi-spectral acquisition device

Spatial Frequency and the Performance of Image-Based Visual Complexity Metrics

Contact Info

Product

Resources

About