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RISS 인기검색어
- 검색키워드
- 저자 : Partha Pratim Banik (검색결과 3 건)
- 무료
- 기관 내 무료
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Regression analysis for LED color detection of visual-MIMO system
Banik, Partha Pratim,Saha, Rappy,Kim, Ki-Doo Elsevier 2018 OPTICS COMMUNICATIONS - Vol.413 No.-
<P><B>Abstract</B></P> <P>Color detection from a light emitting diode (LED) array using a smartphone camera is very difficult in a visual multiple-input multiple-output (visual-MIMO) system. In this paper, we propose a method to determine the LED color using a smartphone camera by applying regression analysis. We employ a multivariate regression model to identify the LED color. After taking a picture of an LED array, we select the LED array region, and detect the LED using an image processing algorithm. We then apply the k-means clustering algorithm to determine the number of potential colors for feature extraction of each LED. Finally, we apply the multivariate regression model to predict the color of the transmitted LEDs. In this paper, we show our results for three types of environmental light condition: room environmental light, low environmental light (560 lux), and strong environmental light (2450 lux). We compare the results of our proposed algorithm from the analysis of training and test R-Square (%) values, percentage of closeness of transmitted and predicted colors, and we also mention about the number of distorted test data points from the analysis of distortion bar graph in CIE1931 color space.</P> <P><B>Highlights</B></P> <P> <UL> <LI> LED color detection method is proposed using regression analysis for visual-MIMO system. </LI> <LI> Smart phone camera is used to take images of LEDs. </LI> <LI> Minimizing the real time illumination noise for color detection and distortion analysis is shown. </LI> </UL> </P>
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LED color prediction using a boosting neural network model for a visual-MIMO system
Banik, Partha Pratim,Saha, Rappy,Kim, Ki-Doo Elsevier 2019 OPTICS COMMUNICATIONS - Vol.437 No.-
<P><B>Abstract</B></P> <P>Color decision of Light-emitting diode (LED) by smartphone cameras is a challenging area in visual- multiple-input multiple-output (MIMO) systems. In this study, we use a generalized color modulation (GCM) technique for a visual-MIMO system. We propose a boosting neural network (BNN) model that can predict LED color from an LED image. To develop this learning model, we use LED image pixels as input features by resizing all LED images to 10 × 10 pixels through bicubic anti-aliasing interpolation. The model is trained in three stages: (1) select the coefficient of the activation function, (2) train each feature to build weak learners, and (3) train the weak learners to predict LED color. Then, we make a symbol decision by measuring the minimum Euclidean distance between the predicted color of the received symbol and transmitted symbol colors. We evaluate our prediction by measuring the root-mean-square error (RMSE) of our test dataset at different environmental light intensities. We also measure the average closeness accuracy and symbol error rate (SER) performance of the proposed method with respect to transmission distances and different sizes of constellation diagrams. Finally, we compare the performance of our proposed BNN model with that of a multiple-linear-regression method.</P> <P><B>Highlights</B></P> <P> <UL> <LI> BNN is proposed for determining LED color of visual-MIMO system. </LI> <LI> Smart phone camera is used to take images of LED array. </LI> <LI> GCM technique is used for modulation in visual-MIMO system. </LI> <LI> Using BNN, visual-MIMO system performance is evaluated for different distances. </LI> <LI> BNN model of visual-MIMO system is compared with multiple regression model. </LI> </UL> </P>
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